  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.

Populations and Evolution;Machine Learning;Neural and Evolutionary Computing