essay covid 19 response mechanism of the philippines

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essay covid 19 response mechanism of the philippines

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COVID-19 Response in the Philippines

20 January 2022

The Philippines has been severely affected by COVID-19. According to latest WHO figures , as of 17 January 2022, the Philippines had recorded over 3 million confirmed cases of COVID-19 with over 52,700 deaths. Since March 2020, the country has taken strict measures to halt the spread of the virus, including lockdowns such as Enhanced Community Quarantines.

The impact of COVID-19 has especially been significant on TB and HIV. In 2020, the National TB Control Program recorded a marked decrease in TB testing as well as notification for TB and drug-resistant TB (DR-TB). In 2021, COVID-19 cases surged again as the Delta variant spread.

In 2021, through our COVID-19 Response Mechanism (C19RM), the Global Fund supported the Philippines with over US$37.7 million to fight COVID-19, including to expand COVID-19 testing capacities and support the COVID-19 case management strategy. In addition, interventions focus on COVID-19 mitigation measures for HIV, TB and malaria programs. This includes telemedicine, mobile clinics, bidirectional testing, digital tools to help patients adhere to their treatment, improving TB case finding and transportation networks to transport samples, supporting differentiated service delivery approach to providing HIV services, and integrating information campaigns for COVID-19 and malaria. The programs also support social protection interventions, community-based organization strengthening, addressing human rights barriers to health care and services, and HIV mitigation measures addressing community needs like mental health support and gender-based violence prevention. The Philippines was one of the first countries to develop a strong comprehensive TB adaptive plan to the impact of COVID-19.

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Open access
Published: 21 September 2021

Local government responses for COVID-19 management in the Philippines

Dylan Antonio S. Talabis 1 , 2 ,
Ariel L. Babierra 1 , 2 ,
Christian Alvin H. Buhat 1 , 2 ,
Destiny S. Lutero 1 , 2 ,
Kemuel M. Quindala III 1 , 2 &
Jomar F. Rabajante 1 , 2 , 3

BMC Public Health volume 21 , Article number: 1711 ( 2021 ) Cite this article

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Responses of subnational government units are crucial in the containment of the spread of pathogens in a country. To mitigate the impact of the COVID-19 pandemic, the Philippine national government through its Inter-Agency Task Force on Emerging Infectious Diseases outlined different quarantine measures wherein each level has a corresponding degree of rigidity from keeping only the essential businesses open to allowing all establishments to operate at a certain capacity. Other measures also involve prohibiting individuals at a certain age bracket from going outside of their homes. The local government units (LGUs)–municipalities and provinces–can adopt any of these measures depending on the extent of the pandemic in their locality. The purpose is to keep the number of infections and mortality at bay while minimizing the economic impact of the pandemic. Some LGUs have demonstrated a remarkable response to the COVID-19 pandemic. The purpose of this study is to identify notable non-pharmaceutical interventions of these outlying LGUs in the country using quantitative methods.

Data were taken from public databases such as Philippine Department of Health, Philippine Statistics Authority Census, and Google Community Mobility Reports. These are normalized using Z-transform. For each locality, infection and mortality data (dataset Y ) were compared to the economic, health, and demographic data (dataset X ) using Euclidean metric d =( x − y ) 2 , where x ∈ X and y ∈ Y . If a data pair ( x , y ) exceeds, by two standard deviations, the mean of the Euclidean metric values between the sets X and Y , the pair is assumed to be a ‘good’ outlier.

Our results showed that cluster of cities and provinces in Central Luzon (Region III), CALABARZON (Region IV-A), the National Capital Region (NCR), and Central Visayas (Region VII) are the ‘good’ outliers with respect to factors such as working population, population density, ICU beds, doctors on quarantine, number of frontliners and gross regional domestic product. Among metropolitan cities, Davao was a ‘good’ outlier with respect to demographic factors.

Conclusions

Strict border control, early implementation of lockdowns, establishment of quarantine facilities, effective communication to the public, and monitoring efforts were the defining factors that helped these LGUs curtail the harm that was brought by the pandemic. If these policies are to be standardized, it would help any country’s preparedness for future health emergencies.

Peer Review reports

Introduction

Since the emergence of the COVID-19 pandemic, the number of cases have already reached 82 million worldwide at the end of 2020. In the Philippines, the number of cases exceeded 473,000. As countries around the world face the continuing threat of the COVID-19 pandemic, national governments and health ministries formulate, implement and revise health policies and standards based on recommendations by world health organization (WHO), experiences of other countries, and on-the-ground experiences. Early health measures were primarily aimed at preventing and reducing transmission in populations at risk. These measures differ in scale and speed among countries, as some countries have more resources and are more prepared in terms of healthcare capacity and availability of stringent policies [ 1 , 2 ].

During the first months of the pandemic, several countries struggled to find tolerable, if not the most effective, measures to ‘flatten’ the COVID-19 epidemic curve so that health facilities will not be overwhelmed [ 3 , 4 ]. In responding to the threat of the pandemic, public health policies included epidemiological and socio-economic factors. The success or failure of these policies exposed the strengths or weaknesses of governments as well as the range of inequalities in the society [ 5 , 6 ].

As national governments implemented large-scale ‘blanket’ policies to control the pandemic, local government units (LGUs) have to consider granular policies as well as real-time interventions to address differences in the local COVID-19 transmission dynamics due to heterogeneity and diversity in communities. Some policies in place, such as voluntary physical distancing, wearing of face masks and face shields, mass testing, and school closures, could be effective in one locality but not in another [ 7 – 9 ]. Subnational governments like LGUs are confronted with a health crisis that have economic, social and fiscal impact. While urban areas have been hot spots of the COVID-19 pandemic, there are health facilities that are already well in placed as compared to less developed and deprived rural communities [ 10 ]. The importance of local narratives in addressing subnational concerns are apparent from published experiences in the United States [ 11 ], China [ 12 , 13 ], and India [ 14 ].

In the Philippines, the Inter-Agency Task Force on Emerging Infectious Diseases (IATF) was convened by the national government in January 2020 to monitor a viral outbreak in Wuhan, China. The first case of local transmission of COVID-19 was confirmed on March 7, 2020. Following this, on March 8, the entire country was placed under a State of Public Health Emergency. By March 25, the IATF released a National Action Plan to control the spread of COVID-19. A community quarantine was initially put in place for the national capital region (NCR) starting March 13, 2020 and it was expanded to the whole island of Luzon by March 17. The initial quarantine was extended up to April 30 [ 5 , 15 ]. Several quarantine protocols were then implemented based on evaluation of IATF:

Community Quarantine (CQ) refers to restrictions in mobility between quarantined areas.

In Enhanced Community Quarantine (ECQ), strict home quarantine is implemented and movement of residents is limited to access essential goods and services. Public transportation is suspended. Only economic activities related to essential and utility services are allowed. There is heightened presence of uniformed personnel to enforce community quarantine protocols.

Modified Enhanced Community Quarantine (MECQ) is implemented as a transition phase between ECQ and GCQ. Strict home quarantine and suspension of public transportation are still in place. Mobility restrictions are relaxed for work-related activities. Government offices operates under a skeleton workforce. Manufacturing facilities are allowed to operate with up to 50% of the workforce. Transportation services are only allowed for essential goods and services.

In General Community Quarantine (GCQ), individuals from less susceptible age groups and without health risks are allowed to move within quarantined zones. Public transportation can operate at reduced vehicle capacity observing physical distancing. Government offices may be at full work capacity or under alternative work arrangements. Up to 50% of the workforce in industries (except for leisure and amusement) are allowed to work.

Modified General Community Quarantine (MGCQ) refers to the transition phase between GCQ and the New Normal. All persons are allowed outside their residences. Socio-economic activities are allowed with minimum public health standard.

LGUs are tasked to adopt, coordinate, and implement guidelines concerning COVID-19 in accordance with provincial and local quarantine protocols released by the national government [ 16 ].

In this study, we identified economic and demographic factors that are correlated with epidemiological metrics related to COVID-19, specifically to the number of infected cases and number of deaths [ 17 , 18 ]. At the regional, provincial, and city levels, we investigated the localities that differ with the other localities, and determined the possible reasons why they are outliers compared to the average practices of the others.

We categorized the data into economic, health, and demographic components (See Table 1 ). In the economic setting, we considered the number of people employed and the number of work hours. The number of health facilities provides an insight into the health system of a locality. Population and population density, as well as age distribution and mobility, were used as the demographic indicators. The data (as of November 10, 2020) from these seven factors were analyzed and compared to the number of deaths and cumulative cases in cities, provinces or regions in the Philippines to determine the outlier.

The Philippine government’s administrative structure and the availability of the data affected its range for each factor. Regional data were obtained for the economic component. For the health and demographic components, data from cities and provinces were retrieved from the sources. Due to the NCR exhibiting the highest figures in all key components, an investigation was conducted to identify an outlier among its cities. The z -transform

where x is the actual data, μ is the mean and σ is the standard deviation were applied to normalize the dataset. Two sets of normalized data X and Y were compared by assigning to each pair ( x , y ), where x ∈ X and y ∈ Y , its Euclidean metric d given by d =( x − y ) 2 . Here, the Y ’s are the number of COVID-19 cases and deaths, and X ’s are the other demographic indicators. Since 95% of the data fall within two standard deviations from the mean, this will be the threshold in determining an outlier. This means that if a data pair ( x , y ) exceeds, by two standard deviations, the mean of the Euclidean metric values between the sets X and Y , the pair is assumed to be an outlier.

To identify a good outlier, a bias computation was performed. In this procedure, Y represents the normalized data set for the number of deaths or the number of cases while X represents the normalized data set for every factor that were considered in this study. The bias is computed using the metric

for all x in X and y in Y . To categorize a city, province, or region as a good outlier, the bias corresponding to this locality must exceed two standard deviations from the mean of all the bias computations between the sets X and Y .

Results and discussion

The data used were the reported COVID-19 cases and deaths in the Philippines as of November 10, 2020 which is 240 days since community lockdowns were implemented in the country. Figure 1 shows the different lockdowns implemented per province since March 15. It can be seen that ECQ was implemented in Luzon and major cities in the country in the first few weeks since March 15, and slowly eased into either GCQ or MGCQ as time progressed. By August, the most stringent lockdown was MECQ in the National Capital Region (NCR) and some nearby provinces. Places under MECQ on September were Iloilo City, Bacolod City, and Lanao del Sur, with the last province as the lone community to be placed under MECQ the month after. By November 1, 2020, communities were either placed under GCQ or MGCQ.

COVID-19 community quarantines in Regions III, IVA and VII

Comparison of economic, health, and demographic components and COVID-19 parameters

The economic, health and demographic components were compared to COVID-19 cases and deaths. These comparisons were done for different community levels (regional, provincial, city/metropolitan) (See Tables 2 , 3 , and 4 ). Figure 2 summarizes the correlation of components to COVID-19 cases and deaths at the regional level. In all components, correlations with other parameters to both COVID-19 cases and deaths are close. Every component except Residential Mobility and GRDP have slightly higher correlation coefficient for COVID-19 cases as compared to COVID-19 deaths.

Correlation of components to COVID-19 cases and deaths at the regional level

Among the components, the number of ICU beds component has the highest correlation with COVID-19 parameters. This makes sense as this is one of the first-degree measures of COVID-19 transmission. Population density comes in second, followed by mean hours worked and working population, which are all related to how developed the region is economy-wise. Regions having larger population density also have a huge working population and longer working hours [ 24 ]. Thus, having a huge population density implies high chance of having contact with each other [ 25 , 26 ]. Another component with high correlation to the cases and deaths is the number of doctors on quarantine, which can be looked at two ways; (i) huge infection rate in the region which is the reason the doctors got exposed or are on quarantine, and (ii) lots of doctors on quarantine which resulted to less frontliners taking care of the infected individuals. All definitions of mobility and the GDP are not strongly correlated to any of the COVID-19 measures.

In each data set, outliers were identified depending on their distance from the mean. For simplicity, we denote components that are compared with COVID-19 cases by (C) and with COVID-19 deaths by (D). The summary of outliers among regions in the Philippines is shown in Figs. 3 and 4 . Data is classified according to groups of component. In each outlier region, non-pharmaceutical interventions (NPI) implemented and their timing are identified.

Outliers among regions in the Philippines with respect to COVID-19 cases

Outliers among regions in the Philippines with respect to COVID-19 deaths

Region III is an outlier in terms of working population (C) and the number of ICU beds (C) (see Fig. 5 and Table 5 ). This means that considering the working population of the region, the number of COVID-19 infections are better than that of other regions. Same goes with the number of ICU beds in relation to COVID-19 deaths. Region III is comprised of Aurora, Bataan, Nueva Ecija, Pampanga, Tarlac, Zambales, and Bulacan. This good performance might be attributed to their performance especially on their programs against COVID-19. As early as March 2020, the region had been under a community lockdown together with other regions in Luzon. Being the closest to NCR, Bulacan has been the most likely to have high number of COVID-19 cases in the region. But the province responded by opening infection control centers which offer free healthcare, meals, and rooms for moderate-severe COVID-19 patients [ 27 ]. They have also implemented strict monitoring of entry-exit borders, organization of provincial task force and incident command center, establishment of provincial quarantine facilities for returning overseas Filipino workers, mandated municipal quarantine facilities for asymptomatic cases, and mass testing, among others [ 27 ]. Most of which have been proven effective in reducing the number of COVID-19 cases and deaths [ 28 ].

Outliers among the provinces in Luzon with respect to COVID-19 cases and deaths

Outliers among the provinces in Visayas with respect to COVID-19 cases and deaths

Outliers among the provinces in Mindanao with respect to COVID-19 cases and deaths

Region IV-A is an outlier in terms of population and working population (D) and doctors on quarantine (D) (see Fig. 5 and Table 5 ). Considering their population and working population, the COVID-19 death statistics show better results compared to other regions. Same goes with the number of doctors in the region which are in quarantine in relation to the reported COVID-19 deaths. This shows that the region is doing well in terms of decreasing the COVID-19 fatalities compared to other regions in terms of populations and doctors on quarantine. Region IV-A is comprised of Batangas, Cavite, Laguna, Quezon, and Rizal. Same with Region III, they have been under the community lockdown since March of last year. Provinces of the region such as Rizal have been proactive in responding to the epidemic as they have already suspended classes and distributed face masks even before the nationwide lockdown [ 29 ]. Despite being hit by natural calamities, the region still continue ramping up the response to the pandemic through cash assistance, first aid kits, and spreading awareness [ 30 ].

An interesting result is that NCR, the center of the country and the most densely populated, is a good outlier in terms of GRDP (C) and GRDP (D). Cities in the region launched various programs in order to combat the disease. They have launched mass testings with Quezon City, Taguig City, and Caloocan City starting as early as April 2020. Pasig City started an on-the-go market called Jeepalengke. Navotas, Malabon, and Caloocan recorded the lowest attack rate of the virus. Caloocan city had good strategies for zoning, isolation and even in finding ways to be more effective and efficient. Other programs also include color-coded quarantine pass, and quarantine bands. It is also possible that NCR may just have a very high GRDP compared to other regions. A breakdown of the outliers within NCR can be seen in Fig. 8 .

Outliers in the national capital region with respect to COVID-19 cases and deaths

Region VII is also an outlier in terms of population density (D) and frontliners (D) (see Fig. 6 and Table 5 ). This means that given the population density and the number of frontliners in the region, their COVID-related deaths in the region is better than the rest of the country. This region consists of four provinces (Cebu, Bohol, Negros Oriental, and Siquijor) and three highly urbanized cities (Cebu City, Lapu-Lapu City, and Mandaue City), referred to as metropolitan Cebu. This significant decline may be explained by how the local government responded after they were placed in stricter community quarantine measures despite the rest of the country easing in to more lenient measures. Due to the longer and stricter quarantine in Cebu, the lockdown had a greater impact here than in other areas where restrictions were eased earlier [ 31 ]. Dumaguete was one of the destinations of the first COVID case in the Philippines [ 32 ], their local government was able to keep infections at bay early on. Siquijor was also COVID-19-free for 6 months [ 33 ]. The compounded efforts of the different provinces in the region can account for the region being identified as an outlier.

Among the metropolitan cities, Davao came out as a good outlier in terms of population (C) and working population (C) (see Figs. 7 , 9 , and Table 5 ). This result may be attributed to their early campaign on consistent communication of COVID-19-related concerns to the public [ 34 ]. They were also able to set up transportation for essential workers early on [ 35 ].

Outliers among metropolitan areas in the Philippines with respect to COVID-19 cases and deaths

This study identified outliers in each data group and determined the NPIs implemented in the locality. Economic, health and demographic components were used to identify these outliers. For the regional data, three regions in Luzon and one in Visayas were identified as outliers. Apart from the minimum IATF recommended NPIs, various NPIs were implemented by different regions in containing the spread of COVID-19 in their areas. Some of these NPIs were also implemented in other localities yet these other localities did not come out as outliers. This means that one practice cannot be the sole explanation in determining an outlier. The compounding effects of practices and their timing of implementation are seen to have influenced the results. A deeper analysis of daily data for different trends in the epidemic curve is considered for future research.

Correlation tables, outliers and community quarantine timeline

Availability of data and materials.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

JFR is supported by the Abdus Salam International Centre for Theoretical Physics Associateship Scheme.

This research is funded by the UP System through the UP Resilience Institute.

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Dylan Antonio S. Talabis, Ariel L. Babierra, Christian Alvin H. Buhat, Destiny S. Lutero, Kemuel M. Quindala III & Jomar F. Rabajante

University of the Philippines Resilience Institute, University of the Philippines, Quezon City, Philippines

Faculty of Education, University of the Philippines Open University, Laguna, Philippines

Jomar F. Rabajante

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S. Talabis, D.A., Babierra, A.L., H. Buhat, C.A. et al. Local government responses for COVID-19 management in the Philippines. BMC Public Health 21 , 1711 (2021). https://doi.org/10.1186/s12889-021-11746-0

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DOI : https://doi.org/10.1186/s12889-021-11746-0

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Analysing the Philippines' health and economic response to Covid-19

The Philippines | Economy

The Philippines reported its first Covid-19 case on January 30, 2020, and confirmed its first coronavirus-related fatality three days later. The country was officially placed under a state of calamity for a period of six months on March 17, mandating that national and local authorities mobilise the resources needed to respond to the health crisis. The state of calamity was extended for an additional 12 months in September, facilitating one of the world’s longest, most stringent lockdowns. In March and September of that year the government passed two wide-ranging stimulus packages aimed at helping to mitigate the economic impact of the crisis and aid in the health response.

Movement Restrictions

Covid-19-related restrictions varied, with the government designating each region or metropolitan area one of four quarantine phases, based on contagion figures and in consultation with health officials and local authorities. These labels were re-evaluated every two to four weeks, and local officials had the autonomy to tighten restrictions within their area. The entire island of Luzon – home to over half of the population – was placed under the most stringent grade of restrictions, enhanced community quarantine, on March 16. This remained in force until May 12 across some areas – including the National Capital Region, which accounts for more than one-third of GDP.

By early November 2020 total confirmed cases exceeded 380,000, with over 7000 fatalities. Within the ASEAN region, only Indonesia had officially reported higher case and fatality counts. The Philippines had, however, ramped up testing by this time, with the Department of Health reporting that the country had performed the highest number of tests in South-east Asia as of early August. A curfew remained in place in many areas throughout November and all of the country retained some degree of quarantine restrictions.

Relief Funding

Two weeks after the World Health Organisation officially declared the pandemic, in late March 2020 President Rodrigo Duterte signed the Bayanihan to Heal as One Act – known as Bayanihan 1 – into law. Among the measures included in the stimulus package were economic assistance for disadvantaged families and displaced workers; protocols for coordination between the central government and local government units; a mandate for the public health insurance provider to shoulder the treatment costs for any medical centre employees who contracted the coronavirus; and measures to limit the hoarding and profiteering of essential food, fuel and medical supplies.

Bayanihan 1 was followed by the Bayanihan to Recover as One Act, or Bayanihan 2. Signed into law on September 11, 2020 and valid through December 19 of that year, the P165.5bn ($3.3bn) package included almost P39.5bn ($785.6m) for loans targeting small businesses; P24bn ($477.3m) for the agriculture sector; and P13bn ($258.6m) to assist displaced workers. Bayanihan 2 also mandated the extension of grace periods and zero-interest staggered instalments for rental payments and utility bills incurred by residential occupants and micro-, small and medium-sized businesses during the two strictest lockdown phases.

Recovery Priorities

The administration reinforced the importance of its flagship Build, Build, Build infrastructure development programme throughout the pandemic period, both as a strategy to create jobs in the immediate term and as a means to accelerate economic growth into the future (see Transport & Infrastructure chapter). Meanwhile, efforts to strengthen the digital economy gained momentum, providing an opportunity to grow value added in key segments and broaden financial inclusion. Supplemented by ongoing support for vulnerable groups and targeted strategies that aim to enhance food security and health care, these developments are expected to create a more resilient economy. These shifts will not only help to drive the post-pandemic recovery, but also pave the way for ongoing economic expansion in the years to come.

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The Philippines’ Response to the COVID-19 Pandemic: Learning from Experience and Emerging Stronger to Future Shocks

Celia M. Reyes
COVID-19 pandemic
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COVID-19 policy responses
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crisis response
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The coronavirus disease 2019 (COVID-19) pandemic hit the Philippine economy and society unprecedentedly. To protect the people, the government had to act decisively and identify solutions to contain the rapid spread of the virus and the devastating economic and social disruption caused by the pandemic. This book compiles papers assessing the strategies, policies, and recovery efforts that the government had implemented during the first year of the COVID-19 pandemic. It discusses the challenges that the country had experienced and the government's responses in the areas of health, macroeconomy, food security, labor, social protection, poverty, education, digitalization, fiscal policy, and crisis and risk communication. Learning from these experiences, this book provides recommendations to help the Philippines recover from the current crisis and build better resilience to future shocks.

This publication has been cited 4 times

Alviar, DC. 2023. Sapat ba ang teknolohiya upang epektibong magturo? Mga aral mula sa PIDS . Tutubi News Magazine.
Daily Guardian . 2024. COVID-19 school closures led to significant learning losses – expert . DailyGuardian .
Manila Standard Business. 2023. PIDS: Technology key to learning amid crises . Manila Standard.
Nazario, Dhel. 2023. NAST PHL set to introduce new members, recognize outstanding Filipino scientists . Manila Bulletin.

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100 days of COVID-19 in the Philippines: How WHO supported the Philippine response

Exactly 100 days have passed since the first confirmed COVID-19 case was announced in the Philippines on 30 January 2020, with a 38-year old female from Wuhan testing positive for the novel coronavirus. On the same day, on the other side of the world at the WHO headquarters in Geneva, WHO activated the highest level of alert by declaring COVID-19 as a public health emergency of international concern. The Philippine government mounted a multi-sectoral response to the COVID-19, through the Interagency Task Force (IATF) on Emerging Infectious Diseases chaired by the Department of Health (DOH). Through the National Action Plan (NAP) on COVID-19, the government aims to contain the spread of COVID-19 and mitigate its socioeconomic impacts. The Philippines implemented various actions including a community quarantine in Metro Manila which expanded to Luzon as well as other parts of the country; expanded its testing capacity from one national reference laboratory with the Research Institute of Tropical Medicine (RITM) to 23 licensed testing labs across the country; worked towards ensuring that its health care system can handle surge capacity, including for financing of services and management of cases needing isolation, quarantine and hospitalization; and addressed the social and economic impact to the community including by providing social amelioration to low income families. The World Health Organization (WHO) has been working with Ministries of Health worldwide to prepare and respond to COVID-19. In the Philippines, WHO country office in the Philippines and its partners have been working with the Department of Health and subnational authorities to respond to the pandemic. The country level response is done with support from the WHO regional office and headquarters.

Surveillance

Surveillance is a critical component and is used to detect cases of COVID-19 as well as to understand the disease dynamics and trends and identify hotspots of disease transmission. The Department of Health included COVID-19 in the list of nationally notifiable diseases early in the outbreak to ensure that information was being collected to guide appropriate response actions. Existing surveillance systems were capitalized upon to speed up identification of cases as well as identify unusual clusters. Laboratory confirmation is a critical component of the surveillance system but cannot be the only sources of information. The non-specific symptoms and the novel nature of the disease means that the DOH, with support from WHO, are looking at all available information sources to guide response decision making. WHO also provided technical assistance to selected local government units to strengthen field surveillance for timely data for action at the local level.

Contact tracing

Infection prevention and control

Laboratory and therapeutics access

Clinical care

Non-pharmaceutical interventions and mental health

Risk communication and community engagement

Effective communication and engagement with communities is essential for people to understand the situation, know the situation and practice protective measures to protect their health, their family and the larger community. WHO supported and amplified DOH messaging by releasing various communication materials on the risk of COVID-19 and how people can protect themselves through social media and traditional media. WHO also worked with partners such as UNICEF and OCHA in reaching vulnerable groups, getting their feedback and understanding their information needs.

Logistics support

With lots of moving equipment and supplies required for COVID-19, logistics support is an important part of the response. WHO provided technical support to the DOH in the recalibration of PPE requirements by using WHO projection tools, provided cost estimates, and advised on streamlining the distribution flow of PPEs and other essential supplies. WHO also supported DOH in the development of a commodities dashboard that provides real-time PPE stocks at the facility level, as well as assisted in building an information system for tracking essential COVID-19 commodities.

Subnational operations support

Responding to outbreaks in high risk areas

Moving forward with the response

Much more needs to be done to break the chain of COVID-19 transmission. Some of the challenges that the Philippines continues to face are containing transmission of infection, mitigating the impact in high risks communities and confined settings, as well as ensuring the uniform enforcement of non-pharmaceutical interventions that are already in place. The continuation of the community quarantine will have substantial social and economic impact and thus a heightened effort to control transmission of infections through rigorous contact tracing, isolation of cases, quarantine of contacts while ensuring timely and adequate treatment to save lives will continue to be the primary public health measure. In addition, while the government is exerting all its efforts in this current situation, it also needs to prepare its health systems for surge capacity in the event that a wide-scale community transmission occurs.

In the next few days, the government will carefully consider the next steps, especially on deciding whether or not the communty quarantine will be lifted or levels of quarantine will be differentiated based on the situation of provinces. WHO strongly recommends that when the government considers adjusting public health and social measures in the context of COVID-19 the following requirements must be in place:

COVID-19 transmission is controlled through two complementary approaches – breaking chains of transmission by detecting, isolating, testing and treating cases and quarantining contacts and monitoring hot spots of disease circulation
Sufficient public health workforce and health system capacities are in place
Outbreak risks in high-vulnerability settings are minimized
Preventive measures are established in workplaces
Capacity to manage the risk of exporting and importing cases from communities with high risks of transmission
Communities are fully engaged

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A topic modeling analysis on the early phase of COVID-19 response in the Philippines

Affiliations.

1 Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, 100025, Hong Kong SAR.
2 Information Technology and Computer Education Department, Leyte Normal University, Tacloban City, Leyte, 6500, Philippines.
3 Accident and Emergency Department, King's College Hospital: Princess Royal University Hospital Site, Farnborough Common, Kent, BR6 8ND, United Kingdom.
PMID: 34123718
PMCID: PMC8179722
DOI: 10.1016/j.ijdrr.2021.102367

Like many others across the globe, Filipinos continue to suffer from the COVID-19 pandemic. To shed light on how the Philippines initially managed the disease, our paper analyzed the early phase of the government's pandemic response. Using machine learning, we compiled the official press releases issued by the Department of Health from early January to mid-April 2020 where a total of 283,560 datasets amounting to 2.5 megabytes (Mb) were analyzed using the Latent Dirichlet Allocation (LDA) algorithm. Our results revealed five latent themes: the highest effort (40%) centered on "Nationwide Reporting of COVID-19 Status", while "Contact Tracing of Suspected and Infected Individuals" had the least focus at only 11.68%- indicating a lack of priority in this area. Our findings suggest that while the government was ill-prepared in the early phase of the pandemic, it exerted efforts in rearranging its fiscal and operational priorities toward the management of the disease. However, we emphasize that this article should be read and understood with caution. More than a year has already passed since the outbreak in the country and many (in)actions and challenges have adversely impacted its response. These include the Duterte administration's securitization and militarization of pandemic response and its apparent failure to find a balance between the lives and livelihoods of Filipinos, to name a few. We strongly recommend that other scholars study the various aspects of the government's response, i.e., economic, peace and security, agriculture, and business, to assess better how the country responded and continually responds to the pandemic.

Keywords: COVID-19; Government response; Machine learning; Pandemic; Philippines.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Global COVID-19 data as of…

Global COVID-19 data as of May 2020 based on CSSE-JHU online dashboard.

Design algorithm process utilizing modified…

Design algorithm process utilizing modified KDD used by the authors.

Press release topic probability made…

Press release topic probability made by the authors.

Screenshot of the web design…

Screenshot of the web design of the COVID-19 CORe Portal taken by the…

Pictures of locally-made COVID-19 testing…

Pictures of locally-made COVID-19 testing kit (left) and the GINHAWA ventilator (right) compiled…

Apps and platforms used for…

Apps and platforms used for COVID-19 contact tracing from January to April 2020…

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Philippines

Impact Report: COVID-19 Response in the Philippines

In the past six months, UNHCR has mobilized quickly to support the government-led response to COVID-19 in the Philippines. Help us continue to deliver sustainable & vital services including health, water, sanitation and hygiene to many more forcibly displaced families.

UNHCR Philippines, with the support of donors, in coordination with partners, and together with other United Nations agencies in the country, has mobilized rapidly and comprehensively to help the government respond to this unprecedented crisis – particularly in communities with refugees, asylum seekers, internally displaced populations, and persons at risk of statelessness. They are among the most vulnerable to COVID-19, and they cannot be left behind in the response.

PREPOSITIONING TO RESPOND TO A GLOBAL PANDEMIC

Building on its experience in SARS and ebola outbreaks noting that the health crisis is a global challenge that must be addressed through solidarity and cooperation, in March UNHCR endeavoured to mobilize its resources to effectively combat the public health emergency urging governments and partners that the most vulnerable – including refugees, asylum seekers, stateless and the internally displaced – should be able to access health facilities and services in a non-discriminatory manner.

UNHCR has followed the guidance of the World Health Organization (WHO) and identified a number of prevention and response activities in displacement situations that include:

RAPID RESPONSE

Together with other UN agencies in the Philippines, UNHCR augmented government response efforts in containing the spread of the pandemic and decreasing morbidity and mortality, as well as ensuring protection and assistance for forcibly displaced populations.

Life-Saving Assistance

Providing core relief items, hygiene kits, and personal protective equipment to vulnerable communities and to support duty bearers in their response

Ensuring the continuity of direct assistance to refugees and asylum seekers amid the pandemic

Community Level Health Awareness

Conducting awareness raising sessions and distributing localized COVID-19 information materials focusing on hygiene, respiratory symptoms and signs, referral mechanisms, infection prevention information.

SUSTAINABLE RECOVERY

UNHCR joins the government and other humanitarian partners in helping ensure that the people of the Philippines can emerge stronger and be better prepared to withstand future emergencies.

Information Management

UNHCR Philippines has a functional cooperation with government agencies in instituting a systematic information management & reporting mechanism that maps displacement incidences to gauge the extent of protection provided to internally displaced persons (IDPs)

Information management has become a useful tool in providing targeted assistance as information is derived from evidence-based needs, gaps, as well as displacement trends and analysis gathered by a strong Protection Cluster network

Capacity Building

UNHCR is strengthening the information management capacity of government partners to ensure continuity in refugee status determination procedures, protection monitoring, case management, as well as reporting and mitigating potential protection risks for displaced people amid the public health emergency

Quick Impact Projects

UNHCR is mainstreaming and integrating the impact of COVID-19 into the planning and design of quick impact projects, which promote community empowerment, help foster peaceful co-existence, and strengthen resilience of forcibly displaced populations.

As we move from rapid response to sustainable recovery, you can help ensure that more forcibly displaced families in Mindanao are better prepared and equipped to withstand future emergencies. In the next few months, the impact of COVID-19 will be integrated into the design and implementation of quick impact projects, prioritizing water, sanitation, and hygiene (WASH) facilities.

Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis

Lanndon ocampo.

a Department of Industrial Engineering, Cebu Technological University, Corner M.J. Cuenco Ave. & R. Palma St., Cebu City, 6000, Philippines

b Graduate School, Cebu Technological University, Corner M.J. Cuenco Ave. & R. Palma St., Cebu City, 6000, Philippines

Kafferine Yamagishi

c Department of Tourism Management, Cebu Technological University, Corner M.J. Cuenco Ave. & R. Palma St., Cebu City, 6000, Philippines

The COVID-19 pandemic, which started at Wuhan, has shut down world economies, prompting governments to impose drastic lockdown measures of the economy and the society. As these measures are exhausted, non-COVID-19 related issues such as those associated with the mental and physical well-being of people under lockdowns became an emerging concern. As these issues are evident, not to mention the economic downturn, governments are currently looking at designing lockdown relaxation efforts by simultaneously considering both public health and economic restart. Without documented experiences to rely on, governments are resorting to trial-and-error approach in creating a lockdown exit strategy while preventing succeeding waves of cases that may overwhelm healthcare facilities. Thus, this work pioneers the use of the decision-making trial and evaluation laboratory (DEMATEL) method with intuitionistic fuzzy (IF) sets along with the domain of public health and the emerging COVID-19 pandemic. The DEMATEL handles the intertwined causal relationships among guideline protocols for the relaxation strategy. The intuitionistic fuzzy set theory addresses the vagueness and uncertainty of human judgments in the context of the DEMATEL. A case study of the Philippine government response for the lockdown exit is presented to evaluate the applicability of the proposed method. Findings reveal that compliance of minimum public health standards, limited movement of persons, suspension of physical classes, the prohibition of mass gatherings, non-operation of category IV industries, and non-operation of hotels or similar establishments are the most crucial protocols for such strategy. These findings offer practical insights for the government to allocate resources and impose measures to ensure their implementation, as well as for developing mitigation efforts to cushion their socio-economic impacts. Policy insights and avenues for future works are also discussed.

• Lockdown relaxation protocols are managed to balance public health and economic restart.
• It proposes intuitionistic fuzzy DEMATEL analysis in addressing the emerging COVID-19 pandemic.
• The Philippine government response for the lockdown exit is presented to demonstrate the proposed method.
• Findings reveal the six most crucial protocols for the lockdown exit strategy.
• Policy insights are offered for the government to develop mitigation efforts to cushion adverse socio-economic impacts.

1. Introduction

While most countries were celebrating holidays during the second half of December 2019, the streets at Wuhan City in Hubei Province, China were at a brink of a disease outbreak caused by cases of pneumonia of unknown etiology. These cases are believed to be linked to Huanan Seafood Wholesale Market, which trades fish and wild animals, including bats [ 1 ]. Several claims traced the dates of the pneumonia cases back in November 2019, with the first confirmed case in Wuhan on 1 December [ 2 ]. However, the first case has no exposure to the Huanan seafood market, not until 10 December, when a case directly linked to the market was recorded [ 2 ]. The debate on these claims is still developing. Nonetheless, 27 cases were already documented on 31 December, which prompted the local health officials at Wuhan to inform the World Health Organization (WHO) about the developing disease [ 3 ]. The causative agent was identified as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and the WHO named the disease as COVID-19 [ 3 ].

In just 30 days, COVID-19 quickly spread from Wuhan to the rest of China, pushing the public health services in Wuhan City and Hubei Province to their limits [ 4 ]. While some infected patients are asymptomatic, those with symptoms experience anywhere between mild ones (i.e., dry cough, sore throat, and fever) and more severe and fatal symptoms (i.e., organ failure, septic shock, pulmonary edema, severe pneumonia, and ARDS) which require hospitalization [ 3 ]. A review of its virology, epidemiology, clinical manifestations, diagnosis, treatment, and prevention can be found in Ref. [ 2 ]. [ 5 ] summarized why COVID-19 is such a threat: (1) it can kill adult people with existing health problems, and (2) its transmission is quite efficient. As the cases in China were gaining momentum, COVID-19 was starting to leak outside of its epicenter to the rest of the world. This prompted China on 23 January to implement an unprecedented lockdown in recent history, affecting a whopping 40–60 million people across Hubei province [ 3 ]. The measures are considered restrictive and drastic – curtailing some liberties of its citizens [ 6 ]. It suspended all forms of public transportation [ 3 ], implemented heightened mobility restrictions, limited social interaction, and canceled gatherings [ 7 ], and all public affairs within the city of Wuhan and other cities in Hubei were put off [ 8 ]. More aggressive measures were implemented in Wuhan after 7 February. These include locking down of residential buildings and compounds, strict self-quarantining for families, door-to-door inspection for suspected cases, quarantining suspected cases, and close contacts in quarantine spaces [ 9 ]. Along with these measures are the efforts to ramp up the construction of new hospitals and quarantine facilities to manage the surge of cases in the most affected areas [ 10 ].

Such drastic containment measures in China earned praises from the WHO and are considered crucial in curbing the spread of COVID-19 in mid-February [ 6 , 11 ]. The speed and the magnitude of these measures have never been implemented on such a large scale [ 4 ]. In the absence of antiviral drugs and vaccine, China focused on traditional public health outbreak response mechanisms of identifying cases, testing, isolation, contact tracing, social distancing, quarantine, and community containment [ 4 , 10 , 12 ]. It also recommended basic hand hygiene measures such as frequent hand washing and the use of PPE (e.g., face masks) [ 3 ]. These response initiatives were aimed at buying some time for science (e.g., development of vaccines and antiviral drugs, in-depth characterization) to catch up with the spread [ 4 , 13 ]. The timeline of the COVID-19 and the containment measures implemented in Wuhan can be found in Ref. [ 8 ]. Quantitatively identifying which of the stringent measures were most successful could not be carried out by present models [ 14 , 15 ]. What is known and apparent is that the totality of these measures has delayed COVID-19 spread in China [ 14 ]. Despite these efforts [ 3 ], outlined some of the lessons that China fell short in curbing the spread of COVID-19: lack of transparency, travel restriction delay, quarantine delay, public misinformation, emergency announcement delay, and research and development. Nonetheless, quantifying how these factors play in the viral spread and their long-term impact could not be determined yet and is open for discussion.

The staggering cases of the disease on a global scale prompted WHO on 11 March to declare COVID-19 a pandemic. Outside China, two enormous challenges are upfront: (1) effectively containing current and future outbreaks, and (2) treating the infected population promptly and safely [ 11 ]. The draconian measures imposed by China were, at a period, considered unpopular by other countries. Considered as the only way to truly control outbreaks, one-fifth of the world population is in lockdown [ 16 ]. In the absence of established measures to contain the COVID-19 spread at a scale, and no controlled experiment has taken place [ 17 ], countries resorted to trial-and-error initiatives with various strategies being explored to curb its transmission [ 18 ]. For instance, South Korea and Italy introduced lockdowns in early March [ 18 ]. On 24 March, the Indian government has announced a countrywide lockdown for three weeks [ 19 ]. Initially, the United Kingdom was entertaining the idea of “herd immunity”. It was later on obliged to change gears as the model published by an experienced team at Imperial College, London, predicted that at any response scenario, the number of cases requiring ICU would exceed the ‘surge capacity’ of healthcare facilities [ 20 ]. An unprecedented lockdown was then implemented on 23 March. In the Philippines, the government issued a lockdown in the form of an enhanced community quarantine on 12 March. Collectively, 188 countries have been on a pause, stopping religious meetings, sports events, and other social gatherings, while closing their borders and businesses including schools which affect 1.5 billion students [ 17 ].

The drastic measures and lockdowns implemented by most countries worldwide come at a high economic and socio-economic cost, creating ripples of economic shocks and disrupting the functioning of societies [ 21 ]. The economic costs are straightforward, but the social costs have far-reaching consequences [ 22 ]. Observed that the ongoing crisis had caused vulnerability to food insecurity for people who are already economically vulnerable. The lack of food shops in some countries (e.g., UK) has led people to be unable to acquire the food they need as stay-at-home measures compel them to go out or food supplies are not readily available [ 22 ]. [ 6 ] called for greater attention to the health of the people who are not infected by the virus during lockdowns. They highlighted that those who stopped working have worse mental and physical health conditions, as well as distress, and physically active people are more vulnerable to some well-being issues [ 6 ]. Another indirect effect is the growing cases of deteriorating mental health of people in lockdowns [ 23 ]. [ 23 ] identified some additional health problems such as stress, anxiety, symptoms of depression, insomnia, denial, anger, and fear at a global scale. With similar observation [ 24 ], surveyed the general public in China to assess their levels of psychological impact, anxiety, depression, and stress during the initial stage of the outbreak. They found out that more than half reported the psychological effect as moderate-to-severe, and about one-third rated moderate-to-severe anxiety. Collectively, these concerns may weaken the efficacy of the imposed measures to control the viral spread.

As of this writing [11 May 2020], the total number of COVID-19 cases worldwide is 4,117,684, with global deaths of 286,330 [ 25 ]. It is almost widely accepted that with the onset of current global drastic measures of testing, contact tracing, isolating, social distancing, and quarantining, COVID-19 can be contained, at least in its initial spread. However, the rapid acceleration of the disease in different countries and the near-collapse, if not total breakdown, of healthcare systems even in countries with more robust public health mechanisms indicate shortfalls of preparedness of countries in handling pandemics [ 15 ]. As confinement measures are not sustainable in the long run from an economic and social perspective, easing drastic containment measures is a way forward [ 26 ]. The decision to lift draconian measures comes with a trade-off between socio-economic costs and the potential resurgence of cases [ 27 ]. Cautioned that a premature relaxation of interventions and measures could lead to the second wave of cases, and governments must ensure that healthcare capacities are not pushed to its limits while the outbreaks are growing exponentially. A model developed by Ref. [ 27 ] suggests that a sudden and premature lifting of social distancing measures would yield higher health and economic loss. Even without a ‘premature’ relaxation, lifting altogether, the aggressive measures and lockdowns would cause cases to soar high at an exponential rate [ 28 ]. With a low community level of immunity, the attack rate of approximately 2% could initiate a rapid second wave [ 11 ]. In Taiwan, Hong Kong, and Singapore, lifting of lockdown measures has resulted in the second wave of cases, prompting these countries to enforce stricter measures than what was implemented [ 29 ]. With the sustained economic crash, which may result in more non-COVID-19 deaths and social despair linked to prolonged confinement, which may negatively affect people, a well-designed exit strategy is a significant step [ 26 ].

[ 17 ] further elaborated that a lockdown exit strategy must carefully consider the triangulation of “the health of their citizens, the freedoms of their population, and economic constraints”. These factors could be translated into three control knobs for governments: (1) isolation of patients and contact tracing, (2) border restrictions, and (3) social distancing [ 17 ]. Two primary defining criteria on when to turn these knobs for easing lockdown measures are the (1) testing capacity at a national level [ 30 ], and (2) healthcare capacity [ 15 ]. Upscaling of testing capacity is a precursor of efficient contact tracing, isolation, and quarantining, while the capacity of healthcare facilities dictates the allowable number of cases that require ICU beds for treatments. One plausible scheme which is a “drug holiday” approach [ 18 ], popularly adopted by Singapore and Hong Kong as the “suppress and lift” strategy [ 17 ]. In this approach, social distancing measures are eased out, and reimpose when cases start to climb again [ 17 , 18 ]. This strategy would create a series of small outbreaks within the threshold of the healthcare capacity and builds up immunity to the disease [ 18 ]. Another crucial feature of an exit strategy is to carefully monitor the real-time reproduction number (Rt) and the confirmed case fatality rate (cCFR) to inform strategy against a potential second wave to strike a balance between health and economic objectives [ 27 ]. As the resurgence of cases becomes inevitable brought about by relaxing drastic social distancing measures, governments must proactively prepare that the healthcare system has adequate labor, resources, and facilities to minimize mortality [ 15 ]. On the other hand, another exit strategy currently put forward by Ref. [ 26 ] is to release only immunized but virus-free people back to their normal lives to increase community immunity. When the pandemic subsides, younger people who are low risk but virus-free and not immune may be gradually considered. Such a strategy, as argued by Ref. [ 26 ]; would allow shifting from massive social-distancing measures to the testing of symptomatic cases only, isolation of confirmed cases, and contact tracing and quarantining, along with the release of immune people from containment. Despite these suggested post-lockdown relaxation insights, at present, there is no consensus in the scientific communities or guidance on developing an exit strategy with no agreed benchmarks on measuring safe conditions for returning to normal life [ 29 ].

As of 12 May, the Philippines has 11,086 COVID-19 cases, with 726 deaths [ 25 ]. Its largest metropolitan, Metro Manila, has been on a lockdown (i.e., locally termed as ‘enhanced community quarantine’ (ECQ)) since 12 March. After two months, Metro Manila and the other two large cities are still on a restrictive ECQ while the rest of the country is just recently under a relaxed ‘general community quarantine’ (GCQ). The guidelines of these two conditions are set forth by the Inter-Agency Task Force (IATF), which was formed by the Philippine government early this year to supervise and provide recommendations on all government actions regarding the COVID-19 pandemic. The GCQ status is a relaxation of the ECQ, with measures designed to restart economic activities. After a two-month lockdown of the entire country, the transition guidelines from ECQ to GCQ are provided in the [ 31 ]. The guidelines consist of a set of protocols with obvious overlaps that aim to maintain social distancing, proper hygiene, and minimal movement of people to curb the viral spread while gradually restoring some salient socio-economic activities. These protocols, as argued in the emerging literature, are under a trial-and-error approach with neither controlled experiments that would support the effectiveness of such protocols nor information from prior experience on a massive scale. Note also that current models fail to quantitatively identify which of the stringent measures were most successful in the China experience [ 14 , 15 ]. Nevertheless, the government's priority must be keeping mortality at a minimum, and amelioration measures are necessary to cushion the impact of economic collapse [ 21 ]; thus, a careful strike of balance is crucial.

Thus, this work offers a modeling approach based on network analysis on how these protocols are causally interrelated in an attempt to determine those protocols with high priority. Understanding these intertwined relationships provides insights to policymakers on key protocols, and at the same time, identifies those redundant protocols which can be relaxed. This information is crucial for efficient and effective resource allocation decisions that could strike a balance between maintaining public health and economic restart. To address this objective, an intuitionistic fuzzy decision-making trial and evaluation laboratory (DEMATEL) (IF-DEMATEL) is proposed in this work. DEMATEL, developed by the Science and Human Affairs Program of the Battle Memorial Institute of Geneva between 1972 and 1976, is a network analysis approach based on graph theory that handles a complex system of elements connected by causal relationships [ 32 , 33 ]. It achieves two objectives: (1) to determine the causal relationships among elements in a network (e.g., protocols in a set of protocols), and (2) to cluster the elements into the net cause and net effect groups. Due to the inherent uncertainty of eliciting judgments within the DEMATEL framework, an intuitionistic fuzzy set (IFS) theory is adopted in this work. IFS, proposed by Ref. [ 34 ]; is a generalization of the fuzzy set theory proposed by Ref. [ 35 ]; which handles vagueness and uncertainty in computing. While the classical fuzzy set theory introduces a membership function, the IFS extends this concept to include a non-membership function. The use of IF-DEMATEL is popular across different domain applications, such as strategic decisions in the insurance industry [ 36 ], sustainable solid waste management [ 37 ], critical factors in recycling industry [ 38 ], green supply chain [ 39 ], and project risk assessment [ 40 ]. Note that this list is not intended to be comprehensive. However, its application in public health is scant, most especially in the emerging COVID-19 pandemic. Thus, this work is the first of its kind in addressing (1) the use of IF-DEMATEL in the public health domain, and (2) the application of complexity mapping under uncertainty in the management of COVID-19 pandemic as a global public health emergency. It advances the evolving literature of COVID-19 by effectively identifying key protocols for a lockdown exit strategy, which may set as guidelines for relevant policy- and decision-making.

This paper is outlined as follows: Section 2 illustrates the emerging scenario of the Philippine COVID-19 outbreak. Section 3 presents a brief background of IF and DEMATEL. The proposed detailed methodology is described in Section 4 . Section 5 details the results and the findings. Section 6 highlights the implications of these results. It ends with a conclusion and identification of future works in Section 7 .

2. Emerging scenario of the Philippine COVID-19 outbreak

To date, the Philippines is experiencing one of the worst cases of the COVID-19 outbreaks as the second-highest confirmed cases and deaths in the ASEAN, next to Indonesia (COVID-19 Dashboard, 20200), amplified with its weak healthcare system. The COVID-19 outbreak in Wuhan has reached the Philippine media since December 2019. However, no concrete initiatives have been undertaken until the first COVID-19 case in the country, which was confirmed on 30 January with a 38-year-old female Chinese national [ 41 ]). Table 1 presents the timeline of events relevant to the response of the Philippine government to the COVID-19 outbreak. Note that all dates are adjusted to local times unless those declared by the WHO.

Summary of events in the Philippine COVID-19 outbreak.

Date	Relevant events	Reference
30 January		[ ]) [ ]
1 February		[ ]
2 February		[ ])
4 February		[ ])
7 March		[ ])
9 March		[ ]
11 March		[ ]
12 March		[ ]
16 March		[ ])
17 March	for a tentative period of 6 months.	[ ]
19 March		[ ])
25 March		[ ]
12 April		[ ]
24 April		[ ]
30 April		[ ]
May 7, 2020		[ ]

The Philippine Congress, through the Bayanihan to Heal as One Act, allocates US$ 5.37 billion for the COVID-19 pandemic, where US$ 3.9 billion is allotted for the implementation of the emergency subsidy program, and US$ 1.4 for funding health requirements and other services [ 53 ]). The emergency subsidy program for two months covers the basic needs of the 18 million Filipino families [ 57 ]. The strict quarantine measures have put off public and private establishments that generate more than two-thirds of the overall GDP [ 58 ]). The inflation is expected to reach 2.2% in 2020, subsequently 2.4% in 2021 [ 58 ]. has projected GDP growth at 2% this year, with a strong recovery forecast of 6.5% growth in 2021 with the assumption that the pandemic will be curbed in June 2020. The Philippine government has been granted a loan of US$ 100 million from the World Bank to fund the emergency response project in addressing the healthcare needs for the COVID-19 pandemic, and improve public health preparedness [ 59 ]. Furthermore, the US government has also provided US$ 15.2 million in assistance to the country [ 60 ]. The Department of Labor and Employment highlighted that there are more than one million employees in the formal sector that were affected by temporary closures of businesses or flexible work arrangements (CNN Philippines, 2020). Employees in the manufacturing, hotel, restaurants, and tourism industry absorbed most of the impact [ 61 ]. [ 62 ] reported that the economy might lose between US$ 5.4 billion (best case) and US$ 49.5 billion (worse case) due to COVID-19 based on a Leontief input-output model. Specifically, the losses would come from transportation, storage, and communication sector (US$ 232.1–2.4 billion), manufacturing (US$ 1.6–16.9 billion), wholesale and retail (US$ 1.8–14.3 billion), and other services (US$ 823.2 million to 7 billion) [ 62 ].

As of April 2020, seventeen (17) testing centers for COVID-19 were constructed [ 63 ]. With the population of 109 million with a rapid increase of confirmed cases, the healthcare facilities are collapsing with just 89,000 hospital beds, of which 8,779 are isolation beds, 2,546 are ward beds, and only 1,249 are ICU beds, and 1,937 mechanical ventilators [ 64 ]). Currently, the Philippines has 129,000 doctors, of which only 50% are considered active [ 62 ]. As reported by Ref. [ 62 ]; the average age with COVID-19 cases in the Philippines is 53 years old, and the average age of mortality is 65 years old, 70% of which are male. 56% of the confirmed cases and 62% of reported deaths are concentrated in Metro Manila. On 24 April, the Philippine government decided to slowly lift the strict measures by announcing that lower risk community areas would be placed under GCQ [ 55 ]. The set of guidelines proposed on 7 May details the transition protocols from ECQ to GCQ. As long as an effective antiviral drug or vaccine remains unavailable, the government is looking at a future where provinces or cities are observing GCQ protocols. Table 2 summarizes the protocols issued by the IATF on 7 May. For easier recall, a code is assigned to each protocol. These protocols have inherent interrelationships, and redundancies are apparent to some extent. Each protocol requires resources and control measures that may overwhelm the government. Additionally, some protocols may be relaxed for economic and socio-economic purposes without undermining public health concerns over the pandemic. Thus, carefully identifying these protocols is a crucial task that requires attention.

The GCQ protocols with their corresponding codes.

Codes	GCQ Protocols
P1	compliance of minimum public health standards
P2	limited movement of persons
P3	24-hr curfew of minors and senior citizens
P4	work in government at full operational capacity
P5	limited operational capacity of diplomatic missions and international organizations
P6a	full operational capacity of category I industries
P6b	minimum of 50% operational capacity of category II industries
P6c	maximum of 50% operational capacity of category III industries
P7	limited operations of malls and shopping centers
P8	allowed operation of essential public and private construction projects
P9	non-operation of category IV industries
P10	non-operation of hotels or similar establishments
P11	suspension of physical classes
P12	prohibition of mass gatherings
P13	reduced capacity of public transportation

3. Preliminaries

3.1. intuitionistic fuzzy set (ifs) theory.

[ 35 ] proposed the fuzzy set theory (FST) in handling vagueness and uncertainty in computing information. An extension of the FST is the intuitionistic fuzzy set (IFS) theory, which was introduced by Ref. [ 34 ]. IFS is characterized by a membership function, a non-membership function, and a hesitancy degree which express support, opposition, and neutrality in eliciting information [ 38 ]. This is an advantage over the FST as it can better handle the decision-maker's vagueness in the elicitation process, particularly when eliciting judgment [ 39 ]. Detailed three main advantages of the IFS theory. First, it offers the ability to model unknown information via the degree of hesitation. In the practical application (e.g., COVID-19 pandemic) where decision-makers are unsure about their preferences, IFS theory is more suitable in extracting opinion than the FST. Second, it is characterized by three grades of information that can better capture uncertainty comprehensively. Finally, the traditional FST only handles the degree of “agreement” but fails to represent the degree of “disagreement” which is often depicted in eliciting opinion. The following provides some fundamental concepts of the IFS relevant in this work.

[ 65 ]: Suppose X is a finite, non-empty set, and A ⊆ X . A is a standard fuzzy set if ∃ a membership function μ A ( x ) such that μ A ( x ) : X → [ 0,1 ] . The set of 2-tuple A = { x , μ A ( x ) : x ∈ X , μ A ( x ) ∈ [ 0,1 ] } is a fuzzy set where μ A ( x ) is a membership function of x in A .

[ 65 ]: A triangular fuzzy number can be defined as a triplet A = ( l , m , u ) and the membership function μ A ( x ) is as follows:

[ 34 ]: Suppose X is a finite, non-empty set. Then an IFS A in X is defined as

where μ A ( x ) : X → [ 0,1 ] and v A ( x ) : X → [ 0,1 ] such that 0 ≤ μ A ( x ) + v A ( x ) ≤ 1 , x ∈ X . μ A ( x ) and v A ( x ) represent the membership function and the non-membership function, respectively, of x ∈ X to A . π A ( x ) expresses the degree of lack of knowledge of every x ∈ X to A , and 0 ≤ π A ( x ) ≤ 1 . μ A ( x ) , v A ( x ) , and π A ( x ) follow Equation (3)

[ 66 ]: For a fixed universe E , the IFS A can be interpreted as a mapping E → [ 0,1 ] × [ 0,1 ] , and it can be defined by a 2-tuple μ A ( x ) , v A ( x ) where for x ∈ E , μ A ( x ) denotes the degree of membership of x and v A ( x ) denotes the degree of non-membership of x to A ; and μ A ( x ) and v A ( x ) satisfy the condition μ A ( x ) + v A ( x ) ≤ 1 . The set B is a standard fuzzy subset when μ A ( x ) + v A ( x ) = 1 . The crispification operation as a map [ 0,1 ] × [ 0,1 ] → R is introduced. Here, E = R for IFS.

[ 67 ]: Let A be an IFS. By Definition 4 , let D λ be a crispification operator defined by D λ : [ 0,1 ] × [ 0,1 ] → R . The procedure is described in two steps:

(i) transform A into a (standard) fuzzy set;
(ii) evaluate the standard fuzzy set by using a defuzzification method.

For (i), the operator D λ is defined as

with λ ∈ [ 0,1 ] . Note that D λ ( A ) is a standard fuzzy subset with a membership function

In particular, λ = 0.5 is a solution of the minimization problem

where d denotes the Euclidean distance. With λ = 0.5 , the fuzzy set D 0.5 ( A ) is characterized by a membership function

For (ii), any defuzzification process can be adopted. The center of gravity (COG) method is a candidate.

3.2. The DEMATEL method

Developed between 1972 and 1976 by Battelle Memorial Institute of Geneva for a Science and Human Affairs Program, the DEMATEL method is a graph theoretic tool for analyzing a structural model or system characterized by elements (as vertices) and causal relationships among elements (as edges). It divides all elements into two categories: cause and effect. This categorization leads to superior understanding and better realization of the system's elements, which may offer solutions in convoluted problems [ 32 , 33 ]. Using concepts of graph theory and linear algebra, the following describes the computational process of the DEMATEL:

1. Determine the system elements. This process can be obtained via different approaches, which may include a literature review on the domain topic, focus group discussion on the practical problem, expert decisions. Denote p 1 , p 2 , … , p n for these n elements.
2. Generate the direct-relation matrix. An expert group of H = 1,2 , … , N members perform pairwise comparisons of the causal relationships between n elements. This generates a direct-relation matrix X k = ( x i j k ) n × n for the k t h member, k = 1,2 , … , H . x i j represents the casual influence of the element p i on element p j . An evaluation scale of 0, 1, 2, 3, and 4 is used for this causal influence, representing ‘no influence’, ‘low influence’, ‘medium influence’, ‘high influence’, and ‘very high influence’, respectively. The aggregate direct-relation matrix X , ∀ X k , k = 1,2 , … , H , considering that w k ∈ R is assigned to the importance of the k t h member is described in Equation (8) .
3. Normalize the aggregate direction-relation matrix. The normalized direct-relation matrix is calculated using Equation (9) and Equation (10) .
4. Calculate the total relation matrix. Once G is obtained, a continuous decrease in the system's indirect effects along with the powers of G (i.e., G + G 2 + G 3 + ⋯ ) guarantees convergent solutions to the matrix inversion. The total relation matrix T = ( t i j ) n × n is computed using Equation (11) .
5. Categorize the elements into the net cause and net effect. Compute for D and R using Equation (12) and Equation (13) , respectively.

The ( D + R T ) vector (i.e., also known as the “prominence’ vector) represents the relative importance of each element. Those elements in the ( D − R T ) (i.e., also known as the “relation” vector) having t i − t j > 0 , i = j belong to the net cause group, while those elements with t i − t j < 0 , i = j belong to the net effect group.

The prominence-relation map.

4. Proposed procedure: An IF-DEMATEL application to model the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic

The IF-DEMATEL approach in this work consists of the following steps:

Step 1: Identify the lockdown relaxation protocols.

The lockdown relaxation protocols in transitioning from ECQ to GCQ were extracted Executive Order No. 112 [ 31 ] of the Philippine government. They are considered the elements of the system in the DEMATEL approach. The summary of these protocols, along with their corresponding codes, is shown in Table 2 . Note that there are 15 relaxation protocols identified in Table 2 .

Step 2. Set up the direct-relation matrix.

The matrix was completed by a group of two academics and one infectious disease and public health expert with rich knowledge on the dynamics of systems, public health protocols, and local culture and conditions in the Philippines. In a focus group discussion, the group elicited x i j values in IFS on consensus. Open discussions and careful deliberations were made to ensure that those judgments in the initial-direct relation matrix are not whimsical. The group was asked to provide the μ A ( x ) and v A ( x ) values of x i j on the causal influence of p i on p j . The π A ( x ) values of x i j are computed using Equation (3) . Table 3 presents the initial direct-relation matrix in IFS. Each element is represented as a 2-tuple (as in Definition 4 ), i.e., x i j = μ A ( x ) , v A ( x ) .

Step 3. Obtain the corresponding membership function of the equivalent standard fuzzy subset

The initial direct-relation matrix in IFS.

	P1	P2	P3	P4	P5	P6a	P6b	P6c	P7	P8	P9	P10	P11	P12	P13
P1	0	<0.7,0>	<0.9,0>	<0.7,0.1>	<0.6,0.1>	<0.3,0.2>	<0.15,0.7>	<0.1,0.2>	<0.1,0.3>	<0.5,0.1>	<0.9,0>	<0.7,0.1>	<0.9,0>	<0.9,0>	<0.4,0.3>
P2	<0.8,0>	0	<0.4,0>	<0.4,0.2>	<0.3,0.2>	<0.7,0.3>	<0.1,0.6>	<0.3,0.1>	<0.4,0.4>	<0.6,0.3>	<0.9,0>	<0.6,0>	<0.9,0>	<1,0>	<0.6,0.3>
P3	<0.5,0>	<0.9,0>	0	<0,0.1>	<0,0.1>	<0,0.3>	<0.1,0.3>	<0.3,0.1>	<0,0.1>	<0,0.2>	<0,0>	<0,0>	<0.3,0>	<0,0>	<0.4,0>
P4	<0.2,0.6>	<0.5,0.4>	<0.1,0.3>	0	<0.3,0>	<0.3,0	<0.2,0>	<0.1,0>	<0.1,0.1>	<0.3,0>	<0,0>	<0.2,0.1>	<0.4,0>	<0,0.6>	<0,0.5>
P5	<0.1,0.1>	<0.7,0.2>	<0,0.1>	<0.05,0.05>	0	<0,0>	<0,0>	<0,0>	<0,0.05>	<0,0>	<0,0>	<0,0>	<0.1,0>	<0.1,0.3>	<0.4,0.2>
P6a	<0.3,0.3>	<0.1,0.7>	<0.3,0.2>	<0.2,0>	<0,0>	0	<0.2,0>	<0.2,0>	<0.5,0.1>	<0.1,0>	<0,0.1>	<0,0.3>	<0,0>	<0,0.9>	<0,0.7>
P6b	<0.1,0.2>	<0.05,0.6>	<0.1,0.1>	<0.1,0>	<0,0>	<0.6,0>	0	<0.1,0>	<0.2,0.1>	<0.5,0.1>	<0,0>	<0,0.1>	<0,0>	<0,0.8>	<0.3,0.6>
P6c	<0.1,0.1>	<0.3,0.4>	<0,0>	<0.1,0>	<0,0>	<0,0>	<0.1,0>	0	<0.6,0.05>	<0,0.05>	<0,0>	<0,0.5>	<0,0>	<0.3,0.5>	<0.5,0.2>
P7	<0.3,0.6>	<0.5,0.5>	<0.7,0>	<0.2,0>	<0,0>	<0.3,0.5>	<0.2,0.3>	<0.1,0.3>	0	<0,0.1>	<0,0.2>	<0,0>	<0,0>	<0.4,0.6>	<0.3,0.5>
P8	<0.1,0.4>	<0.3,0.3>	<0,0>	<0.4,0>	<0,0>	<0.1,0>	<0.1,0>	<0,0>	<0.1,0>	0	<0,0.2>	<0,0.2>	<0,0>	<0.05,0.3>	<0.1,0.2>
P9	<1,0>	<0.9,0>	<0.2,0>	<0.2,0.2>	<0,0>	<0,0.7>	<0,0.2>	<0,0.1>	<0.8,0>	<0,0.4>	0	<0.7,0>	<0,0>	<1,0>	<0.7,0>
P10	<0.8,0.2>	<0.9,0.1>	<0.2,0>	<0.4,0.2>	<0,0.1>	<0.1,0.5>	<0.1,0.4>	<0,0.3>	<0,0>	<0,0.2>	<0.9,0>	0	<0,0>	<0.8,0.1>	<0.6,0.05>
P11	<1,0>	<1,0>	<0.5,0>	<0.3,0>	<0.05,0.1>	<0,0>	<0,0>	<0,0>	<0,0>	<0,0>	<0,0>	<0,0>	0	<1,0>	<1,0>
P12	<1,0>	<1,0>	<0.4,0>	<0,0.7>	<0,0.1>	<0,0.7>	<0,0.7>	<0.3,0.2>	<0.7,0.1>	<0,0.3>	<0.9,0>	<0.9,0>	<1,0>	0	<0.6,0>
P13	<0.7,0.3>	<0.6,0.1>	<0.2,0>	<0,0.4>	<0.05,0.05>	<0,0.7>	<0.2,0.5>	<0.5,0.4>	<0.6,0.2>	<0.1,0.3>	<0,0>	<0,0>	<0.6,0>	<0.7,0.2>	0

From Table 3 , the next step is to deffuzify the IFS values. We adopted the two-step defuzzification process of [ 67 ]. The first step is to convert the IFS into corresponding standard fuzzy subsets using Equation (7) . For instance,

Table 4 shows the initial-direct relation matrix in standard fuzzy subsets.

Step 4. Defuzzify the standard fuzzy subset values

The initial direct-relation matrix in standard fuzzy subsets.

	P1	P2	P3	P4	P5	P6a	P6b	P6c	P7	P8	P9	P10	P11	P12	P13
P1	0	0.850	0.950	0.800	0.750	0.550	0.225	0.450	0.400	0.700	0.950	0.800	0.950	0.950	0.550
P2	0.900	0	0.700	0.600	0.550	0.700	0.250	0.600	0.500	0.650	0.950	0.800	0.950	1.000	0.650
P3	0.750	0.950	0	0.450	0.450	0.350	0.400	0.600	0.450	0.400	0.500	0.500	0.650	0.500	0.700
P4	0.300	0.550	0.400	0	0.650	0.650	0.600	0.550	0.500	0.650	0.500	0.550	0.700	0.200	0.250
P5	0.500	0.750	0.450	0.500	0	0.500	0.500	0.500	0.475	0.500	0.500	0.500	0.550	0.400	0.600
P6a	0.500	0.200	0.550	0.600	0.500	0	0.600	0.600	0.700	0.550	0.450	0.350	0.500	0.050	0.150
P6b	0.450	0.225	0.500	0.550	0.500	0.800	0	0.550	0.550	0.700	0.500	0.450	0.500	0.100	0.350
P6c	0.500	0.450	0.500	0.550	0.500	0.500	0.550	0	0.775	0.475	0.500	0.250	0.500	0.400	0.650
P7	0.350	0.500	0.550	0.600	0.500	0.400	0.450	0.400	0	0.450	0.400	0.500	0.500	0.400	0.400
P8	0.350	0.500	0.500	0.700	0.500	0.550	0.550	0.500	0.550	0	0.400	0.400	0.500	0.375	0.450
P9	1.000	0.950	0.600	0.500	0.500	0.150	0.400	0.450	0.900	0.300	0	0.850	0.500	1.000	0.850
P10	0.800	0.900	0.600	0.600	0.450	0.300	0.350	0.350	0.500	0.400	0.950	0	0.500	0.850	0.775
P11	1.000	1.000	0.750	0.650	0.475	0.500	0.500	0.500	0.500	0.500	0.500	0.500	0	1.000	1.000
P12	1.000	1.000	0.700	0.150	0.450	0.150	0.150	0.550	0.800	0.350	0.950	0.950	1.000	0	0.800
P13	0.700	0.750	0.600	0.300	0.500	0.150	0.350	0.550	0.700	0.400	0.500	0.500	0.800	0.750	0

The final step of the defuzzification process of [ 67 ] is to adopt a defuzzification function f that would map f : μ ( x ) → R . To carry out this step, the membership function values in Table 4 are assigned to a triangular fuzzy number = ( 0,4,4 ) . See Definition 2 . Using Equation (1) with l = 0 , m = 4 , u = 4 , the following equation can be set up. Analogous to Equation (1) , we have

where μ ( x ˜ ) is the membership function value shown in Table 4 , l , m , u are parameters of a triangular fuzzy number and the x ˜ is the corresponding crisp or defuzzified value. As an example,

The initial-direct relation matrix in crisp values is presented in Table 5 .

Step 5. Obtain the normalized direct-relation matrix.

The initial direct-relation matrix in crisp values.

	P1	P2	P3	P4	P5	P6a	P6b	P6c	P7	P8	P9	P10	P11	P12	P13
P1	0	3.4	3.8	3.2	3.0	2.2	0.9	1.8	1.6	2.8	3.8	3.2	3.8	3.8	2.2
P2	3.6	0	2.8	2.4	2.2	2.8	1.0	2.4	2.0	2.6	3.8	3.2	3.8	4.0	2.6
P3	3.0	3.8	0	1.8	1.8	1.4	1.6	2.4	1.8	1.6	2.0	2.0	2.6	2.0	2.8
P4	1.2	2.2	1.6	0	2.6	2.6	2.4	2.2	2.0	2.6	2.0	2.2	2.8	0.8	1.0
P5	2.0	3.0	1.8	2.0	0	2.0	2.0	2.0	1.9	2.0	2.0	2.0	2.2	1.6	2.4
P6a	2.0	0.8	2.2	2.4	2.0	0	2.4	2.4	2.8	2.2	1.8	1.4	2.0	0.2	0.6
P6b	1.8	0.9	2.0	2.2	2.0	3.2	0	2.2	2.2	2.8	2.0	1.8	2.0	0.4	1.4
P6c	2.0	1.8	2.0	2.2	2.0	2.0	2.2	0	3.1	1.9	2.0	1.0	2.0	1.6	2.6
P7	1.4	2.0	2.2	2.4	2.0	1.6	1.8	1.6	0	1.8	1.6	2.0	2.0	1.6	1.6
P8	1.4	2.0	2.0	2.8	2.0	2.2	2.2	2.0	2.2	0	1.6	1.6	2.0	1.5	1.8
P9	4.0	3.8	2.4	2.0	2.0	0.6	1.6	1.8	3.6	1.2	0	3.4	2.0	4.0	3.4
P10	3.2	3.6	2.4	2.4	1.8	1.2	1.4	1.4	2.0	1.6	3.8	0	2.0	3.4	3.1
P11	4.0	4.0	3.0	2.6	1.9	2.0	2.0	2.0	2.0	2.0	2.0	2.0	0	4.0	4.0
P12	4.0	4.0	2.8	0.6	1.8	0.6	0.6	2.2	3.2	1.4	3.8	3.8	4.0	0	3.2
P13	2.8	3.0	2.4	1.2	2.0	0.6	1.4	2.2	2.8	1.6	2.0	2.0	3.2	3.0	0

Using Equation (9) and Equation (10) , the normalized direct-relation matrix is computed with g = 39.5 . It is shown in Table 6 .

Step 6. Generate the total relation matrix

Normalized direct-relation matrix.

	P1	P2	P3	P4	P5	P6a	P6b	P6c	P7	P8	P9	P10	P11	P12	P13
P1	0	0.08608	0.09620	0.08101	0.07595	0.05570	0.02278	0.04557	0.04051	0.07089	0.09620	0.08101	0.09620	0.09620	0.05570
P2	0.09114	0	0.07089	0.06076	0.05570	0.07089	0.02532	0.06076	0.05063	0.06582	0.09620	0.08101	0.09620	0.10127	0.06582
P3	0.07595	0.09620	0	0.04557	0.04557	0.03544	0.04051	0.06076	0.04557	0.04051	0.05063	0.05063	0.06582	0.05063	0.07089
P4	0.03038	0.05570	0.04051	0	0.06582	0.06582	0.06076	0.05570	0.05063	0.06582	0.05063	0.05570	0.07089	0.02025	0.02532
P5	0.05063	0.07595	0.04557	0.05063	0	0.05063	0.05063	0.05063	0.04810	0.05063	0.05063	0.05063	0.05570	0.04051	0.06076
P6a	0.05063	0.02025	0.05570	0.06076	0.05063	0	0.06076	0.06076	0.07089	0.05570	0.04557	0.03544	0.05063	0.00506	0.01519
P6b	0.04557	0.02278	0.05063	0.05570	0.05063	0.08101	0	0.05570	0.05570	0.07089	0.05063	0.04557	0.05063	0.01013	0.03544
P6c	0.05063	0.04557	0.05063	0.05570	0.05063	0.05063	0.05570	0	0.07848	0.04810	0.05063	0.02532	0.05063	0.04051	0.06582
P7	0.03544	0.05063	0.05570	0.06076	0.05063	0.04051	0.04557	0.04051	0	0.04557	0.04051	0.05063	0.05063	0.04051	0.04051
P8	0.03544	0.05063	0.05063	0.07089	0.05063	0.05570	0.05570	0.05063	0.05570	0	0.04051	0.04051	0.05063	0.03797	0.04557
P9	0.10127	0.09620	0.06076	0.05063	0.05063	0.01519	0.04051	0.04557	0.09114	0.03038	0	0.08608	0.05063	0.10127	0.08608
P10	0.08101	0.09114	0.06076	0.06076	0.04557	0.03038	0.03544	0.03544	0.05063	0.04051	0.09620	0	0.05063	0.08608	0.07848
P11	0.10127	0.10127	0.07595	0.06582	0.04810	0.05063	0.05063	0.05063	0.05063	0.05063	0.05063	0.05063	0	0.10127	0.10127
P12	0.10127	0.10127	0.07089	0.01519	0.04557	0.01519	0.01519	0.05570	0.08101	0.03544	0.09620	0.09620	0.10127	0	0.08101
P13	0.07089	0.07595	0.06076	0.03038	0.05063	0.01519	0.03544	0.05570	0.07089	0.04051	0.05063	0.05063	0.08101	0.07595	0

The total relation matrix is obtained using Equation (11) and is shown in Table 7 . The corresponding ( D + R T ) and ( D − R T ) vectors are presented in Table 8 . Likewise, the categorization of protocols according to net cause or net effect is shown in Table 8 . These vectors were computed following Equation (12) and Equation (13) .

Step 7. Construct the prominence-relation map

Total relation matrix.

	P1	P2	P3	P4	P5	P6a	P6b	P6c	P7	P8	P9	P10	P11	P12	P13
P1	0.34211	0.44113	0.39759	0.34902	0.33677	0.27659	0.23325	0.30408	0.33857	0.32014	0.40713	0.37274	0.42513	0.40042	0.36184
P2	0.42360	0.35816	0.37401	0.32945	0.31709	0.28780	0.23369	0.31533	0.34639	0.31378	0.40520	0.37046	0.42276	0.40298	0.36826
P3	0.33828	0.37019	0.24351	0.25971	0.25329	0.21227	0.20361	0.26207	0.27841	0.24009	0.29878	0.28102	0.32714	0.29362	0.30792
P4	0.26312	0.29756	0.25221	0.19336	0.24728	0.22213	0.20716	0.23427	0.25635	0.24077	0.26611	0.25534	0.29670	0.23158	0.23660
P5	0.29411	0.32929	0.26794	0.24875	0.19409	0.21373	0.20231	0.23806	0.26382	0.23469	0.27855	0.26252	0.29677	0.26306	0.27931
P6a	0.25155	0.23656	0.24092	0.22919	0.21279	0.14226	0.19068	0.21768	0.25007	0.21142	0.23471	0.21269	0.25111	0.19111	0.20140
P6b	0.26069	0.25235	0.24834	0.23501	0.22289	0.22585	0.14169	0.22347	0.24877	0.23467	0.25157	0.23300	0.26409	0.20757	0.23110
P6c	0.28332	0.29186	0.26402	0.24612	0.23561	0.20776	0.20240	0.18323	0.28389	0.22579	0.26811	0.23124	0.28285	0.25236	0.27457
P7	0.25088	0.27666	0.25006	0.23428	0.21943	0.18524	0.18027	0.20662	0.19200	0.20811	0.24175	0.23699	0.26297	0.23511	0.23483
P8	0.26016	0.28597	0.25481	0.25252	0.22814	0.20719	0.19720	0.22443	0.25468	0.17324	0.25063	0.23626	0.27311	0.24043	0.24757
P9	0.40736	0.42097	0.34297	0.29926	0.29331	0.22136	0.22910	0.28205	0.35908	0.26381	0.29588	0.35527	0.36023	0.38246	0.36361
P10	0.36980	0.39478	0.32379	0.29187	0.27282	0.22153	0.21303	0.25805	0.30619	0.25745	0.36431	0.25777	0.33935	0.35004	0.33833
P11	0.41743	0.43481	0.36622	0.32195	0.30009	0.26237	0.24660	0.29672	0.33299	0.29153	0.35267	0.33175	0.32333	0.38934	0.38519
P12	0.41729	0.43525	0.35923	0.27447	0.29350	0.22468	0.21065	0.29569	0.35597	0.27226	0.39045	0.36971	0.41038	0.30080	0.36887
P13	0.33277	0.35220	0.29930	0.24360	0.25564	0.19113	0.19696	0.25530	0.29931	0.23738	0.29696	0.28005	0.33859	0.31533	0.24164

The prominence and relation vectors.

Codes	GCQ Protocols				Rank		Rank	Category
P1	compliance of minimum public health standards	5.30651	4.91247	10.21898	2	0.39403	2	net cause
P2	limited movement of persons	5.26895	5.17773	10.44669	1	0.09122	7	net cause
P3	24-hr curfew of minors and senior citizens	4.16989	4.48491	8.65480	7	−0.31502	14	net effect
P4	work in government at full operational capacity	3.70056	4.00857	7.70912	11	−0.30801	13	net effect
P5	limited operational capacity of diplomatic missions and international organizations	3.86700	3.88275	7.74975	10	−0.01574	8	net effect
P6a	full operational capacity of category I industries	3.27413	3.30188	6.57601	14	−0.02775	9	net effect
P6b	minimum of 50% operational capacity of category II industries	3.48107	3.08861	6.56968	15	0.39246	3	net cause
P6c	maximum of 50% operational capacity of category III industries	3.73314	3.79703	7.53017	12	−0.06389	10	net effect
P7	limited operations of malls and shopping centers	3.41520	4.36649	7.78169	9	−0.95129	15	net effect
P8	allowed operation of essential public and private construction projects	3.58635	3.72513	7.31147	13	−0.13878	11	net effect
P9	non-operation of category IV industries	4.87670	4.60281	9.47951	4	0.27389	4	net cause
P10	non-operation of hotels or similar establishments	4.55911	4.28682	8.84593	6	0.27229	5	net cause
P11	suspension of physical classes	5.05299	4.87451	9.92750	3	0.17848	6	net cause
P12	prohibition of mass gatherings	4.97918	4.45620	9.43539	5	0.52298	1	net cause
P13	reduced capacity of public transportation	4.13615	4.44102	8.57716	8	−0.30487	12	net effect

The prominence-relation map, similar to Fig. 1 , is constructed based on ( D + R T , D − R T ) coordinates. This map is illustrated in Fig. 2 .

The prominence-relation map of lockdown relaxation protocols for Philippine COVID-19 response.

5. Results and discussion

The results show that compliance of minimum public health standards (P1), limited movement of persons (P2), minimum of 50% operational capacity of category II industries (P6b), non-operation of category IV industries (P9), non-operation of hotels or similar establishments (P10), suspension of physical classes (P11), and prohibition of mass gatherings (P12) are categorized into a net cause group. They impact the entire set of guidelines, and their attainment or non-attainment affects the balance of public health and socio-economic performance. Thus, they should be given more attention by the IATF. These protocols in the net cause group have a more influential impact ( D ) than influenced impact ( R ). On the other hand, the net effect group contains the 24-hr curfew of minors and senior citizens (P3), work in government at full operational capacity (P4), limited operational capacity of diplomatic missions and international organizations (P5), full operational capacity of category I industries (P6a), maximum of 50% operational capacity of category III industries (P6c), limited operations of malls and shopping centers (P7), allowed operation of essential public and private construction projects (P8), and reduced capacity of public transportation (P13). They tend to be easily influenced by other protocols as their ( D − R T ) values are negative, which implies that the influential impact ( D ) of these protocols are lower than their influenced impact ( R ).

The ( D + R T ) scores describe the relative significance or prominence of the protocols. In this work, the limited movement of persons (P2) yields the highest ( D + R T ) score; thus, it must be considered as a relatively important for the lockdown exit strategy. This finding also implies that this protocol possesses the highest impact, both received and given. This protocol is central to the set of guidelines by the IAFT. Also, this result is supported by the insights of [ 7 , 68 ] on how China curb the disease spread. Most countries (e.g., South Korea) who have suppressed the first wave of cases found themselves in a situation where a spike of a new wave of cases emerges just days after they ease down the lockdown measures, particularly allowing people to move around on purpose beyond non-essential things such as leisure, going out to parks, restaurants, malls, bars, and opening of schools. The ranking of protocols according to the ( D + R T ) scores is described as follows: P 2 ≻ P 1 ≻ P 11 ≻ P 9 ≻ P 12 ≻ P 10 ≻ P 3 ≻ P 13 ≻ P 7 ≻ P 5 ≻ P 4 ≻ P 6 c ≻ P 8 ≻ P 6 a ≻ P 6 b . The ( D + R T ) scores yield the following ranking: P 12 ≻ P 1 ≻ P 6 b ≻ P 9 ≻ P 10 ≻ P 11 ≻ P 2 ≻ P 5 ≻ P 6 a ≻ P 6 c ≻ P 8 ≻ P 13 ≻ P 4 ≻ P 3 ≻ P 7 .

Overall, identifying the critical protocols must simultaneously consider both ( D + R T ) and ( D − R T ) vectors. To achieve this, we refer to Fig. 1 and categorize all protocols into four distinct categories: minor key factors (low prominence, high relation), key factors (high prominence, high relation, indirect factors (high prominence, low relation), and independent factors (low prominence, low relation. Based on Fig. 2 , the minor key factors comprise limited operational capacity of diplomatic missions and international organizations (P5), minimum of 50% operational capacity of category II industries (P6b), limited operations of malls and shopping centers (P7), allowed operation of essential public and private construction projects (P8). The key factors include compliance of minimum public health standards (P1), limited movement of persons (P2), non-operation of category IV industries (P9), non-operation of hotels or similar establishments (P10), suspension of physical classes (P11), and prohibition of mass gatherings (P12). The indirect factors category is composed of 24-hr curfew of minors and senior citizens (P3) and reduced capacity of public transportation (P13). The independent factors consist of work in government at full operational capacity (P4), full operational capacity of category I industries (P6a), and maximum of 50% operational capacity of category III industries (P6c). We focus our attention on the key factors category and identify the most crucial protocols. In this category, the minimum public health standards protocol (P1) yields the most important one. Thus, the IATF must concentrate its resources and efforts to ensure that minimum public health standards are strictly observed during the GCQ. This finding is consistent with the observations of [ 3 ] on how China responded to the disease spread. Although those drastic lockdown measures are relaxed and people start to move around, relaxing public health standards (e.g., proper hygiene, wearing of masks, disinfecting public spaces, physical distancing) would stimulate a new surge of cases. The rank order of the key factors is as follows: P 1 ≻ P 2 ≻ P 11 ≻ P 12 ≻ P 9 ≻ P 10 .

6. Policy insights for the lockdown exit strategy

The Philippine government has become more efficient and transparent with the release of guidelines and protocols to curb the spread of COVID-19. The key factors in Fig. 2 represent the most crucial lockdown exit protocols, which would provide a balance between public health and economic restart. The government should allocate its resources and plan and implement strict control and monitoring mechanisms on these priority protocols as they impact other protocols for the successful attainment of GCQ's purpose. This work provides better insights to further streamline the lockdown exit strategy of the Philippine government.

The protocol on ensuring minimum public health standards (P1) yields the most critical protocol for implementing the GCQ. This protocol enforces social distancing, wearing of face masks, body temperature checks, provision of sanitation stations, immune system boosting, and disinfecting public spaces at all times. While the GCQ allows movements of people to restart the economy and the society, observing public health standards shields the general health of the community from such movements. It is straightforward to note that compliance to minimum health standards impacts most protocols and serves as a precursor in observing other protocols in the list. Thus, the IATF must establish control measures in those socio-economic activities allowed in the GCQ guidelines so that public health standards appropriate in responding to the COVID-19 pandemic are maintained. For instance, the DOH of the Philippine government has issued “Guidelines on the Risk-Based Public Health Standards for COVID-19 Mitigation” on 27 April [ 69 ] that guides the roles of various stakeholders in maintaining risk-based public health standards. Control measures must be heightened to ensure its strict implementation in the GCQ.

Closely linked to maintaining public health standards is the protocol that there must be limited movement of persons (P2). While P2 supports economic restart through cross-border movements, it also ensures that those movements are just related to essential activities to support the economy. P2 implies that public transportation is reduced, mall operations are limited, suspension of classes, and flexible work arrangements, among others. Managing food capacity and demand at a scale supports P2 by limiting the number of customers through booking and reservations to avoid long queues in supermarkets, retail stores, and food establishments. Establishments may venture into online platforms and delivery service as part of their augmented product. This protocol must be upheld and strictly monitored in GCQ by imposing measures that limit the movement of people. Note that people in lockdowns became impatient in responding to government measures that disrupt their daily lives. This stimulates people to go back to their pre-COVID-19 normal, which would drastically increase movements. Experiences in Hong Kong, Singapore, and South Korea reveal that after easing lockdown measures, the number of new cases surge in just a matter of days as the movement of people became uncontrollable.

The education sector absorbs a massive impact for these draconian measures. Suspension of physical classes (P11) has driven all academic institutions to shift towards online classes, distance learning, flexible learning, and other alternative modes of learning to cushion the disruption amidst the pandemic. With these platforms, some fundamental challenges become apparent for all stakeholders, including students, teachers, and administrators. Poor yet expensive internet connection in the Philippines, limited access to the internet in most rural communities, inadequate skills and experience of both teachers and students in such platforms, and the unavailability of those platforms on a school-wide basis are some of those challenges. Despite these challenges, suspension of physical classes must be maintained as a crucial protocol under GCQ conditions. As an augmentation, the government must support the establishment of necessary infrastructure to enable schools to venture for digital platforms in education as the new normal. The government, along with the academic institutions, must respond to these changes by promptly planning support services for all stakeholders, since personal interaction may not be possible without the availability of the COVID-19 vaccine. Issues in developing countries like the Philippines, such as Internet connectivity, availability of technologies (gadgets), and the technical capacity to go digital must be promptly addressed.

Gatherings such as conferences, festivals, concerts, sports events, and weddings, among others, would discourage social distancing, as huge crowds become uncontrollable. Thus, the prohibition of mass gatherings (P12) must be central to the lockdown exit strategy. To balance economic underpinnings, the government must implement a critical assessment on a per event basis. Permits to event organizers must be secured for proper response whether to suspend, cancel, or consider reducing the number of attendees. Recreation and leisure activities contribute to the overall mental and physical well-being of people under lockdowns [ 23 , 24 ]. However, the non-operation of category IV industries (P9) protocol discourages engagement of leisure activities, which induce sharing or touching of equipment that can spread the disease. To balance with economic goals, Category IV industries may work closely with the government to establish a comprehensive and robust risk assessment and management of activities during GCQ. With the travel restrictions imposed on global destinations beginning in January 2020, the tourism industry has been on a sore end, worsen with the uncertainty and stigma of tourists out of fear for safety. In support of the category IV industries, it can gradually operate some of the establishments such as museums, sightseeing, and some sports activities that do not involve sharing or touching of equipment and where social distancing can be easily implemented and monitored. Furthermore, the government must allocate subsidies toward destination promotion and may invest in virtual tourism during post-lockdown.

The non-operation of category IV industries (P9) is supported by the non-operation of hotels or similar establishments (P10) with the limited operation of the accommodation establishments due to travel restrictions and stigma. Hotels may position their core products in response to the GCQ conditions. For instance, hotels may shift towards highlighting their in-house restaurants through offering deliveries, take-out services, and catering bulk order delivery for small household gatherings. In the case of reopening hotels, content marketing can be adopted through the creation of initiatives that would build strong and profitable relationships with the market. For instance, hygiene standards in hotels and the safety measures of the hotel in response to the pandemic may be highlighted in the promotion campaign, as this is one of the major concerns of the traveling public. Moreover, the government must still strictly impose social distancing measures in hotels and monitor hygiene practices of these establishments as part of the new normal.

7. Conclusion and future works

As economic shutdowns were implemented and the mental and physical well-being of people under lockdowns in response to the COVID-19 pandemic becomes increasingly evident, governments around the world are already planning, if not implementing, relaxation efforts of those drastic measures implemented early this year. However, the emerging literature, as well as practical experiences of those countries who are already lifting those harsh measures, cautions the resurgence of a new wave of cases that may burden (again) the healthcare systems. Thus, a careful lockdown exit strategy is crucial for governments in order to balance the two conflicting objectives: (1) keep mortality at minimum, and (2) initiate an economic restart. Nevertheless, without controlled experiments and documented experience on such a massive scale, governments resort to trial-and-error approach on designing effectively a post-lockdown strategy. In this work, we demonstrate how network modeling helps in providing insights into the relaxation approach. To address those two objectives, as mentioned above, an intuitionistic fuzzy DEMATEL (IF-DEMATEL) approach is highlighted in this work. A case study in modeling the Philippine relaxation protocols is provided here to demonstrate the applicability of the IF-DEMATEL.

Transitioning from an ECQ (lockdown) status to a more relaxed GCQ status has been initiated by the Philippine government, and 15 GCQ protocols were introduced. This work aims to identify key protocols for the government to invest its resources and provide strict control measures to ensure its implementation. Results suggest that compliance of minimum public health standards, limited movement of persons, minimum of 50% operational capacity of category II industries, non-operation of category IV industries, non-operation of hotels or similar establishments, suspension of physical classes, and prohibition of mass gatherings protocols are part of the cause group which implies that they impact the rest of the guideline protocols, and they are influential in attaining the GCQ objectives. Findings also reveal that six important protocols must be given more attention, and more control measures are necessary. They include, according to priority degree of importance, compliance of minimum public health standards, limited movement of persons, suspension of physical classes, the prohibition of mass gatherings, non-operation of category IV industries, and non-operation of hotels or similar establishments. The Philippine government, through its pandemic task force, must ensure that these six protocols must receive more resources (e.g., funds, workforce) and strict measures must be put in place for their implementation, as well as for developing mitigation efforts to cushion their impacts on the economy and the society. These insights are crucial in resource allocation decisions and policy formulation for the government. Finally, this work reveals that the use of network modeling under uncertainty, specifically IF-DEMATEL, has considerable potential for public health studies in bringing in systemic insights for decision- and policymaking. This work, along with its use of the IF-DEMATEL, is the first of its kind in addressing the emerging COVID-19 pandemic.

Nevertheless, this work is not free from limitations. First, the limited number of experts sets a stage for future research which could handle a significant number of stakeholders, and decision- and policymakers. Future research may explore the same methodological approach to justify the validity of the findings of this work. Second, the proposed method is flexible if new protocols are introduced or removed from the current set of guideline protocols. Future work could address the sensitivity and the implications in the number and kind of protocols when changes are introduced to better respond to the evolving pandemic. Third, the application of the proposed approach is limited to a Philippine case with different culture, geography, political settings, and environment. The findings may not be extended directly to other countries or regions. Thus, future work could also adopt the proposed approach to other countries in developing their lockdown exit protocols. Fourth, the use of other fuzzy DEMATEL extensions (e.g., hesitant fuzzy sets, type-2 fuzzy sets, neutrosophic sets) could be explored and compared to the findings of this work. Fifth, other network modeling techniques such as system dynamics modeling, interpretative structural modeling technique, among others, could also be adopted in future works. Sixth, some predictive modeling techniques for assessing the impact of lockdown exit protocols, such as the adaptive neuro-fuzzy inference system [ 70 , 71 ], can be explored in future work. Finally, the use of multi-attribute decision-making techniques in prioritization problems along the domains of public health or the emerging COVID-19 pandemic is an interesting platform for future research.

CRediT authorship contribution statement

Lanndon Ocampo: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Writing - original draft, Writing - review & editing. Kafferine Yamagishi: Formal analysis, Visualization, Writing - original draft.

Biographies

Lanndon Ocampo is an Associate Professor in the Department of Industrial Engineering at Cebu Technological University (Philippines). He received his Ph.D. in Industrial Engineering from De La Salle University (Philippines) and his MEng and BSc (cum laude) degrees in Industrial Engineering as well as MSc in Mathematics from the University of San Carlos (Philippines). He has authored over 90 international peer-reviewed journal papers and has presented papers at over 30 research conferences. His research interests include optimization, multi-attribute decision-making, decision science, systemic risk analysis, and sustainable manufacturing. He is currently the Editor-in-Chief of the International Journal of Applied Industrial Engineering (IGI-Global). He is a 2017 Outstanding Young Scientist awardee by the National Academy of Science and Technology, Philippines (NAST PH), and a 2018 Outstanding Cebuano awardee in the field of Science and Technology. He is named as one of 2018 THE ASIAN SCIENTIST 100 – an annual listing of the region's top researchers, academics, and innovators. Most recently, he is conferred as the 2019 Achievement Awardee of the National Research Council of the Philippines (NRCP) under the Division of Engineering and Industrial Research.

Kafferine Yamagishi is an Assistant Professor, and currently the Chair of the Department of Tourism Management, College of Management and Entrepreneurship at Cebu Technological University, Philippines. She attained her Master of Management major in Tourism Management at the University of San Carlos (Philippines), where she is currently taking up her Doctor of Philosophy degree in Business Administration. She received her Certification in Professional Education and attained her Master of Arts in Education major in Administration and Supervision at Cebu Technological University. She graduated Bachelor of Science in Tourism (cum laude) at the University of San Jose-Recoletos, Philippines. Before joining academia, she worked both in the hospitality and tourism industry. She currently has four published articles in Scopus-indexed journals. Also, she has presented papers to research conferences throughout her academic career. Her research interests include tourism management, destination planning, tourism marketing, and events management.

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Advancements in fish vaccination: current innovations and future horizons in aquaculture health management.

1. Introduction

2. fish immune system, 3. bacterial, viral, and parasitic diseases in fish, 4. current licensed vaccines for bacterial diseases, 4.1. edwardsiellosis in fish, 4.2. enteric septicemia of catfish, 4.3. bacterial kidney disease, 4.4. flavobacteriosis/columnaris disease, 4.5. furunculosis, 4.6. piscine streptococcosis, 4.7. enteric red mouth disease/yersiniosis, 4.8. lactococcosis, 5. current licensed vaccines for viral diseases, 5.1. current licensed vaccines for parasitic diseases, 5.2. challenges and limitations in developing vaccines for fish, 6. summary and conclusions, conflicts of interest.

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Click here to enlarge figure

Name of the Diseases	Causative Agent	Fish It Affects
Bacterial
Atypical furunculosis	Aeromonas salmonicida	Salmonids, spotted wolfish, Atlantic cod
Motile aeromonid septicemia	Aeromonas hydrophila, A. caviae, A. veronii biovar sobria	Freshwater fish species, including catfish and brass
Vibriosis	Vibrio spp., including V. harveyi, V. vulnificus, V. alginolyticus, and V. parahaemolyticus	Marine fish including salmonids, yellowtail, halibut, amberjack
Enteric septicemia	Edwardsiella ictaluri	Catfish
Edwardsiellosis	Edwardsiella tarda	Catfish, striped bass, tilapia, sea bream
Tuberculosis	Mycobacterium marinum, M. fortuitum, M. chelonae	Marine, brackish, and freshwater fish, including sea bass, tropical aquarium fish
Rainbow trout fry syndrome/Bacterial Cold-Water Disease	Flavobacterium psychrophilum	Salmonids, freshwater fish
Columnaris	Flavobacterium columnare	Cyprinids, trout, tilapia
Streptococcosis	Streptococcus agalactiae	Tilapia, bass, rainbow trout
Streptococcosis	Streptococcus parauberis	Olive flounder, rainbow trout, tilapia, bass
Streptococcosis	Streptococcusiniae	Atlantic salmon, rainbow trout, and tilapia
Enteric redmouth disease/Yersiniosis	Yersinia ruckeri	Salmonids, rainbow trout, eel, minnows, sturgeon, and crustaceans
Lactococcosis	Lactococcus garvieae	Rainbow trout, yellowtail, catfish, olive flounder, greytail mullet, amberjack, kingfish
Viral
Tilapia Lake Virus	Tilapia Tilapinevirus	Tilapia and hybrid tilapia fish
Infectious Hemorrhagic necrosis virus	Novirhabdovirus	Trout and salmon
Infectious salmon anemia	Orthomyxovirus	Atlantic salmon, rainbow trout, coho salmon
Infectious pancreatic necrosis	Birnavirus	Salmonids, sea brass, sea bream, Pacific cod
Koi Herpes Virus	Herpesvirus	Cyprinus carpio
Red Sea Bream Iridovirus	Iridovirus	Marine fish species including red sea bream, japanese seabass, and striped jack
Salmonid Alphavirus	Alphavirus	Atlantic salmon, rainbow trout
Iridoviral disease	Iridovirus	Amberjack, yellowtail, red sea bream
	Parasites
Costiasis	Ichthyobodo necotor	Several freshwater and saltwater fish
Salmon Poisoning disease	Nanophyetus salmincola	Salmon, several freshwater fish
White Spot	Ichthyophthirius mulifiliis	Freshwater fish
Sea Lice	Lepeophtheirus solmonis	Marine salmonids
Whirling Disease	Myxobolus cerebralis	Trout, salmon, whitefish
Myxosporeans	Myxobolus genera	Freshwater and marine fish
Microsporean	Pleistophora genera	Freshwater and marine fish

Disease	Pathogen	Host	Type of Vaccine	Route of Delivery	Trade Name	Country
Enteric septicaemia of catfish (ESC)	Edwardsiella ictaluri	catfish	Live attenuated	Immersion	Aquavac-ESC	US
Bacterial Kidney Disease (BKD)	Renibacterium salmoninarum	salmonids	Live attenuated	IP	Renogen	US Canada Chile
Flavobacteriosis/Columnaris	Flavobacterium columnare Flavobacterium maritimus	cyprinids, salmonids, catfish	Live attenuated	Immersion	Aquavac-Col	US Canada Chile
			Inactivated	IP	Alpha Ject IPNVFlevo 0.025	Chile
			Killed bacterin	Immersion	FryVacc 1	US Canada
			Killed bacterin	Immersion	FryVacc 2	Chile
Furunculosis	Aeromonas salmonicida	Atlantic salmon and rainbow trout	Inactivated, oil-based	IP	AlphaJect 3000	Denmark Finland Iceland Ireland Norway Sweden
					Alpha Ject 2.2	UK
					Alpha Ject 4-1, Alpha Ject 5-1	Chile
					Alpha Ject 6-2	Norway The Faroe Islands
					Alpha Ject micro 7 ILA	Norway The Faroe Islands
			Subunit vaccine	IP	Norvax Minova 6	Norway
			Inactivated bacterin	IP	AquaVac-FNM	UK Ireland Spain France
			Killed bacterin	IP	Lipogen Forte, Furogen Dip, Forte VI	US Canada
Streptococcosis	Streptococcus iniae	tilapia and seabass	Inactivated	IP or Bath	Norvax Strep Si, Aquavac Strep Sa	Vietnam Honduras Indonesia
	Streptococcus iniae	tilapia	Killed	IP	Aquavac-Garvetil	Honduras Venezuela Ecuador The Philippines Indonesia
	Streptococcus agalactiae	tilapia	Inactivated	IP	AlphaJect micro1 TiLa	Brazil Colombia Honduras Indonesia Panama
	Streptococcusparauberis	turbot	Inactivated	IP	Icthiovac-STR	Spain
Vibriosis	V. anguillarum V. ordalii	Atlantic salmon	Inactivated, oil-based	IP	Alpha Ject micro 7 ILA, Alpha Ject 6-2	Norway The Faroe Islands
			Inactivated, oil-based	IP	Alpha Ject 5-1, Alpha Ject 4-1,	Chile
			Inactivated, oil-based	IP	Alpha Ject Micro-4	Canada
			Subunit vaccine	IP	Norvax Minova 6	Norway
			Inactivated, oil-based	IP	Alpha Ject micro 6	Ireland UK The Faroe Islands Norway
		sea bass	Inactivated, oil-based	IP	Alpha Ject micro 2000	Croatia Spain Greece France
		Atlantic salmon	Inactivated, oil-based	IP	Alpha Ject Micro-3	Chile
		Atlantic salmon and rainbow trout	Inactivated, oil-based	IP	Alpha Ject 5-3	Iceland Norway
		Atlantic salmon and rainbow trout	Inactivated, oil-based	IP	AlphaJect 3000	Denmark Finland Iceland Ireland Norway Sweden
		Sea bass	Inactivated, oil-based	Dip	ALPHA DIP Vib	Croatia Cyprus Greece Italy Portugal Spain
		Sea bass	Inactivated, oil-based	Bath/ Immersion	ALPHA DIP Vibrio	Turkey
		Atlantic salmon	Inactivated, oil-based	IP	Alpha Ject 2-2	UK
		salmonids	Killed bacterin	IP	Furogen Dip, Forte VI, Lipogen Forte	US Canada
		salmonids	Killed bacterin	Bath/ Immersion	Vibrogen-2	US Canada
		European sea bass	Inactivated bacterin	IP	AquaVac Vibrio Pasteurella	Greece Middle East
		rainbow trout	Inactivated bacterin	Oral/ Immersion	AquaVac Vibrio, AquaVac Vibrio Oral Boost	Finland UK Ireland Spain Greece

Virus	Type of Virus (RNA/DNA)	Fish Host	Trade Name (If Applicable)	Type of Vaccine	Delivery Method	Licensed for Use in the Following Countries	Description
SAV	RNA	Atlantic salmon	Norvax Compact PD	Inactivated	Intraperitoneal Injection	Norway Chile UK	A monovalent vaccine which contains an inactivated strain of SAV subtype 1.
SAV	RNA	Atlantic salmon	Aquavac PD7	Inactivated	Intraperitoneal Injection	Norway	A polyvalent vaccine which contains seven strains to protect against pancreatic disease, infectious pancreatic necrosis, furunculosis, cold-water vibriosis, vibriosis and winter ulcers. Specifically, to protect against SAV, it contains an inactivated strain of SAV subtype 1.
SAV	RNA	Atlantic salmon	Aquavac PD3	Inactivated	Intraperitoneal Injection	UK	A polyvalent vaccine which contains an inactivated strain of SAV subtype 1, as well as infectious pancreatic necrosis and furunculosis.
SAV	RNA	Atlantic salmon	Alphaject Micro 1 PD	Inactivated	Intraperitoneal Injection	UK Norway	A monovalent vaccine which contains the inactivated SAV subtype 3, the SAV strain most dominant in Norway.
IPNV	RNA	Atlantic salmon, rainbow trout	AlphaJect 1000	Inactivated	Intraperitoneal Injection	Chile Norway UK	A monovalent vaccine containing an inactivated form of the virus.
IPNV	RNA	Atlantic salmon	Birnagen Forte	Inactivated	Intraperitoneal Injection	Canada UK	A monovalent vaccine containing inactivated bacterins and virulins.
IPNV	RNA	Atlantic salmon	Aquavac IPN Oral	Recombinant	Oral	US Canada Chile Middle East	A monovalent vaccine containing capsid proteins VP2 and VP3.
IPNV	RNA	Atlantic salmon, Pacific salmon, chinook salmon, rainbow trout	Blueguard IPNV Oral	Inactivated	Oral	Chile	A monovalent vaccine containing two inactivated strains of IPNV.
IPNV	RNA	Rainbow trout, Atlantic salmon, Pacific Salmon, chinook salmon	Blueguard IPN Inyectable	Inactivated	Intraperitoneal Injection	Chile	A monovalent vaccine containing two strains of inactivated IPNV.
IPNV	RNA	Atlantic salmon	AlphaJect IPNV-Flavo 0.025	Inactivated	Intraperitoneal Injection	Chile	A bivalent vaccine protecting against IPNV and Flavobacteriosis.
IPNV	RNA	Atlantic salmon, Pacific salmon, rainbow trout	AlphaJect Micro 2	Inactivated	Intraperitoneal Injection	Chile	A bivalent vaccine protecting against IPNV and SRS.
IPNV	RNA	Atlantic salmon	AlphaJect 2-2	Inactivated	Intraperitoneal Injection	UK	A bivalent vaccine protecting against IPNV and Furunculosis.
IPNV	RNA	Atlantic salmon	AlphaJect Micro 3	Inactivated	Intraperitoneal Injection	Chile	A trivalent vaccine protecting against IPNV, SRS, and Vibriosis.
IPNV	RNA	Atlantic salmon, rainbow trout	blueguard SRS+IPN+Vibrio	Inactivated	Intraperitoneal Injection	Chile	A trivalent vaccine which includes two strains of inactivated IPNV and inactivated bacterins to protect against SRS and Vibrio.
IPNV	RNA	Atlantic salmon	AlphaJect 4-1	Inactivated	Intraperitoneal Injection	Chile	A polyvalent vaccine protecting against Furunculosis, SRS, Vibriosis, and IPNV.
IPNV	RNA	Atlantic salmon	Pentium Forte Plus	Inactivated	Intraperitoneal Injection	Norway	Contains inactivated whole virus of IPNV, and also protects against Furunculosis, Classical Vibriosis, coldwater vibriosis, and Winter Ulcer.
IPNV	RNA	Atlantic Salmon	Norvax Minova 6	Subunit, inactivated	Intraperitoneal Injection	UK Norway	A multivalent vaccine which protects against Furunculosis, classical vibriosis, coldwater vibriosis, wound disease and IPNV. It contains a subunit VP2 capsid protein.
IPNV	RNA	Atlantic salmon	AlphaJect Micro 6	Inactivated	Intraperitoneal Injection	Norway United Kingdom The Faroe Islands Ireland	A multivalent vaccine protecting against Furunculosis, Vibriosis, cold-water vibriosis, Winter sore, and IPNV.
IPNV	RNA	Atlantic Salmon	AlphaJect 6-2	Inactivated	Intraperitoneal Injection	Norway The Faroe Islands	A polyvalent vaccine protecting against Furunculosis, Vibriosis. Coldwater vibriosis, Winter sore, and IPNV.
IPNV and ISA	RNA	Atlantic salmon	AlphaJect Micro 4-2	Inactivated	Intraperitoneal Injection	Chile	A multivalent vaccine protecting against IPNV, Infectious Salmon Anemia (ISA), Vibriosis, and Furunculosis.
IPNV and ISA	RNA	Atlantic salmon	AlphaJect 5-1	Inactivated	Intraperitoneal Injection	Chile	A polyvalent vaccine protecting against Furunculosis, SRS, Vibriosis, ISA, and IPNV.
IPNV and ISA	RNA	Atlantic salmon	AlphaJect Micro 7	Inactivated	Intraperitoneal Injection	Norway The Faroe Islands	A multivalent vaccine protecting against, Furunculosis, Vibriosis, Coldwater vibriosis, Winter sore, IPNV, and (ISA).
IPNV and ISA	RNA	Atlantic salmon	Blueguard SRS+IPN+VO+ISA	Subunit and Inactivated	Intraperitoneal Injection	Chile	A polyvalent vaccine containing subunit ISA, inactivated IPNV strain, and bacterins. It protects against ISA, IPNV, SRS, and Vibriosis.
IPNV and ISA	RNA	Atlantic salmon	Blueguard IPN+SRS+AS+VO+ISA inyectable	Subunit and Inactivated	Intraperitoneal Injection	Chile	A polyvalent vaccine containing subunit ISA, inactivated IPNV strain, and bacterins. It protects against ISA, IPN, SRS, vibriosis, and furunculosis.
ISA	RNA	Atlantic salmon	AlphaJect Micro 1 ISA	Inactivated	Intraperitoneal Injection	Chile	A monovalent vaccine that includes an inactivated strain of ISA.
ISA	RNA	Salmonids	Forte VII	Inactivated	Intraperitoneal Injection	Canada	A polyvalent vaccine which contains inactivated ISA and bacterin. It protects against ISA, Furunculosis, and Vibriosis.
RSIV	DNA	Red sea bream, yellowtail and sea brass	n.a	Formalin	Intraperitoneal	Japan	A monovalent formalin-based vaccine that fights against RSIV. This was the first vaccine made against the virus.
RSIV	DNA	Red sea bream, yellowtail and sea brass	AQUAVAC IridoV	Formalin, oil-adjuvant	Intraperitoneal	Singapore	A monovalent vaccine with an inactivated strain of RSIV which targets tilapia and Asian sea bass.
IHNV	RNA	Salmonids including rainbow trout, steelhead trout and Atlantic salmon	Apex-IHN	DNA	Intramuscular Injection	Canada, USA	A DNA plasmid vaccine targeting IHNV in salmonids.

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Rathor, G.S.; Swain, B. Advancements in Fish Vaccination: Current Innovations and Future Horizons in Aquaculture Health Management. Appl. Sci. 2024 , 14 , 5672. https://doi.org/10.3390/app14135672

Rathor GS, Swain B. Advancements in Fish Vaccination: Current Innovations and Future Horizons in Aquaculture Health Management. Applied Sciences . 2024; 14(13):5672. https://doi.org/10.3390/app14135672

Rathor, Garima S., and Banikalyan Swain. 2024. "Advancements in Fish Vaccination: Current Innovations and Future Horizons in Aquaculture Health Management" Applied Sciences 14, no. 13: 5672. https://doi.org/10.3390/app14135672

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3.2. The DEMATEL method

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