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

Abstract

<jats:p>Background The United States has the highest numbers of confirmed cases of COVID-19, where they took up nearly half in the hot spot states of New York, New Jersey, Connecticut, and California. The workforce in these states was required to work from home except for essential services. It is necessary to evaluate an appropriate date for resumption of business since premature reopening of economy will lead to broader spread of COVID-19, while the opposite situation would cause greater loss of economy. Methods To consider pre-symptomatic and asymptomatic transmission of COIVD-19, it is crucial to evaluate the unobserved numbers of unidentified infectious individuals but not the observed number of confirmed cases, which reflect the real-time risks of different stage of infectious disease. We proposed an epidemic model in considering the pre-symptomatic transmission and asymptomatic transmission of COVID-19 to evaluate the real-time risk of epidemic for the states of New York, New Jersey and Connecticut, and compared with California state (where it effectively phased reopened on May 8) for assessments of the appropriate Monday for resumption of business. Results The predicted numbers of unidentified infectious individuals per 100,000 for states of New York, New Jersey and Connecticut which are close to those in California state are 12.23 with 95% CI (10.68, 13.57) on June 1, 25.65 with 95% CI (20.04, 30.43) on June 15, 28.49 with 95% CI (19.10, 38.65) on June 22, respectively, which may cause 11.23%, 15.64% and 17.32% higher than the estimated number of cumulative confirmed cases on July 11, if the second wave of the infection would happen after people return to work. Conclusions It may be feasible for states of New York, New Jersey and Connecticut to reopen business on June 1 (or even May 18), June 15 and June 22. For the period after resumption of work, if the number of unidentified infectious individuals is still non-zero, the risks for the second wave of the infection would never vanish.</jats:p>

Estimating the Number of Infected Cases in COVID-19 Pandemic

“…Our result implies that the proportion of cases never captured by any medical systems out of all the infected cases is about 87.94/220.54 = 39.87% as of Apr 20, 2020. This is not far from estimations given by Tian et al (2020) which predicates that the ratio of unidentified cases in NY, NJ, and CA by July 11, 2020 in the range between 18.49% and 33.20%. Our estimate is also consistent to a meta-analysis of over 10 studies (Byambasuren et al, 2020) which reports an asymptomatic ratio in the range of 15%-40%.…”

“…Indeed, estimation through CFR may be misleading (Bottcher et al, 2020). Another class of methods are based on epidemiology models such as the susceptible, infectious, recovered, and death (SIRD) model or its variants (Richterich, 2020;Tian et al, 2020). .…”

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”

“…Detection of heterogeneity patterns among spatial regions could reveal interesting insights for the study of COVID-19. Tian et al (2021) proposed a modified version of the SIR model which has finer-partitioned compartments, which provides more accurate depiction of the role of quarantine policies in the fight against the COVID-19 pandemic. Four U.S. states are used as examples.…”

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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.

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This time is different: model-based evaluation of the implications of SARS-CoV-2 infection kinetics for disease control

“…While a number of studies have looked at intervention strategies in specific scenarios (Davis et al ., 2020; Kerr et al ., 2020; Tian et al ., 2020), there is a need for a quantitative investigation that can be applied broadly to questions of disease control strategy. To better understand the impact of SARS-CoV-2 transmission on COVID-19 disease control, we used a time-dependent model of SARS-CoV-2 infectivity.…”