An observation-based quantitative approach to predict the spread of infectious diseases within a cluster

Contact tracing, early diagnosis, and social distancing are crucial to contain outbreaks of infectious diseases, especially in the presence of asymptomatic infectives. Therefore, identifying the individuals at the highest risk of infectious disease and prioritizing them for testing is necessary to ensure both the public health and the cost-effectiveness of screening strategies. In this paper, we present an efficient quantitative approach to predict the spread of infectious diseases within a cluster by exploiting a stochastic model of disease evolution in an individual alongside observations of contacts, symptoms, and results of diagnostic tests. Specifically, we present an iterative solution technique to estimate the probability that a subject is infectious and not isolated over time given the observations acquired up to that time, achieving computational efficiency by disregarding dependencies among observations. We generated multiple synthetic data sets of observations, featuring different social network topologies, numbers of individuals, and densities of internal contacts, while accounting for noisy, erroneous, and missing observations. For each data set, we performed extensive experiments by varying the stochastic parameters of our approach from those used to derive a ground truth by performing stochastic simulation. Experimental results demonstrate that our approach effectively ranks subjects according to their probability of being infectious and not isolated, achieving high accuracy with respect to the ground truth. Notably, our approach outperforms an alternative baseline that accounts for dependencies among observations while maintaining a comparable runtime.