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Abstract Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.
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This content will become publicly available on May 26, 2027
The behavioural spillover effect: modelling behavioural interdependencies in multi-pathogen dynamics
During the recent pandemic, a rise in COVID-19 cases was followed by a decline in influenza. In the absence of cross-immunity, a potential explanation for the observed pattern is behavioural: non-pharmaceutical interventions (NPIs) designed and promoted for one disease also reduce the spread of others. We study short-term and long-term dynamics of two pathogens where NPIs targeting one pathogen indirectly influence the spread of another – a phenomenon we term behavioural spillover. We examine how perceived risk of and response to one disease substantially alter the spread of other pathogens, revealing how waves of different pathogens emerge over time as a result of behavioural interdependencies and human response. Our analysis identifies the parameter space where two diseases simultaneously co-exist, and where shifts in prevalence occur. Our findings are consistent with observations from the COVID-19 pandemic, where NPIs contributed to significant declines in infections such as influenza, pneumonia, and Lyme disease.
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- Award ID(s):
- 2229819
- PAR ID:
- 10685197
- Publisher / Repository:
- Taylor & Francis
- Date Published:
- Journal Name:
- Journal of Biological Dynamics
- Volume:
- 20
- Issue:
- 1
- ISSN:
- 1751-3758
- Subject(s) / Keyword(s):
- Epidemic models, risk response, equilibria analysis, identifiability, system dynamics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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