Dear Colleagues,
The Division of Biostatistics at the Department of Preventive Medicine invites you to attend the following seminar.
Date: Monday, July 27th, 2020
Time: 2 P.M.
ZOOM Virtual Room Connection: https://tennessee.zoom.us/j/99322680124
Presenter: Dr. Abolfazl Mollalo, Baldwin Wallace University (http://healthgislab.com/)
Title: “Spatial Variations of the COVID-19 Incidence in the United States: A GIS-based Approach”
Abstract: The outbreak of COVID19 in the United States is posing an unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could potentially explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependencies and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity.
The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R²: 68.1%) with the lowest AICc compared to the others.
In the second study we added mortality rates of several infectious and chronic diseases as explanatory variables and examined the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates using 57 variables. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukaemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates.
The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level.
Background reading:
https://www.sciencedirect.com/science/article/pii/S0048969720324013 and https://www.mdpi.com/1660-4601/17/12/4204
About the speaker:
Dr Abe Mollalo received his PhD in Medical Geography at the University of Florida with an emphasis on the application of data science in examining geospatial distribution of infectious diseases. He is currently an Assistant Professor at the Department of Public Health at Baldwin Wallace University. His research interests lie primarily in spatial & space-time analysis and modelings of major respiratory diseases in the United States, such as Asthma, COPD, Tuberculosis, and COVID-19.
We look forward to seeing you all among us.
Chi-Yang Chiu, Ph.D.
Assistant Professor of Biostatistics
Department of Preventive Medicine, UTHSC