Development of a Choice-framework for Covid vaccines in India using a multi-criteria decision analysis approach.

BACKGROUND: Vaccines were crucial in controlling the Covid-19 pandemic. As more vaccines receive regulatory approval, stakeholders will be faced with several options and must make an appropriate choice for themselves. We proposed a multi-criteria decision analysis (MCDA) framework to guide decision-makers in comparing vaccines for the Indian context. METHODS: We adhered to the ISPOR guidance for the MCDA process. Seven vaccine options were compared under ten criteria. Through three virtual workshops, we obtained opinions and weights from citizens, private-sector hospitals, and public health organisations. Available evidence was rescaled and incorporated into the performance matrix. The final score for each vaccine was calculated for the different groups. We performed different sensitivity analyses to assess the consistency of the rank list. RESULTS: The cost, efficacy and operational score of the vaccines had the highest weights among the stakeholders. From the six scenario groups, Janssen had the highest score in four. This was driven by the advantage of having a single dose of vaccination. In the probabilistic sensitivity analysis for the overall group, Covaxin, Janssen, and Sputnik were the first three options. The participants expressed that availability, WHO approvals and safety, among others, would be crucial when considering vaccines. CONCLUSIONS: The MCDA process has not been capitalised on in healthcare decision-making in India and LMICs. Considering the available data and stakeholder preference at the time of the study, Covaxin, Janssen, and Sputnik were preferred options. The choice framework with the dynamic performance matrix is a valuable tool that could be adapted to different population groups and extended based on increasing vaccine options and emerging evidence. *ISPOR - The Professional Society for Health Economics and Outcomes Research.

Policy Analysis of Low-Carbon Energy Transition in Senegal Using a Multi-Criteria Decision Approach Based on Principal Component Analysis

Senegal has been investing in the development of its energy sector for decades. By using a novel multi-criteria decision analysis (MCDA) based on the principal component analysis (PCA) method, this paper develops an approach to determine the effectiveness of Senegal's policies in supporting low-carbon development. This was determined using six criteria (C1 to C6) and 17 policies selected from the review of Senegal's energy system. In order to determine the optimal weighting of the six criteria, a PCA is performed. In our approach, the best weighted factor is the normalized version of the best linear combination of the initial criteria with the maximum summarized information. Proper weighted factors are determined through the percentage of the information provided by the six criteria kept by the principal components. The percentage of information is statistically a fit of goodness of a principal component. The higher it is, the more statistically important the corresponding principal component is. Among the six principal components obtained, the first principal component (comp1) best summarizes the values of criteria C1 to C6 for each policy. It contains 81.15% of the information on energy policies presented by the six criteria and was used to rank the policies. Future research should take into account that when the number of criteria is high, the share of information explained by the first principal component could be lower (less than 50% of the total variance). In this case, the use of a single principal component would be detrimental to the analysis. For such cases, we recommend a higher dimensional visualization (using two or three components), or a new PCA should be performed on the principal components. This approach presented in our study can serve as an important benchmark for energy projects and policy evaluation.

Key Factors for Enhancing Home Care Workers' Intention to Stay by Multiple-Criteria Decision Analysis.

The ageing population is increasing rapidly in Taiwan, where the ageing rate exceeds even that of Japan, the United States and France. The increase in the disabled population and the impact of the COVID-19 pandemic have resulted in an increase in the demand for long-term professional care, and the shortage of home care workers is one of the most important issues in the development of such care. This study explores the key factors that promote the retention of home care workers through multiple-criteria decision making (MCDM) to help managers of long-term care institutions retain home care talent. A hybrid model of multiple-criteria decision analysis (MCDA) combining Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the analytic network process (ANP) was employed for relative analysis. Through literature discussion and interviews with experts, all factors that promote the retention and desire of home care workers were collected, and a hierarchical MCDM structure was constructed. Then, the hybrid MCDM model of DEMATEL and the ANP was used to analyze the questionnaire data of seven experts to evaluate the factor weights. According to the study results, the key direct factors are improving job satisfaction, supervisor leadership ability and respect, while salary and benefits are the indirect factor. This study uses the MCDA research method and establishes a framework by analyzing the facets and criteria of different factors to promote the retention of home care workers. The results will enable institutions to formulate relevant approaches to the key factors that promote the retention of domestic service personnel and to strengthen the intention of Taiwan's home care workers to stay in the long-term care industry.

Multi-Criteria Decision Analysis to Prioritize People for COVID-19 Vaccination When Vaccines Are in Short Supply.

COVID-19 pandemic underscored the need for a rapid tool supporting decision-makers in prioritizing patients in the immediate and overwhelming context of pandemics, where shortages in different healthcare resources are faced. We have proposed Multi-Criteria Decision Analysis (MCDA) to create a system of criteria and weights to prioritize uses of COVID-19 vaccines in groups of people at significantly higher risk of severe COVID-19 disease or death, when vaccines are in short supply, for use in Tunisia. The prioritization criteria and the levels within each criterion were identified based on available COVID-19 evidence with a focus on the criteria selected by Tunisian scientific committees. To determine the weights for the criteria and levels, reflecting their relative importance, a panel of frontline physicians treating COVID-19 were invited to participate in an online survey using 1,000 minds MCDA software (www.1000minds.com) which implements the PAPRIKA (Potentially All Pairwise RanKings of all possible Alternatives) method. Ten criteria and twenty-three levels have been selected for prioritizing the uses of COVID-19 vaccines in groups of people at significantly higher risk of severe disease or death. Among the invited physicians, sixty have completed the survey. The obtained scores were, in decreasing order of importance (mean weights in parentheses, summing to 100%). Obesity (16.2%), Age (12.7%), Chronic pulmonary diseases (10.8%), Chronic cardiovascular conditions (10.3%), Bone marrow or organ transplantation (10.1%), Immunodeficiency or Immunosuppression (9.6%), Diabetes (9%), Renal failure (8.4%), evolutive cancer (6.9%), and high blood pressure (6%). MCDA-based prioritization scoring system comprising explicit criteria and weights provides an adaptable and multicriteria approach that can assist policy-makers to prioritize uses of COVID-19 vaccines.

Multi-criteria analysis applied to humanitarian assistance: an approach based on ELECTRE-MOr.

The recent increase in the number of cases of COVID-19 in Brazil and worldwide, caused by the Omicron Variant, has brought to light concern to the population and the government, especially in the states most affected by the pandemic. To find a way to help combat the pandemic, a case study was conducted to acquire new speedboats by the Brazilian Navy (BN), through the application of the ELECTRE-MOr multicriteria method. The boats would be employed as mobile hospitals, aiming to perform first aid and evacuation of patients from riverside regions to qualified hospitals. Another important use would be the transport of vaccines, medicines and basic supplies for riverside populations, such as water, food and hygiene materials. For the proposed analysis, we consulted three specialists from the BN, who evaluated eight boat models in seven tactical, operational and medical criteria. After the application of the method, the Guardian 25 and RAC boats were chosen to be employed in humanitarian assistance. This study brings a valuable contribution to academia and society since it represents the application of a multi-criteria decision-aid method in the state of the art to contribute to the solution of a real problem that affects millions of people in Brazil and worldwide.

Obsolescence Assessment Approach: Case of Mechanical Ventilators Under the Covid-19 Environment

Support treatment for patients with severe Covid19 symptoms is based on mechanical ventilation. As a result of the spread of the coronavirus, various medical institutions found it necessary to acquire more mechanical ventilators, causing an increase in the demand for their production. The production, distribution, and use conditions present a potential medium- and long-term risk concerning the equipment’s operation, safety, and effectiveness. In the face of the pandemic, the care that must be implemented to guarantee patient safety, the effectiveness of the equipment, and the quality of care, should be a high priority. This paper presents a first approach to analyzing the causes that can lead to a potential state of obsolescence in mechanical ventilators used to care for patients with Covid19. We collected information about mechanical ventilators, identified the factors that may cause a failure in the device, applied Multi-Criteria Decision Analysis for three possible outcomes, and compared the results with the expert opinion of clinical engineers. We found a total of 30 factors and classified them into associated, operation, and external concerning the ventilator. The results show that the factors found are associated with the technical field. The knowledge of the operation of the equipment’s subsystems and familiarity with its components is essential. The contrast with the expert opinion and the non-inclusion of factors such as the economic ones led to the development of more in-depth work on this topic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Energy efficiency in residential buildings amid COVID-19: A holistic comparative analysis between old and new normal occupancies.

Stringent lockdowns have been one of the defining features of the COVID-19 pandemic. Lockdowns have brought about drastic changes in living styles, including increased residential occupancy and telework practices predicted to last long. The variation in occupancy pattern and energy use needs to be assessed at the household level. Consequently, the new occupancy times will impact the performance of energy efficiency measures. To address these gaps, this work uses a real case study, a two-story residential building in the Okanagan Valley (British Columbia, Canada). Further, steady-state building energy simulations are performed on the HOT2000 tool to evaluate the resiliency of energy efficiency measures under a full lockdown. Three-year monitored energy data is analyzed to study the implications of COVID-19 lockdowns on HVAC and non-HVAC loads at a monthly temporal scale. The results show a marked change in energy use patterns and a higher increase in May 2020 compared to the previous two years. Calibrated energy models built on HOT2000 are then used to study the impacts of pre-COVID-19 (old normal occupancy) and post-COVID-19 (new normal occupancy) on energy upgrades performance. The simulations show that under higher occupancy times, the annual electricity use increased by 16.4%, while natural gas use decreased by 7.6%. The results indicate that overall residential buildings following pre-COVID-19 occupancy schedules had higher energy-saving potential than those with new normal occupancy. In addition, the variation in occupancy and stakeholder preferences directly impact the ranking of energy efficiency measures. Furthermore, this study identifies energy efficiency measures that provide flexibility for the decision-makers by identifying low-cost options feasible under a range of occupancy schedules.

A Proposal to Assess the Benefits of Urban Ecosystem Services

The Millennium Ecosystem Assessment in 2005 defined and categorized the concept of Ecosystem Services and the strategic role of natural capital. The need to rethink our cities and public spaces is even more pressing in the COVID-19 era. In this context, green strategies could be the answer to the new demands raised by citizens about the built and natural environment. Green roofs, along with the other green spaces, form the city’s green network, contribute to improving the quality of life and wellbeing of citizens. The present contribution aims to evaluate green roofs from an ecosystem perspective, by considering the evidence of their benefits on inhabitants’ wellbeing, their ability to mitigate climate change and preserve biodiversity. A proposal for an integrated evaluation model is presented to take into account the different dimensions of value in the study of Ecosystem Services (ESs) and to support decision makers (DMs) in the definition of actions able to increase the quality of life in cities. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Classification of Non-pharmaceutical Anti-COVID Interventions Based on Novel FTOPSIS-Sort Models

Assigning alternatives to predefined ordered categories under multicriteria conditions is the essence of multi-criteria sorting problematic. The family of fuzzy multi-criteria sorting models with the common name FTOPSIS-Sort are introduced based on the fuzzy extension of Multi-Criteria Decision Analysis (MCDA) ordinary method TOPSIS with the use of different approaches to assess functions of fuzzy numbers and different fuzzy ranking methods. The features of adjusting Fuzzy TOPSIS (FTOPSIS) models to sorting problematic are presented. The developed FTOPSIS-Sort models are implemented for multi-criteria sorting of non-pharmaceutical interventions against COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An application of the PROMETHEE II method for the comparison of energy requalification strategies to design Post-Carbon Cities

A resilient, diversified, and efficient energy system, comprising multiple energy carriers and high-efficiency infrastructure, is the way to decarbonise the European economy in line with the Paris Agreement, the UN 2030 Agenda for Sustainable Development, and the various recovery plans after the COVID-19 pandemic period. To achieve these goals, a key role is played by the private construction sector, which can reduce economic and environmental impacts and accelerate the green transition. Nevertheless, while traditionally decision-making problems in large urban transformations were supported by economic assessment based on Life Cycle Thinking and Cost-Benefit Analysis (CBA) approaches, these are now obsolete. Indeed, the sustainable neighbourhood paradigm requires the assessment of different aspects, considering both economic and extra-economic criteria, as well as different points of view, involving all stakeholders. In this context, the paper proposes a multi-stage assessment procedure that first investigates the energy performance, through a dynamic simulation model, and then the socio-economic performance of regeneration operations at the neighbourhood scale, through a Multi-Criteria Decision Analysis (MCDA). The model based on the proposed Preference Ranking Organisation Method for Enrichment Evaluations II (PROMETHEE II) aims to support local decision makers (DMs) in choosing which retrofit operations to implement and finance. The methodology was applied to a real-world case study in Turin (Italy), where various sustainable measures were ranked using multiple criteria to determine the best transformation scenario.

Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia.

COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km2) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km2) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km2), and the remaining 1.17% (6.12 km2) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.

Checking consistency for Group-PAHP: a case study of tourism facilities in COVID-19 pandemic.

The pandemic situation due COVID-19 highlighted a great vulnerability of tourism systems in the world, defined a scenario characterized by strong uncertainties, unfavorable prospects and widespread fragility (Michie 2020). Our work proposes the use of Multi-Criteria Decision Aiding (MCDA) for analyzing the potentiality of local territory development through the improvement of the tourism facilities. More precisely, we propose the use of the Parsimonious AHP (Abastante et al. 2019) for group choices to analyze a decision-making problem for the improvement of tourism facilities. As the complexity of the decision-making problem and the number of decision-makers grow, there may be problems of consistency of judgments and therefore problems of consistency of the matrices (Brunelli and Cavallo 2020a). Consistency is difficult to achieve in the real situation (Maturo et al. 2005). Our work aims to verify in a 4-step process the errors of consistency that occurs in Pairwise Comparison Matrices with the use of Parsimonious AHP for group choices. Furthermore, we propose a new innovative tool for decision makers to tackle complex problems, with multiple decision categories, a large number of alternatives and several criteria.

Measuring the Attractiveness of Cities to Receive Investments in Regional Airport Infrastructure

The vast Brazilian territory and the accelerated economic growth of the cities of the country’s interior in recent years have created a favourable environment for the expansion of regional aviation. In 2015, the Brazilian Government launched a program of investments in regional airports equipping them to receive commercial flights. However, the economic crisis and the scarcity of resources drive the prioritisation of projects with a greater economic and social return. This article aims to present a multicriteria decision aid (MCDA) model to measure cities’ attractiveness to receive investments in regional airports. The MCDA approach can deal with multiple indicators and different points of view and provide systematised steps for supporting decision-makers. For this purpose, we selected 12 criteria among the evaluation parameters identified in the literature, which led to the construction of the evaluation model and elaborating the ranking of the localities participating in the investment program. This study can contribute scientifically by proposing the use of an MCDA approach to support decisions related to logistics and infrastructure. It can help managers and practitioners provide a structured and systematised model to address decisions related to airport investments.

Assessing IoT challenges in supply chain: A comparative study before and during- COVID-19 using interval valued neutrosophic analytical hierarchy process

Although the Internet of Things (IoT) has spawned a new breed of smart factories within supply chains, the latest pandemic has ushered in unparalleled supply chain disturbances. Following the challenges identified in the literature, we interview top experts to evaluate the significance of these challenges. We apply a multi-criteria decision analysis (MCDA) tool, analytical hierarchy process (AHP) in combination with interval-valued neutrosophic numbers (IVN). The critical part of this research is that we also perform a comparative analysis by focusing on before- and during- the pandemic periods individually to better assess the impact of the latest pandemic on the IoT challenges. Our study also includes a comprehensive, systematic literature review to bring the readers up-to-date.

A multicriteria decision analysis framework to measure equitable healthcare access during COVID-19.

The ongoing novel coronavirus (COVID-19) pandemic has highlighted the need for individuals to have easy access to healthcare facilities for treatment as well as vaccinations. The surge in COVID-19 hospitalizations during 2020 also underscored the fact that accessibility to nearby hospitals for testing, treatment and vaccination sites is crucial for patients with fever or respiratory symptoms. Although necessary, quantifying healthcare access is challenging as it depends on a complex interaction between underlying socioeconomic and physical factors. In this case study, we deployed a Multi Criteria Decision Analysis (MCDA) approach to uncover the barriers and their effect on healthcare access. Using a least cost path (LCP) analysis we quantified the costs associated with healthcare access from each census block group in the Los Angeles metropolitan area (LA Metro) to the nearest hospital. Social vulnerability reported by the Centers for Disease Control and Prevention (CDC), the daily number of COVID-19 cases from the Los Angeles open data portal and built environment characteristics (slope of the street, car ownership, population density distribution, walkability, traffic collision density, and speed limit) were used to quantify overall accessibility index for the entire study area. Our results showed that the census block groups with a social vulnerability index above 0.75 (high vulnerability) had low accessibility owing to the higher cost of access to nearby hospitals. These areas were also coincident with the hotspots for COVID-19 cases and deaths which highlighted the inequitable exposure of socially disadvantaged populations to COVID-19 infections and how the pandemic impacts were exacerbated by the synergistic effect of socioeconomic status and built environment characteristics of the locations where the disadvantaged populations resided. The framework proposed herein could be adapted to geo-target testing/vaccination sites and improve accessibility to healthcare facilities in general and more specifically among the socially vulnerable populations residing in urban areas to reduce their overall health risks during future pandemic outbreaks.

Strategic support for the distribution of vaccines against Covid-19 to Brazilian remote areas: A multicriteria approach in the light of the ELECTRE-MOr method.

The pandemic caused by the new coronavirus has brought to light a series of concerns for the Brazilian population and government departments due to the different costly consequences that it has generated. It has also mobilized different strategic fronts that plan and implement several mitigating measures against the virus. Besides, the search for solutions for adequate care for individuals in need of support has been constant. This work uses ELECTRE-MOr, a Multi-Criteria Decision Aid (MCDA) method, to support the logistic plan for the vaccine distribution throughout Brazil, essentially to remote areas. The method allows an objective and structured classification of ideal types of thermal boxes for the storage of immunobiological inside the Cold Chain, presenting the best alternative that maintains the quality of materials until the final destination and has the best cost-benefit. Currently, the ELECTRE-MOr model is under development in a computational tool in Python, allowing the use of the method intuitively and clearly, enabling professionals of any area of expertise to apply it.

A practical approach for geographic prioritization and targeting of insecticide-treated net distribution campaigns during public health emergencies and in resource-limited settings.

BACKGROUND: The use of data in targeting malaria control efforts is essential for optimal use of resources. This work provides a practical mechanism for prioritizing geographic areas for insecticide-treated net (ITN) distribution campaigns in settings with limited resources. METHODS: A GIS-based weighted approach was adopted to categorize and rank administrative units based on data that can be applied in various country contexts where Plasmodium falciparum transmission is reported. Malaria intervention and risk factors were used to rank local government areas (LGAs) in Nigeria for prioritization during mass ITN distribution campaigns. Each factor was assigned a unique weight that was obtained through application of the analytic hierarchy process (AHP). The weight was then multiplied by a value based on natural groupings inherent in the data, or the presence or absence of a given intervention. Risk scores for each factor were then summated to generate a composite unique risk score for each LGA. This risk score was translated into a prioritization map which ranks each LGA from low to high priority in terms of timing of ITN distributions. RESULTS: A case study using data from Nigeria showed that a major component that influenced the prioritization scheme was ITN access. Sensitivity analysis results indicate that changes to the methodology used to quantify ITN access did not modify outputs substantially. Some 120 LGAs were categorized as 'extremely high' or 'high' priority when a spatially interpolated ITN access layer was used. When prioritization scores were calculated using DHS-reported state level ITN access, 108 (90.0%) of the 120 LGAs were also categorized as being extremely high or high priority. The geospatial heterogeneity found among input risk factors suggests that a range of variables and covariates should be considered when using data to inform ITN distributions. CONCLUSION: The authors provide a tool for prioritizing regions in terms of timing of ITN distributions. It serves as a base upon which a wider range of vector control interventions could be targeted. Its value added can be found in its potential for application in multiple country contexts, expediated timeframe for producing outputs, and its use of systematically collected malaria indicators in informing prioritization.

Applying the mars method to the sponsor contract negotiations

Sport is nowadays an important part of social and economic life. It contributes to economic growth and employment, increases expected life span of people and facilitates better lifestyles. It also helps to avoid healthcare costs. In 2020, the global sports market was valued at nearly US$388.3 billion with yearly growth averaging at 3.4% (since 2015). However, it must be emphasised that the market value fell from US$458.8 billion in 2019 at a-15.4% rate. The lockdown and social distancing norms as well as economic slowdown due to the COVID-19 is found to be the major reason for this decline. Though, the market is expected to revive and reach the value of nearly US$600.0 billion by 2025, and by 2030 – US$826.0 billion. The sports market is divided into segments by revenue source into the following ones: media rights, sponsorship, merchandising, and tickets. The largest segment of the sports market, reaching 38% of the total in 2020, is media rights. However, sports sponsorship market is booming: it was worth of US$57.0 billion in 2020 and it is projected to reach the level of US$89.6 billion in 2027 (at a rate of 6.72%). According to Brandessence Market Research athlete endorsements have been shown to generate a 4% increase in sales (an average of US$10 million in additional sales annually) and nearly a 0.25% increase in stock returns. It is therefore obvious that behind the physical activity sport is a big business now and negotiations, for example with potential sponsors, are an indispensable part of this business. It is extremely important in a negotiation process to learn about the preferences and expectations of the decision-maker as well as build a negotiation offer scoring system facilitating the conduct of negotiations. These are exceptionally challenging but necessary tasks. Therefore, this article aims to present the application of one of the multi-criteria methods, namely the MARS method, for this purpose. More specifically, MARS will be applied to evaluate the negotiation template for sponsor contract negotiations. The method can ease and accelerate the negotiation process due to its properties such as, for instance, clarity, fit to reality and user-friendliness. © JPES.

GIS-based COVID-19 vulnerability mapping in the West Bank, Palestine.

From the beginning of the COVID-19 pandemic, the world stands idly by in the face of the virus spreading. The prediction of highly vulnerable population and the implementation of proper actions are very important steps to break the infection chain of any virus. This will, in turn, reduce the economic and social impact of this virus outbreak. In this study, the COVID-19 vulnerability map for the West Bank, Palestine was developed. Analytic Hierarchy Process (AHP) was used to develop the COVID-19 vulnerability map. The Geographic Information system (GIS) in combination with multi-criteria decision analysis (MCDA) was adopted to estimate the COVID-19 vulnerability index (CVI) based on some selected potential criteria including population, population density, elderly population, accommodation and food service activities, school students, chronic diseases, hospital beds, health insurance, and pharmacy. The results of this study show that Nablus, Jerusalem, and Hebron governorates are under very high vulnerability. Tulkarm, Ramallah & Al-Bireh and Jenin governorates are high vulnerable to COVID-19. Additionally, 82 % of the West Bank population are under high to very high COVID-19 vulnerability classes. Moreover, 14% and 4 % are medium and low to very low vulnerable, respectively. The obtained results are of high value to help decision-makers to take proper actions as early as possible mainly in the highly COVID-19 vulnerable governorates to control the risk associated with the potential outbreak of the virus and accordingly to protect social life and to sustain economic conditions.

The CAPACITI Decision-Support Tool for National Immunization Programs.

OBJECTIVES: Immunization programs in low-income and middle-income countries (LMICs) are faced with an ever-growing number of vaccines of public health importance recommended by the World Health Organization, while also financing a greater proportion of the program through domestic resources. More than ever, national immunization programs must be equipped to contextualize global guidance and make choices that are best suited to their setting. The CAPACITI decision-support tool has been developed in collaboration with national immunization program decision makers in LMICs to structure and document an evidence-based, context-specific process for prioritizing or selecting among multiple vaccination products, services, or strategies. METHODS: The CAPACITI decision-support tool is based on multi-criteria decision analysis, as a structured way to incorporate multiple sources of evidence and stakeholder perspectives. The tool has been developed iteratively in consultation with 12 countries across Africa, Asia, and the Americas. RESULTS: The tool is flexible to existing country processes and can follow any type of multi-criteria decision analysis or a hybrid approach. It is structured into 5 sections: decision question, criteria for decision making, evidence assessment, appraisal, and recommendation. The Excel-based tool guides the user through the steps and document discussions in a transparent manner, with an emphasis on stakeholder engagement and country ownership. CONCLUSIONS: Pilot countries valued the CAPACITI decision-support tool as a means to consider multiple criteria and stakeholder perspectives and to evaluate trade-offs and the impact of data quality. With use, it is expected that LMICs will tailor steps to their context and streamline the tool for decision making.