COVID-19 Cumulative Case Research Articles

A mathematical model to study herbal and modern treatments against COVID-19

Abstract In this paper, we propose a two-group deterministic COVID-19 model which takes into account educational campaigns and the fact that people infected with COVID-19 may choose either modern (allopathic) medicine, traditional medicine or may combine the two modes of treatment. The model is analysed in the case where modern medicine is the only mode of treatment and when traditional medicine is taken as an adjuvant (or another mode of treatment). We prove in the first case that the model has a disease-free equilibrium (DFE), globally asymptotically stable when the control reproduction number is less than one and whenever it is greater than one, we prove the local asymptotic stability of the endemic equilibrium. In the second case, we prove that, misconceptions in the population lead to a backward bifurcation phenomenon, which makes the control of the disease more difficult. We derive using the Lyapunov method that a threshold T $\mathcal{T}$ ensures the global asymptotic stability of DFE in some cases when its value is less than one. Both models are fitted using daily COVID-19 cumulative cases reported from January to February 2022 in South Africa. We found a control reproduction number less than one, meaning that COVID-19 will be eliminated. Comparison of the two models fits highlights that misconceptions should be taken into account to accurately describe the dynamics of COVID-19 in South Africa. Numerically, we prove that educational campaigns should focus on preventive measures and both traditional and allopathic medicine health care systems should complement each other in the fight against COVID-19.

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.

Investigating the relationship between cross-national suicide rates and COVID-19 first and second waves spread across the world: An exploratory study

IntroductionCOVID-19 spreads between people in close contact. Social isolation, which is linked with increased suicide risk, prevents COVID-19 from spreading. Suicide and COVID-19 may therefore represent two antagonistic phenomena. Specifically, we tested whether previous cross-national suicide rates inversely correlate with COVID-19 cases and deaths across countries. Material and methodsWe ran unadjusted bivariate correlations between the most updated (2016) cross-national Age-Standardised suicide rates and COVID-19 cumulative cases and deaths (as of: 30/08/2020, 11/10/2020 and 30/05/2021) across countries; and we controlled for WHO Income group, WHO region, suicide data quality, and urbanicity. ResultsSuicide rates negatively correlated with COVID-19 cumulative cases up to 30/08/2020 (r=−0.14, P=.064) and up to 11/10/2020 at an almost significant level (r=−0.149, P=.050) across 174 countries. As of 11/10/2020 this correlation became significant when controlling for WHO region (r=−0.17, P=.028), data quality (r=−0.181, P=.017) and urbanicity (r=−0.172, P=.039); and as of 30/08/2020 when adjusting for WHO region (r=−0.15, P=.047) and data quality a (r=−0.16, P=.036). No significant correlations between suicide rates and COVID-19 deaths were found. ConclusionsThere seems to be an inverse correlation between previous cross-national suicide rates and COVID-19 cumulative cases across countries. Suicide and COVID-19 appear to behave, to some degree, as antagonistic phenomena, which challenges their prevention.

Health behaviors and the risk of COVID-19 incidence: A Bayesian hierarchical spatial analysis

ObjectivesEffective infection control measures, based on a sound understanding of geographical community-specific health behavioral characteristics, should be implemented from the early stage of disease transmission. However, few studies have explored health behaviors as a possible contributor to COVID-19 infection in the spatial context. We investigated health behaviors as potential factors of COVID-19 incidence in the early phase of transmission in the spatial context. MethodsWe extracted COVID-19 cumulative case data as of February 25, 2021—one day prior to nationwide COVID-19 vaccination commencement—regarding health behaviors and covariates, including health condition and socio-economic factors, at the municipal level from publicly available datasets. The spatial autocorrelation of incidence was analyzed using Global Moran’s I statistics. The associations between health behaviors and COVID-19 incidence were examined using Besag–York–Mollie models to deal with spatial autocorrelation of residuals. ResultsThe COVID-19 incidence had positive spatial autocorrelation across South Korea (I = 0.584, p = 0.001). The results suggest that individuals vaccinated against influenza in the preceding year had a negative association with COVID-19 incidence (relative risk=0.913, 95 % Credible Interval=0.838–0.997), even after adjusting for covariates. ConclusionsOur ecological study suggests an association between COVID-19 infection and health behaviors, especially influenza vaccination, in the early stage of COVID-19 transmission at the municipal level.

The predictors of COVID-19 mortality among health systems parameters: an ecological study across 203 countries

BackgroundHealth systems responsiveness is the key to addressing infectious disease threats such as pandemics. The paper outlines an assessment of health systems resilience by exploring the association of health systems and Global Health Security (GHS) parameters with case load and mortality resulting from COVID-19 across 203 countries using an ecological design.MethodologyCorrelation analysis was performed to assess the relationship of each of the indicators with COVID 19 cases and deaths per million population. Stepwise multiple regression models were developed to determine the predictors of COVID-19 cumulative cases and deaths per million population separately.ResultsGlobal health security indicators seemed to have a strong association when analyzed individually but those did not necessarily translate into less burden of cases or deaths in the multivariable analysis. The predictors of cumulative deaths per million population included general government expenditure on health as a proportion of general government expenditure, responsiveness of the system to prevent the emergence and release of pathogens and governance related voice and accountability.ConclusionTo conclude, health financing parameters and preventive activities with regard to emergence of pathogens were better predictors of cumulative COVID-19 cases and deaths per million population compared to other health systems and global health security indicators.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses

BackgroundThere is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts.MethodsThis study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000.ResultsNinety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 90 days) have a better fit.ConclusionOur findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.

COVID-19 metrics across parliamentary constituencies and districts in India

ABSTRACT In India, Parliamentary Constituencies (PCs) could serve as a regional unit of COVID-19 monitoring that facilitates evidence-based policy decisions. In this study, we presented the first estimates of COVID-19 cumulative cases and deaths per 100,000 population and the case fatality rate (CFR) between 7 January 2020 and 31 January 2021 across PCs and districts of India. We adopted a novel geographic information science-based methodology called crosswalk to estimate COVID-19 outcomes at the PC-level from district-level information. We found a substantial variation of COVID-19 burden within each state and across the country. Access to PC-level and district-level COVID-19 information can enhance both central and regional governmental accountability of safe reopening policies.

Effects of country success on COVID-19 cumulative cases and excess deaths in 169 countries

COVID-19 has caused over 260 million confirmed cases and over 5 million deaths globally. The results of statistical and multiple criteria analyses on the success of 169 countries and on COVID-19 cumulative cases and excess deaths show that the prosperity of a country relates directly to the consequences due to the pandemic. The topic of this article is the Country Success and COVID-19 (CSC) Map of the World. As a country’s success grows, this map shows how cumulative cases of COVID-19 increase; at the same time, excess deaths decrease. The indicators in the system of criteria regarding country success and sustainability are interrelated. Conditional country successes remain quite similar, despite changes to the numbers of countries and their indicators. Likewise, the seven clusters of countries under consideration group together independently of which system of indicators had been applied for their analysis. The 2020 Inglehart–Welzel Cultural Map of the World, which is grounded on surveys, and the CSC Map, which is grounded on statistical indicators, have axes that correlate with one another significantly. The CSC Map Model explains over 63% of the dispersions pertinent to COVID-19 cumulative cases, over 52% of COVID-19 excess deaths, and over 95% of country success variables. The layout of the clusters on the CSC Map changes little over time. Upon performance of the correlation analysis, it was established that strong and statistically significant relationships exist between 169 countries success and sustainability linked with their current air quality score (r = 0.602, p < 0.01) and the environmental performance index (EPI) score (r = 0.931, p < 0.01). The results obtained show that when a country’s EPI score and current air quality improve by 1%, excess deaths decrease, respectively, by 2.33 and 1.55%. Global integrated analysis on country successes, COVID-19 cumulative cases, and excess deaths comprise this study.

Spatially refined time-varying reproduction numbers of SARS-CoV-2 in Arkansas and Kentucky and their relationship to population size and public health policy, March – November 2020

PurposeTo examine the time-varying reproduction number, Rt, for COVID-19 in Arkansas and Kentucky and investigate the impact of policies and preventative measures on the variability in Rt.MethodsArkansas and Kentucky county-level COVID-19 cumulative case count data (March 6-November 7, 2020) were obtained. Rt was estimated using the R package ‘EpiEstim’, by county, region (Delta, non-Delta, Appalachian, non-Appalachian), and policy measures.ResultsThe Rt was initially high, falling below 1 in May or June depending on the region, before stabilizing around 1 in the later months. The median Rt for Arkansas and Kentucky at the end of the study were 1.15 (95% credible interval [CrI], 1.13, 1.18) and 1.10 (95% CrI, 1.08, 1.12), respectively, and remained above 1 for the non-Appalachian region. Rt decreased when facial coverings were mandated, changing by -10.64% (95% CrI, -10.60%, -10.70%) in Arkansas and -5.93% (95% CrI, -4.31%, -7.65%) in Kentucky. The trends in Rt estimates were mostly associated with the implementation and relaxation of social distancing measures.ConclusionsArkansas and Kentucky maintained a median Rt above 1 during the entire study period. Changes in Rt estimates allow quantitative estimates of potential impact of policies such as facemask mandate.

A study on the estimation of COVID-19 case with the adaptive kalman filter (AKF) in USA, Germany, United Kingdom, Italy, France, Russia, Brazil, India, Turkey, Spain, Peru, Colombia, South Africa, Argentina, Iran, Pakistan

In this paper, COVID-19 cumulative cases are estimated with AKF based on total COVID-19 cases between January-September 9, 2020 in USA, Germany, United Kingdom, Italy, France, Russia, Brazil, India, Turkey, Spain, Peru, Colombia, South Africa, Argentina, Iran, Pakistan. The cumulative covid-19 cases time-series was modeled with a stochastic dynamic linear model (DLM). The estimation performance of the models is measured by the calculation of mean square error (MSE) and coefficient of determination ( ). Calculated MSE and values showed that the model and AKF could be used to estimate the number of cases in these countries. In this study, firstly, the cumulative number of cases was estimated. Secondly, using these estimates number of daily cases was calculated. Thirdly, the reproduction number was obtained by using these number of daily cases. The model and estimation method used is suitable. The AKF algorithm uses only the number of cases in the last day. We propose that the model and estimation method under consideration is a convenient tool for calculating the reproduction number depending on time.

Modelling and analysis of fractional-order vaccination model for control of COVID-19 outbreak using real data.

In this paper, we construct the SV1V2EIR model to reveal the impact of two-dose vaccination on COVID-19 by using Caputo fractional derivative. The feasibility region of the proposed model and equilibrium points is derived. The basic reproduction number of the model is derived by using the next-generation matrix method. The local and global stability analysis is performed for both the disease-free and endemic equilibrium states. The present model is validated using real data reported for COVID-19 cumulative cases for the Republic of India from 1 January 2022 to 30 April 2022. Next, we conduct the sensitivity analysis to examine the effects of model parameters that affect the basic reproduction number. The Laplace Adomian decomposition method (LADM) is implemented to obtain an approximate solution. Finally, the graphical results are presented to examine the impact of the first dose of vaccine, the second dose of vaccine, disease transmission rate, and Caputo fractional derivatives to support our theoretical results.

Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study

In this paper, a mathematical model is formulated to study the transmission dynamics of the novel coronavirus infection under the effect of treatment. The compartmental model is firstly formulated using a system of nonlinear ordinary differential equations. Then, with the help of Caputo operator, the model is reformulated in order to obtain deeper insights into disease dynamics. The basic mathematical features of the time fractional model are rigorously presented. The nonlinear least square procedure is implemented in order to parameterize the model using COVID-19 cumulative cases in Saudi Arabia for the selected time period. The important threshold parameter called the basic reproduction number is evaluated based on the estimated parameters and is found R0≈1.60. The fractional Lyapunov approach is used to prove the global stability of the model around the disease free equilibrium point. Moreover, the model in Caputo sense is solved numerically via an efficient numerical scheme known as the fractional Adamas-Bashforth-Molten approach. Finally, the model is simulated to present the graphical impact of memory index and various intervention strategies such as social-distancing, disinfection of the virus from environment and treatment rate on the pandemic peaks. This study emphasizes the important role of various scenarios in these intervention strategies in curtailing the burden of COVID-19.

Association of air pollution and meteorological variables with the two waves of COVID-19 pandemic in Delhi: A critical analysis

Various countries across the globe have been affected by different COVID-19 waves at different points in time and with varying levels of virulence. With the backdrop of the two COVID-19 waves that broke out in Delhi, this study examines the variations in the concentrations of criteria pollutants, air quality, and meteorological variables across the waves and their influence on COVID-19 morbidity/mortality. Descriptive statistics, violin plots, and Spearman rank correlation tests were employed to assess the variations in environmental parameters and investigate their associations with COVID-19 incidence under the two waves. The susceptible-infected-recovered model and multiple linear regression were used to assess the wave-wise basic reproduction number (R0) and infection spreading trajectory of the virus. Our results show that the first wave in Delhi had three successive peaks and valleys, and the first peak of the second wave was the tallest, indicating the severity of per-day infection cases. During the analysed period (April 2020 and April 2021), concentrations of criteria pollutants varied across the waves, and air pollution was substantially higher during the second wave. In addition, the results revealed that during the second wave, NO2 maintained a significant negative relationship with COVID-19 (cases per day), while SO2 had a negative relationship with COVID-19 (cumulative cases) during the first wave. Our results also show a significant positive association of O3 with COVID-19 deaths during the first wave and cumulative cases and deaths during the second wave. The study indicates that a higher relative humidity in Delhi had a negative relation with COVID-19 cumulative cases and mortality during the first wave. The study confirms that the estimated R0 was marginally different during the two waves, and the spread of COVID-19 new cases followed a cubic growth trajectory. The findings of this study provide valuable information for policymakers in handling COVID-19 waves in various cities.

Overview and cross-validation of COVID-19 forecasting univariate models

Researchers have been working with different models to forecast COVID-19 cases. Many of their estimates are not accurate. This study aims to propose the best model to forecast COVID-19 cumulative cases using a machine learning technic. It is a work that focused on time series univariate models because there are too many debates about the quality of the pandemic data. To increase the likelihood of the findings, we avoided many variables modeling and proposed a robust process to forecast COVID-19 cumulative cases. It will help international institutions to take optimal decisions about the world economy and response to the pandemic. Consequently, we used the data titled “Coronavirus Pandemic (COVID-19)” from “Our World in Data” about cases from 22 January 2020 to 30 November 2020. We computed Error Trend Season (ETS), Exponential smoothing with multiplicative error-trend, and ARIMA on the training data sets. In addition, we calculated the Mean Absolute Percentage Error (MAPE) per model. Among those models, we notice that ETS (with additive error-trend and no season) has the smallest MAPE statistics compared to the others. The findings revealed that with the ETS model we need at least 100 days to have good forecasts with a MAPE threshold of 1%.

Situation assessment and natural dynamics of COVID-19 pandemic in Nigeria, 31 May 2020

BackgroundThe coronavirus disease (COVID-19) remains a global public health issue due to its high transmission and case fatality rate. There is apprehension on how to curb the spread and mitigate the socio-economic impacts of the pandemic, but timely and reliable daily confirmed cases' estimates are pertinent to the pandemic's containment. This study therefore conducted a situation assessment and applied simple predictive models to explore COVID-19 progression in Nigeria as at 31 May 2020. MethodsData used for this study were extracted from the websites of the European Centre for Disease Control (World Bank data) and Nigeria Centre for Disease Control. Besides descriptive statistics, four predictive models were fitted to investigate the pandemic natural dynamics. ResultsThe case fatality rate of COVID-19 was 2.8%. A higher number of confirmed cases of COVID-19 was reported daily after the relaxation of lockdown than before and during lockdown. Of the 36 states in Nigeria, including the Federal Capital Territory, 35 have been affected with COVID-19. Most active cases were in Lagos (n = 4064; 59.2%), followed by Kano (n = 669; 9.2%). The percentage of COVID-19 recovery in Nigeria (29.5%) was lower compared to South Africa (50.3%), but higher compared to Kenya (24.1%). The cubic polynomial model had the best fit. The projected value for COVID-19 cumulative cases for 30 June 2020 in Nigeria was 27,993 (95% C.I: 27,001–28,986). ConclusionThe daily confirmed cases of COVID-19 are increasing in Nigeria. Increasing testing capacity for the disease may further reveal more confirmed cases. As observed in this study, the cubic polynomial model currently offers a better prediction of the future COVID-19 cases in Nigeria.

Modelling the Cumulative Number of COVID-19 Cases

Each country has its own characteristics of COVID-19 infection trajectory and epidemic waves. Differences in government-implemented restrictions and social regulations result in variability of the virus transmissions and spread dynamics. This in turn results in various shapes of the growth function used to represent and describe the propagation of infection. Statistical methods are applied to fit non-linear functions to represent daily time-series data of the cumulative numbers of COVID-19 cases. The aim of this work is to fit various statistical models to the cumulative number of COVID-19 cases. Also to overview various types of the existed numerical methodologies. The data (since December 31, 2019) are available for almost each country in the world. As the examples, we used daily time-series data of the cumulative number of COVID-19 cases in Poland, Italy, Canada, and the USA. Non-linear approximations are applied to represent these time series data. The fitted functions allow us to investigate the dynamics of the pandemic. The constructed approximations are compositions of a few nonlinear functions, which describe the growth process of the COVID-19 infection trajectories. Two Gompertz functions and cumulative distribution functions (cdf) were estimated for the data of Poland and Italy (using the cdf for the normal distribution) and for the data of Canada and the USA (using the cdf for the gamma distribution). An analytical (parametric) functions representation of the number of COVID-19 cumulative cases is a useful tool to study the propagation of epidemics.

Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR).

Since COVID-19 is extremely threatening to human health, it is significant to determine its impact factors to curb the virus spread. To tackle the complexity of COVID-19 expansion on a spatial–temporal scale, this research appropriately analyzed the spatial–temporal heterogeneity at the county-level in Texas. First, the impact factors of COVID-19 are captured on social, economic, and environmental multiple facets, and the communality is extracted through principal component analysis (PCA). Second, this research uses COVID-19 cumulative case as the dependent variable and the common factors as the independent variables. According to the virus prevalence hierarchy, the spatial–temporal disparity is categorized into four quarters in the GWR analysis model. The findings exhibited that GWR models provide higher fitness and more geodata-oriented information than OLS models. In El Paso, Odessa, Midland, Randall, and Potter County areas in Texas, population, hospitalization, and age structures are presented as static, positive influences on COVID-19 cumulative cases, indicating that they should adopt stringent strategies in curbing COVID-19. Winter is the most sensitive season for the virus spread, implying that the last quarter should be paid more attention to preventing the virus and taking precautions. This research is expected to provide references for the prevention and control of COVID-19 and related infectious diseases and evidence for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.

The spread of COVID-19 outbreak in the first 120\u2009days: a comparison between Nigeria and seven other countries

BackgroundCOVID-19 is an emerging public health emergency of international concern. The trajectory of the global spread is worrisome, particularly in heavily populated countries such as Nigeria. The study objective was to assess and compare the pattern of COVID-19 spread in Nigeria and seven other countries during the first 120 days of the outbreak.MethodsData was extracted from the World Bank’s website. A descriptive analysis was conducted as well as modelling of COVID-19 spread from day one through day 120 in Nigeria and seven other countries. Model fitting was conducted using linear, quadratic, cubic and exponential regression methods (α=0.05).ResultsThe COVID-19 spread pattern in Nigeria was similar to the patterns in Egypt, Ghana and Cameroun. The daily death distribution in Nigeria was similar to those of six out of the seven countries considered. There was an increasing trend in the daily COVID-19 confirmed cases in Nigeria. During the lockdown, the growth rate in Nigeria was 5.85 (R2=0.728, p< 0.001); however, it was 8.42 (R2=0.625, p< 0.001) after the lockdown was relaxed. The cubic polynomial model (CPM) provided the best fit for predicting COVID-19 cumulative cases across all the countries investigated and there was a clear deviation from the exponential growth model. Using the CPM, the predicted number of cases in Nigeria at 3-month (30 September 2020) was 155,467 (95% CI:151,111-159,824, p< 0.001), all things being equal.ConclusionsImprovement in COVID-19 control measures and strict compliance with the COVID-19 recommended protocols are essential. A contingency plan is needed to provide care for the active cases in case the predicted target is attained.

Covid-19 Pandemic and the Market Performance Analysis: Evidence From Indonesia

This paper empirically identified the impact of the Covid-19 pandemic on market performance in Indonesia. We use a cointegrating regression model of FM-OLS and D-OLS along with Cubic Spline missing data interpolation, summary unit root test, unit root test with Dickey-Fuller breakpoint selection, and Johansen cointegration test. Daily time series data of Covid-19 cumulative case, exchange rate, IDX composite, and gold commodity price were analyzed around 119 days after the first announced case in Indonesia. The finding from FM-OLS and D-OLS analysis showed that the Covid-19 pandemic has a positive and significant impact on exchange rate and gold commodity price. The Covid-19 pandemic impact is appeared to be negative and significant in explaining IDX composite price. It seems that during the Covid-19 pandemic, people prefer to do transactions in the commodity market than neither capital nor money market. The diagnostic of cointegrating regression showed that all variables are integrated of order one I (1), and the long-run cointegration among variables in each equation exists. From the unit root test with break point selection, this study revealed that Covid-19 pandemic has an extreme impact on Indonesian market performance only in the first month after the case was announced. The government responses to mitigate the spread of the Covid-19 pandemic and to minimize the possibly profound impact of market performance are appeared to be successful. Besides, it seems that people's fear is diminished, quite high in the first month, and began to shrink in the following month.

The effect of area deprivation on COVID-19 risk in Louisiana.

BackgroundLouisiana in the summer of 2020 had the highest per capita case count for COVID-19 in the United States and COVID-19 deaths disproportionately affects the African American population. Neighborhood deprivation has been observed to be associated with poorer health outcomes. The purpose of this study was to examine the relationship between neighborhood deprivation and COVID-19 in Louisiana.MethodsThe Area Deprivation Index (ADI) was calculated and used to classify neighborhood deprivation at the census tract level. A total of 17 US census variables were used to calculate the ADI for each of the 1148 census tracts in Louisiana. The data were extracted from the American Community Survey (ACS) 2018. The neighborhoods were categorized into quintiles as well as low and high deprivation. The publicly available COVID-19 cumulative case counts by census tract were obtained from the Louisiana Department of Health website on July 31, 2020. Descriptive and Poisson regression analyses were performed.ResultsNeighborhoods in Louisiana were substantially different with respect to deprivation. The ADI ranged from 136.00 for the most deprived neighborhood and –33.87 in the least deprived neighborhood. We observed that individuals residing in the most deprived neighborhoods had almost a 40% higher risk of COVID-19 compared to those residing in the least deprived neighborhoods.ConclusionWhile the majority of previous studies were focused on very limited socio-environmental factors such as crowding and income, this study used a composite area-based deprivation index to examine the role of neighborhood environment on COVID-19. We observed a positive relationship between neighborhood deprivation and COVID-19 risk in Louisiana. The study findings can be utilized to promote public health preventions measures besides social distancing, wearing a mask while in public and frequent handwashing in vulnerable neighborhoods with greater deprivation.