Publication date: Feb 06, 2026
Background: The COVID-19 pandemic was strongly shaped by the interaction between population behaviour and transmission dynamics. Standard mathematical models do not account for this interaction, however. Objective: we tested whether adding a mechanistic representation of population behavioural dynamics improves the ability of a mathematical model to explain and predict COVID-19 pandemic waves. Methods: We compared a standard Susceptible-Infected-Recovered (SIR) model to a variant (SIRx) with a mechanistic representation of behavioural processes, including two-way coupling between behaviour and transmission dynamics. We used approximate Bayesian computation to parameterise the models with SARS-CoV-2 case incidence and the Oxford stringency index from 13 European countries. Models were fitted to the Spring 2020 wave, and their out-of-sample prediction for the Summer/Fall 2020 wave was tested. Outcome measures included the Akaike Information Criterion (AICc), the area between empirical and model epidemic curves, and predicted timing/magnitude of the second wave. Results: The average AICc for the SIRx model across all 13 countries was lower (-2638 {+/-} 345 versus -2295 {+/-} 212 for SIR), meaning that the SIRx model explains the data more parsimoniously. The average area-between-curves was also lower (0.072 {+/-} 0.071 versus 0.16 {+/-} 0.11). The predicted peak magnitude for the SIRx model (0.0015 {+/-} 0.0014) was closer to the data (0.0006 {+/-} 0.0005) than the SIR prediction (0.0083 {+/-} 0.0090). The average day-of-peak for the SIRx model (283 {+/-} 19 days from first data point) was also closer to the data (278 {+/-} 25), than the SIR prediction (253 {+/-} 31), although the 95% credible intervals for individual countries were very large. Conclusion: Coupling behavioural and disease dynamics improves the ability of mathematical models to explain and predict crucial features of pandemic waves.
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | COVID-19 pandemic |
| disease | MESH | included |
| disease | MESH | infectious diseases |
| disease | MESH | face |
| disease | MESH | severe acute respiratory syndrome |
| disease | MESH | influenza |
| disease | MESH | infection |
| disease | MESH | AIC |
| pathway | REACTOME | Reproduction |
| disease | MESH | GPS |
| drug | DRUGBANK | Tropicamide |
| disease | MESH | Dis |