Accuracy of State-Level Surveillance during Emerging Outbreaks of Respiratory Viruses: A Model-Based Assessment

Yuwen Gu, Elise DeDoncker, Richard VanEnk, Rajib Paul, Susan Peters, Gillian Stoltman and Diana Prieto
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Yuwen Gu: Western Michigan University, Kalamazoo, MI, USA
Elise DeDoncker: Western Michigan University, Kalamazoo, MI, USA
Richard VanEnk: Bronson Methodist Hospital, Portage, MI, USA
Rajib Paul: University of North Carolina Charlotte College of Health and Human Services, Charlotte, NC, USA
Susan Peters: Michigan State University College of Human Medicine, East Lansing, MI, USA
Gillian Stoltman: Western Michigan University Homer Stryker MD School of Medicine, Kalamazoo, MI
Diana Prieto: Johns Hopkins University Carey Business School, Baltimore, MD, USA

Medical Decision Making, 2021, vol. 41, issue 8, 1004-1016

Abstract: It is long perceived that the more data collection, the more knowledge emerges about the real disease progression. During emergencies like the H1N1 and the severe acute respiratory syndrome coronavirus 2 pandemics, public health surveillance requested increased testing to address the exacerbated demand. However, it is currently unknown how accurately surveillance portrays disease progression through incidence and confirmed case trends. State surveillance, unlike commercial testing, can process specimens based on the upcoming demand (e.g., with testing restrictions). Hence, proper assessment of accuracy may lead to improvements for a robust infrastructure. Using the H1N1 pandemic experience, we developed a simulation that models the true unobserved influenza incidence trend in the State of Michigan, as well as trends observed at different data collection points of the surveillance system. We calculated the growth rate, or speed at which each trend increases during the pandemic growth phase, and we performed statistical experiments to assess the biases (or differences) between growth rates of unobserved and observed trends. We highlight the following results: 1) emergency-driven high-risk perception increases reporting, which leads to reduction of biases in the growth rates; 2) the best predicted growth rates are those estimated from the trend of specimens submitted to the surveillance point that receives reports from a variety of health care providers; and 3) under several criteria to queue specimens for viral subtyping with limited capacity, the best-performing criterion was to queue first-come, first-serve restricted to specimens with higher hospitalization risk. Under this criterion, the lab released capacity to subtype specimens for each day in the trend, which reduced the growth rate bias the most compared to other queuing criteria. Future research should investigate additional restrictions to the queue.

Keywords: Disease spread modeling; influenza surveillance evaluation; influenza surveillance modeling; respiratory virus surveillance (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:41:y:2021:i:8:p:1004-1016

DOI: 10.1177/0272989X211022276

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