A dynamic microsimulation model for epidemics

Abstract

A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.

A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic

“…A wealth of modelling studies of non-pharmaceutical interventions have been published since the beginning of the pandemic, using frameworks including stochastic and deterministic compartmental models, branching processes, network models, and agent-based microsimulation models, covering policies ranging from contact tracing to travel restrictions to school closures, as well as possible exit strategies when interventions are withdrawn [33][34][35][36][37][38][39][40]. In the present study we introduce an approach which can model the interplay between household structure and control policy in a deterministic compartmental setting; while microsimulation models of COVID-19 have been developed which can account for household structure, they must be implemented through repeat simulation [41]. Although one study has considered a similar formalism to ours based on self-consistent equations in the context of NPIs during the COVID-19, it did not attempt to combine household structure with stratification based on age or other risk classes [9].…”

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

“…It involves the development and use of mathematical and computational techniques to describe the spread, evolution and control of epidemic disease [4,5]. The models in use are enormously varied and employ a wide range of techniques, including mechanistic mathematical approaches [6,7], statistical models trained from disease data [8][9][10][11] and computational micro-simulation of agents [12][13][14][15][16] as well as complex model emulators [17]. Each aims to generate outputs to help understand the past, current or future course of an epidemic while considering contextspecific strategies for mitigating spread.…”

An Open‐Source Model for Projecting Small Area Demographic and Land‐Use Change

“…This framing of the models as tools for assessing alternative scenarios is important because there is the potential for results to influence real‐world events in so much that investment decisions based on simulated growth would serve to enable that particular outcome over other alternatives. The models can also be utilized in other noninfrastructure planning contexts where high‐resolution demographic data are required, for example individual level data have been used as an input to a disease transition model (Spooner et al 2021). This work is ongoing and as such various improvements and extensions are in progress.…”