Managing Congregations of People by Predicting Likelihood of a Person being Infected by a Contagious Disease like the COVID Virus

Pandemics such as Covid-19 change the status quo. The things we take for granted become no longer true. When the disease is contagious then in-person communication is fraught with danger for all parties. Despite all these dangers people still required to meet in person for various reasons that could range from personal to official to government business. Can we have a way to understand the risk of a person being infected as compared to another person so that we can make decisions of segregating the two people or to decline entry to a person? In this paper we provide an architecture and an approach that caters to estimating a score for each person based on GPS (Global Positioning System) locations. A score is computed by an application running in cloud and receiving as input the GPS trajectory information from each person's device (like a mobile or a smart watch). With the scores of any two people in hand one can predict who is more likely to be infected. We complement this approach with extensive simulation results to validate our approach. Our results show that we can achieve a high accuracy (80% to 90%) of predicting which person (of the two being compared) has an infection.