Abstract
Following the emergence of a novel coronavirus1 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions such as closure of schools and national lockdowns. We study the impact of major interventions across 11 European countries for the period from the start of COVID-19 until the 4th of May 2020 when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. We use partial pooling of information between countries with both individual and shared effects on the reproduction number. Pooling allows more information to be used, helps overcome data idiosyncrasies, and enables more timely estimates. Our model relies on fixed estimates of some epidemiological parameters such as the infection fatality rate, does not include importation or subnational variation and assumes that changes in the reproduction number are an immediate response to interventions rather than gradual changes in behavior. Amidst the ongoing pandemic, we rely on death data that is incomplete, with systematic biases in reporting, and subject to future consolidation. We estimate that, for all the countries we consider, current interventions have been sufficient to drive the reproduction number $$R_t$$Rt below 1 (probability $$R_tbackslash,$$Rt< 1.0 is 99.9%) and achieve epidemic control. We estimate that, across all 11 countries, between 12 and 15 million individuals have been infected with SARS-CoV-2 up to 4th May, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions and lockdown in particular have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.
Seth Flaxman
Associate Professor
My research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and social science.
Swapnil Mishra
Assistant Professor of Machine Learning and Public Health
I primarily work at intersection of public health, machine learning and Bayesian modelling.
Juliette Unwin
Lecturer in Statistical Science
I develop methods to solve questions related to infectious disease outbreaks.
Helen Coupland
PhD Student
Uses machine learning approaches to examine the dynamics of exposure events that give rise to health outcomes.
Charles Whittaker
Sir Henry Wellcome Postdoctoral Fellow
My work involves modelling infectious disease transmission dynamics.
Samir Bhatt
Professor of Machine Learning, Statistics and Public Health
I focus on mathematical, statistical and computer science tools to answer questions about human health.