The coronavirus disease 2019 (COVID-19) is a new disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We propose a design, data-driven modelling, planning and predictive analytics tool for nowcasting and a forecasting of both epidemic and economic consequences of COVID-19 spread. The proposed data-driven model is based on mean-field-type game-theoretic interaction that integrates the decision-making of the authorities, firms, and individuals together with pandemic aspects on comorbidities (diabetes type 2, hypertension, asthma, high blood pressure) and a testing strategy per area/city/province/country. The epidemic aspect takes into consideration several variables such as age, gender, locality, position, family size, average revenue per family, mobility from/to hotspots, cities, countries. The internal infection status has 17 compartmental states including testing status, hospitalization, untested infectious persons and pre-existing health conditions per person. The economic aspect includes total hours worked pre-COVID-19 vs total hours worked during COVID-19 for producing essential, moderate-essential and less-essential goods, total consumption per person and economic incentives from firms and authorities. The data-driven model incorporates dynamically changing data sets and has been tested successfully in 66 countries.
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