Background Kuwait had its first COVID-19 in late February, and until October 6, 2020 it recorded 108,268 cases and 632 deaths. Despite implementing one of the strictest control measures-including a three-week complete lockdown, there was no sign of a declining epidemic curve. The objective of the current analyses is to determine, hypothetically, the optimal timing and duration of a full lockdown in Kuwait that would result in controlling new infections and lead to a substantial reduction in case hospitalizations. Methods The analysis was conducted using a stochastic Continuous-Time Markov Chain (CTMC), eight state model that depicts the disease transmission and spread of SARS-CoV 2. Transmission of infection occurs between individuals through social contacts at home, in schools, at work, and during other communal activities. Results The model shows that a lockdown 10 days before the epidemic peak for 90 days is optimal but a more realistic duration of 45 days can achieve about a 45% reduction in both new infections and case hospitalizations. Conclusions In the view of the forthcoming waves of the COVID19 pandemic anticipated in Kuwait using a correctly-timed and sufficiently long lockdown represents a workable management strategy that encompasses the most stringent form of social distancing with the ability to significantly reduce transmissions and hospitalizations. Supplementary Information The online version contains supplementary material available at 10.1186/s13690-021-00778-y.
【저자키워드】 COVID-19, Hospitalization, Kuwait, Stochastic model, Lockdown timing, Lockdown duration, Actual incidence, 【초록키워드】 COVID19, pandemic, lockdown, social distancing, Infection, Transmission, Spread, Schools, Epidemic, management, hospitalizations, Analysis, SARS-CoV 2, deaths, supplementary material, chain, individual, activities, social contact, Complete, Markov, the epidemic, Result, significantly, the disease, conducted, eight, determine, occur, analysis, reduce, reduction in, recorded, anticipated, declining, 【제목키워드】 lockdown, Markov chain,