Modelling infectious disease transmission potential as a function of human behaviour

Mathematical modelling is an important public health tool for aiding understanding the spread of respiratory infectious diseases, such as influenza or COVID-19, and for quantifying the effects of behavioural interventions. However, such models rarely explicitly appeals to theories of human behaviour to justify model assumptions. Here we propose a novel mathematical model of disease transmission via fomites (luggage trays) at airport security screening during an outbreak. Our model incorporates the self-protective behaviour of using hand sanitiser gel in line with the extended parallel processing model (EPPM) of behaviour. We find that changing model assumptions of human behaviour in line with the EPPM gives qualitatively different results on the optimal placement of hand sanitiser gels within an airport compared to the model with naive behavioural assumptions. Specifically, that it is preferable to place hand sanitiser gels after luggage screening in most scenarios, however in situations where individuals perceive high threat and low efficacy this strategy may need to be reviewed. This model demonstrates how existing behavioural theories can be incorporated into mathematical models of infectious disease.

Modelling the effect of a border closure between Switzerland and Italy on the spatiotemporal spread of COVID-19 in Switzerland

We present an approach to extend the Endemic-Epidemic (EE) modelling framework for the analysis of infectious disease data. In its spatiotemporal application, spatial dependencies have originally been captured by a power law applied to static neighbourhood matrices. We propose to adjust these weight matrices over time to reflect changes in spatial connectivity between geographical units. We illustrate this extension by modelling the spread of coronavirus disease 2019 (COVID-19) between Swiss and bordering Italian regions in the first wave of the COVID-19 pandemic. We adjust the spatial weights with data describing the daily changes in population mobility patterns, and indicators of border closures describing the state of travel restrictions since the beginning of the pandemic. We use these time-dependent weights to fit an EE model to the region-stratified time series of new COVID-19 cases. We then adjust the weight matrices to reflect two counterfactual scenarios of border closures and draw counterfactual predictions based on these, to retrospectively assess the usefulness of border closures. We observed that predictions based on a scenario where no closure of the Swiss-Italian border occurred increased the number of cumulative cases in Switzerland by a factor of 2.5 over the study period. Conversely, a closure of the Swiss-Italian border two weeks earlier than implemented would have resulted in only a 12% decrease in the number of cases and merely delayed the epidemic spread by a couple weeks. Despite limitations in the current study, we believe it provides useful insight into modelling the effect of epidemic countermeasures on the spatiotemporal spread of COVID-19.