Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network. In this study, we examine how four processing decisions, including temporal sampling window (TSW), spatial threshold of contact (SpTh), minimum contact duration (MCD), and temporal aggregation (daily or hourly) influence the information content of contact data (indicated by changes in entropy) as well as disease transmission model dynamics. We found that changes made to information content by processing decisions translated to significant impacts to the transmission dynamics of disease models using the contact data. In particular, we found that SpTh had the largest independent influence on information content, and that some output metrics (R 0 , time to peak infection) were more sensitive to changes in information than others (epidemic extent). These findings suggest that insights gained from transmission modeling using dynamic contact data can be influenced by processing decisions alone, emphasizing the need to carefully consideration them prior to using contact-based models to conduct analyses, compare different datasets, or inform policy decisions.

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Intervention strategies for minimizing indoor SARS-CoV-2 transmission are often based on anecdotal evidence because there is little evidence-based research to support them. We developed a spatially-explicit agent-based model for simulating indoor respiratory pathogen transmission, and used it to compare effects of four interventions on reducing individual-level SARS-CoV-2 transmission risk by simulating a well-known case study. We found that imposing movement restrictions and efficacious mask usage appear to have the greatest effects on reducing infection risk, but multiple concurrent interventions are required to minimize the proportion of susceptible individuals infected. Social distancing had little effect on reducing transmission if individuals move during the gathering. Furthermore, our results suggest that there is potential for ventilation airflow to expose susceptible people to aerosolized pathogens even if they are relatively far from infectious individuals. Maximizing rates of aerosol removal is the key to successful transmission-risk reduction when using ventilation systems as intervention tools.Article Summary LineImposing mask usage requirements, group size restrictions, duration limits, and social distancing policies can have additive, and in some cases multiplicative protective effects on SARS-CoV-2 infection risk during indoor events.

African swine fever (ASF) is considered the most impactful transboundary swine disease. In the absence of effective vaccines, control strategies are heavily dependent on mass depopulation and shipment restrictions. Here, we developed a nested multiscale model for the transmission of ASF, combining a spatially explicit network model of animal shipments with a deterministic compartmental model for the dynamics of two ASF strains within 3 km × 3 km pixels in one Brazilian state. The model outcomes are epidemic duration, number of secondary infected farms and pigs, and distance of ASF spread. The model also shows the spatial distribution of ASF epidemics. We analyzed quarantine‐based control interventions in the context of mortality trigger thresholds for the deployment of control strategies. The mean epidemic duration of a moderately virulent strain was 11.2 days, assuming the first infection is detected (best‐case scenario), and 15.9 days when detection is triggered at 10% mortality. For a highly virulent strain, the epidemic duration was 6.5 days and 13.1 days, respectively. The distance from the source to infected locations and the spatial distribution was not dependent on strain virulence. Under the best‐case scenario, we projected an average number of infected farms of 23.77 farms and 18.8 farms for the moderate and highly virulent strains, respectively. At 10% mortality‐trigger, the predicted number of infected farms was on average 46.27 farms and 42.96 farms, respectively. We also demonstrated that the establishment of ring quarantine zones regardless of size (i.e. 5 km, 15 km) was outperformed by backward animal movement tracking. The proposed modelling framework provides an evaluation of ASF epidemic potential, providing a ranking of quarantine‐based control strategies that could assist animal health authorities in planning the national preparedness and response plan.