Abstract
Together with seasonal effects inducing outdoor or indoor activities, the gradual easing of prophylaxis caused second and third waves of SARS-CoV-2 to emerge in various countries. Interestingly, data indicate that the proportion of infections belonging to the elderly is particularly small during periods of low prevalence and continuously increases as case numbers increase. This effect leads to additional stress on the health care system during periods of high prevalence. Furthermore, infections peak with a slight delay of about a week among the elderly compared to the younger age groups. Here, we provide a mechanistic explanation for this phenomenology attributable to a heterogeneous prophylaxis induced by the age-specific severity of the disease. We model the dynamical adoption of prophylaxis through a two-strategy game and couple it with an SIR spreading model. Our results also indicate that the mixing of contacts among the age groups strongly determines the delay between their peaks in prevalence and the temporal variation in the distribution of cases.This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.
Highlights
The study of the interplay between human behaviour and the spreading of epidemics has received great attention since the early 2000s [1,2,3] and is fundamental in many modelling approaches to the spread of SARS-CoV-2 [4]
We model the dynamical adoption of prophylaxis through a twostrategy game and couple it with an SIR spreading model
By analyzing the available information of confirmed SARS-CoV-2 infections, we have shown how the case distribution among age groups temporally varies through time
Summary
The study of the interplay between human behaviour and the spreading of epidemics has received great attention since the early 2000s [1,2,3] and is fundamental in many modelling approaches to the spread of SARS-CoV-2 [4]. The behavioural model is coupled to the evolution of a spreading dynamics This framework allows us to qualitatively understand and mechanistically explain the emergence of the temporal variation over age groups as well as the observed delayed peak in prevalence. The adoption of prophylactic measures largely determines the course of the epidemic Central actors, such as public health authorities [26,27,28], and external factors, for example seasonality [29,30], influence the spread of the epidemic. In a continuous time mean field model, all these measures reduce the transmission rate either through a reduction of contacts or the transmission probability and are essentially equivalent [31] This equivalence enables us to subsume the different measures into one category: the adoption of prophylactic measures. The variable Ri subsumes the fraction of recovered individuals in group i
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