Analyzing the spread of infectious disease using a probabilistic model

Abstract

Infectious diseases are those that can be transmitted from person to person upon some form of contact. In this regard, airborne infectious diseases can wreak quite a havoc as they have a high degree of infectiousness and can easily infect a healthy person who comes in proximity of an infected person for a specific interval of time. The situation can take the form of an epidemic in no time if the outbreak of a disease is not checked at an earlier stage. In this paper, we simulate the spread of airborne infectious disease in the city population. Disease transmission from an infected person to a healthy person is modeled based on proximity and contact time. We analyze how population density affects the spread of disease. Moreover, we also analyze how practices like wearing a mask and hotspot lockdowns might slow down the spread of infection. Finally, we analyze how an epidemic mitigates when a certain fraction of the population becomes immune to the disease. Observations and inferences drawn from the simulation results can help make policies to tackle the spread of airborne infectious disease in a city community.