ABSTRACT
Non-pharmaceutical interventions (NPIs) to mitigate the spread of SARS-CoV-2 were often implemented under considerable uncertainty and a lack of scientific evidence. Assessing the effectiveness of the individual interventions is critical to inform future preparedness response plans. Here we quantify the impact of 4,579 NPIs implemented in 76 territories on the effective reproduction number, Rt, of COVID-19. We use a hierarchically coded data set of NPIs and propose a novel modelling approach that combines four computational techniques, which together allow for a worldwide consensus rank of the NPIs based on their effectiveness in mitigating the spread of COVID-19. We show how the effectiveness of individual NPIs strongly varies across countries and world regions, and in relation to human and economic development as well as different dimensions of governance. We quantify the effectiveness of each NPI with respect to the epidemic age of its adoption, i.e., how early into the epidemics. The emerging picture is one in which no one-fits-all solution exists, and no single NPI alone can decrease Rt below one and that a combination of NPIs is necessary to curb the spread of the virus. We show that there are NPIs considerably less intrusive and costly than lockdowns that are also highly effective, such as certain risk communication strategies and voluntary measures that strengthen the healthcare system. By allowing to simulate "what-if" scenarios at the country level, our approach opens the way for planning the most likely effectiveness of future NPIs.
ABSTRACT
The goal of this analysis is to estimate the effects of the diverse government intervention measures implemented to mitigate the spread of the Covid-19 epidemic. We use a process model based on a compartmental epidemiological framework Susceptible-Infected-Recovered-Dead (SIRD). Analysis of case data with such a mechanism-based model has advantages over purely phenomenological approaches because the parameters of the SIRD model can be calibrated using prior knowledge. This approach can be used to investigate how governmental interventions have affected the Covid-19-related transmission and mortality rate during the epidemic.
