Estimating the global reduction in transmission and rise in detection capacity of the novel coronavirus SARS-CoV-2 in early 2020

Abstract

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 4.2 billions individuals. Based on a region-specific infection fatality ratio, we inferred the time-varying probability of case detection and the time-varying decline in transmissiblity. As a validation, the predicted total number of infected was close to that found in serosurveys; more importantly, the inferred probability of detection strongly correlated with 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 10 millions deaths in the regions studied over the first four months of 2020. In contrast, symptom-based testing and isolation of positive cases 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. The latter is explained by the limited number of tests available, and the fact that increasing test capacity often 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.

Consistent pattern of epidemic slowing across many geographies led to longer, flatter initial waves of the COVID-19 pandemic

“…A large number of studies have used available databases of interventions [8][9][10], coupled with statistical methods, to estimate the impact of different interventions on the effective reproduction number [15][16][17][18][19][20][21][22][23][24][25][26][27][51][52][53][54]. Our approach is different from these studies in three major aspects: (i) we use a mechanistic and not a statistical model, (ii) we do not try to model or explain the impact of specific interventions, and (iii) our compartmental model estimates the change in both the susceptible population and the transmission whereas effective reproduction number based studies only estimate the product of the two, i.e.…”

“…The transmission of the SARS-Cov-2 virus is often quantified using the time-varying reproduction number, R(t), which represents the mean number of secondary cases that a single primary case will infect. Many studies have focused on estimating the impact of different interventions on R(t) under the implicit assumption that interventions are similar between different populations [16][17][18][19][20][21][22][23][24][25][26][27]. However, the analytical approach in these studies conditions on the assumption that the interventions as measured are the main drivers of changes in R(t), with transmissibility assumed to be constant otherwise.…”

The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection

“…A good measure to evaluate the prevalence of the disease in the population is the number of detected cases, which depends on the testing effort. In the context of COVID-19, the average detection rate on the 8 th of May 2020 across a large number of countries, mainly in Europe and North America, was inferred to be around 30% at best (Belloir and Blanquart, 2021; Russell et al, 2020). However, test capacity is not the only limiting factor to detect infected individuals.…”