ArticleScientific reports2021
Plateaus, rebounds and the effects of individual behaviours in epidemics.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Modeling the impact of hospitalization-induced behavioral changes on the spread of COVID-19 in New York City.Infectious Disease Modelling · 2025Article
- Smart epidemic control: A hybrid model blending ODEs and agent-based simulations for optimal, real-world intervention planning.PLoS computational biology · 2025Article
- Using an epidemiological model to explore the interplay between sharing and advertising in viral videos.Scientific reports · 2024Article
- Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants.Water research · 2023Article
- Epidemic modeling with heterogeneity and social diffusion.Journal of mathematical biology · 2023Article
- Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation.PloS one · 2023Article
- Has the United States Reached a Plateau in Overdoses Caused by Synthetic Opioids After the Onset of the COVID-19 Pandemic? Examination of Centers for Disease Control and Prevention Data to November 2021.Frontiers in psychiatry · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plateaus and rebounds of various epidemiological indicators are widely reported in Covid-19 pandemics studies but have not been explained so far. Here, we address this problem and explain the appearance of these patterns. We start with an empirical study of an original dataset obtained from highly precise measurements of SARS-CoV-2 concentration in wastewater over nine months in several treatment plants around the Thau lagoon in France. Among various features, we observe that the concentration displays plateaus at different dates in various locations but at the same level. In order to understand these facts, we introduce a new mathematical model that takes into account the heterogeneity and the natural variability of individual behaviours. Our model shows that the distribution of risky behaviours appears as the key ingredient for understanding the observed temporal patterns of epidemics.
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