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

During a pandemic outbreak, it is crucial to build socially resilient and sustainable communities. Data-driven solutions that account for the requirements of both exist military applications and global strategies are necessary for this task. There has been a lot of interest in the smartphone-based Digital Proximity Tracing Technology (DPTT) for reducing, confining, and tracking the ongoing COVID-19 epidemic with population acceptability insights and the effectiveness of the function. This study contrasts DPTTs with Data-Driven Epidemic Intelligence Strategies (DDEIS) to emphasize their distinctions and provide a new perspective on how to deal with them. DDEIS's core features might be used to provide a social as well as technological solution for stopping the spread of disease, protecting the public's health, and getting cities back to normal as soon as possible. The influence of human behavior is considered while weighing the benefits and drawbacks of society decision making and individual choice. The SEIR (Susceptible-Exposed-Infectious-Recovered) epidemiology model offers early information on user preferences in a DPTT. The suggested model's influence on the Covid-19 dissemination dynamics is assessed, and the findings are given.