The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Outbreak analytics: a developing data science for informing the response to emerging pathogens
Open accessAbstract
Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
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
Evolutionary trees from DNA sequences: A maximum likelihood approach.
Felsenstein, J. (1981) · Journal of Molecular Evolution · doi:10.1007/bf01734359 · Cited by 14,571
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
Vos, T., Abajobir, A. A., Abate, K. H., Abbafati, C., Abbas, K. M., Abd-Allah, F., Abdulkader, R. S., Abdulle, A. M., Abebo, T. A., Abera, S. F., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)32154-2 · Cited by 13,220
Self-citationVos, T., Flaxman, A. D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., Shibuya, K., Salomon, J. A., Abdalla, S., Aboyans, V., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61729-2 · Cited by 8,291
BEAST 2: a software platform for Bayesian evolutionary analysis
Bouckaert, R., Heled, J., Kühnert, D., Vaughan, T., Wu, C.-H., Xie, D., Suchard, M. A., Rambaut, A., & Drummond, A. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003537 · Cited by 6,527
Nextstrain: real-time tracking of pathogen evolution
Hadfield, J., Megill, C., Bell, S. M., Huddleston, J., Potter, B., Callender, C., Sagulenko, P., Bedford, T., & Neher, R. A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty407 · Cited by 3,154
Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections
WHO Ebola Response Team (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1411100 · Cited by 1,674
A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics
Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt133 · Cited by 1,530
Self-citationDonnelly, C. A., Ghani, A. C., Leung, G. M., Hedley, A. J., Fraser, C., Riley, S., Abu-Raddad, L. J., Ho, L.-M., Thach, T.-Q., Chau, P., et al. (2003) · The Lancet · doi:10.1016/s0140-6736(03)13410-1 · Cited by 940
Microreact: visualizing and sharing data for genomic epidemiology and phylogeography
Argimón, S., Abudahab, K., Goater, R. J. E., Fedosejev, A., Bhai, J., Glasner, C., Feil, E. J., Holden, M. T. G., Yeats, C. A., Grundmann, H., et al. (2016) · Microbial Genomics · doi:10.1099/mgen.0.000093 · Cited by 732
Cited by
Wu, J. T., Leung, K., & Leung, G. M. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30260-9 · Cited by 4,389
Oude Munnink, B. B., Nieuwenhuijse, D. F., Stein, M., O’Toole, Á., Haverkate, M., Mollers, M., Kamga, S. K., Schapendonk, C., Pronk, M., Lexmond, P., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0997-y · Cited by 306
Ratnayake, R., Tammaro, M., Tiffany, A., Kongelf, A., Polonsky, J. A., & McClelland, A. (2020) · The Lancet Planetary Health · doi:10.1016/s2542-5196(20)30221-7 · Cited by 60
Self-citationPedrotti, C. H. S., Accorsi, T. A. D., Moreira, F. T., Lima, K. D. A., Köhler, K. F., Gaz, M. V. B., Chiamolera, M., Cunha, G. A., Neto, A. S., Morbeck, R. A., et al. (2022) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2022.04.059 · Cited by 6
Metadata
- Authors
- Polonsky, J. A., Baidjoe, A., Kamvar, Z. N., Cori, A., Durski, K., Edmunds, W. J., Eggo, R. M., Funk, S., Kaiser, L., Keating, P., de Waroux, O. L. P., Marks, M., Moraga, P., Morgan, O., Nouvellet, P., Ratnayake, R., Roberts, C. H., Whitworth, J., & Jombart, T.
- Venue
- Philosophical Transactions of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2019-05-20
- Volume
- 374
- Issue
- 1776
- Pages
- 20180276
- ISSN
- 0962-8436 (ISSN-L)
- 1471-2970
- OpenAlex
- W2946778829
- Cited by
- 174
- References
- 16
- FWCI
- 18.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Outbreak analytics: a developing data science for informing the response to emerging pathogens. https://app.cochat.ai/papers/10.1098/rstb.2018.0276
- The paper
- Polonsky, J. A., Baidjoe, A., Kamvar, Z. N., Cori, A., Durski, K., Edmunds, W. J., Eggo, R. M., Funk, S., Kaiser, L., Keating, P., de Waroux, O. L. P., Marks, M., Moraga, P., Morgan, O., Nouvellet, P., Ratnayake, R., Roberts, C. H., Whitworth, J., & Jombart, T. (2019). Outbreak analytics: a developing data science for informing the response to emerging pathogens. Philosophical Transactions of the Royal Society B: Biological Sciences. https://doi.org/10.1098/rstb.2018.0276
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.
