Speaker
Description
Nursing home residents pay every year a devastating tribute to respiratory virus outbreaks. While antiviral treatments are effective if administered shorty after symptom onset or, even better, as pre- or post-exposure prophylaxis (Pep or PreP, respectively), they remain dramatically underused. This is due to the limited number of studies that have evaluated their efficacy in nursing homes, both at the individual level to prevent hospitalization and at the population level to break transmission chains.
In order to predict the impact of antiviral deployment during an outbreak, we developed a multi-scale mathematical model integrating viral dynamics and association with the risk of hospitalization of the three main respiratory viruses (SARS-CoV-2, Influenza, RSV). This provided us with a model that can reproduce the time-dependent change in the risk of transmission and predict the impact of treatment. Next, the model was calibrated using a contact matrix developed in geriatric wards to reproduce the frequency and the heterogeneity of contacts among residents and staff members.
In the context of SARS-CoV-2, we predicted that treating symptomatic cases can reduce the risk of severe disease by 50% during an outbreak, but may not have a substantial impact on the attack rate. Instead, treating positive contact residents as PeP or PreP could reduce by more than 50% the attack rate during an outbreak, and by more than 70% the risk of sever disease, irrespective of the virus reproductive ratio. We will also show how these results apply to Influenza and RSV, two viruses with very patterns of viral dynamics and transmission rates. Our work can be used to optimize antiviral treatments and reduce the burden of respiratory viruses in nursing homes.
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