Library

Description: Agent-based epidemiological models have been applied widely successfully during the SARS-CoV-2 pandemic and assisted policymakers in assessing the effectiveness of intervention strategies. The computational complexity of agent-based models is still challenging, and therefore it is important to utilize modern multi-core systems as good as possible. In this paper, we are presenting our work on parallelizing the epidemiological simulation model MATSim Episim. Episim combines a large-scale person-centric human mobility model with a mechanistic model of infection and a person-centric disease progression model. In general, the parallelization of agent-based models with an inherent sequential structure — in the case of epidemiological models, the temporal order of the individual movements of the agents — is challenging. Especially when the underlying social network is irregular and dynamic, they require frequent communication between the processing elements. In Episim, however, we were able to take advantage of the fact that people are not contagious on the same day they become infected, and therefore immediate health synchronization is not required. By parallelizing some of the most computationally intensive submodels, we are now able to run MATSim Episim simulations up to eight times faster than the serial version. This makes it feasible to increase the number of agents, e.g. to run simulations for the whole of Germany instead of just Berlin as before.

Description: Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. Methods We compared July 2022 projections from the European COVID-19 Scenario Modelling Hub. Five modelling teams projected incidence in Belgium, the Netherlands, and Spain. We compared projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. Results. By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. Conclusions. We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts. Data availability All code and data available on Github: https://github.com/covid19-forecast-hub-europe/aggregation-info-loss