Possible effects of mixed prevention strategy for COVID-19 epidemic: massive testing, quarantine and social distancing

Abstract

Background The pandemic coronavirus disease 2019 (COVID-19) has spread and caused enormous and serious damages to many countries worldwide. One of the most typical interventions is the social distancing such as lockdown that would contribute to reduce the number of contacts among undiagnosed individuals. However, prolongation of the period of such a restrictive intervention could hugely affect the social and economic systems, and the outbreak will come back if the strong social distancing policy will end earlier due to the economic damage. Therefore, the social distancing policy should be followed by massive testing accompanied with quarantine to eradicate the infection. Methods In this paper, we construct a mathematical model and discuss the effect of massive testing with quarantine, which would be less likely to affect the social and economic systems, and its efficacy has been proved in South Korea, Taiwan, Vietnam and Hong Kong. Results By numerical calculation, we show that the control reproduction number is monotone decreasing and convex downward with respect to the testing rate, which implies that the improvement of the testing rate would highly contribute to reduce the epidemic size if the original testing rate is small. Moreover, we show that the recurrence of the COVID-19 epidemic in Japan could be possible after the lifting of the state of emergency if there is no massive testing and quarantine. Conclusions If we have entered into an explosive phase of the epidemic, the massive testing could be a strong tool to prevent the disease as long as the positively reacted individuals will be effectively quarantined, no matter whether the positive reaction is pseudo or not. Since total population could be seen as a superposition of smaller communities, we could understand how testing and quarantine policy might be powerful to control the disease.

Modelling population-wide screening of SARS-CoV-2 infection for containing COVID-19 pandemic in Okinawa, Japan

“…To estimate R in Okinawa, we applied the least-squares method as in [17] under the assumption that the identification rate is 0.1 to the data of number of daily reported cases of COVID-19 in Okinawa from October 1 to November 30, 2020. We then obtained R=1.22, and the asymptotic and symptomatic infection rates were calculated as in [13]. The sensitivity and specificity in usual diagnosis were assumed as 90% and 100%, respectively [18].…”

“…In the numerical simulation, we used an SEIQR compartmental model as in [13], in which the susceptible, exposed (asymptomatic infectious), infectious, quarantined and recovered populations are considered. As in [13], we assumed that, for COVID-19, the average incubation period is 5 days [14], the average infectious period is 10 days [15], the average quarantine period is 14 days and the reproduction numbers for asymptomatic and symptomatic infections are 0.44 R and 0.56 R, respectively [16], where R denotes the reproduction number. To estimate R in Okinawa, we applied the least-squares method as in [17] under the assumption that the identification rate is 0.1 to the data of number of daily reported cases of COVID-19 in Okinawa from October 1 to November 30, 2020.…”

On Confinement and Quarantine Concerns on an SEIAR Epidemic Model with Simulated Parameterizations for the COVID-19 Pandemic

“…The effects of total or partial quarantine interventions imply that the transmission rates decrease since the number of contagious contacts decreases. See, for instance, References [38,39]. The quarantines on the susceptible have the qualitative effect of an impulsive vaccination since part of the susceptible population is kept away of infective contacts.…”

“…This property implies that the disease-free equilibrium point is a global attractor and the disease becomes asymptotically removed as a result. Those above issues have been important in the last months with regard to the evolution of the COVID-19 pandemic around the world (See References [27,29,[37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]). In particular, an "ad hoc" SEIR model parameterization related to the COVID-19 pandemic is investigated in Reference [37] including delayed re-susceptibility caused by the infection.…”

“…Additionally, a kind of autoregressive model average model (ARMA), known as an ARIMA model for prediction of COVID-19, is presented and simulated in Reference [38] for the data of several countries. The effects of different phases of quarantine actions in the values of the transmission rate are studied in Reference [39]. See Reference [42] for a discussion of related simulated numerical results on the COVID-19 outbreak in Italy.…”