To better control the SARS-CoV-2 pandemic, it is essential to quantify the impact of control measures and the fraction of infected individuals that are detected. To this end we developed a deterministic transmission model based on the renewal equation and fitted the model to daily case and death data in the first few months of 2020 in 79 countries and states, representing more than 4 billions individuals. Based on a region-specific infected fatality ratio, we inferred the time-varying probability of case detection and the time-varying decline in transmissiblity. The model was validated by the good correlation between the predicted total number of infected and that found in serosurveys; and most importantly by the strong correlation between the inferred probability of detection and the number of daily tests per inhabitant, with 50% detection achieved with 0.003 daily tests per inhabitants. Most of the decline in transmission was explained by the reductions in transmissibility (social distancing), which avoided 107 deaths in the regions studied over the first four months of 2020. In contrast, symptom-based testing and isolation was not an efficient way to control the spread of the disease, as a large part of transmission happens before symptoms and only a small fraction of infected individuals was typically detected. We developed a phenomenological model to link the number of daily tests with the probability of detection and verified the prediction that increasing test capacity increases the probability of detection less than proportionally. Together these results suggest that little control can be achieved by symptom-based testing and isolation alone.
Estimating the global reduction in transmission and rise in detection capacity of the novel coronavirus SARS-CoV-2 in early 2020