Countries should aim to lower the reproduction number R close to 1.0 for the short-term mitigation of COVID-19 outbreaks
Hochberg, M. E.
Show abstract
The COVID-19 pandemic is still in its early stages and given the speed and magnitude of local outbreaks it is urgent to understand how mitigation measures translate into changes in key epidemiological and clinical outcomes. Here, we employ a mathematical model to explore the short-term consequences of lowering the reproduction number [R]0 and delaying measures on total infections and fatalities. The positive implications of mitigation generally accrue as these measures are adopted early, with the most striking effects seen when the reproductive number is lowered to a level [R]C{approx}1.0. As the delay in adopting measures exceeds approximately the half-way point to the peak of an outbreak, the effects of lowering [R]0 markedly decrease. Aiming for reproduction numbers close to 1.0 can substantially reduce fatality probabilities over short time scales, particularly for larger populations. We conclude that research is urgently needed on how mitigation measures impact [R]0 and how these can be optimized so as to achieve [R]C{approx}1.0 whilst supporting individual freedoms, society and the economy.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Model-informed optimal allocation of limited resources to mitigate infectious disease outbreaks in societies at war 96%
- The Complex Interplay Between Risk Tolerance and the Spread of Infectious Diseases 95%
- Interventions targeting nonsymptomatic cases can be important to prevent local outbreaks: SARS-CoV-2 as a case-study 95%
Similar papers in this journal
- Vaccine escape in a heterogeneous population: insights for SARS-CoV-2 from a simple model 95%
- Cost and social distancing dynamics in a mathematical model of COVID-19 with application to Ontario, Canada 95%
- Optimal health and economic impact of non-pharmaceutical intervention measures prior and post vaccination in England: a mathematical modelling study 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.