The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Analysis and forecast of COVID-19 spreading in China, Italy and France
Open accessAbstract
Highlights • Epidemic spreading• COVID19• SIR model• Recursive relations and non linear fitting
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048
Cited by
Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy
Fattorini, D., & Regoli, F. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.114732 · Cited by 616
Silva, P. C. L., Batista, P. V. C., Lima, H. S., Alves, M. A., Guimarães, F. G., & Silva, R. C. P. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110088 · Cited by 389
A review on COVID-19 forecasting models
Rahimi, I., Chen, F., & Gandomi, A. H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-020-05626-8 · Cited by 282
Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2021) · IEEE Access · doi:10.1109/access.2021.3093633 · Cited by 286
D'Arienzo, M., & Coniglio, A. (2020) · Biosafety and Health · doi:10.1016/j.bsheal.2020.03.004 · Cited by 251
Metadata
- Authors
- Fanelli, D., & Piazza, F.
- Venue
- Chaos, Solitons & Fractals
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 134
- Pages
- 109761
- ISSN
- 0960-0779 (ISSN-L)
- OpenAlex
- W3011771926
- Cited by
- 963
- References
- 3
- FWCI
- 34.68
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Analysis and forecast of COVID-19 spreading in China, Italy and France. https://app.cochat.ai/papers/10.1016/j.chaos.2020.109761
- The paper
- Fanelli, D., & Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals. https://doi.org/10.1016/j.chaos.2020.109761
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
