ArticlePloS one2017
Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Ensemble-labeling of infectious disease time series to evaluate early warning systems.Infectious Disease Modelling · 2026Article
- Reframing early warning for Nipah virus in Bangladesh: A shift from detection to anticipation.PLoS neglected tropical diseases · 2026Article
- Evaluation and Comparison of Statistical Methods for Early Detection of Respiratory Symptom Outbreaks: A Real-World Observational Study From Iran.Health science reports · 2025Article
- An integrated framework for modelling respiratory disease transmission and designing surveillance networks using a sentinel index.Royal Society open science · 2025Article
- Primary health care data-based early warning system for dengue outbreaks: a nationwide case study in Brazil.Lancet regional health. Americas · 2025Article
- Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records.BMC medical research methodology · 2025Article
- Evaluation of Rolling Surveillance Methods in Context of Prior Aberrations: A Simulation Study With Routine Data From Low- and Middle-Income Countries.Statistics in medicine · 2025Article
- Adapting the Flexible Farrington Algorithm for daily situational awareness and alert system to support public health decision-making during the SARS-CoV-2 epidemic in England.Epidemiology and infection · 2025Article
- Exploring Options for Syndromic Surveillance in Aquaculture: Outbreak Detection of Salmon Pancreas Disease Using Production Data from Norwegian Farms.Transboundary and emerging diseases · 2024Article
- Early warning system using primary health care data in the post-COVID-19 pandemic era: Brazil nationwide case-study.Cadernos de saude publica · 2024Article
- Outbreak detection algorithms based on generalized linear model: a review with new practical examples.BMC medical research methodology · 2023Review
- Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods.Veterinary research · 2023Article
- Comparison of statistical methods for the early detection of disease outbreaks in small population settings.IJID regions · 2023Article
- Epidemiological cluster identification using multiple data sources: an approach using logistic regression.Microbial genomics · 2023Article
- A statistical algorithm for outbreak detection in multisite settings: an application to sick leave monitoring.Bioinformatics advances · 2023Article
- Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease.PLoS computational biology · 2022Article
- Excess deaths from non-COVID-19-related causes in Japan and 47 prefectures from January 2020 through May 2021 by place of death.SSM - population health · 2022Article
- Changes in marriage, divorce and births during the COVID-19 pandemic in Japan.BMJ global health · 2022Article
- Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.Frontiers in public health · 2022Review
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Authors and funding
2 authors.
Funding
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Abstract
The objective of this paper is to evaluate a panel of statistical algorithms for temporal outbreak detection. Based on a large dataset of simulated weekly surveillance time series, we performed a systematic assessment of 21 statistical algorithms, 19 implemented in the R package surveillance and two other methods. We estimated false positive rate (FPR), probability of detection (POD), probability of detection during the first week, sensitivity, specificity, negative and positive predictive values and F1-measure for each detection method. Then, to identify the factors associated with these performance measures, we ran multivariate Poisson regression models adjusted for the characteristics of the simulated time series (trend, seasonality, dispersion, outbreak sizes, etc.). The FPR ranged from 0.7% to 59.9% and the POD from 43.3% to 88.7%. Some methods had a very high specificity, up to 99.4%, but a low sensitivity. Methods with a high sensitivity (up to 79.5%) had a low specificity. All methods had a high negative predictive value, over 94%, while positive predictive values ranged from 6.5% to 68.4%. Multivariate Poisson regression models showed that performance measures were strongly influenced by the characteristics of time series. Past or current outbreak size and duration strongly influenced detection performances.
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