COVID-19 Risk Minimization Decision Making Strategy Using Data-Driven Model

Abstract

In order to establish social resilient and sustainable cities during the pandemic outbreak, it is essential to forecast the epidemic trends and trace infection by means of data-driven solution addressing the requirements of local operational defense applications and global strategies. The smartphone based Digital Proximity Tracing Technology (DPTT) has obtained a great deal of interest with the ongoing COVID-19 pandemic in terms of mitigation, containing and monitoring with the population acceptance insights and effectiveness of the function. The DPTTs and Data-Driven Epidemic Intelligence Strategies (DDEIS) are compared in this paper to identify the shortcomings and propose a novel solution to overcome them. In terms of epidemic resurgence risk minimization, guaranteeing public health safety and quick return of cities to normalcy, a social as well as technological solution may be provided by incorporating the key features of DDEIS. The role of human behavior is taken into consideration while assessing its limitations and benefits for policy making as well as individual decision making. The epidemiological model of SEIR (Susceptible–Exposed–Infectious–Recovered) provides preliminary data for the preferences of users in a DPTT. The impact of the proposed model on the spread dynamics of Covid-19 is evaluated and the results are presented.