Analysis of COVID-19 cases' spatial dependence in US counties reveals health inequalities

On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I = 0.174 and 0.264, p < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran I = 0.264, p < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran I = –0.203, p < 0.0001) and deaths (Moran I = –0.137, p < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I = 0.08, p < 0.0001) and deaths (Moran I = 0.15, p < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease

The prevalence and contextual correlates of non-communicable diseases among inter-provincial migrants and non-migrants in South Africa

BACKGROUND: The socioeconomic conditions of different environments manifest in varying experiences of illnesses. Even as migrants do transit across these different environments for various reasons, including settlement, they are bound to have peculiar experiences of diseases, which could be traced to lifestyle, gender, adaptation, and reactions to specific social, economic, psychological and climatic conditions. Paying attention to such unique scenarios, our study examines the prevalence and contextual correlates of non-communicable diseases among inter-provincial migrants and non-migrants in South Africa. METHODS: Data was from the National Income Dynamics Study (NIDS), waves 5 of 2017, which comprised of 28,055 respondents aged 15–64 years made up of 22,849 inter-provincial non-migrants and 5206 inter-provincial migrants. A composite dependent/outcome variable of non-communicable diseases (NCDs) was generated for the study and data analysis involved descriptive statistics, chi Square analysis and multilevel logistic regression analysis. RESULTS: More migrants (19.81%) than non-migrants (16.69%) reported prevalence of NCDs. With the exception of household size for migrants and smoking for non-migrants, the prevalence of NCDs showed significant differences in all the community, behavioral, and individual variables. The factors in the full model, which significantly increased odds of NCDs among the migrants and the non-migrants, were older populations, the non-Blacks, and those with higher education levels. On the one hand, being married, having a household with 4–6 persons, and being residents of urban areas significantly increased odds of NCDs among the migrant population. While on the other, living in coastal provinces, being a female, and belonging to the category of those who earn more than 10,000 Rands were significantly associated with increased odds of NCDs among the non-migrants. CONCLUSIONS: These findings, therefore, among other things underscore the need for increased education and awareness campaigns, especially among the older populations on the preventive and mitigative strategies for NCDs. In addition, changes in lifestyles with regard to smoking and physical exercises should be more emphasized in specific contextual situations for the migrant and non-migrant populations, as highlighted by the results of this study

Is Nigeria really on top of COVID-19? Message from effective reproduction number

Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population.In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) - this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020.In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37-2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%.Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed

The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa.

Corona virus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first detected in the city of Wuhan, China in December 2019. Although, the disease appeared in Africa later than other regions, it has now spread to virtually all countries on the continent. We provide early spatio-temporal dynamics of COVID-19 within the first 62 days of the disease's appearance on the African continent. We used a two-parameter hurdle Poisson model to simultaneously analyse the zero counts and the frequency of occurrence. We investigate the effects of important healthcare capacities including hospital beds and number of medical doctors in different countries. The results show that cases of the pandemic vary geographically across Africa with notably high incidence in neighbouring countries particularly in West and North Africa. The burden of the disease (per 100 000) mostly impacted Djibouti, Tunisia, Morocco and Algeria. Temporally, during the first 4 weeks, the burden was highest in Senegal, Egypt and Mauritania, but by mid-April it shifted to Somalia, Chad, Guinea, Tanzania, Gabon, Sudan and Zimbabwe. Currently, Namibia, Angola, South Sudan, Burundi and Uganda have the least burden. These findings could be useful in guiding epidemiological interventions and the allocation of scarce resources based on heterogeneity of the disease patterns