ArticleProceedings of the National Academy of Sciences of the United States of America2020
The turning point and end of an expanding epidemic cannot be precisely forecast.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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68 citing papers in PubMed.
- A real-time forecasting framework for emerging infectious diseases affecting animal populations.PLoS computational biology · 2026Article
- Leveraging mathematical models to predict and control T-cell activation.PLoS computational biology · 2026Article
- Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity.Scientific reports · 2026Article
- The tale of two assumptions: incorporating healthcare-seeking behaviour in epidemic forecasting.BMC infectious diseases · 2025Article
- Improving outbreak forecasts through model augmentation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A modular approach to forecasting COVID-19 hospital bed occupancy.Communications medicine · 2025Article
- Socio-environmental modeling shows physics-like confidence with water modeling surpassing it in numerical claims.iScience · 2025Article
- Article
- Quantum-Like Approaches Unveil the Intrinsic Limits of Predictability in Compartmental Models.Entropy (Basel, Switzerland) · 2024Article
- Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand.PLoS computational biology · 2024Article
- Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19.Scientific reports · 2023Article
- Fundamental bound on epidemic overshoot in the SIR model.Journal of the Royal Society, Interface · 2023Article
- Inferring China's excess mortality during the COVID-19 pandemic using online mourning and funeral search volume.Scientific reports · 2023Article
- Accurately summarizing an outbreak using epidemiological models takes time.Royal Society open science · 2023Article
- An adaptive testing strategy for efficient utilization of healthcare resources during an epidemic.Journal of theoretical biology · 2023Article
- A KdV-SIR equation and its analytical solutions: An application for COVID-19 data analysis.Chaos, solitons, and fractals · 2023Article
- Forecasting COVID-19 spreading through an ensemble of classical and machine learning models: Spain's case study.Scientific reports · 2023Article
- Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.eLife · 2023Article
- Stochastic simulation of successive waves of COVID-19 in the province of Barcelona.Infectious Disease Modelling · 2023Article
- TransCode: Uncovering COVID-19 transmission patterns via deep learning.Infectious diseases of poverty · 2023Article
8 more citing papers are in PubMed but not listed here.
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No grant is acknowledged in the PubMed record.
Abstract
Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained; neither can the end of the epidemic after the turning point. A susceptible-infected-removed (SIR) model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slowdown in the number of newly infected individuals during the expansion phase allows one to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the midterm is not due to wrong or incomplete data, since it persists in error-free, synthetically produced datasets and does not necessarily improve by using larger datasets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective, or phenomenological models and supports that only probabilities of different outcomes can be confidently given.
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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.