Analysis of COVID-19 Cases' Spatial Dependence in US Counties Reveals Health Inequalities

Trends in the mobility of primary healthcare human resources in underdeveloped regions of western China from 2000 to 2021: Evidence from Nanning

OBJECTIVE

This research aimed to identify the fundamental and geographic characteristics of the primary healthcare personnel mobility in Nanning from 2000 to 2021 and clarify the determinants that affect their transition to non-primary healthcare institutions.

METHODS

Through utilizing the Primary Healthcare Personnel Database (PHPD) for 2000-2021, the study conducts descriptive statistical analysis on demographic, economic, and professional aspects of healthcare personnel mobility across healthcare reform phases. Geographic Information Systems (QGIS) were used to map mobility patterns, and R software was employed to calculate spatial autocorrelation (Moran's I). Logistic regression identified factors that influenced the transition to non-primary institutions.

RESULTS

Primary healthcare personnel mobility is divided into four phases: initial (2000-2008), turning point (2009-2011), rapid development (2012-2020), and decline (2021). The rapid development stage saw increased mobility with no spatial clustering in inflow and outflow. From 2016 to 2020, primary healthcare worker mobility reached its peak, in which the most significant movement occurred between township health centers and other institutions. Aside from their transition to primary medical institutions, the primary movement of grassroots health personnel predominantly directs towards secondary general hospitals, tertiary general hospitals, and secondary specialized hospitals. Since 2012, the number and mobility distance of primary healthcare workers have become noticeably larger and remained at a higher level from 2016 to 2020. The main migration of primary healthcare personnel occurred in their districts (counties). Key transition factors include gender, education, ethnicity, professional category, general practice registration, and administrative division.

CONCLUSIONS

This study provides evidence of the features of primary healthcare personnel mobility in the less developed western regions of China, in which Nanning was taken as a case study. It uncovers the factors that impact the flow of primary healthcare personnel to non-primary healthcare institutions. These findings are helpful to policy refinement and support the retention of primary healthcare workers.

A Novel Electronic Record System for Documentation and Efficient Workflow for Community Health Workers: Development and Usability Study

BACKGROUND

The COVID-19 pandemic added to the decades of evidence that public health institutions are routinely stretched beyond their capacity. Community health workers (CHWs) can be a crucial extension of public health resources to address health inequities, but systems to document CHW efforts are often fragmented and prone to unneeded redundancy, errors, and inefficiency.

OBJECTIVE

We sought to develop a more efficient data collection system for recording the wide range of community-based efforts performed by CHWs.

METHODS

The Communities Organizing to Promote Equity (COPE) project is an initiative to address health disparities across Kansas, in part, through the deployment of CHWs. Our team iteratively designed and refined the features of a novel data collection system for CHWs. Pilot tests with CHWs occurred over several months to ensure that the functionality supported their daily use. Following implementation of the database, procedures were set to sustain the collection of feedback from CHWs, community partners, and organizations with similar systems to continually modify the database to meet the needs of users. A continuous quality improvement process was conducted monthly to evaluate CHW performance; feedback was exchanged at team and individual levels regarding the continuous quality improvement results and opportunities for improvement. Further, a 15-item feedback survey was distributed to all 33 COPE CHWs and supervisors for assessing the feasibility of database features, accessibility, and overall satisfaction.

RESULTS

At launch, the database had 60 active users in 20 counties. Documented client interactions begin with needs assessments (modified versions of the Arizona Self-sufficiency Matrix and PRAPARE [Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences]) and continue with the longitudinal tracking of progress toward goals. A user-specific automated alerts-based dashboard displays clients needing follow-up and upcoming events. The database contains over 55,000 documented encounters across more than 5079 clients. Available resources from over 2500 community organizations have been documented. Survey data indicated that 84% (27/32) of the respondents considered the overall navigation of the database as very easy. The majority of the respondents indicated they were overall very satisfied (14/32, 44%) or satisfied (15/32, 48%) with the database. Open-ended responses indicated the database features, documentation of community organizations and visual confirmation of consent form and data storage on a Health Insurance Portability and Accountability Act-compliant record system, improved client engagement, enrollment processes, and identification of resources.

CONCLUSIONS

Our database extends beyond conventional electronic medical records and provides flexibility for ever-changing needs. The COPE database provides real-world data on CHW accomplishments, thereby improving the uniformity of data collection to enhance monitoring and evaluation. This database can serve as a model for community-based documentation systems and be adapted for use in other community settings.

State variation in neighborhood COVID-19 burden across the United States

BACKGROUND

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on.

METHODS

We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment.

RESULTS

We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state.

CONCLUSIONS

Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.

The impact of urban spatial environment on COVID-19: a case study in Beijing

Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities' ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, volume ratio, density of commercial facilities, density of service facilities, and density of transportation facilities were positively correlated with COVID-19 Outbreak Frequency, while water and greenery coverage was negatively correlated with it. (ii) The correlation between COVID-19 Outbreak Frequency and the internal spatial environmental elements of neighborhoods of different grades differed. House price and the number of households were positively correlated with the COVID-19 Outbreak Frequency in low-end neighborhoods, while the number of households was positively correlated with the COVID-19 Outbreak Frequency in mid-end neighborhoods. In order to achieve spatial justice, society should strive to address the inequality phenomena of income gaps and residential differentiation, and promote fair distribution of spatial environments.

COVID-19 Testing Trend: A Retrospective Analysis of the Three Major Pandemic Waves in Punjab, Pakistan

BACKGROUND/OBJECTIVES

There is some evidence in the literature of under-testing of COVID-19 cases in Pakistan. This study aims to explore COVID-19 testing trends and the factors affecting them in a lower middle-income country for future infectious disease policy-making and intervention strategies.

METHODOLOGY

The study was conducted as a serial cross-sectional study during the three major peaks from March 2020 to June 2021 on 1616 participants in Punjab, Pakistan. This is the first study to explore COVID-19 testing trends in association with flu-like symptoms (FLS) and the factors affecting all three major waves in Pakistan.

RESULTS

The results show that in all three waves, only 18.8% reported COVID-19 tested despite that 86.7% thought they had already had COVID-19, with 51.3% reporting having FLS and 35.6% with exposure to FLS from their families and 19.8% of positive testing rate among their family members. Out of the survey participants, 66% received vaccination, and over 80% had their eligible family members immunized. Fear of contracting COVID-19 was 69.7% in all three waves. Factors positively associated with the uptake of testing were the age group of 31-40 years with an adjusted odds ratio of 3.27 (95% confidence interval (CI): 2.09-5.12) for the second wave and an adjusted odds ratio of 13.75 (95% CI: 9.43-20.01) for the third wave and traveling abroad with odds of 3.08 times when the reference was inland traveling. The adjusted odds ratio to test for FLS was 1.62 (95% CI: 1.21-2.16).

CONCLUSION

In this study, there is convincing evidence of COVID-19 under-testing and thus under-reporting. This study also suggests that fear-based interventions may be counterproductive; however, economic factors such as education, employment, and traveling are significant in guiding the behavior for infectious disease prevention and management.

Supplemental Nutrition Assistance Program Education reductions during COVID-19 may have exacerbated health inequities

OBJECTIVE

Describe, and assess disparities in, the changes in Supplemental Nutrition Assistance Program Education (SNAP-Ed) that occurred the year before vs. the year when COVID-19 restrictions were implemented.

DESIGN

Observational study comparing reach, intensity, and dose of California Local Health Department (LHD) SNAP-Ed interventions in Federal Fiscal years 2019 and 2020 (FFY19, FFY20).

ANALYSIS

Student t-tests determined significance of differences in the number of Direct Education (DE) programs, Policy, Systems and Environmental change (PSE) sites, people reached, and intervention intensity and dose between FFY19 and FFY20 using data reported online by LHDs. Linear regression assessed associations between census tract-level characteristics (urbanicity; percentages of population with income <185% of federal poverty level, under 18 years of age, and belonging to various racial/ethnic groups; and California Healthy Places Index) and changes in number of DE programs, PSE sites, people reached, and intervention dose between FFY19 and FFY20.

RESULTS

From FFY19 to FFY20, the number of DE programs, PSE sites, people reached, and census tract-level intervention intensity and dose decreased. Higher census tract poverty, higher proportions of Black and Latino residents, and less healthy neighborhood conditions were associated with greater decreases in some intervention characteristics including PSE sites, PSE reach, DE programs, and DE dose.

CONCLUSIONS AND IMPLICATIONS

These reductions in LHD SNAP-Ed interventions indicate reduced access to education and environments that support healthy eating and obesity prevention during a time when this support was especially needed to reduce risk of COVID-19 infection and complications. Disproportionately reduced access, may have worsened health disparities in already-disadvantaged communities. Assuring maintenance of SNAP-Ed interventions, especially in disadvantaged communities, should be a priority during public health emergencies.

State Variation in Neighborhood COVID-19 Burden: Findings from the COVID Neighborhood Project

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 burden has been distributed across neighborhoods, a known geographic unit of both risk and resilience, and is hampering efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities. Using spatially-referenced data from 21 states at the ZIP code or census tract level, we documented how the distribution of COVID-19 at the neighborhood-level varies significantly within and between states. The median case count per neighborhood (IQR) in Oregon was 3,608 (2,487) per 100,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (IQR) was 8,142 (11,031) per 100,000. We also found that the association between features of the neighborhood social environment and burden varied in magnitude and direction by state. Our findings underscore the importance of local contexts when addressing the long-term social and economic fallout communities will face from COVID-19.

The Most Vulnerable Hispanic Immigrants in New York City: Structural Racism and Gendered Differences in COVID-19 Deaths

This paper explores the structural and group-specific factors explaining the excess death rates experienced by the Hispanic population in New York City during the peak years of the coronavirus pandemic. Neighborhood-level analysis of Census data allows an exploration of the relation between Hispanic COVID-19 deaths and spatial concentration, conceived in this study as a proxy for structural racism. This analysis also provides a more detailed exploration of the role of gender in understanding the effects of spatial segregation among different Hispanic subgroups, as gender has emerged as a significant variable in explaining the structural and social effects of COVID-19. Our results show a positive correlation between COVID-19 death rates and the share of Hispanic neighborhood residents. However, for men, this correlation cannot be explained by the characteristics of the neighborhood, as it is for women. In sum, we find: (a) differences in mortality risks between Hispanic men and women; (b) that weathering effects increase mortality risks the longer Hispanic immigrant groups reside in the U.S.; (c) that Hispanic males experience greater contagion and mortality risks associated with the workplace; and (d) we find evidence corroborating the importance of access to health insurance and citizenship status in reducing mortality risks. The findings propose revisiting the Hispanic health paradox with the use of structural racism and gendered frameworks.

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

BACKGROUND

COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.

METHODS

We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination.

RESULTS

Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions.

CONCLUSIONS

This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Geographic disparities in COVID-19 testing and outcomes in Florida

BACKGROUND

Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida.  METHODS: Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff's circular spatial scan statistics or Tango's flexible spatial scan statistics and their locations were visually displayed using QGIS.

RESULTS

Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1-3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases.

CONCLUSIONS

Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.

Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.

Ambulances Required to Relieve Overcapacity Hospitals: A Novel Measure of Hospital Strain During the COVID-19 Pandemic in the United States

Study objective

Methods

Results

Conclusion

Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA

Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models to assess the influence of socio-economic and health conditions on COVID-19 mortality in contiguous USA. For a start, stepwise and exploratory regression models were employed to isolate the main explanatory variables for COVID-19 mortality from the ensemble 33 socio-economic and health parameters between January 1st and 16th of September 2020. Preliminary results showed that only five out of the examined variables (case fatality rate, vulnerable population, poverty, percentage of adults that report no leisure-time physical activity, and percentage of the population with access to places for physical activity) can explain the variability of COVID-19 mortality across the Counties of contiguous USA within the study period. Consequently, we employ three global and two local regression algorithms to model the relationship between COVID-19 and the isolated socio-economic and health variables. The outcomes of the regression analyses show that the adopted models can explain 61%-81% of COVID-19 mortality across the contiguous USA within the study period. However, MGWR yielded the highest R² (0.81) and lowest AICc values (4031), emphasizing that it is the most efficient among the adopted regression models. The computed average adjusted R² values show that local regression models (mean adj. R² = 0.80) outperformed the global regression models (mean adj. R² = 0.64), indicating that the former is ideal for modeling spatial causal relationships. The GIS-based optimized cluster analyses results show that hotspots for COVID-19 mortality as well as socioeconomic variables are mostly delineated in the South, Mid-West and Northeast of contiguous USA. COVID-19 mortality exhibited positive and significant association with black race (0.51), minority (0.48) and poverty (0.34). Whereas, the percentage of persons that attended college was negatively associated with poverty (-0.51), obesity (-0.50) and diabetes (-0.45). Results show that education is crucial to improve socio-economic and health conditions of the Americans. We conclude that investing in people's standard of living would reduce the vulnerability of an entire population.

The Geographical Distribution and Influencing Factors of COVID-19 in China

The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person’s perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.

Performance Evaluation of Lateral Flow Assays for Coronavirus Disease-19 Serology

The coronavirus disease of 2019 (COVID-19) pandemic, caused by infection with the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has undoubtedly resulted in significant morbidities, mortalities, and economic disruptions across the globe. Affordable and scalable tools to monitor the transmission dynamics of the SARS-CoV-2 virus and the longevity of induced antibodies will be paramount to monitor and control the pandemic as multiple waves continue to rage in many countries. Serologic assays detect humoral responses to the virus, to determine seroprevalence in target populations, or induction of antibodies at the individual level following either natural infection or vaccination. With multiple vaccines rolling out globally, serologic assays to detect anti-SARS-CoV-2 antibodies will be important tools to monitor the development of herd immunity. To address this need, serologic lateral flow assays (LFAs), which can be easily implemented for both population surveillance and home use, will be vital to monitor the evolution of the pandemic and inform containment measures. Such assays are particularly important for monitoring the transmission dynamics and durability of immunity generated by natural infections and vaccination, particularly in resource-limited settings. In this review, we discuss considerations for evaluating the accuracy of these LFAs, their suitability for different use cases, and implementation opportunities.

Spatial Clustering of County-Level COVID-19 Rates in the U.S

Despite the widespread prevalence of cases associated with the coronavirus disease 2019 (COVID-19) pandemic, little is known about the spatial clustering of COVID-19 in the United States. Data on COVID-19 cases were used to identify U.S. counties that have both high and low COVID-19 incident proportions and clusters. Our results suggest that there are a variety of sociodemographic variables that are associated with the severity of COVID-19 county-level incident proportions. As the pandemic evolved, communities of color were disproportionately impacted. Subsequently, it shifted from communities of color and metropolitan areas to rural areas in the U.S. Our final period showed limited differences in county characteristics, suggesting that COVID-19 infections were more widespread. The findings might address the systemic barriers and health disparities that may result in high incident proportions of COVID-19 clusters.

A Spatial Analysis of COVID-19 in African Countries: Evaluating the Effects of Socio-Economic Vulnerabilities and Neighbouring

The ongoing highly contagious coronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China, in December 2019, has now become a global public health problem. Using publicly available data from the COVID-19 data repository of Our World in Data, we aimed to investigate the influences of spatial socio-economic vulnerabilities and neighbourliness on the COVID-19 burden in African countries. We analyzed the first wave (January-September 2020) and second wave (October 2020 to May 2021) of the COVID-19 pandemic using spatial statistics regression models. As of 31 May 2021, there was a total of 4,748,948 confirmed COVID-19 cases, with an average, median, and range per country of 101,041, 26,963, and 2191 to 1,665,617, respectively. We found that COVID-19 prevalence in an Africa country was highly dependent on those of neighbouring Africa countries as well as its economic wealth, transparency, and proportion of the population aged 65 or older (p-value < 0.05). Our finding regarding the high COVID-19 burden in countries with better transparency and higher economic wealth is surprising and counterintuitive. We believe this is a reflection on the differences in COVID-19 testing capacity, which is mostly higher in more developed countries, or data modification by less transparent governments. Country-wide integrated COVID suppression strategies such as limiting human mobility from more urbanized to less urbanized countries, as well as an understanding of a county's social-economic characteristics, could prepare a country to promptly and effectively respond to future outbreaks of highly contagious viral infections such as COVID-19.

Spatial Analysis of the Sociodemographic Characteristics, Comorbidities, Hospitalization, Signs, and Symptoms Among Hospitalized Coronavirus Disease 2019 Cases in the State of Rio De Janeiro, Brazil

After more than 1 year from the beginning of the pandemic, the coronavirus disease 2019 (COVID-19) has reached all continents. The number of infected people is still increasing, and Brazil is among the countries with the highest number of registered cases in the world. In this study, we investigated the profile of hospitalized COVID-19 cases and the eventual clusters of similar areas, using geographic information systems. The study was conducted using secondary data. Variables such as sociodemographic characteristics, comorbidities, hospitalization, signs, and symptoms among confirmed cases were considered for each microregion/city of the state of Rio de Janeiro. These proportions were used when calculating the Global Moran's I. The local indicator of spatial association was used to identify local clusters. A significant global spatial auto correlation was found in 28% of the variables. The presence of spatial autocorrelation indicates that the proportions of patients with COVID-19 according to these characteristics are spatially oriented. Moran maps reveal 2 clusters, 1 of high proportions and 1 of low proportions. Understanding the geographic patterns of COVID-19 may assist public health investigators, contributing to actions to prevent and control the pandemic in the state.

Bayesian spatial modelling of Ebola outbreaks in Democratic Republic of Congo through the INLA-SPDE approach

Ebola virus (EBV) disease is a globally acknowledged public health emergency, endemic in the west and equatorial Africa. To understand the epidemiology especially the dynamic pattern of EBV disease, we analyse the EBV case notification data for confirmed cases and reported deaths of the ongoing outbreak in the Democratic Republic of Congo (DRC) between 2018 and 2019, and examined the impact of reported violence on the spread of the virus. Using fully Bayesian geo-statistical analysis through stochastic partial differential equations (SPDE) allows us to quantify the spatial patterns at every point of the spatial domain. Parameter estimation was based on the integrated nested Laplace approximation (INLA). Our findings revealed a positive association between violent events in the affected areas and the reported EBV cases (posterior mean = 0.024, 95% CI: 0.005, 0.045) and deaths (posterior mean = 0.022, 95% CI: 0.005, 0.041). Translating to an increase of 2.4% and 2.2% in the relative risks of EBV cases and deaths associated with a unit increase in violent events (one additional Ebola case is associated with an average of 45 violent events). We also observed clusters of EBV cases and deaths spread to neighbouring locations in similar manners. Findings from the study are therefore useful for hot spot identification, location-specific disease surveillance and intervention.