ArticlePhysica D. Nonlinear phenomena2020
Size and timescale of epidemics in the SIR framework.
Article in Physica D. Nonlinear phenomena, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed.
- Relative contributions of public and domestic transmission domains in cholera outbreaks in displacement camps: an exploratory agent-based modeling study.Epidemiology and infection · 2026Article
- Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications.Journal of mathematical biology · 2025Article
- Difference in Rumor Dissemination and Debunking Before and After the Relaxation of COVID-19 Prevention and Control Measures in China: Infodemiology Study.Journal of medical Internet research · 2024Article
- A standardised differential privacy framework for epidemiological modeling with mobile phone data.PLOS digital health · 2023Article
- The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19.Infectious Disease Modelling · 2023Article
- Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil.Physica D. Nonlinear phenomena · 2022Article
- The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading.Scientific reports · 2022Article
- Estimating a continuously varying offset between multivariate time series with application to COVID-19 in the United States.The European physical journal. Special topics · 2022Article
- The changing economic relationship between some of the major COVID-19 impacted countries with prominent wealth: a comparative study from the view point of stock markets.The European physical journal. Special topics · 2022Article
- Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use?Physica D. Nonlinear phenomena · 2021Article
- Trends in COVID-19 prevalence and mortality: A year in review.Physica D. Nonlinear phenomena · 2021Article
- Nonlinear science against the COVID-19 pandemic.Physica D. Nonlinear phenomena · 2021Article
- Explicit formulae for the peak time of an epidemic from the SIR model.Physica D. Nonlinear phenomena · 2021Article
- Epidemic models with discrete state structures.Physica D. Nonlinear phenomena · 2021Article
- Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19.Physica A · 2021Article
- Association between COVID-19 cases and international equity indices.Physica D. Nonlinear phenomena · 2021Article
- Modeling of COVID-19 propagation with compartment models.Mathematische Semesterberichte · 2021Article
- Apparent scaling of virus surface roughness-An example from the pandemic SARS-nCoV.Physica D. Nonlinear phenomena · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The most important features to assess the severity of an epidemic are its size and its timescale. We discuss these features in a systematic way in the context of SIR and SIR-type models. We investigate in detail how the size and timescale of the epidemic can be changed by acting on the parameters characterizing the model. Using these results and having as guideline the COVID-19 epidemic in Italy, we compare the efficiency of different containment strategies for contrasting an epidemic diffusion such as social distancing, lockdown, tracing, early detection and isolation.
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.