ArticleScience (New York, N.Y.)2020
Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions.
Article in Science (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 370 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
370 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Data-driven strategies for model-informed decision-making during the COVID-19 pandemic: a systematic review.BMJ open · 2026Pooled it
- The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review.European journal of epidemiology · 2022Pooled it
- Effects of a 12-Week Web-Based Weight Loss Program for Adults With Overweight and Obesity on COVIDAge and Lifestyle-Related Cardiometabolic Risk Factors: A Randomized Controlled Trial.Frontiers in public health · 2022Trial
- A real-time forecasting framework for emerging infectious diseases affecting animal populations.PLoS computational biology · 2026Article
- Optimizing infectious disease mitigation under dynamic conditions.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Mechanics of pandemics.EClinicalMedicine · 2026Review
- Mechanistic Understanding of Pandemic Dynamics: A Multiscale Algorithmic Framework.Life (Basel, Switzerland) · 2026Article
- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- Networked SIRS model with Kalman filter state estimation for epidemic monitoring in Europe.Communications medicine · 2026Article
- Rapid Epidemiological Data Collection on Social Media for COVID-19: Comparative Study Between Online Surveys and Conventional Cohorts.Journal of medical Internet research · 2026Observational
- Survey on mathematical modeling of infectious disease dynamics: insights and applications.BMC infectious diseases · 2026Review
- Automated model discovery based on COVID-19 epidemiological data from Thuringia, Germany.PloS one · 2026Article
- Systematic Determinants of Global COVID-19 Burden: Longitudinal Time-Series Analysis Using Big Data-Driven Artificial Intelligence.Journal of medical Internet research · 2025Article
- Data assimilation for estimating time-varying reproduction numbers.Journal of the Royal Society, Interface · 2025Article
- Daily profile of COVID-19 infections in Europe - a biophysical perspective.Biophysical reviews · 2025Review
- How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics.Risk analysis : an official publication of the Society for Risk Analysis · 2025Review
- Real-time forecasting of data revisions in epidemic surveillance streams.PLoS computational biology · 2025Article
- Modeling the Complete Dynamics of the SARS-CoV-2 Pandemic of Germany and Its Federal States Using Multiple Levels of Data.Viruses · 2025Article
- Bayesian changepoint detection for epidemic models.Scientific reports · 2025Article
- An agent-based model of COVID- 19 in the food industry for assessing public health and economic impacts of infection control strategies.Scientific reports · 2025Article
310 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As coronavirus disease 2019 (COVID-19) is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyzed the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detected change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we could quantify the effect of interventions and incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.