Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States.

Abolfazl Mollalo,Kiara M Rivera,Behzad Vahedi

doi:10.3390/ijerph17124204

Abstract

Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. 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 leukemia, 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. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* (p < 0.05) in a geographic information system environment. 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.

Highlights

Novel coronavirus disease (COVID-19) has rapidly spread worldwide, becoming a global health threat [1]
217 counties were identified as hotspots (p < 0.05), which were mainly located in the northeastern regions of the continental United States, western Georgia, central Ohio, southern Louisiana, and northeast Iowa
The Boruta algorithm and Pearson’s correlation analysis selected 34 variables as less correlated and important variables (Supplementary Materials), which were fed as inputs to Artificial neural networks (ANNs)

Summary

Introduction

Novel coronavirus disease (COVID-19) has rapidly spread worldwide, becoming a global health threat [1]. The disease was first identified in Wuhan, China, and continued to spread out across the world [2]. According to the World Health Organization [3], as of 4 June 2020, there have been more than 6.4 million confirmed cases and over 380 thousand deaths worldwide. These statistics have surpassed the number of deaths and cases for Middle East respiratory syndrome (MERS) and severe acute respiratory disorder (SARS) since their outbreaks [4].

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Discussion

Conclusion