A New Extension of State-Space SIR Model to Account for Underreporting- An Application to the COVID-19 transmission in California and Florida

Abstract

In the absence of sufficient testing capacity for COVID-19, a substantial number of infecteds are expected to remain undetected. Since the undetected cases are not quarantined, they are expected to transmit the infection at a much higher rate than their quarantined counterparts. That is, under the lack of extensive random testing, the actual prevalence and incidence of the SARS-CoV-2 infection may be entirely different from that being reported. Thus, it is imperative that the information on the percentage of undetected (or unreported) cases be considered while estimating the parameters and forecasting the transmission dynamics of the epidemic.In this paper, we have developed a new version of the basic susceptible-infected-removed (SIR) compartmental model, called the susceptible-infected (quarantined/ free) -recovered-deceased [SI(Q/F)RD] model, to incorporate the impact of undetected cases on the transmission dynamics of the epidemic. Further, we have presented a Dirichlet-Beta state-space formulation of the SI(Q/F)RD model for the estimation of its parameters using posterior realizations from Gibbs sampling procedure. As a demonstration, the proposed methodology is implemented to forecast the COVID-19 transmission in California and Florida.HighlightsData calibrated for underreporting using excess deaths and case fatality rate.A new extension of SIR compartmental model, called SI(Q/F)RD, is introduced.A Dirichlet-Beta state-space formulation of the SI(Q/F)RD model is developed.Gibbs sampling used to estimate the Bayesian hierarchical state-space model.Proposed methodology is applied on the COVID-19 data of California and Florida.

Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

“…Wu et al, 2020 [ 22 ] reported that the actual number of infections was estimated to be 3 to 20 times higher than the confirmed cases for various states of the United States of America. Lu et al, 2020 [ 23 ] showed that there were drastic cases of underreporting in many countries worldwide.…”

Selection of models and parameter estimation for monitoring the COVID-19 epidemic in Brazil via Bayesian inference

“…However, the significant problem was that many health systems underestimated the effect of the coronavirus (Ujvari, 2020). Soon, other aspects can be incorporated into the models, according to the disease cycle or socioeconomic aspect of the country, such as hospitalization dynamic (Bekker et al, 2023;Kozyreff, 2021), under-reporting cases (Chen et al, 2022;Deo & Grover, 2021), containment strategies (Das et al, 2021), age of individuals (Franco, 2021), macroeconomic impacts (Chan, 2022), outbreak recurrence (Muñoz-Fernández; Seoane; & Seoane-Sepúlveda, 2021), and other aspects. It is noted that the models are limited, as they work with possible assumptions about the spread of diseases.…”