A venue-based population-wide individual-based microsimulation model for COVID-19 transmission
Understanding infectious disease transmission requires insight into who interacts with whom, where and how these interactions take place, and under which conditions. While individual-based models (IBMs) allow interactions to be represented at the level of individuals, most models aggregate information on interaction partners (e.g. by age), without specifying where contacts occur, how they take place, or which individuals are co-present in the same setting. As a result, interactions outside households, schools or workplaces are commonly represented using aggregated community structures, and detailed mobility or venue-level contact data are rarely available. This poses a key challenge for microsimulation-based transmission modelling.
We present a methodological extension of the STRIDE individual-based model (Willem et al., 2021) that introduces an explicit, population-wide representation of community venues. Starting from aggregated community interaction pools, individuals are assigned to specific venue types (i.a. such as shops, restaurants, and other social locations) using empirical time-use data. This venue-based decomposition makes it possible to explicitly represent where interactions occur at the population scale, without relying on detailed mobility trajectories. Such fine-grained representations are straightforward when modelling a single setting, but become substantially more challenging when extending to all venues across an entire population.
The venue-based structure allows heterogeneous environmental characteristics to be incorporated at the setting level, including ventilation, occupancy, and exposure duration. Within this framework, we explicitly integrate multiple transmission pathways (i.a. close-range droplet transmission and airborne transmission) within STRIDE. The contribution of each pathway depends on individual behaviour and venue-specific conditions. Most epidemiological models, including IBMs, focus on a single dominant transmission route. By contrast, jointly modelling multiple pathways across all venues makes it possible to examine how these routes combine and interact to shape transmission at the population scale.
A key challenge is the limited availability of venue-level data. To this end, we developed an algorithm to redistribute aggregated community contacts across venues based on occupancy sizes and the time individuals spend in each setting. Where time-use data were unavailable, venue attendance patterns were imputed using age-stratified contact information. Many additional venue-specific characteristics required for transmission modelling, (i.a., contact duration, proximity, and environmental parameters) were not directly observed. In such cases, we introduced assumptions, guided as much as possible by existing literature.
By representing individuals and venues explicitly, the model can study superspreading caused by differences in contacts, infectiousness, and venue conditions. Because empirical data on the drivers of superspreading are limited, variability in key characteristics was introduced using mathematically defined distributions, allowing heterogeneity to be explored systematically. Our new model was applied to a computer-generated population of 600,000 virtual individuals designed to statistically mirror the Belgian population, enabling the simulation of intervention scenarios corresponding to Belgium’s first COVID-19 lockdown. Moreover, it allowed us to investigate a variety of what if scenarios, including ventilation interventions.
Overall, this work demonstrates how venue-based microsimulation with heterogeneous transmission and individual-level variability can enhance the realism and policy relevance of population-scale infectious disease models.