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"

Yishu Xue, Hou-Cheng Yang, Yuqing Pan, Guanyu Hu
2021 Journal of Data Science  
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) . Specifically, the authors divided the infectious compartment into those who exhibit symptoms, and asymptomatic carriers. Instead of using a recovered/removed compartment, the authors
more » ... sumed that individuals in the infectious compartment eventually end up confirmed and hospitalized or quarantined, or self-healed without being confirmed. This novel segregation is of practical value as it matches the current practices in fighting COVID-19. In the rest of this discussion, we comment on the approach that the authors have proposed, and suggest some possible extension of the work for future research. The Proposed SIHC Model 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. Many variants of the SIR model or its extensions have been used to model the development of COVID-19 from different aspects (see, Wang et al., 2020; Yang et al., 2020; Hu and Geng, 2020 ). 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 also be described using differential equations: with ρ being transmissibility, and θ(t) denoting the time-varying average per-person contact number, D H being the average duration from infection to self-healing, D C being the time taken * Corresponding author.
doi:10.6339/21-jds994c fatcat:reitpbqgxjcftjn6g7cmh4tc4u