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Networks are widely used to model the contact structure within a population and in the resulting models of disease spread. While networks provide a high degree of realism, the analysis of the exact model is out of reach and even numerical methods fail for modest network size. Hence, mean-field models (e.g. pairwise) focusing on describing the evolution of some summary statistics from the exact model gained a lot of traction over the last few decades. In this paper we revisit the problem of deriving triple closures for pairwise models and we investigate in detail the assumptions behind some of the well-known closures as well as their validity. Using a top-down approach we start at the level of the entire graph and work down to the level of triples and combine this with information around nodes and pairs. We use our approach to derive many of the existing closures and propose new ones and theoretically connect the two well-studied models of multinomial link and Poisson link selection. The theoretical work is backed up by numerical examples to highlight where the commonly used assumptions may fail and provide some recommendations for how to choose the most appropriate closure when using graphs with no or modest degree heterogeneity.
The course of an epidemic exhibits average growth dynamics determined by features of the pathogen and the population, yet also features significant variability reflecting the stochastic nature of disease spread. The interplay of biological, social, s
We have established a novel mathematical model that considers various aspects of the spreading of the virus, including, the transmission based on being in the latent period, environment to human transmission, governmental decisions, and control measu
School environments are thought to play an important role in the community spread of airborne infections (e.g., influenza) because of the high mixing rates of school children. The closure of schools has therefore been proposed as efficient mitigation
The COVID-19 pandemic has demonstrated how disruptive emergent disease outbreaks can be and how useful epidemic models are for quantifying risks of local outbreaks. Here we develop an analytical approach to calculate the dynamics and likelihood of ou
We provide a description of the Epidemics on Networks (EoN) python package designed for studying disease spread in static networks. The package consists of over $100$ methods available for users to perform stochastic simulation of a range of differen