A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities

As the COVID-19 pandemic spread worldwide, it has become clearer that prevalence of certain comorbidities in a given population could make it more vulnerable to serious outcomes of that disease, including fatality. Indeed, it might be insightful from a health policy perspective to identify clusters of populations in terms of the associations between their prevalent comorbidities and the observed COVID-19 specific death rates. In this study, we described a mixture of polynomial time series (MoPTS) model to simultaneously identify (a) three clusters of 86 U.S. cities in terms of their dynamic death rates, and (b) the different associations of those rates with 5 key comorbidities among the populations in the clusters. We also described an EM algorithm for efficient maximum likelihood estimation of the model parameters

Transition from Social Vulnerability to Resiliency vis-à-vis COVID-19

The COVID-19 pandemic has revealed systemic deficiencies in preparing and planning for disasters, with profound health, economic, social, political, and humanitarian consequences. When preparing for pandemics, social vulnerability needs to be assessed using vulnerability indices to identify which populations are at greater risk. In this context, we examined the possible association of social vulnerabilities in U.S. cities with COVID-19 case fatality ratios. Post-pandemic return to normalcy is fraught with uncertainty over the ability of different communities to recover with varying degrees of resilience. Towards this, we recommend use of a community resiliency planning framework, along with modeling and eval

Health analytics and disease modeling for better understanding of healthcare-associated infections

Healthcare-associated infections (HAIs) are a growing challenge and a major cause of health concern worldwide. It is difficult to understand precisely the dynamics of spread of hospital-acquired infections owing to the usual involvement of different populations, risk factors, environments, and pathogens. Mathematical and computational models have proved to be useful tools in providing realistic representations of HAI dynamics and the means of evaluating interventions to minimize the risk of HAIs