ArticleChaos (Woodbury, N.Y.)2020
Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation.
Article in Chaos (Woodbury, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The COVID-19 Pandemic and Health and Care Workers: Findings From a Systematic Review and Meta-Analysis (2020-2021).International journal of public health · 2023Pooled it
- Grocery Shopping Under Simplified Marginal Value Theorem Predictions.Human nature (Hawthorne, N.Y.) · 2024Article
- COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies.Scientific reports · 2023Article
- Empirical study and model simulation of global stock market dynamics during COVID-19.Chaos, solitons, and fractals · 2022Article
- The Ockham's razor for estimating the needs of ICU beds during a pandemic.Annals of intensive care · 2021Article
- Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19.Disaster medicine and public health preparedness · 2021Article
- Understanding the Challenges and Uncertainties of Seroprevalence Studies for SARS-CoV-2.International journal of environmental research and public health · 2021Review
- Measuring differences between phenomenological growth models applied to epidemiology.Mathematical biosciences · 2021Article
- Suppressing the impact of the COVID-19 pandemic using controlled testing and isolation.Scientific reports · 2021Article
- Reproducing country-wide COVID-19 dynamics can require the usage of a set of SIR systems.PeerJ · 2021Article
- Structural identifiability and observability of compartmental models of the COVID-19 pandemic.Annual reviews in control · 2021Review
- A stretched logistic equation for pandemic spreading.Chaos, solitons, and fractals · 2020Article
- On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy.Communications in nonlinear science & numerical simulation · 2020Article
- Long-term forecasts of the COVID-19 epidemic: a dangerous idea.Revista da Sociedade Brasileira de Medicina Tropical · 2020Article
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
Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data from different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity, using a susceptible-exposed-infected-recovered model, where the parameters are stochastically perturbed to simulate the difficulty in detecting patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics.
Indexed as
Identifiers
What OpenQuestion holds
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.