Publication date: Jul 07, 2025
The initial rollout of COVID-19 vaccines has been challenged by logistical issues, limited availability of doses, scarce healthcare capacity, spotty acceptance, and the emergence of variants of concern. Non-pharmaceutical interventions (NPIs) have been critical to support these phases. However, vaccines may have prompted behavioural relaxation, potentially reducing NPIs adherence. Epidemic models have explored this phenomenon, but they have not been validated against data. Moreover, recent surveys provide conflicting results on the matter. The extent of behavioural relaxation induced by COVID-19 vaccines is still unclear. Here, we aim to study this phenomenon in four regions. We implement five realistic epidemic models which include age structure, multiple virus strains, NPIs, and vaccinations. One of the models acts as a baseline, while the others extend it including different behavioural relaxation mechanisms. First, we calibrate the baseline model and run counterfactual scenarios to quantify the impact of vaccinations and NPIs. Our results confirm the critical role of both in reducing infection and mortality rates. Second, using different metrics, we calibrate the behavioural models and compare them to each other and to the baseline. Including behavioural relaxation leads to a better fit of weekly deaths in three regions. However, the improvements are limited to a [Formula: see text] reduction in weighted mean absolute percentage errors and these gains are generally offset by models’ increased complexity. Overall, we do not find clear signs of behavioural relaxation induced by COVID-19 vaccines on weekly deaths. Furthermore, our results suggest that if this phenomenon occurred, it generally involved only a minority of the population. Our work contributes to the retrospective validation of epidemic models developed amid the COVID-19 Pandemic and underscores the issue of non-identifiability of complex social mechanisms.
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| Concepts | Keywords |
|---|---|
| Pandemic | Baseline |
| Retrospective | Behavioural |
| Run | Covid |
| Vaccinations | Critical |
| Weekly | Epidemic |
| Induced | |
| Limited | |
| Models | |
| Non | |
| Npis | |
| Phenomenon | |
| Reducing | |
| Relaxation | |
| Rollout | |
| Vaccines |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | IDO | role |
| disease | MESH | infection |
| drug | DRUGBANK | Tropicamide |
| disease | MESH | COVID-19 Pandemic |
| disease | MESH | Long Covid |
| disease | IDO | history |
| disease | IDO | process |
| pathway | REACTOME | Reproduction |
| drug | DRUGBANK | Coenzyme M |
| drug | DRUGBANK | Trestolone |
| disease | IDO | susceptibility |
| drug | DRUGBANK | Ilex paraguariensis leaf |
| drug | DRUGBANK | Iron |
| drug | DRUGBANK | Abacavir |
| drug | DRUGBANK | Naproxen |
| drug | DRUGBANK | L-Valine |
| disease | IDO | country |
| disease | MESH | death |
| disease | MESH | breakthrough infection |
| drug | DRUGBANK | Trimebutine |
| disease | IDO | algorithm |
| drug | DRUGBANK | Ademetionine |
| drug | DRUGBANK | (S)-Des-Me-Ampa |
| disease | IDO | cell |
| disease | MESH | infectious diseases |
| drug | DRUGBANK | Carboxyamidotriazole |
| drug | DRUGBANK | Methylphenidate |
| disease | IDO | contact tracing |
| drug | DRUGBANK | Guanosine |
| disease | MESH | Tuberculosis |
| pathway | KEGG | Tuberculosis |
| drug | DRUGBANK | L-Phenylalanine |
| drug | DRUGBANK | Delorazepam |
| drug | DRUGBANK | Albendazole |