Abstract
Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”, Authors: Yishu Xue, Hou-Cheng Yang, Yuqing Pan
Highlights
Tian et al (2021) proposed the Susceptible-Unidentified infectious-Self-healing without being confirmed-Confirmed cases (SIHC) model that divides the population into four compartments as opposed to three, which is assumed by the popular Susceptible-Infectious-Recovered model (SIR; Kermack and McKendrick, 1927)
Instead of using a recovered/removed compartment, the authors assumed that individuals in the infectious compartment eventually end up confirmed and hospitalized or quarantined, or self-healed without being confirmed
Many variants of the SIR model or its extensions have been used to model the development of COVID-19 from different aspects
Summary
There is a notable amount of work done by statisticians and biostatisticians since the breakout of COVID-19. One of the most frequently used model, the SIR model, segregate the population into three compartments, and used three differential equations to depict the evolution of each. The proposed model takes into consideration hospitalization/quarantine (compartment C) and self-healing (compartment H ), and similar to the original SIR model, the evolution of the four compartments can be described using differential equations: dS(t) = −ρθ (t)S(t) I (t) , dt. DC with ρ being transmissibility, and θ (t) denoting the time-varying average per-person contact number, DH being the average duration from infection to self-healing, DC being the time taken. From infection to test confirmation, and N denoting the population, which is assumed to be constant over the duration of the pandemic.
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