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BackgroundEvidence suggests that the risk of Coronavirus Disease 2019 (COVID-19) varies geographically due to differences in population characteristics. Therefore, the objectives of this study were to identify: (a) geographic disparities of COVID-19 risk in the Greater St. Louis area of Missouri, USA; (b) predictors of the identified disparities. MethodsData on COVID-19 incidence and chronic disease hospitalizations were obtained from the Departments of Health and Missouri Hospital Association, respectively. Socioeconomic and demographic data were obtained from the 2018 American Community Survey while population mobility data were obtained from the SafeGraph website. Choropleth maps were used to identify geographic disparities of COVID-19 risk and its predictors at the ZIP Code Tabulation Area (ZCTA) spatial scale. Global negative binomial and local geographically weighted negative binomial models were used to identify predictors of ZCTA-level geographic disparities of COVID-19 risk. ResultsThere were geographic disparities in COVID-19 risk. Risks tended to be higher in ZCTAs with high percentages of the population with a bachelors degree (p<0.0001) and obesity hospitalizations (p<0.0001). Conversely, risks tended to be lower in ZCTAs with high percentages of the population working in agriculture (p<0.0001). However, the association between agricultural occupation and COVID-19 risk was modified by per capita between ZCTA visits. Areas that had both high per capita between ZCTA visits and high percentages of the population employed in agriculture had high COVID-19 risks. The strength of association between agricultural occupation and COVID-19 risk varied by geographic location. ConclusionsGeographic Information Systems, global and local models are useful for identifying geographic disparities and predictors of COVID-19 risk. Geographic disparities of COVID-19 risk exist in the St. Louis area and are explained by differences in sociodemographic factors, population movements, and obesity hospitalization risks. The latter is particularly concerning due to the growing prevalence of obesity and the known immunological impairments among obese individuals. Therefore, future studies need to focus on improving our understanding of the relationships between COVID-19 vaccination efficacy, obesity and waning of immunity among obese individuals so as to better guide vaccination regimens, reduce disparities and improve population health for all.
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BackgroundCOVID-19 has overwhelmed the US healthcare system, with over 44 million cases and over 700,000 deaths as of October 6, 2021. There is evidence that some communities are disproportionately affected. This may result in geographic disparities in COVID-19 hospitalization risk that, if identified, could guide control efforts. Therefore, the objective of this study is to investigate Zip Code Tabulation Area (ZCTA)-level geographic disparities and identify predictors of COVID-19 hospitalization risk in the St. Louis area. MethodsHospitalization data for COVID-19 and several chronic diseases were obtained from the Missouri Hospital Association. ZCTA-level data on socioeconomic and demographic factors were obtained from the US Census Bureau American Community Survey. Age-adjusted COVID-19 and several chronic disease hospitalization risks were calculated. Geographic disparities in distribution of COVID-19 age-adjusted hospitalization risk, socioeconomic and demographic factors as well as chronic disease risks were investigated using choropleth maps. Predictors of ZCTA-level COVID-19 hospitalization risks were investigated using global negative binomial and local geographically weighted negative binomial models. ResultsThere were geographic disparities of COVID-19 hospitalization risks. COVID-19 hospitalization risks were significantly higher in ZCTAs with high diabetes hospitalization risks (p<0.0001), high risks of COVID-19 cases (p<0.0001), as well as high percentages of black population (p=0.0416) and populations with some college education (p=0.0005). The coefficients of the first three predictors varied across ZCTAs, implying that the associations between COVID-19 hospitalization risks and these predictors varied by geographic location. This implies that a "one-size-fits-all" approach may not be appropriate for management and control. ConclusionsThere is evidence of geographic disparities in COVID-19 hospitalization risks that are driven by differences in socioeconomic, demographic and health-related factors. The impacts of these factors vary by geographical location with some factors being more important predictors in some locales than others. Use of both global and local models leads to a better understanding of the determinants of geographic disparities in health outcomes and utilization of health services. These findings are useful for informing health planning to identify geographic areas likely to have high numbers of individuals needing hospitalization as well as guiding vaccination efforts.
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The ongoing pandemic disease COVID-19 has caused worldwide social and financial disruption. As many countries are engaged in designing vaccines, the harmful second and third waves of COVID-19 have already appeared in many countries. To investigate changes in transmission rates and the effect of social distancing in the USA, we formulate a system of ordinary differential equations using data of confirmed cases and deaths in these states: California, Texas, Florida, Georgia, Illinois, Louisiana, Michigan, and Missouri in the USA to be able to investigate changes in transmission rates of the outbreak and effect of social distancing. Our models and the corresponding parameter estimations show social distancing reduces the transmission by 60% to 90%, and thus obeying the movement restriction rules plays a crucial rule to reduce the magnitudes of the outbreak waves. Our analysis shows the current management restrictions do not sufficiently slow the disease propagation.
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As the pandemic of Coronavirus Disease 2019 (COVID-19) rages throughout the world, accurate modeling of the dynamics thereof is essential. However, since the availability and quality of data varies dramatically from region to region, accurate modeling directly from a global perspective is difficult, if not altogether impossible. Nevertheless, via local data collected by certain regions, it is possible to develop accurate local prediction tools, which may be coupled to develop global models. In this study, we analyze the dynamics of local outbreaks of COVID-19 via a coupled system of ordinary differential equations (ODEs). Utilizing the large amount of data available from the ebbing outbreak in Hubei, China as a testbed, we estimate the basic reproductive number,[R] 0 of COVID-19 and predict the total cases, total deaths, and other features of the Hubei outbreak with a high level of accuracy. Through numerical experiments, we observe the effects of quarantine, social distancing, and COVID-19 testing on the dynamics of the outbreak. Using knowledge gleaned from the Hubei outbreak, we apply our model to analyze the dynamics of outbreak in Turkey. We provide forecasts for the peak of the outbreak and the total number of cases/deaths in Turkey, for varying levels of social distancing, quarantine, and COVID-19 testing.
