Attitudes towards booster, testing and isolation, and their impact on COVID-19 response in winter 2022/2023 in France, Belgium, and Italy

Abstract

European countries are focusing on testing, isolation, and boosting strategies to counter the 2022/2023 winter surge due to Omicron subvariants. However, widespread pandemic fatigue and limited compliance potentially undermine mitigation efforts. To establish a baseline for interventions, we ran a multicountry survey to assess respondents’ willingness to receive booster vaccination and comply with testing and isolation mandates. The vast majority of survey participants (N=4,594) was willing to adhere to testing (>91%) and rapid isolation (>88%) across the three countries. Pronounced differences emerged in the declared senior adherence to booster vaccination (73% in France, 94% in Belgium, 86% in Italy). Next, we inferred the vaccine-induced population immunity profile at the winter start from prior vaccination data, immunity waning, and declared booster uptake. Integrating survey and estimated immunity data in a branching process epidemic spreading model, we evaluated the effectiveness and costs of current protocols in France, Belgium, and Italy to manage the winter wave. Model results estimate that testing and isolation protocols would confer significant benefit in reducing transmission (17-24%) with declared adherence. Achieving a mitigating level similar to the French protocol, the Belgian protocol would require 30% fewer tests and avoid the long isolation periods of the Italian protocol (average of 6 days vs. 11). A cost barrier to test would significantly decrease adherence in France and Belgium, undermining protocols’ effectiveness. Simpler mandates for isolation may increase awareness and actual compliance, reducing testing costs, without compromising mitigation. High booster vaccination uptake remains key for the control of the winter wave.

Publication
On medRxiv
Eugenio Valdano
Eugenio Valdano
Researcher (Chargé de recherche)

I study infectious disease epidemiology using data-rich mathematical models.