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Forecasting COVID-19 Cases Based on Social Distancing in Maryland, USA: A Time–Series Approach

Published online by Cambridge University Press:  19 May 2021

Raul Cruz-Cano Open the ORCID record for Raul Cruz-Cano [Opens in a new window] ,
Tianzhou Ma ,
Yifan Yu ,
Minha Lee  and
Hongjie Liu
Raul Cruz-Cano*
Affiliation:
Department of Epidemiology and Biostatistics, University of Maryland, College Park, MarylandUSA
Tianzhou Ma
Affiliation:
Department of Epidemiology and Biostatistics, University of Maryland, College Park, MarylandUSA
Yifan Yu
Affiliation:
Department of Epidemiology and Biostatistics, University of Maryland, College Park, MarylandUSA
Minha Lee
Affiliation:
Maryland Institute of Transportation, University of Maryland, College Park, MarylandUSA
Hongjie Liu
Affiliation:
Department of Epidemiology and Biostatistics, University of Maryland, College Park, MarylandUSA
*
Corresponding author: Raul Cruz-Cano, Email raulcruz@umd.edu.
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Abstract

Objective:

Our objective is to forecast the number of coronavirus disease 2019 (COVID-19) cases in the state of Maryland, United States, using transfer function time series (TS) models based on a Social Distancing Index (SDI) and determine how their parameters relate to the pandemic mechanics.

Methods:

A moving window of 2 mo was used to train the transfer function TS model that was then tested on the next week data. After accounting for a secular trend and weekly cycle of the SDI, a high correlation was documented between it and the daily caseload 9 days later. Similar patterns were also observed on the daily COVID-19 cases and incorporated in our models.

Results:

In most cases, the proposed models provide a reasonable performance that was, on average, moderately better than that delivered by TS models based only on previous observations. The model coefficients associated with the SDI were statistically significant for most of the training/test sets.

Conclusions:

Our proposed models that incorporate SDI can forecast the number of COVID-19 cases in a region. Their parameters have real-life interpretations and, hence, can help understand the inner workings of the epidemic. The methods detailed here can help local health governments and other agencies adjust their response to the epidemic.

Keywords

environmental exposurepublic healthvital statistics
Type
Brief Report
Information
Disaster Medicine and Public Health Preparedness , Volume 16 , Issue 5 , October 2022 , pp. 1798 - 1801
Copyright
© Society for Disaster Medicine and Public Health, Inc. 2021

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