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Long-term care facilities have been widely affected by the COVID-19 pandemic.Retirement homes are particularly vulnerable due to the higher mortality riskof infected elderly individuals. Once an outbreak occurs, suppressing thespread of the virus in retirement homes is challenging because the residentsare in contact with each other, and isolation measures cannot be widelyenforced. Regular testing strategies, on the other hand, have been shown toeffectively prevent outbreaks in retirement homes. However, high frequencytesting may consume substantial staff working time, which results in atrade-off between the time invested in testing, and the time spent providingessential care to residents. Thus, developing an optimal testing strategy iscrucial to proactively detect infections while guaranteeing efficient use oflimited staff time in these facilities. Although numerous efforts have beenmade to prevent the virus from spreading in long-term care facilities, this isthe first study to develop testing strategies based on formal optimizationmethods. This paper proposes two novel optimization models for testingschedules. The models aim to minimize the risk of infection in retirementhomes, considering the trade-off between the probability of infection and staffworkload. We employ a probabilistic approach in conjunction with theoptimization models, to compute the risk of infection, including contact rates,incidence status, and the probability of infection of the residents. To solvethe models, we propose an enhanced local search algorithm by leveraging thesymmetry property of the optimal solution. We perform several experiments withrealistically sized instances and show that the proposed approach can deriveoptimal testing strategies.

Modeling COVID-19 optimal testing strategies in long-term care facilities: An optimization-based approach