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National responses to the Covid-19 pandemic varied markedly across countries,from business-as-usual to complete shutdowns. Policies aimed at disrupting theviral transmission cycle and preventing the healthcare system from beingoverwhelmed, simultaneously exact an economic toll. We developed a interventionpolicy model that comprised the relative human, economic and healthcare costsof non-pharmaceutical epidemic intervention and arrived at the optimal strategyusing the neuroevolution algorithm. The proposed model finds the minimumrequired reduction in contact rates to maintain the burden on the healthcaresystem below the maximum capacity. We find that such a policy renders a sharpincrease in the control strength at the early stages of the epidemic, followedby a steady increase in the subsequent ten weeks as the epidemic approaches itspeak, and finally control strength is gradually decreased as the populationmoves towards herd immunity. We have also shown how such a model can provide anefficient adaptive intervention policy at different stages of the epidemicwithout having access to the entire history of its progression in thepopulation. This work emphasizes the importance of imposing interventionmeasures early and provides insights into adaptive intervention policies tominimize the economic impacts of the epidemic without putting an extra burdenon the healthcare system.

Optimal non-pharmaceutical intervention policy for Covid-19 epidemic via neuroevolution algorithm