Skip to Content

Coronavirus information for Feinberg.

Download the full-sized PDF of Dynamic Panel EstimateBased Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation
Download the file

Actions

Download Analytics Citations

Export to: EndNote

Collections

This file is in the following collections:

COVID-19 Community

Dynamic Panel EstimateBased Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation Open Access (recommended)

Oehmke JF, Oehmke TB, Singh LN, Post LA. Dynamic Panel Estimate-Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation. Journal of Medical Internet Research. 2020;22(9):12.

Descriptions

Resource type(s)
Article
Keyword
COVID-19
models
surveillance
COVID-19 surveillance system
dynamic panel data
infectious disease modeling
reopening America
COVID-19 guidelines
COVID-19 health policy
Rights
Attribution 4.0 International

Creator
Oehmke, James Francis
Oehmke, Theresa B.
Post, Lori Ann
Singh, Lauren Nadya
Abstract
Background: SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. Objective: The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. Methods: Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. Results: A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (chi(2)(10)=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (chi(2)(946) 935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. Conclusions: DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties. the country will be COVID-19-free only when there is an effective vaccine, and the social end of the pandemic will occur before the medical end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.
Related URL
Publisher
JMIR PUBLICATIONS, INC
Date Created
2020-09-22
Original Identifier
(PMID) 32915762
DOI
10.2196/20924

File Details

File Properties
Mime type: application/pdf
File size: 320.6 kB