Predictors of COVID-19 epidemics in countries of the World Health Organization African Region

Healthcare system resilience and adaptability to pandemic disruptions in the United States

Understanding healthcare system resilience has become paramount, particularly in the wake of the COVID-19 pandemic, which imposed unprecedented burdens on healthcare services and severely impacted public health. Resilience is defined as the system's ability to absorb, recover from and adapt to disruptions; however, despite extensive studies on this subject, we still lack empirical evidence and mathematical tools to quantify its adaptability (the ability of the system to adjust to and learn from disruptions). By analyzing millions of patients' electronic medical records across US states, we find that the COVID-19 pandemic caused two successive waves of disruptions within the healthcare systems, enabling natural experiment analysis of the adaptive capacity of each system to adapt to past disruptions. We generalized the quantification framework and found that the US healthcare systems exhibit substantial adaptability (ρ = 0.58) but only a moderate level of resilience (r = 0.70). When considering system responses across racial groups, Black and Hispanic groups were more severely impacted by pandemic disruptions than white and Asian groups. Physician abundance was the key characteristic for determining healthcare system resilience. Our results offer vital guidance in designing resilient and sustainable healthcare systems to prepare for future waves of disruptions akin to COVID-19 pandemics.

A Digital Mask-Voiceprint System for Postpandemic Surveillance and Tracing Based on the STRONG Strategy

Lockdowns and border closures due to COVID-19 imposed mental, social, and financial hardships in many societies. Living with the virus and resuming normal life are increasingly being advocated due to decreasing virus severity and widespread vaccine coverage. However, current trends indicate a continued absence of effective contingency plans to stop the next more virulent variant of the pandemic. The COVID-19-related mask waste crisis has also caused serious environmental problems and virus spreads. It is timely and important to consider how to precisely implement surveillance for the dynamic clearance of COVID-19 and how to efficiently manage discarded masks to minimize disease transmission and environmental hazards. In this viewpoint, we sought to address this issue by proposing an appropriate strategy for intelligent surveillance of infected cases and centralized management of mask waste. Such an intelligent strategy against COVID-19, consisting of wearable mask sample collectors (masklect) and voiceprints and based on the STRONG (Spatiotemporal Reporting Over Network and GPS) strategy, could enable the resumption of social activities and economic recovery and ensure a safe public health environment sustainably.

Country-level predictors of COVID-19 mortality

This study aimed to identify country-level predictors of COVID-19 mortality, after controlling for diverse potential factors, and utilizing current worldwide mortality data. COVID-19 deaths, as well as geographic, demographic, socioeconomic, healthcare, population health, and pandemic-related variables, were obtained for 152 countries. Continuous variables were examined with Spearman's correlation, categorical variables with ANOVA or Welch's Heteroscedastic F Test, and country-level independent predictors of COVID-19 mortality identified by weighted generalized additive models. This study identified independent mortality predictors in six limited models, comprising groups of related variables. However, in the full model, only WHO region, percent of population ≥ 65 years, Corruption Perception Index, hospital beds/100,000 population, and COVID-19 cases/100,000 population were predictive of mortality, with model accounting for 80.7% of variance. These findings suggest areas for focused intervention in the event of similar future public health emergencies, including prioritization of the elderly, optimizing healthcare capacity, and improving deficient health sector-related governance.

COVID-19 pandemic and socio-environmental inequality: A narrative review

Background and Aims

The COVID-19 pandemic has provided preliminary evidence of the existence of health, social, and environmental inequalities. This inequality encompasses inadequate access to safe water, clean air, and wastewater management, as well as limited socioeconomic and educational opportunities. These issues have not received sufficient attention during the pandemic. The purpose of this narrative review is to provide a comprehensive summary and analysis of the existing literature on a specific topic, ultimately leading to a conclusion based on the evidence presented.

Methods

The search methodology for this study involved conducting comprehensive searches of scientific databases, including PubMed, ScienceDirect, LILACS, and Google Scholar, from 2019 to 2023. The study focused on a specific theme and its relevant aspects related to global environmental health and society. Keywords such as COVID-19, inequities, and environmental health were used for searching. Additionally, the Boolean operator "AND" was used to combine these descriptors.

Results

Unequal exposure to air pollution has been reported in Africa, as well as in large parts of Asia and Latin America, according to the data that has been obtained. The pandemic has also resulted in a surge in healthcare waste generation, exacerbating the environmental impact of solid waste. Furthermore, there is evidence indicating significant disparities in the severe lack of access to sanitation services between developing nations and low-income regions. The issues related to water availability, accessibility, and quality are subject to debate. It has been reported that SARS-CoV-2 is present not only in untreated/raw water, but also in water bodies that act as reservoirs. Moreover, insufficient education, poverty, and low household income have been identified as the most significant risk factors for COVID-19 infection and mortality.

Conclusion

It is evident that addressing socio-environmental inequality and striving to narrow the gap by prioritizing vulnerable populations are imperative.

A Review of the Machine Learning Algorithms for Covid-19 Case Analysis

The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemic prediction, researchers and authorities have given more attention to simple statistical and epidemiological methodologies. The inadequacy and absence of medical testing for diagnosing and identifying a solution is one of the key challenges in preventing the spread of COVID-19. A few statistical-based improvements are being strengthened to answer this challenge, resulting in a partial resolution up to a certain level. ML have advocated a wide range of intelligence-based approaches, frameworks, and equipment to cope with the issues of the medical industry. The application of inventive structure, such as ML and other in handling COVID-19 relevant outbreak difficulties, has been investigated in this article. The major goal of this article is to 1) Examining the impact of the data type and data nature, as well as obstacles in data processing for COVID-19. 2) Better grasp the importance of intelligent approaches like ML for the COVID-19 pandemic. 3) The development of improved ML algorithms and types of ML for COVID-19 prognosis. 4) Examining the effectiveness and influence of various strategies in COVID-19 pandemic. 5) To target on certain potential issues in COVID-19 diagnosis in order to motivate academics to innovate and expand their knowledge and research into additional COVID-19-affected industries.

Spatio-temporal evolution of the COVID-19 across African countries

The aim of this study is to make a comparative study on the reproduction number R ₀ computed at the beginning of each wave for African countries and to understand the reasons for the disparities between them. The study covers the two first years of the COVID-19 pandemic and for 30 African countries. It links pandemic variables, reproduction number R ₀, demographic variable, median age of the population, economic variables, GDP and CHE per capita, and climatic variables, mean temperature at the beginning of each waves. The results show that the diffusion of COVID-19 in Africa was heterogeneous even between geographical proximal countries. The difference of the basic reproduction number R ₀ values is very large between countries and is significantly correlated with economic and climatic variables GDP and temperature and to a less extent with the mean age of the population.

COVID-19 Vehicle Based on an Efficient Mutual Authentication Scheme for 5G-Enabled Vehicular Fog Computing

The COVID-19 pandemic is currently having disastrous effects on every part of human life everywhere in the world. There have been terrible losses for the entire human race in all nations and areas. It is crucial to take good precautions and prevent COVID-19 because of its high infectiousness and fatality rate. One of the key spreading routes has been identified to be transportation systems. Therefore, improving infection tracking and healthcare monitoring for high-mobility transportation systems is impractical for pandemic control. In order to enhance driving enjoyment and road safety, 5G-enabled vehicular fog computing may gather and interpret pertinent vehicle data, which open the door to non-contact autonomous healthcare monitoring. Due to the urgent need to contain the automotive pandemic, this paper proposes a COVID-19 vehicle based on an efficient mutual authentication scheme for 5G-enabled vehicular fog computing. The proposed scheme consists of two different aspects of the special flag, SF = 0 and SF = 1, denoting normal and COVID-19 vehicles, respectively. The proposed scheme satisfies privacy and security requirements as well as achieves COVID-19 and healthcare solutions. Finally, the performance evaluation section shows that the proposed scheme is more efficient in terms of communication and computation costs as compared to most recent related works.

Anxiety, depression, insomnia symptoms & associated factors among young to middle-aged adults during the resurgent epidemic of COVID-19: a cross-sectional study

The coronavirus disease 2019 (COVID-19) pandemic is a public health emergency of international concern. However, its stress on the mental health of young to middle-aged adults is largely unexplored. This study aimed to evaluate the mental health difficulties during the resurgent phase of COVID-19 among young to middle-aged adults in China. There were 1,478 participants with a median age of 26 years (IQR, 23 - 30), including 535 males (36.2%). The prevalence of anxiety, depression, and insomnia were 8.6%, 11.4%, and 13.7%, respectively. Participants aged 29 - 59 years (OR, 95% CI: 2.46, 1.23 - 4.91) and females (2.49, 1.55 - 4.01) had a higher risk of anxiety. Education status, worried level about the current COVID-19, and the level of COVID-19's impact on life were significantly associated with the prevalence of anxiety. Besides, the level of COVID-19's impact on life was positively related to the prevalence of depression and insomnia. Our study provided novel evidence of psychological difficulties among young to middle-aged adults during the resurgent stage of the COVID-19 epidemic. Psychological intervention should be continuously implemented to prevent long-term psychological comorbidities during the COVID-19 epidemic.

Using multiagent modeling to forecast the spatiotemporal development of the COVID-19 pandemic in Poland

In the article, the authors present a multi-agent model that simulates the development of the COVID-19 pandemic at the regional level. The developed what-if system is a multi-agent generalization of the SEIR epidemiological model, which enables predicting the pandemic's course in various regions of Poland, taking into account Poland's spatial and demographic diversity, the residents' level of mobility, and, primarily, the level of restrictions imposed and the associated compliance. The developed simulation system considers detailed topographic data and the residents' professional and private lifestyles specific to the community. A numerical agent represents each resident in the system, thus providing a highly detailed model of social interactions and the pandemic's development. The developed model, made publicly available as free software, was tested in three representative regions of Poland. As the obtained results indicate, implementing social distancing and limiting mobility is crucial for impeding a pandemic before the development of an effective vaccine. It is also essential to consider a given community's social, demographic, and topographic specificity and apply measures appropriate for a given region.

A highly powerful non-specific strategy to reduce COVID-19 deaths

The coronavirus disease 2019 (COVID‐19) pandemic caused by the coronavirus severe acute respiratory syndrome coronavirus 2 remains risky worldwide. We elucidate here that good IDM (isolation, disinfection, and maintenance of health) is powerful to reduce COVID‐19 deaths based on the striking differences in COVID‐19 case fatality rates among various scenarios. IDM means keeping COVID‐19 cases away from each other and from other people, disinfecting their living environments, and maintaining their health through good nutrition, rest, and treatment of symptoms and pre‐existing diseases (not through specific antiviral therapy). Good IDM could reduce COVID‐19 deaths by more than 85% in 2020 and more than 99% in 2022. This is consistent with the fact that good IDM can minimize co‐infections and maintain body functions and the fact that COVID‐19 has become less pathogenic (this fact was supported with three novel data in this report). Although IDM has been frequently implemented worldwide to some degree, IDM has not been highlighted sufficiently. Good IDM is relative, nonspecific, flexible, and feasible in many countries, and can reduce deaths of some other relatively mild infectious diseases. IDM, vaccines, and antivirals aid each other to reduce COVID‐19 deaths. The IDM concept and strategy can aid people to improve their health behavior and fight against COVID‐19 and future pandemics worldwide.

Multiple striking differences in COVID‐19 case fatality rate were calculated.

These differences support that a strategy could reduce COVID‐19 deaths by more than 85% in 2020 and more than 99% in 2022.

The strategy is based on isolation, disinfection, and health maintenance.

The strategy can reduce co‐infections and maintain body functions.

The strategy has been implemented frequently but insufficiently worldwide.

The strategy is non‐specific and can reduce deaths of other mild infectious diseases.

COVID-19 in the 47 countries of the WHO African region: a modelling analysis of past trends and future patterns

Background

COVID-19 has affected the African region in many ways. We aimed to generate robust information on the transmission dynamics of COVID-19 in this region since the beginning of the pandemic and throughout 2022.

Methods

For each of the 47 countries of the WHO African region, we consolidated COVID-19 data from reported infections and deaths (from WHO statistics); published literature on socioecological, biophysical, and public health interventions; and immunity status and variants of concern, to build a dynamic and comprehensive picture of COVID-19 burden. The model is consolidated through a partially observed Markov decision process, with a Fourier series to produce observed patterns over time based on the SEIRD (denoting susceptible, exposed, infected, recovered, and dead) modelling framework. The model was set up to run weekly, by country, from the date the first infection was reported in each country until Dec 31, 2021. New variants were introduced into the model based on sequenced data reported by countries. The models were then extrapolated until the end of 2022 and included three scenarios based on possible new variants with varying transmissibility, severity, or immunogenicity.

Findings

Between Jan 1, 2020, and Dec 31, 2021, our model estimates the number of SARS-CoV-2 infections in the African region to be 505·6 million (95% CI 476·0–536·2), inferring that only 1·4% (one in 71) of SARS-CoV-2 infections in the region were reported. Deaths are estimated at 439 500 (95% CI 344 374–574 785), with 35·3% (one in three) of these reported as COVID-19-related deaths. Although the number of infections were similar between 2020 and 2021, 81% of the deaths were in 2021. 52·3% (95% CI 43·5–95·2) of the region's population is estimated to have some SARS-CoV-2 immunity, given vaccination coverage of 14·7% as of Dec 31, 2021. By the end of 2022, we estimate that infections will remain high, at around 166·2 million (95% CI 157·5–174·9) infections, but deaths will substantially reduce to 22 563 (14 970–38 831).

Interpretation

The African region is estimated to have had a similar number of COVID-19 infections to that of the rest of the world, but with fewer deaths. Our model suggests that the current approach to SARS-CoV-2 testing is missing most infections. These results are consistent with findings from representative seroprevalence studies. There is, therefore, a need for surveillance of hospitalisations, comorbidities, and the emergence of new variants of concern, and scale-up of representative seroprevalence studies, as core response strategies.

Funding

None.

Predicting the transmission of SARS-CoV-2 in Africa: the case of Namibia

SARS-CoV-2 transmission models have been fairly inaccurate in their predictions for Africa. Here, based on an analysis of surveillance data from Namibia, we conclude that it is necessary to include spatial demography, and travel, in SARS-CoV-2 transmission models in order to make more accurate predictions for COVID-19 epidemics in Africa.

Coordination mechanisms for COVID-19 in the WHO Regional office for Africa

Aim

This study describes the coordination mechanisms that have been used for management of the COVID 19 pandemic in the WHO AFRO region; relate the patterns of the disease (length of time between onset of coordination and first case; length of the wave of the disease and peak attack rate) to coordination mechanisms established at the national level, and document best practices and lessons learned.

Method

We did a retrospective policy tracing of the COVID-19 coordination mechanisms from March 2020 (when first cases of COVID-19 in the AFRO region were reported) to the end of the third wave in September 2021. Data sources were from document and Literature review of COVID-19 response strategies, plans, regulations, press releases, government websites, grey and peer-reviewed literature. The data was extracted to Excel file database and coded then analysed using Stata (version 15). Analysis was done through descriptive statistical analysis (using measures of central tendencies (mean, SD, and median) and measures of central dispersion (range)), multiple linear regression, and thematic analysis of qualitative data.

Results

There are three distinct layered coordination mechanisms (strategic, operational, and tactical) that were either implemented singularly or in tandem with another coordination mechanism. 87.23% (n = 41) of the countries initiated strategic coordination, and 59.57% (n = 28) initiated some form of operational coordination. Some of countries (n = 26,55.32%) provided operational coordination using functional Public Health Emergency Operation Centres (PHEOCs) which were activated for the response. 31.91% (n = 15) of the countries initiated some form of tactical coordination which involved the decentralisation of the operations at the local/grassroot level/district/ county levels. Decentralisation strategies played a key role in coordination, as was the innovative strategies by the countries; some coordination mechanisms built on already existing coordination systems and the heads of states were effective in the success of the coordination process. Financing posed challenge to majority of the countries in initiating coordination.

Conclusion

Coordinating an emergency is a multidimensional process that includes having decision-makers and institutional agents define and prioritise policies and norms that contain the spread of the disease, regulate activities and behaviour and citizens, and respond to personnel who coordinate prevention.

Estimating and explaining cross-country variation in the effectiveness of non-pharmaceutical interventions during COVID-19

To control the COVID-19 pandemic, countries around the world have implemented non-pharmaceutical interventions (NPIs), such as school closures or stay-at-home orders. Previous work has estimated the effectiveness of NPIs, yet without examining variation in NPI effectiveness across countries. Based on data from the first epidemic wave of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=40$$\end{document}n=40 countries, we estimate country-specific differences in the effectiveness of NPIs via a semi-mechanistic Bayesian hierarchical model. Our estimates reveal substantial variation between countries, indicating that NPIs have been more effective in some countries (e. g. Switzerland, New Zealand, and Iceland) as compared to others (e. g. Singapore, South Africa, and France). We then explain differences in the effectiveness of NPIs through 12 country characteristics (e. g. population age, urbanization, employment, etc.). A positive association with country-specific effectiveness of NPIs was found for government effectiveness, gross domestic product (GDP) per capita, population ages 65+, and health expenditures. Conversely, a negative association with effectiveness of NPIs was found for the share of informal employment, average household size and population density. Overall, the wealth and demographic structure of a country can explain variation in the effectiveness of NPIs.

A Look at COVID-19 Global Health Situation, 1-Year Post Declaration of the Pandemic

The new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared a pandemic on 11 March 2020 by the World Health Organization (WHO). The impacts of COVID-19 have changed over the past year globally. There were 116 million confirmed cases of COVID-19 in more than 220 countries, including 2.5 million deaths, as reported at the end of the first week of March 2021. Throughout this time, different variants of SARS-CoV-2 have emerged. In early March, the United States of America (USA) led in both confirmed cases and casualties, while India followed in the number of confirmed cases and Brazil in the number of deaths. Vaccines are available in the USA and worldwide to help combat COVID-19. The level of preparedness among multisectoral communities played a role in transmission rates; therefore, lessons learned from past outbreaks, alongside this pandemic, are crucial in establishing policies and regulations to reduce and/or prevent the spread. This narrative literature review provides an update on the global spread of the COVID-19 outbreak, and the current impact of the pandemic 1-year after the declaration, preparedness, and mitigation efforts since the outbreak.

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Pandemic: Are Africa's Prevalence and Mortality Rates Relatively Low?

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the cause of coronavirus disease 19 (COVID-19), has been rapidly spreading since December 2019, and within a few months, it turned out to be a global pandemic. The disease affects primarily the lungs, but its pathogenesis spreads to other organs as well. However, its mortality rates vary, and in the majority of infected people, there are no serious consequences. Many factors including advanced age, preexisting health conditions, and genetic predispositions are believed to exacerbate outcomes of COVID-19. The virus contains several structural proteins including the spike (S) protein with subunits for binding, fusion, and internalization into host cells following interaction with host cell receptors and proteases (ACE2 and TMPRSS2, respectively) to cause the subsequent pathology. Although the pandemic has spread into all countries, most of Africa is thought of as having relatively less prevalence and mortality. Several hypotheses have been forwarded as reasons for this and include warmer weather conditions, vaccination with BCG (i.e., trained immunity), and previous malaria infection. From genetics or metabolic points of view, it has been proposed that most African populations could be protected to some degree because they lack some genetic susceptibility risk factors or have low-level expression of allelic variants, such as ACE2 and TMPRSS2 that are thought to be involved in increased infection risk or disease severity. The frequency of occurrence of α-1 antitrypsin (an inhibitor of a tissue-degrading protease, thereby protecting target host tissues including the lung) deficiency is also reported to be low in most African populations. More recently, infections in Africa appear to be on the rise. In general, there are few studies on the epidemiology and pathogenesis of the disease in African contexts, and the overall costs and human life losses due to the pandemic in Africa will be determined by all factors and conditions interacting in complex ways.

On the resilience of health systems: A methodological exploration across countries in the WHO African Region

The need for resilient health systems is recognized as important for the attainment of health outcomes, given the current shocks to health services. Resilience has been defined as the capacity to “prepare and effectively respond to crises; maintain core functions; and, informed by lessons learnt, reorganize if conditions require it”. There is however a recognized dichotomy between its conceptualization in literature, and its application in practice. We propose two mutually reinforcing categories of resilience, representing resilience targeted at potentially known shocks, and the inherent health system resilience, needed to respond to unpredictable shock events. We determined capacities for each of these categories, and explored this methodological proposition by computing country-specific scores against each capacity, for the 47 Member States of the WHO African Region. We assessed face validity of the computed index, to ensure derived values were representative of the different elements of resilience, and were predictive of health outcomes, and computed bias-corrected non-parametric confidence intervals of the emergency preparedness and response (EPR) and inherent system resilience (ISR) sub-indices, as well as the overall resilience index, using 1000 bootstrap replicates. We also explored the internal consistency and scale reliability of the index, by calculating Cronbach alphas for the various proposed capacities and their corresponding attributes. We computed overall resilience to be 48.4 out of a possible 100 in the 47 assessed countries, with generally lower levels of ISR. For ISR, the capacities were weakest for transformation capacity, followed by mobilization of resources, awareness of own capacities, self-regulation and finally diversity of services respectively. This paper aims to contribute to the growing body of empirical evidence on health systems and service resilience, which is of great importance to the functionality and performance of health systems, particularly in the context of COVID-19. It provides a methodological reflection for monitoring health system resilience, revealing areas of improvement in the provision of essential health services during shock events, and builds a case for the need for mechanisms, at country level, that address both specific and non-specific shocks to the health system, ultimately for the attainment of improved health outcomes.

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification

The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.