Background: Controlling the transmission of respiratory infections such as influenza andCOVID-19 is a critical public health priority. Non-pharmaceutical intervention policies such as community quarantines, closures and travel bans are often implemented in emergencies but many of them are disruptive and difficult to maintain for extended periods of time. A promising alternative recommended by the CDC for influenza is requiring individuals showing fever symptoms to remain isolated at home until they are fever-free for at least one day, but there is limited evidence to support the effectiveness of such symptom-based isolation policies.
Methods:Here we introduce a computational model of symptom-based isolation that accounts for the timing of symptoms, viral shedding and the population structure. It was validated on outbreaks of influenza in schools and modified to account for COVID-19. It was then used to estimate the outbreak curves and the attack rates (the proportion of the population infected) under one or more days of fever-based isolation.Results: Using the model we find evidence that symptom-based isolation policies could reduce the attack rates of both influenza and COVID-19 outbreaks, and flatten the outbreak curves.Specifically, we found that across a range of influenza scenarios, a CDC-recommended policy of one day isolation following fever can reduce the attack rate from 27% of the population to 12%, : medRxiv preprint implementing one day post-fever isolation would reduce the attack rate from 79% to 71%, and there is possible benefit from isolation for six days. In both influenza and COVID-19, the policies are predicted to reduce the peak number of infected but not shorten the outbreak duration.Conclusions : Symptom-based isolation could be an important tool to control influenza and COVID-19 outbreaks in schools, and potentially other settings. We recommend that schools implement a post-fever isolation policy of two days for influenza and six days for COVID-19.