ABSTRACT
Conventional techniques of epidemic modeling are based on compartmental models, where population groups are transitioning from one compartment to another - for example, S, I, or R, (Susceptible, Infectious, or Recovered). Then, they focus on learning macroscopic properties of disease spreading, such as the transition rates between compartments. Although these models are useful in studying epidemic dynamics, they lack the granularity needed for analyzing individual behaviors during an epidemic and understanding the relationship between individual decisions and the spread of the disease. In this paper, we develop microscopic models of spatiotemporal epidemic dynamics informed by mobility patterns of individuals and their interactions. In contrast to macroscopic models, microscopic epidemic models focus on individuals and their properties, such as their activity level, mobility behaviors, and impact of mobility behavior changes. Our microscopic spatiotemporal epidemic model allows to: (i) assess the risk of infection of an individual based on mobility patterns;(ii) assess the risk of infection associated with specific geographic areas and points-of-interest (POIs);(iii) assess the risk of infection of a trip in an urban environment;(iv) provide trip recommendation for mitigating the risk of infection;and (v) assess targeted intervention strategies that aim to control the epidemic spreading. Our work provides an evidence-based data-driven model to inform individuals about the infection risks associated with their mobility behavior during a pandemic, providing at the same time safer alternatives. It can also inform public policy about the effectiveness of targeted intervention strategies that aim to contain or mitigate the epidemic spread compared to horizontal measures. © 2022 ACM.