ArticlePloS one2016
Measuring Spatial Dependence for Infectious Disease Epidemiology.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03631719 (World Mosquito Program - Colombia), which is not on this map. Cited by 24 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.
World Mosquito Program - Colombia (WMP-COLOMBIA): The Impact of City-wide Deployment of Wolbachia-infected Mosquitoes on Arboviral Disease Incidence in Medellin and Bello, Colombia
Who cites it
24 citing papers in PubMed, 49 citations in OpenAlex.
- Disruption of spatiotemporal clustering in dengue cases by wMel Wolbachia in Yogyakarta, Indonesia.Scientific reports · 2022Trial
- Spatiotemporal effects on dengue incidence based on a large cluster randomized study.Statistical methods in medical research · 2025Article
- A systematic review of antimicrobial resistance transmission inferences at the human-livestock interface in Africa.npj antimicrobials and resistance · 2025Article
- Spatiotemporal Modeling of Cholera, Uvira, Democratic Republic of the Congo, 2016-2020.Emerging infectious diseases · 2024Article
- Genomic and Phylogenetic Analysis ofPathogens (Basel, Switzerland) · 2023Article
- The spatial signature of Plasmodium vivax and Plasmodium falciparum infections: quantifying the clustering of infections in cross-sectional surveys and cohort studies.Malaria journal · 2023Article
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- Article
- COVID-19 outbreak and Urban dynamics: regional variations in India.GeoJournal · 2022Article
- Developments in statistical inference when assessing spatiotemporal disease clustering with the tau statistic.Spatial statistics · 2021Article
- Spatial Point Pattern Analysis Identifies Mechanisms Shaping the Skin Parasite Landscape inFrontiers in immunology · 2021Article
- Understanding the spatio-temporal pattern of COVID-19 outbreak in India using GIS and India's response in managing the pandemic.Regional science policy & practice · 2020Article
- Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- An information-theoretic approach to study spatial dependencies in small datasets.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- Quantifying the localized relationship between vector containment activities and dengue incidence in a real-world setting: A spatial and time series modelling analysis based on geo-located data from Pakistan.PLoS neglected tropical diseases · 2020Article
- Characterizing the impact of spatial clustering of susceptibility for measles elimination.Vaccine · 2019Article
- Spatial Patterns of Extensively Drug-Resistant Tuberculosis Transmission in KwaZulu-Natal, South Africa.The Journal of infectious diseases · 2018Article
- Micro-Hotspots of Risk in Urban Cholera Epidemics.The Journal of infectious diseases · 2018Article
- Article
- The potential impact of case-area targeted interventions in response to cholera outbreaks: A modeling study.PLoS medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Global spatial clustering is the tendency of points, here cases of infectious disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of disease transmission, thereby providing insights into the mechanism of spread, and informing optimal surveillance and control. Here the authors present an interpretable measure of spatial clustering, τ, which can be understood as a measure of relative risk. When biological or temporal information can be used to identify sets of potentially linked and likely unlinked cases, this measure can be estimated without knowledge of the underlying population distribution. The greater our ability to distinguish closely related (i.e., separated by few generations of transmission) from more distantly related cases, the more closely τ will track the true scale of transmission. The authors illustrate this approach using examples from the analyses of HIV, dengue and measles, and provide an R package implementing the methods described. The statistic presented, and measures of global clustering in general, can be powerful tools for analysis of spatially resolved data on infectious diseases.
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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.