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

National responses to the Covid-19 pandemic varied markedly across countries, from business-as-usual to complete shutdowns. Policies aimed at disrupting the viral transmission cycle and preventing the overwhelming of healthcare systems inevitably exact an economic toll.

We developed an intervention policy model that comprised the relative human, implementation and healthcare costs of non-pharmaceutical epidemic interventions and identified the optimal strategy using a neuroevolution algorithm. The proposed model finds the minimum required reduction in transmission rates to maintain the burden on the healthcare system below the maximum capacity.

We find that such a policy renders a sharp increase in the control strength during the early stages of the epidemic, followed by a steady increase in the subsequent ten weeks as the epidemic approaches its peak, and finally the control strength is gradually decreased as the population moves towards herd immunity. We have also shown how such a model can provide an efficient adaptive intervention policy at different stages of the epidemic without having access to the entire history of its progression in the population.

This work emphasizes the importance of imposing intervention measures early and provides insights into adaptive intervention policies to minimize the economic impacts of the epidemic without putting an extra burden on the healthcare system.

We developed an intervention policy model that comprised the relative human, implementation and healthcare costs of non-pharmaceutical epidemic interventions and identified the optimal strategy using a neuroevolution algorithm. Our work emphasizes the importance of imposing intervention measures early and provides insights into adaptive intervention policies to minimize the economic impacts of the epidemic without putting an extra burden on the healthcare system.