Importance of untested infectious individuals for the suppression of COVID-19 epidemics

Francisco Perez-Reche; Norval Strachan

Here, we use data from the outbreaks in Germany, Hubei (China), Italy, Spain and UK to calibrate a mathematical model that accounts for the force of infection associated with both tested (reported) and untested infectious individuals (see a scheme of the simplest version of the model in Figure 1 and more details in Methods). In order to compare outbreaks in different regions/countries, we fit the model independently to each outbreak. The calibrated model is used to study the effect of two suppression strategies: Interventions aiming for a reduction of transmission at the population level (representing, e.g., social distancing or a lockdown) and local interventions consisting in isolation of both tested and untested infectious individuals. Figure 1 . Simplest version of the model used to simulate the SARS-CoV-2 epidemic. At any given time, individuals can belong to one of 7 compartments: (susceptible to SARS-CoV-2), (exposed but not yet infectious), (infectious tested), (recovered from tested infected), (dead), (infected untested) and (recovered from untested infected). It is assumed that all deaths are associated with tested infectious individuals. Recovered individuals are assumed to be immune to the virus. The transition rates between compartments are indicated on the arrows. is the rate at which susceptible individuals become exposed. The force of infection, ( ), accounts for both tested and untested individuals. Here, is the size of the modelled population. The mean incubation period is −1 . The proportion of exposed individuals that become infected and are tested defines the testing rate, . Tested infectious individuals die or recover at a rate . The parameter