Cumulative Confirmed Cases Of COVID-19 Research Articles

Mathematical modelling of COVID-19 dynamics using SVEAIQHR model

Abstract In this study, we formulate an eight-compartment mathematical model with vaccination as one of the compartments to analyze the dynamics of COVID-19 transmission. We examine the model’s qualitative properties, such as positivity and boundedness of solutions, and stability analysis of the illness-free equilibrium with respect to the basic reproduction number. We estimate ten significant parameters and also compute the magnitude of the basic reproduction number for India by fitting the proposed model to daily confirmed and cumulative confirmed COVID-19 cases in India. Sensitivity analysis with respect to basic reproduction number is conducted, and the main parameters that impact the widespread of disease are determined. We further extend this model to an optimal control problem by including four non-pharmaceutical and pharmaceutical intervention measures as control functions. Our numerical results show that the four control strategy has greater impact than the three control strategies, two control strategies, and single control strategies on reducing the dynamics of COVID-19 transmission.

Gaining further insights into the COVID-19 pandemic in Australia: Evidence using capture-recapture methods

ObjectiveWe re-examined the reported number of COVID-19 cases in Australia and across its states during the first wave of the pandemic. We provided estimates of the total number of cases, adjusted for under-reporting. MethodsPublicly available data sourced from Australian governments at federal, state and territory levels included records on cumulative confirmed COVID-19 cases and cumulative deaths occurring in Australia and across its states on a daily basis. Lower bound and upper bound estimates of the total number of COVID-19 cases in Australia and across its states, that included the undetected cases that have not been recorded, were estimated. ResultsOn January 25, 2020, Australia recorded its first 4 cases of COVID-19 and the first death occurred on March 3, 2020. On April 1, 2020, 4864 cases had been reported with 21 deaths. Our estimation showed that on April 1, 2020, the minimum and maximum number of COVID-19 cases in Australia were in fact 10,160 (95 % CI: 9781–10,538) and 21,748 (95 % CI: 21,607–22,014) respectively. We estimated that the total number of cases were at least twice and at most four times the observed cases recorded. These differences were also found at the state level where in New South Wales there was a minimum and maximum of 207 and 447 cases in total for every 100 reported cases, while in Victoria these figures were much lower at 157 and 265 respectively for every 100 reported cases. ConclusionCase ascertainment during the pandemic is known to have been underestimated due to difficulties in testing and contact tracing, amongst others. Capture-recapture methods provided a measure of the gap between the official number of cases recorded and the actual number during the first wave of the pandemic.

Application of optimized LSTM in prediction of the cumulative confirmed cases of COVID-19

This paper proposes an optimized Long Short-Term Memory (LSTM+) model for predicting cumulative confirmed cases of COVID-19 in Germany, the UK, Italy, and Japan. The LSTM+ model incorporates two key optimizations: (1) fine-adjustment of parameters and (2) a ‘re-prediction’ process that utilizes the latest prediction results from the previous iteration. The performance of the LSTM+ model is evaluated and compared with that of Backpropagation (BP) and traditional LSTM models. The results demonstrate that the LSTM+ model significantly outperforms both BP and LSTM models, achieving a Mean Absolute Percentage Error (MAPE) of less than 0.6%. Additionally, two illustrative examples employing the LSTM+ model further validate its general applicability and practical performance for predicting cumulative confirmed COVID-19 cases.

Do spatial and sociodemographic factors affect the transmission pattern of COVID-19? Evidence from Surabaya City, Indonesia

The COVID-19 pandemic has attracted the attention of researchers from different disciplines, including urban studies. Researchers have revealed different spatial and sociodemographic factors affecting both the transmission of COVID-19 and the number of cases and deaths. This study aims to understand the association between regional spatial and sociodemographic characteristics and the dynamics of COVID-19 in Surabaya city, Indonesia, a COVID-19 epicenter. The analysis is based on the COVID-19 cumulative cases per 100,000 population from March to December 2020. To investigate the spatial and sociodemographic factors affecting the transmission of COVID-19, spatial autocorrelation and nonparametric correlation analyses were used. The Moran's I index results revealed that COVID-19 cases are clustered in specific parts of Surabaya. Significant agglomerations were found between high-case regions and low-case regions. In addition, 14 sub-districts were identified as hotspots in the southern Surabaya. The association between spatial and sociodemographic factors and COVID-19 cases was explored using Spearman rank correlation and Kendall tau analysis. The study found that population density, distance to hotspot, distance to airport, and distance to bus station had negative correlation, while the total area and distance to port had a positive correlation with the cumulative confirmed COVID-19 cases per 100,000 population in Surabaya.

Understanding Covid-19 Situation Using Mathematical Modeling

In light of the absence of an effective vaccine or specific antiviral treatments, mathematical modeling assumes a crucial role in enhancing our comprehension of disease dynamics and in devising strategies to manage the rapid spread of infectious diseases. Particularly during this period, forecasting holds paramount significance for healthcare planning and for effectively addressing the COVID-19 pandemic. To elucidate the dynamics of the COVID-19 outbreak, this study introduces an extended SEIR compartment model, refined with the inclusion of contact tracing and hospitalization strategies. The model is calibrated employing daily COVID-19 data encompassing various Indian regions, including Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal, and the entirety of India. Employing the least squares method, we estimate sensitive parameters after conducting a sensitivity analysis, which we approach using partial rank correlation coefficient techniques. Our exploration focuses on the relative significance of system parameters, with a dedicated sensitivity analysis centered on the reproductive number R₀. To assess the model's resilience across parameter variations, we compute R₀ sensitivity indices. Our findings underscore the effectiveness of strategies that involve reducing disease transmission coefficients (s) and clinical outbreak rates (qa) in controlling COVID-19 outbreaks. Our study generates short-term predictions for daily and cumulative confirmed COVID-19 cases across the five Indian provinces. These projections reveal distinct trends, with certain states demonstrating steady exponential growth, while others exhibit a decline in daily new cases. Examining the long-term perspective, our model predicts oscillatory dynamics for COVID-19 cases in India, suggesting the potential for the disease to follow a seasonal pattern. Consequently, our simulation points towards a power law trend in coronavirus cases in India by the close of September 2020, further contributing to our understanding of the disease's progression.

The Dynamic Impact of Covid-19 Pandemic on Environment-friendly Energy Industry

Most of the current research on green energy and the pandemic focuses on how green investments changed after the epidemic and how the pandemic affected the local economy. The correlation between the rate of return on green energy and the number of confirmed cases in China and the rest of the globe is the main topic of this research. The daily cumulative confirmed Covid-19 cases in China and the rest of the world, and daily price of green energy are used as time series variables. They are daily data from the end of January 2020 to the end of July 2022. The study's findings largely show how the price of green energy has changed in relation to the pandemic's progression. A VAR (12) model makes a forecast on how the pandemic, which is expected to become stationary after the tenth period, would affect the price of green energy. The ARMA-GARCH model's findings demonstrate a statistically significant negative correlation between the price of green energy and the number of verified cases. In other words, compared to the pandemic, the price of green energy is lower. The pandemic has had no impact on the rate of return of green energy throughout the specified period, though.

Short-term effects of COVID-19 on the risk of traumatic fractures in China cities

This study aimed to investigate the association between COVID-19 and fracture risk and provide a targeted reference for the world through China’s experience. A nationally representative sample of COVID-19 prevalence areas selected using stratified random sampling was retrospectively analyzed. Age, sex, fracture site, mechanism of injury, and concurrent fractures of traumatic fracture patients in selected hospitals were collected from 10 January to 10 July 2020. The epidemiologic characteristics of traumatic fractures and the association between COVID-19 and fracture risk were explored using descriptive epidemiological methods and a distributed lag nonlinear model. A total of 67,249 patients (52.3% males, 49.4 ± 19.4 years old) with 68,989 fractures were included. The highest proportion of fractures were in the tibia and fibula (14.9%), followed by the femur (13.6%) and ulna and radius (12.5%). Low-energy fractures accounted for 23.3%. With the increase in newly confirmed COVID-19 cases, fracture risk decreased for children, young and middle-aged adults, elderly men, high-energy fractures, and residents in regions with < 1000 cumulative confirmed COVID-19 cases. Fracture risk decreased sharply in all residents except elderly women, for low-energy fractures, and in regions with > 1000 cumulative confirmed COVID-19 cases when newly confirmed COVID-19 cases increased in China. Primary (home) prevention measures are emphasized to prevent traumatic fractures.

Spatiotemporal Characteristics and Patterns of the COVID-19 Pandemic in China: An Empirical Study Based on 413 Cities or Regions.

The global economy was stagnant and even regressed since the outbreak of COVID-19. Exploring the spatiotemporal characteristics and patterns of COVID-19 pandemic spread may contribute to more scientific and effective pandemic prevention and control. This paper attempts to investigate the spatiotemporal characteristics in cumulative confirmed COVID-19 cases, mortality, and cure rate in 413 Chinese cities or regions using the data officially disclosed by the government. The results showed that: (1) The pandemic development can be divided into five stages: early stage (sustained growth), early mid-stage (accelerated growth), mid-stage (rapid growth), late mid-stage (slow growth), and late-stage (stable disappearance); (2) the cumulative number of confirmed COVID-19 cases remained constant in Wuhan, whilst the mortality tended to rise faster from the early stage to the late-stage and the cure rate moved from the southeast to the northwest; (3) the three indicators mentioned above showed significant and positive spatial correlation. Moran’s I curve demonstrated an inverted “V” trend in cumulative confirmed COVID-19 cases; the mortality curve was generally flat; the cure rate curve tended to rise. There are apparent differences in the local spatial autocorrelation pattern of the three primary indicators.

Tropical Climate Less Affects Covid19 Transmission than Population Density: Perspective of Indonesia

Introduction: Indonesia is ranked the 4th most populous country in the world. Since Covid19 is highly transmissible from human to human, Indonesia might suffer a long period of the Covid19 pandemic than other less-populous countries. This study aimed to find the correlations of tropical climate, population density and confounding factors with Covid19 progression in Indonesia from March to August 2020. Methods: The climatological data, population density, laboratory testing, and the confirmed Covid19 cases were statistically analyzed. The correlations between each data were performed with Pearson’s Correlation Coefficient using a Statistical Package for the Social Sciences. The values of statistical significance were considered at 95% and 99% confidence intervals. Results and Discussion: Indonesia recorded more than 1,315 confirmed Covid19 cases in almost all provinces (30 out of 34) during the dry season (March to August 2020). During the early pandemic, DKI Jakarta and East Java have been the epicenters of the pandemic in Indonesia. Humidity and precipitation have a weak negative correlation, while the temperatures have a weak positive correlation. Population density and laboratory testing have a strong positive and significant correlation with the cumulative confirmed Covid19 cases. Conclusion: Our study indicates that tropical climate less affects the cumulative Covid19 case in Indonesia than population density and laboratory testing capacity.

High SARS-CoV-2 Seroprevalence among Healthcare Workers in Bamako, Mali.

In Mali, a country in West Africa, cumulative confirmed COVID-19 cases and deaths among healthcare workers (HCWs) remain enigmatically low, despite a series of waves, circulation of SARS-CoV-2 variants, the country’s weak healthcare system, and a general lack of adherence to public health mitigation measures. The goal of the study was to determine whether exposure is important by assessing the seroprevalence of anti-SARS-CoV-2 IgG antibodies in HCWs. The study was conducted between November 2020 and June 2021. HCWs in the major hospitals where COVID-19 cases were being cared for in the capital city, Bamako, Mali, were recruited. During the study period, vaccinations were not yet available. The ELISA of the IgG against the spike protein was optimized and quantitatively measured. A total of 240 HCWs were enrolled in the study, of which seropositivity was observed in 147 cases (61.8%). A continuous increase in the seropositivity was observed, over time, during the study period, from 50% at the beginning to 70% at the end of the study. HCWs who provided direct care to COVID-19 patients and were potentially highly exposed did not have the highest seropositivity rate. Vulnerable HCWs with comorbidities such as obesity, diabetes, and asthma had even higher seropositivity rates at 77.8%, 75.0%, and 66.7%, respectively. Overall, HCWs had high SARS-CoV-2 seroprevalence, likely reflecting a “herd” immunity level, which could be protective at some degrees. These data suggest that the low number of cases and deaths among HCWs in Mali is not due to a lack of occupational exposure to the virus but rather related to other factors that need to be investigated.

Short-term Forecasting of Cumulative Confirmed Covid-19 Cases Pandemic in Somalia

Somalia has recorded the first confirmed Covid-19 case and first death case on March 16, and April 08, 2020, respectively. Since its arrival, it had infected 2,603 people and took the lives of 88 people while 577 patients were recovered as of 14 June, 2020. To fight this pandemic, the government requires to make the necessary plans accordingly. To plan effectively, the government needs to answer this question: what will be the effect of Covid-19 cases in the country? To answer this question accurately and objectively, forecasting the spread of confirmed Covid-19 cases will be vital. To this regard, this paper provides real times forecasts of Covid-19 cases employing Holt's linear trend model without seasonality. Provided that the data employed is accurate and the past pattern of the disease will continue in the future, this model is powerful to produce real time forecasts in the future with some degree of uncertainty. With the help of these forecasts, the government can make evidence based decisions by utilizing the scarce resource available at its disposal.

Clustering Regency and City in East Java Based on Population Density and Cumulative Confirmed COVID-19 Cases

Coronavirus is a big family of viruses that causes acute respiratory syndrome and mediates human-to-human by the environment. A factor that affects the spread of infectious diseases is population density. Therefore, it is necessary to study the effect of population density on infectious diseases like COVID-19. The research analyzed the effect of the population density of each regency in East Java on cumulative confirmed COVID-19 cases until December 9, 2020. The research applied quantitative method using the agglomerative hierarchical clustering method. The clustering method included single, average, and complete linkages. The results of clustering using single linkage and average linkages have the same results for the population density of Jember Regency. This regency has the lowest effect for the cumulative confirmed COVID-19 cases. Then, complete linkage obtains that Banyuwangi Regency and Gresik Regency has the population density with the lowest effect for the cumulative number of confirmed COVID-19 cases. The results of clustering with single, average, and complete linkages have the same results for population density with a big effect on the cumulative number of confirmed COVID-19 cases in Surabaya City. The results of best clustering regencies or cities that population density affects the number of

A fractional-order model for COVID-19 and tuberculosis co-infection using Atangana–Baleanu derivative

This paper considers and analyzes a fractional order model for COVID-19 and tuberculosis co-infection, using the Atangana–Baleanu derivative. The existence and uniqueness of the model solutions are established by applying the fixed point theorem. It is shown that the model is locally asymptotically stable when the reproduction number is less than one. The global stability analysis of the disease free equilibrium points is also carried out. The model was simulated using data relevant to both diseases in New Delhi, India. Fitting the model to the cumulative confirmed COVID-19 cases for New Delhi from March 1, 2021 to June 26, 2021, COVID-19 and TB contact rates and some other important parameters of the model are estimated. The numerical method used combines the two-step Lagrange polynomial and the fundamental theorem of fractional calculus and has been shown to be highly accurate and efficient, user-friendly and converges quickly to the exact solution even with a large step of discretization. Simulations of the Fractional order model revealed that reducing the risk of COVID-19 infection by latently-infected TB individuals will not only bring down the burden of COVID-19, but will also reduce the co-infection of both diseases in the population. Also, the conditions for the co-existence or elimination of both diseases from the population are established.

MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS

The novel Coronavirus (COVID-19) has significantly affected millions of people around the world since the first notification until nowadays. The rapid spread of the virus has dramatically increased the workload of healthcare systems in many countries. Therefore, the need for efficient use of the healthcare system leads researchers to forecast the trend of virus spread. For this purpose, Machine Learning (ML) and Artificial Intelligence (AI) applications have intensively used to struggle against the coronavirus outbreak. In this study, Temporal Convolutional Network (TCN) is applied for modeling the cumulative confirmed COVID-19 cases and forecasting the spread of it in various European countries using time series data. It is also presented that numerical examples for comparing performances of TCN against Long-Short Term Memory (LSTM) and Gates Recurrent Units(GRU) in terms of computation time, root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE). Simulation results indicate that the Temporal Convolutional Networks used in this manuscript performs better than other models for forecasting the cumulative confirmed COVID-19 cases.

Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States

BackgroundThe spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage.MethodsThis was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately.ResultsOn both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation.ConclusionsNeighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis

BackgroundPopulation mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases.ObjectiveThe aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina.MethodsThis longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting.ResultsPopulation mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%.ConclusionsUsing Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

Forecasting cumulative COVID-19 cases in Malaysia and rising to unprecedented levels

Background: COVID-19 outbreak is being studied throughout the world. Adding more analysis to date strengthening the information about the illness. Here, we analysis the data of Malaysian Ministry of Health from February 15, 2020 until January 10, 2021 was analysed using linear regression model statistical analysis with aim to forecast the trend.&#x0D; Materials and Methods: This study reviewed the data by Malaysia Ministry of Health from February 15, 2020, until January 10, 2021. Linear regression model statistical analysis was used for predictive modelling. The forecasting of the linear trend of the Covid-19 outbreak prediction is purposed to estimate the number of confirm cases according to the number of recoveries patients.&#x0D; Results: Malaysia is currently anticipating another lockdown restriction as new confirmed case of COVID-19 hit new record high. The cumulative confirmed Covid-19 cases in MCO predicted a sharp increase. At the first of March, 2021, the predicted cumulative confirmed Covid-19 cases are 319,477 cases.&#x0D; Conclusions: Covid-19 cases projected to 315766 by end of February 2021 with 3000- 4000 daily cases predicted. Initiative and proactive measurement by Malaysian government hopefully can reduce the number of cases and flatten the infection curve.&#x0D; Bangladesh Journal of Medical Science Vol.20(3) 2021 p.504-510

Persistence and extinction criteria of Covid-19 pandemic: India as a case study

Novel coronavirus has altered the socio-economic condition of the whole world through its devastating effects on the human population. Mathematical models and computation techniques may play an important role in understanding this epidemic and contribute a lot in policy making to control the infection in a more systematic and effective way. In this paper, we have proposed a deterministic mathematical model for the Covid-19 pandemic taking into account the different epidemiological status of individuals of a given geographical region and analyzed it with respect to the basic reproduction number. Uncertainty is obvious in the case of a growing epidemic and it multiplies if the disease etiology is unknown. Taking into account the uncertainty in the epidemiological parameters, we extended the deterministic system into a stochastic system through random parameter perturbations in three epidemiological parameters. Analyzing the model, we determined the disease persistence and eradication conditions. The asymptotic behavior of the stochastic solution around the coexistence equilibrium of the deterministic model was also presented. As a case study, we considered the Covid-19 pandemic of India and estimated the model parameters from the epidemic data. We demonstrated different analytical results and predicted the course of the epidemic. Our simulation results indicate that the epidemic in India may continue up to third week of July 2021 and the cumulative confirmed Covid-19 cases may vary from to Such results may be useful from management and policy development viewpoints.

Impact of social media advertisements on the transmission dynamics of COVID-19 pandemic in India.

In this paper, we propose a mathematical model to assess the impact of social media advertisements in combating the coronavirus pandemic in India. We assume that dissemination of awareness among susceptible individuals modifies public attitudes and behaviours towards this contagious disease which results in reducing the chance of contact with the coronavirus and hence decreasing the disease transmission. Moreover, the individual’s behavioral response in the presence of global information campaigns accelerate the rate of hospitalization of symptomatic individuals and also encourage the asymptomatic individuals for conducting health protocols, such as self-isolation, social distancing, etc. We calibrate the proposed model with the cumulative confirmed COVID-19 cases for the Republic of India. We estimate eight epidemiologically important parameters, and also the size of basic reproduction number for India. We find that the basic reproduction number for India is greater than unity, which represents the substantial outbreak of COVID-19 in the country. Sophisticated techniques of sensitivity analysis are employed to determine the impacts of model parameters on basic reproduction number and symptomatic infected population. Our results reveal that to reduce disease burden in India, non-pharmaceutical interventions strategies should be implemented effectively to decrease basic reproduction number below unity. Continuous propagation of awareness through the internet and social media platforms should be regularly circulated by the health authorities/government officials for hospitalization of symptomatic individuals and quarantine of asymptomatic individuals to control the prevalence of disease in India.

Investigation of the factors potentially responsible for the significant different prevalence of COVID-19 between African-Africans and African-Americans

<abstract><sec> <title>Background</title> The COVID-19 pandemic has resulted in more than 145 million cases and 3 million deaths as of April 23, 2021. Compared with the other continents, Africa had a relatively lower prevalence of COVID-19. However, the African-American population showed a higher COVID-19 susceptibility than the other U.S. populations. </sec><sec> <title>Methods</title> To investigate the factors that are potentially responsible for the different susceptibility of COVID-19 between African-Africans and African-Americans, we collected the data of cumulative confirmed COVID-19 cases and deaths, temperature, humidity, wind speed, age distribution, incidences of age-related diseases, lung diseases, and HIV, and smoking rate in 40 African countries and 50 U.S. states and New York city. </sec><sec> <title>Results</title> We found that old age, smoking, and age-related diseases (such as cardiovascular diseases, COPD, diabetes, hypertension, neoplasms, and stroke) were potential risk factors for COVID-19, while chronic kidney disease, tuberculosis, young age, and temperature were potential protective factors. </sec><sec> <title>Conclusions</title> The significant differences in the age distribution, incidences of age-related diseases, lung diseases, and HIV, smoking rate, temperature, and humidity could be responsible for the markedly different prevalence of COVID-19 between African-Africans and African-Americans. </sec></abstract>