Optimal non-pharmaceutical intervention policy for Covid-19 epidemic via neuroevolution algorithm
This work addresses the challenge of designing adaptive epidemic control policies for public health officials, though it is incremental as it applies an existing algorithm to a specific domain.
The study tackled the problem of optimizing non-pharmaceutical intervention policies for the Covid-19 epidemic to balance healthcare capacity and economic costs, using a neuroevolution algorithm to find strategies that minimize required contact rate reductions while preventing healthcare overload, with results showing a sharp early increase in control followed by gradual adjustments over ten weeks.
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 healthcare system from being overwhelmed, simultaneously exact an economic toll. We developed a intervention policy model that comprised the relative human, economic and healthcare costs of non-pharmaceutical epidemic intervention and arrived at the optimal strategy using the neuroevolution algorithm. The proposed model finds the minimum required reduction in contact 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 at the early stages of the epidemic, followed by a steady increase in the subsequent ten weeks as the epidemic approaches its peak, and finally 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.