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

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 (T1) to 20 August (T212) 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 (T1) to 0.022 (T212). 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 T212. 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.

Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review

“…Around 3% of the studies modeled the effect of asymptomatic individuals on the growth curve of the epidemic. The time-varying SIR model of Peng et al [68] confirmed a 20% contribution of asymptomatic infections to the total infections. Also, Tomochi and Kono [49] included a compartment I in the basic SIR model and reported asymptomatic infections to account for 15% of the COVID-19 infections.…”

Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy

“…Number of deaths per cause of death from 2011 to 2017 in Italy. Legend: [1] = Infectious and parasitic diseases, [2] = Tumors, [3] = Psychic disorders, diseases of the nervous system and organs of the senses, [4] = Diseases of the circulatory system, [5] = Diseases of the respiratory system, [6] = Diseases of the digestive system, [7] = Other morbid states, [8] = Poorly defined symptoms, signs, and morbid states, [9] = External causes of trauma and poisoning.…”

“…Ordinary least squares (OLS) regressions are among the most adopted model by scientists due to their simplicity and efficacy. Specifically, OLS multiple and simple regressions have often been used to predict the course of COVID-19 cases and deaths, both individually and in conjunction with other epidemiological models such as Susceptible-Infected-Recovered (SIR) [8][9][10]. This literature showed that linear regressions are valuable short-term forecasting tools when the necessary assumptions are satisfied.…”

A novel CO2-based demand-controlled ventilation strategy to limit the spread of COVID-19 in the indoor environment

“…However, this parameter is highly uncertain in real-world scenarios: It needs to know the number of infections in population, but large portion of infections are not reported, especially those who are asymptomatic. Researchers, including CDC, have conducted several studies [ 47 , [64] , [65] , [66] ] on the prediction for unreported ratio using epidemiological models, to better reflect the full burden of COVID-19. To adapt the current study to general use, the unreported ratio used to calculate the prevalence rate should be updated according to the latest report from the CDC/the department of health or the latest related studies, which could reflect the current epidemiological scenario more accurately.…”