  A model-based signal processing framework is proposed for pandemic trend
forecasting and control, by using non-pharmaceutical interventions (NPI) at
regional and country levels worldwide. The control objective is to prescribe
quantifiable NPI strategies at different levels of stringency, which balance
between human factors (such as new cases and death rates) and cost of
intervention per region/country. Due to infrastructural disparities and
differences in priorities of regions and countries, strategists are given the
flexibility to weight between different NPIs and to select the desired balance
between the human factor and overall NPI cost.
  The proposed framework is based on a \textit{finite-horizon optimal control}
(FHOC) formulation of the bi-objective problem and the FHOC is numerically
solved by using an ad hoc \textit{extended Kalman filtering/smoothing}
framework for optimal NPI estimation and pandemic trend forecasting. The
algorithm enables strategists to select the desired balance between the human
factor and NPI cost with a set of weights and parameters. The parameters of the
model are partially selected by epidemiological facts from COVID-19 studies,
and partially trained by using machine learning techniques. The developed
algorithm is applied on ground truth data from the Oxford COVID-19 Government
Response Tracker project, which has categorized and quantified the regional
responses to the pandemic for more than 300 countries and regions worldwide,
since January 2020. The dataset was used for NPI-based prediction and
prescription during the XPRIZE Pandemic Response Challenge.
