Age-dependent effects in the transmission and control of COVID-19 epidemics

Epidemiological InfectiologyTransversal
Davies NG et al
Nature Med

Main result

All the models explained the incident cases well, but the one without infectious susceptibility or clinical fraction varying with age did not reproduce the age distribution of the cases. The model with infectious susceptibility and clinical fraction varying with the age reproduced the observed cases better than all the others. 

People under 20 are about half less susceptible to be infected compared to people over 20.
79 % of infections are subclinical (asymptomatic or paucisymptomatic) in 10-19 year olds, compared to 31% for those over 70 years old. 
Therefore, age has an influence on susceptibility and severity of the disease in this model.
The effect of school closings is modest: lowering the peak incidence by 10-19% and delaying the temporal peak by 1-6 days according to the hypotheses, because children are less susceptible. 
In cities with older populations, the projection of infections with clinical presentations was greater whereas subclinical infections was more important in young cities. The age distribution did not vary the R0. 


Age has an effect on the Covid-19 epidemic by influencing both the infectious susceptibility and the severity of clinical presentation


Strength of evidence Moderate

- Modelling from various data sources
- Multiple sensitivity analyses

- Information from the early stages of the epidemic is therefore subject to uncertainty
- Fixed fraction of clinical cases in the model
- Hypothesis that subclinical infections are less infectious than clinical infections
- Age stratification


Understand whether the fact that Covid-19 infections are more often subclinical in young people and especially in children is because age influences infectious vulnerability or/and the severity of clinical presentation.


Covid-19 transmission modelling age-dependant :  
4 variations of this model: 
- One with age-dependant susceptibility to infection;
- One with age-dependant clinical fraction (proportion of individuals with symptoms);
- One with age-independant susceptibility and clinical fraction;
- One with age-dependant susceptibility and clinical fraction.
Models were adjusted to two sources of Wuhan outbreak data: a case report time series and 4 snapshots of the age distribution of cases. 
Taking into account closings ofschools during the holidays as well as movement restrictions in Wuhan.
Estimation of susceptibility and clinical fraction as a function of age from data from 6 countries and 6 studies. Then applying the model to different cities around the world with different age distributions.

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