Prioritizing Infection Prevention and Control Activities for SARS-CoV-2 (COVID-19): A Multi-Criteria Decision-Analysis Method

Implementation of Health Policies in the COVID-19 Pandemic Phases of Myanmar and How the Population Approach Influenced Their Success Rate

The impact of the COVID-19 pandemic is continuing in developing countries, and post-pandemic individuals are still suffering mentally and physically. Many researchers have tried to find the causes and risks that can impact the spread of disease. Among the causes and risks identified, socioeconomic factors and health policies played an important role in determining the transmission of the disease. However, the significance of these factors for the spread of infection is different depending on the country. In this editorial, we discuss the implementation of health policies in Myanmar and their effect on infection transmission.

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

Public awareness and perception towards COVID-19 in Sub-Saharan African countries during the lockdown

Background: The coronavirus disease (COVID-19) outbreak has caused a universal health crisis resulting in significant morbidities and mortalities particularly among high-risk groups. This study sought to determine regional factors associated with knowledge and attitude towards COVID-19 mitigation practices and risk perception of contracting the disease in Sub-Saharan African (SSA) countries. Methods: A cross-sectional anonymous online study was conducted among 1970 participants between April and May 2020, during the lockdown in many SSA countries. Recruitment of participants was via WhatsApp, Facebook and emails using authors’ networks. The outcome variables were KAP (knowledge, attitudes and practice) of COVID-19 and analysis of variance (ANOVA) with post hoc test was run to assess the level of KAP by four regions in SSA. Simple and multiple linear regression (MLR) analyses were performed to examine factors associated with the outcome measures in the four SSA regions. Results: Mean knowledge (P=0.707) and risk perception (P=0.904) scores by four regions in SSA did not differ significantly. However, the mean attitude score was higher among West Africans compared with Southern (P=0.019) and Central Africans (P=0.003). MLR analysis revealed that among those living in West (adjusted coefficient β=-0.83 95% CI: -1.19, -0.48) and Southern Africa (β=-0.91 95% CI: -1.42, -0.40), having a primary or secondary education was associated with a decrease in knowledge scores while not being worried about COVID-19 decreased risk perception scores across the four SSA regions(West [β=-6.57, 95% CI: -7.53, -5.62], East [β=-6.24: 95% CI: -8.34,-4.15], Central [β=-6.51, 95% CI: -8.70, -4.31], and Southern Africa [β=-6.06: 95% CI: -7.51, -4.60]). Except among Southern Africans, participants who practiced self-isolation had positive attitude towards COVID-19. Conclusion: Future research on health education regarding COVID-19 or a future related pandemic in SSA should target people with lower education, those who do not self-isolate, those living in Southern and Western Africa and not worried about contracting COVID-19.

Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature

Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

Development and Validation of Structured COVID Perception Interview Guide (COPING) for Assessing the Acute Impact of COVID-19 Diagnosis

Objective  Structured COVID Perception INterview Guide (COPING) is a novel tool developed to understand the acute impact after receiving the diagnosis of coronavirus disease 2019 (COVID-19) in the Indian setting. The approach carries importance for interviewing patients in a state of shock immediately after receiving the diagnosis of COVID. The tool is developed emphasizing the grief, stigma, and acute psychological perception in the immediate aftermath of receiving the positive test results of COVID-19. Since COVID-19 is characteristically different from other infectious illnesses, a structured interview guide could help to address the concerns related to acute loss of health.

Materials and Methods  This study follows a mixed method design conducted from August 2020 to January 2021. In-depth telephonic interviews with mild to moderate COVID patients admitted to a tertiary hospital in central India was followed by development of COPING questionnaire.

Statistical Analysis  Item-Content Validity Index (I-CVI) and Scale-Content Validity Index Universal Agreement (S-CVI/UA) was computed. Factor analysis, Bartlett's test, and Kaiser–Meier–Olkin measure of sampling adequacy was performed. Principal component analysis, scree plots, and parallel analysis with varimax rotation was used to determine the number of factors to extract. For measuring internal consistency, Cronbach's α was computed.

Results  Out of 40 items, the final tool had 15 items after computing content validity, performing factor analysis and achieving desired level of internal consistency (Cronbach's α  = 0.702). Five domains identified after factor analysis were awareness, grief/bereavement, stigma, social reciprocity, and stress adaptation/coping.

Conclusion  COPING is a valid and reliable interview guide for Indian setting that will allow the assessment of perception of patients with acute COVID-19 infection. Taking into consideration the mental health implications of COVID-19, the availability of such a validated and reliable tool is a timely step to address the public health problem and assist the ongoing research on COVID-19 and similar illnesses in the future.

Prediction of highly vulnerable areas to COVID-19 outbreaks using spatial model: Case study of Cairo Governorate, Egypt

COVID-19 has affected over 170 countries around the world. Alarming rate has increased with the increase of infected cases and death rates. Whereas, the World Health Organization (WHO) had declared the COVID-19 virus as a pandemic on 11th March 2020. Preparations were made to face the spread of COVID-19, as predicting the most probable risk areas by using spatial models. Prediction spatial models of COVID-19 risk areas can help the governmental authorities to generate sustainable strategies and set up suitable protocols to control the pandemic. This research presents an attempt of a potential spatial prediction modeling of COVID-19 risk areas in Cairo governorate-Egypt. Four indicator models (demographic, residential, environmental and topographic) were developed using geomatics technology based on the guidelines of the UN-habitat sustainable development goals (SDGs) target (11 & 3). Five predicted scenarios were generated for the most pandemic probability areas by the integration of the four indicator models. The results showed that there are common areas in all scenarios for highly COVID-19 pandemic risk areas. These common risk areas were found in (El Marag, El Salam, Ain Shams, El Mataria, El Gammaleya, Manshiat Nasser, El Mosky, Bolak, Hadaak El Koba, and El Sharbeya) districts. The hotspots zones are characterized by overcrowding, high population density and economic activities, large family size, poor infrastructure service and low rate of education. Moreover, it was noticed that crowding points resulted in traffic density and air pollution, which may affect the pandemic spread. The accuracy assessment results displayed that, the environmental predicted scenario was more consistent with the official data of the Egyptian Ministry of Health and Population) MOHP), while the residential one was less convenient. The result of this study supports the health sector by predicting the hot spots areas. The present study is aimed to develop a proactive plan to confront the pandemic before spreading in the Cairo governorate-Egypt. Also, the proposed prediction model can be an effective aid for decision-makers across the world working on containment strategies to minimize the spread of Coronavirus.

Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review

The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling an accurate decision for different fields against COVID-19.

Measuring vaccination willingness in response to COVID-19 using a multi-criteria-decision making method

The pandemic COVID-19 is continued to the massive burden of morbidity and mortality while disrupting economies and societies all over the world. At an earlier stage, wearing a face mask, social distancing, and hand hygiene were suggested to limit the transmission of this infection. The WHO, CDC, and other governing bodies were doing an effort to develop the coronavirus vaccine. Now COVID-19 vaccines are available to protect people against the coronavirus. People are hesitant about whether to receive a vaccination or do not to vaccinate. This study was aimed to analyze the COVID-19 vaccination willingness level of the general public of Pakistan to manage the COVID-19 disease. A multi-criteria decision-making method known as an analytical hierarchical method was applied to determine the COVID-19 vaccination willingness level of the public. The significant determinants of COVID-19 vaccination willingness were cues to action, perceived benefits, positive attitude, government recommendation, as well as perceived stress scoring high weights to the vaccination. Determinants of willingness to uptake the COVID-19 vaccine were individual decision, vaccine origin, adapting to change, and perceived barriers high obstacles to vaccinating. The determinants relating to the COVID-19 vaccine may help to increase the uptake of the vaccination program. The government may need communication campaigns to reinforce the benefits of the vaccine.

An AHP-based regional COVID-19 vulnerability model and its application in China

Since the COVID-19 outbreak, four cities-Wuhan, Beijing, Urumqi and Dalian-have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40808-021-01244-y.

A cross-sectional study of infection control measures against COVID-19 and psychological distress among Japanese workers

OBJECTIVES

This study examined the relationship between the status of infection control efforts against COVID-19 in the workplace and workers' mental health using a large-scale Internet-based study.

METHODS

This cross-sectional study was based on an Internet monitoring survey conducted during the third wave of the COVID-19 epidemic in Japan. Of the 33 302 people who participated in the survey, 27 036 were included in the analyses. Participants answered whether or not each of 10 different infection control measures was in place at their workplace (eg, wearing masks at all times during working hours). A Kessler 6 (K6) score of ≥13 was defined as mild psychological distress. The odds ratios (ORs) of psychological distress associated with infection control measures at the workplace were estimated using a multilevel logistic model nested in the prefectures of residence.

RESULTS

The OR of subjects working at facilities with 4 or 5 infection control measures for psychological distress was 1.19 (95% confidence interval [CI]: 1.05-1.34, P = .010), that in facilities with 2 or 3 infection control measures was 1.43 (95% CI: 1.25-1.64, P < .001), and that in facilities with 1 or no infection control measures was 1.87 (95% CI: 1.63-2.14, P < .001) compared to subjects whose workplaces had ≥6 infection control measures.

CONCLUSION

Our findings suggest that proactive COVID-19 infection control measures can influence the mental health of workers.