COVID-19 Epidemic and Opening of the Schools: Artificial Intelligence-Based Long-Term Adaptive Policy Making to Control the Pandemic Diseases

Abstract

Even though the COVID-19 pandemic has endured to be a serious threat for the societies, the state authorities have been seeking policies to re-open the schools and universities. It is clear that opening of the schools will cause more COVID-19 casualties, but the key question is how many students should attend the schools daily while keeping the casualties under control. In this paper, an artificial intelligence based long-term policy making algorithm has been developed to generate time varying policies for opening of the schools part-by-part. The key aim of the algorithm is to produce policies which maximize the number of the students attending the schools while minimizing the pandemic casualties under the worst-case uncertainties. The proposed algorithm consists of a multi-input-multi-output, uncertain, and adaptive background parametric model which is externally manipulated by the produced adaptive policy. Its long-term predictor assesses the possible future casualties under the current policy and its policy maker generates alternative solutions that minimize the future casualties. The results confirm that the proposed algorithm is able to generate effective policies which minimize the COVID-19 casualties while maximize the number of the students attending the schools under the worst-case uncertainties.