Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

Zhenghong Peng, Siya Ao, Lingbo Liu, Shuming Bao, Tao Hu, Hao Wu and Ru Wang
Additional contact information
Zhenghong Peng: Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China
Siya Ao: Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China
Lingbo Liu: Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
Shuming Bao: China Data Institute, Ann Arbor, MI 48108, USA
Tao Hu: Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
Hao Wu: Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China
Ru Wang: Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China

IJERPH, 2021, vol. 18, issue 3, 1-13

Abstract: Background: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. Methods: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Results: Based on the US county-level COVID-19 data from 22 January (T 1 ) to 20 August (T 212 ) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007–0.157 (mean = 0.048), 7.31–185.6 (mean = 38.89), and 0.04–2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T 1 ) to 0.022 (T 212 ). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7–14.0) and IFR was 0.70% (95%CI 0.52–0.95%) at T 212 . Interpretation: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

Keywords: SIR; time-varying parameters; unreported infection rate; infection fatality rate; COVID-19 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/3/1090/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/3/1090/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:3:p:1090-:d:487392

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().