ArticlePLoS computational biology2018
A graph-based evidence synthesis approach to detecting outbreak clusters: An application to dog rabies.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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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.
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Who cites it
25 citing papers in PubMed.
- Molecular Epidemiology to Decipher the Transmission Networks of Mpox in an Outbreak Scenario: Applications in Thailand During the 2023 Global Health Emergency.The Journal of infectious diseases · 2026Article
- Biosurveillance and early outbreak detection of rabies in settings with limited laboratory capacity using spatiotemporal clustering and a machine learning framework.Scientific reports · 2026Article
- Generalizing an outbreak cluster detection method for two groups: an application to rabies.Royal Society open science · 2025Article
- Contrasting the impact and cost-effectiveness of successive intervention strategies in response to Ebola in the Democratic Republic of the Congo, 2018-2020.BMJ global health · 2025Article
- Integrated population clustering and genomic epidemiology with PopPIPE.Microbial genomics · 2025Article
- Examining the molecular clock hypothesis for the contemporary evolution of the rabies virus.PLoS pathogens · 2024Article
- Seamless, rapid, and accurate analyses of outbreak genomic data using splitGenome research · 2024Article
- Updating Reproduction Number Estimates for Mpox in the Democratic Republic of Congo Using Surveillance Data.The American journal of tropical medicine and hygiene · 2024Article
- Introduction and Spread of Dengue Virus 3, Florida, USA, May 2022-April 2023.Emerging infectious diseases · 2024Article
- Use of whole genome sequencing for surveillance and control of foodborne diseases:Frontiers in microbiology · 2024Review
- Identification of dynamical changes of rabies transmission under quarantine: Community-based measures towards rabies elimination.PLoS computational biology · 2023Article
- Observational
- Epidemiological cluster identification using multiple data sources: an approach using logistic regression.Microbial genomics · 2023Article
- Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics.PLoS computational biology · 2022Article
- Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection.Frontiers in public health · 2022Article
- Mathematical modelling and phylodynamics for the study of dog rabies dynamics and control: A scoping review.PLoS neglected tropical diseases · 2021Article
- Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: a statistical analysis in Tunisia from February 29 to May 5, 2020.BMC infectious diseases · 2020Article
- [New aspects of rabies control].Bulletin de l'Academie nationale de medecine · 2020Review
- Meteorological impact on the COVID-19 pandemic: A study across eight severely affected regions in South America.The Science of the total environment · 2020Article
- Inferring evolutionary pathways and directed genotype networks of foodborne pathogens.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new framework for combining several data streams, e.g. temporal, spatial and genetic data, to identify clusters of related cases of an infectious disease. Our method explicitly accounts for underreporting, and allows incorporating preexisting information about the disease, such as its serial interval, spatial kernel, and mutation rate. We define, for each data stream, a graph connecting all cases, with edges weighted by the corresponding pairwise distance between cases. Each graph is then pruned by removing distances greater than a given cutoff, defined based on preexisting information on the disease and assumptions on the reporting rate. The pruned graphs corresponding to different data streams are then merged by intersection to combine all data types; connected components define clusters of cases related for all types of data. Estimates of the reproduction number (the average number of secondary cases infected by an infectious individual in a large population), and the rate of importation of the disease into the population, are also derived. We test our approach on simulated data and illustrate it using data on dog rabies in Central African Republic. We show that the outbreak clusters identified using our method are consistent with structures previously identified by more complex, computationally intensive approaches.
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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.