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

Well parameterized epidemiological models including accurate representation of contacts are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here, we fit age-stratified models, including re-estimation of relative contact rates between age classes, to public data describing the 2020–2021 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing approximate Bayesian computation (ABC) methodology with model-based proposals (MBPs) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalization rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrization of dynamic transmission models that can inform data-driven public health policy and interventions. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Section: Resultsmentioning

confidence: 99%

Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

Pooley

Doeschl‐Wilson

Marion

2022

“…Adjustments to the underlying model—such as adding or removing transitions, changing transition rates, or substituting one submodel for another—are immediately reflected in the analysis. This tight feedback loop gives practical and visual tools that can be rapidly refined [ 27 ] and used to determine which policy decisions or outcomes are robust to model changes. In these examples, the analysis is treated externally to the model with the analysis being run directly on the ODE derived from the Petri net.…”

Section: Calibrating and Analysing Modelsmentioning

confidence: 99%

An algebraic framework for structured epidemic modelling

Libkind

Baas

Halter

et al. 2022

Pandemic management requires that scientists rapidly formulate and analyse epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific modelling workflows detach the structure of a model—its submodels and their interactions—from its implementation in software. Consequently, incorporating local changes to model components may require global edits to the code base through a manual, time-intensive and error-prone process. We propose a compositional modelling framework that uses high-level algebraic structures to capture domain-specific scientific knowledge and bridge the gap between how scientists think about models and the code that implements them. These algebraic structures, grounded in applied category theory, simplify and expedite modelling tasks such as model specification, stratification, analysis and calibration. With their structure made explicit, models also become easier to communicate, criticize and refine in light of stakeholder feedback. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.

“…Mitchell et al [ 17 ] emphasize the importance of provenance, describing a pipeline for managing data and promoting a practice of transparency and reproducibility in scientific workflows that should be adopted in all modelling endeavours. Chen et al [ 18 ] tackle visualization and make recommendations for the challenges surrounding communication of results.…”

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confidence: 99%

“…Panovska-Griffiths et al [5] and Hinch et al [6] are focused on the English COVID-19 epidemics and both estimate the transmissibility of variants and evaluate interventions, with the former combining the ABM with a statistical regression and making a policy contribution with the results, while the latter is taking a geospatial approach. improved methods [3][4][5][6][7][8] fitting data [3,[9][10][11] agent-based models [5,9,10,12,13] model relationships [5,6,14,15] estimating R (t) [8,11,13,16] methodology recommendations [17,18] variants […”

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confidence: 99%

See 1 more Smart Citation

Technical challenges of modelling real-life epidemics and examples of overcoming these

Panovska‐Griffiths

Waites

Ackland

2022

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of mathematical modelling in informing and advising policy decision-making. Effective practice of mathematical modelling has challenges. These can be around the technical modelling framework and how different techniques are combined, the appropriate use of mathematical formalisms or computational languages to accurately capture the intended mechanism or process being studied, in transparency and robustness of models and numerical code, in simulating the appropriate scenarios via explicitly identifying underlying assumptions about the process in nature and simplifying approximations to facilitate modelling, in correctly quantifying the uncertainty of the model parameters and projections, in taking into account the variable quality of data sources, and applying established software engineering practices to avoid duplication of effort and ensure reproducibility of numerical results. Via a collection of 16 technical papers, this special issue aims to address some of these challenges alongside showcasing the usefulness of modelling as applied in this pandemic. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.