title: Exact and approximate moment closures for non-Markovian network epidemics.
abstract: Moment-closure techniques are commonly used to generate low-dimensional deterministic models to approximate the average dynamics of stochastic systems on networks. The quality of such closures is usually difficult to asses and furthermore the relationship between model assumptions and closure accuracy are often difficult, if not impossible, to quantify. Here we carefully examine some commonly used moment closures, in particular a new one based on the concept of maximum entropy, for approximating the spread of epidemics on networks by reconstructing the probability distributions over triplets based on those over pairs. We consider various models (SI, SIR, SEIR and Reed-Frost-type) under Markovian and non-Markovian assumption characterising the latent and infectious periods. We initially study with care two special networks, namely the open triplet and closed triangle, for which we can obtain analytical results. We then explore numerically the exactness of moment closures for a wide range of larger motifs, thus gaining understanding of the factors that introduce errors in the approximations, in particular the presence of a random duration of the infectious period and the presence of overlapping triangles in a network. We also derive a simpler and more intuitive proof than previously available concerning the known result that pair-based moment closure is exact for the Markovian SIR model on tree-like networks under pure initial conditions. We also extend such a result to all infectious models, Markovian and non-Markovian, in which susceptibles escape infection independently from each infected neighbour and for which infectives cannot regain susceptible status, provided the network is tree-like and initial conditions are pure. This works represent a valuable step in enriching intuition and deepening understanding of the assumptions behind moment closure approximations and for putting them on a more rigorous mathematical footing.
authors.affiliation: Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK. Electronic address: l.pellis@warwick.ac.uk.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Pellis
authors.affiliation: School of Mathematics, University of Manchester, Manchester M13 9PL, UK; Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK. Electronic address: thomas.house@manchester.ac.uk.
authors.forename: Thomas
authors.initials: T
authors.lastname: House
authors.affiliation: Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK. Electronic address: M.J.Keeling@warwick.ac.uk.
authors.forename: Matt J
authors.initials: MJ
authors.lastname: Keeling
mesh_terms: D003141:Communicable Diseases / Q000453:epidemiology; D004198:Disease Susceptibility; D058872:Epidemics; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D011336:Probability; D013997:Time Factors
keywords: Approximate dynamics; Maximum Entropy; Pair approximation; Pairwise model; SIR epidemic