County-Level Sociodemographic Factors Associated with COVID-19 Incidence and Mortality in North and South Carolina.

BACKGROUND There is limited research regarding associations between county-level factors and COVID-19 incidence and mortality. While the Carolinas are geographically connected, they are not homogeneous, with statewide political and intra-state socioeconomic differences leading to heterogeneous spread between and within states.METHODS Infection and mortality data from Johns Hopkins University during the 7 months since the first reported case in the Carolinas was combined with county-level socioeconomic/demographic factors. Time series imputations were performed whenever county-level reported infections were implausible. Multivariate Poisson regression models were fitted to extract incidence (infection and mortality) rate ratios by county-level factor. State-level differences in filtered trends were also calculated. Geospatial maps and Kaplan-Meier curves were constructed stratifying by median county-level factor. Differences between North and South Carolina were identified.RESULTS Incidence and mortality rates were lower in North Carolina than South Carolina. Statistically significant higher incidence and mortality rates were associated with counties in both states with higher proportions of Black/African American populations and those without health insurance aged < 65 years. Counties with larger populations aged ≥ 75 years were associated with increased mortality (but decreased incidence) rates.LIMITATIONS COVID-19 data contained multiple inconsistencies, so imputation was needed, and covariate-based data was not synchronous and potentially insufficient in granularity given the epidemiology of the disease. County-level analyses imply within-county homogeneity, an assumption increasingly breached by larger counties.CONCLUSION While statewide interventions were initially implemented, inter-county racial/ethnic and socioeconomic variability points to the need for more heterogeneous interventions, including policies, as populations within particular counties may be at higher risk.

An immunologic model for rapid vaccine assessment -- a clinical trial in a test tube.

While the duration and size of human clinical trials may be difficult to reduce, there are several parameters in pre-clinical vaccine development that may be possible to further optimise. By increasing the accuracy of the models used for pre-clinical vaccine testing, it should be possible to increase the probability that any particular vaccine candidate will be successful in human trials. In addition, an improved model will allow the collection of increasingly more-informative data in pre-clinical tests, thus aiding the rational design and formulation of candidates entered into clinical evaluation. An acceleration and increase in sophistication of pre-clinical vaccine development will thus require the advent of more physiologically-accurate models of the human immune system, coupled with substantial advances in the mechanistic understanding of vaccine efficacy, achieved by using this model. We believe the best viable option available is to use human cells and/or tissues in a functional in vitro model of human physiology. Not only will this more accurately model human diseases, it will also eliminate any ethical, moral and scientific issues involved with use of live humans and animals. An in vitro model, termed "MIMIC" (Modular IMmune In vitro Construct), was designed and developed to reflect the human immune system in a well-based format. The MIMIC System is a laboratory-based methodology that replicates the human immune system response. It is highly automated, and can be used to simulate a clinical trial for a diverse population, without putting human subjects at risk. The MIMIC System uses the circulating immune cells of individual donors to recapitulate each individual human immune response by maintaining the autonomy of the donor. Thus, an in vitro test system has been created that is functionally equivalent to the donor's own immune system and is designed to respond in a similar manner to the in vivo response.