Quantifying the Effect of Quarantine Control and Optimizing its Cost in COVID-19 Pandemic.

The novel coronavirus has been spreading worldwide and emerged as a public health crisis. As the rapid rise of infected population count, a wide variety of stringent non-pharmaceutical interventions have been taken by cities and countries around the globe, including mobility reduction, social distancing and regional lockdown. The efficacy of these interventions is hard to quantify as individuals violate policies, travel inadvertently or deliberately, and spread the virus without themselves being infected. Furthermore, the publicly available pandemic data on infectious rates and other epidemiological data are unreliable and limited, and are even underestimated. In this paper, we intend to interpret and forecast the spreading dynamics of Covid-19 and quantify the efficacy of quarantine control adopted by Wuhan, Italy, South Korea and the United States of America, employing a hybrid model of an epidemiological model and a data-driven neural network model. Furthermore, since the Covid-19 has prompted global travel restrictions, aggravated unemployment, and influenced the global economy, which exemplify the great societal cost of interventions in the battle of halting Covid-19 spreading. We intend to develop optimal quarantine control under which the tradeoff between Covid-19 containment and the societal cost of quarantine control can be optimized. Optimal quarantine control enables communities have opportunities to catch their breath to reserve healthcare resources preemptively, while the Covid-19 spreading can be halted. Our results unequivocally indicate that governments that taken stringent interventions starting from the initial stage can efficiently halt the spreading of Covid-19; furthermore, the total societal cost of such interventions is greatly smaller.