COVID-19: measuring the impact on healthcare demand and capacity and exploring intervention scenarios

Yperen, James Van; Campillo-Funollet, Eduard; Rebecca, Inkpen,; Memon, Anjum; Madzvamuse, Anotida

doi:10.48550/arxiv.2012.15392

Cited by 1 publication

(1 citation statement)

References 17 publications

(19 reference statements)

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“…Bastos et al, 2019;McGough et al, 2020) and epidemic surveillance, i.e., early identification of emerging epidemics (Unkel et al, 2012). Mechanistic/state-space models, which are based on a mathematical description of the spreading process, allow one to make predictions about the future course of an epidemic (Shaman and Karspeck, 2012;Tizzoni et al, 2012;Nsoesie et al, 2013) as well as simulating intervention strategies (Chao et al, 2011;Di Lauro et al, 2021b,a;Van Yperen et al, 2020). Many such models rely on a compartmentalisation of a population of N individuals according to the individual's disease status.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic predictions of SIS epidemics on networks based on population-level observations

Zerenner¹,

Lauro²,

Dashti³

et al. 2021

Preprint

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We predict the future course of ongoing susceptible-infected-susceptible (SIS) epidemics on regular, Erdős-Rényi and Barabási-Albert networks. It is known that the contact network influences the spread of an epidemic within a population. Therefore, observations of an epidemic, in this case at the population-level, contain information about the underlying network. This information, in turn, is useful for predicting the future course of an ongoing epidemic. To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model. The surrogate model is based on a birth-and-death process; the effect of the underlying network is described by a parametric model for the birth rates. We demonstrate empirically that the surrogate model captures the intrinsic stochasticity of the epidemic once it reaches a point from which it will not die out. Bayesian parameter inference allows for uncertainty about the model parameters and the class of the underlying network to be incorporated directly into probabilistic predictions. An evaluation of a number of scenarios shows that in most cases the resulting prediction intervals adequately quantify the prediction uncertainty. As long as the population-level data is available over a long-enough period, even if not sampled frequently, the model leads to excellent predictions where the underlying network is correctly identified and prediction uncertainty mainly reflects the intrinsic stochasticity of the spreading epidemic. For predictions inferred from shorter observational periods, uncertainty about parameters and network class dominate prediction uncertainty. The proposed method relies on minimal data at population-level, which is always likely to be available. This, combined with its numerical efficiency, makes the proposed method attractive to be used either as a standalone inference and prediction scheme or in conjunction with other inference and/or predictive models.

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