ReviewFrontiers in veterinary science2020
An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research.
Review in Frontiers in veterinary science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- A systematic review of spatial and temporal epidemiological approaches, focus on lung cancer risk associated with particulate matter.BMC public health · 2024Pooled it
- Allostatic Load Measurement: A Systematic Review of Reviews, Database Inventory, and Considerations for Neighborhood Research.International journal of environmental research and public health · 2022Pooled it
- Spatial epidemiology of lumpy skin disease outbreaks in Thailand: proximity patterns, case concentrations, spatio-temporal clusters, and interpolated case distribution.Frontiers in veterinary science · 2026Article
- Geospatial analysis of leptospirosis clusters and risk factors in two provinces of the Dominican Republic.PLoS neglected tropical diseases · 2025Article
- ClusterRadar: An interactive web-tool for the multi-method exploration of spatial clusters over time.PloS one · 2025Article
- Spatiotemporal Patterns of Hospitalizations Among Older Adults With Co-Presence of Cancer and Dementia in US Counties: 2013-2018.Journal of applied gerontology : the official journal of the Southern Gerontological Society · 2024Article
- Epidemiological Assessment of African Swine Fever Spread in the Dominican Republic.Pathogens (Basel, Switzerland) · 2023Article
- Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression.One health (Amsterdam, Netherlands) · 2022Article
- A scoping review of foot-and-mouth disease risk, based on spatial and spatio-temporal analysis of outbreaks in endemic settings.Transboundary and emerging diseases · 2022Article
- A reanalysis: Do hog farms cause disease in North Carolina neighborhoods?Frontiers in veterinary science · 2022Article
- Apathogenic proxies for transmission dynamics of a fatal virus.Frontiers in veterinary science · 2022Article
- "The devil is in the detail": geographical inequalities of femicides in Ecuador.International journal for equity in health · 2021Article
- Ecological and Socio-Economic Determinants of Livestock Animal Leptospirosis in the Russian Arctic.Frontiers in veterinary science · 2021Article
- The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis.Frontiers in veterinary science · 2021Article
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 2 institutions in 2 countries.
Funding
Abstract
Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques provides a list of options for researchers who are new to population-level spatial eco-epidemiology. Here, we are conducting a narrative review to provide an overview of the multiple available SATs, and introducing a framework for choosing among them when addressing common research questions across disciplines. The framework is comprised of three stages: (a) pre-hypothesis testing stage, in which hypotheses regarding the spatial dependence of events are generated; (b) primary hypothesis testing stage, in which the existence of spatial dependence and patterns are tested; and (c) secondary-hypothesis testing and spatial modeling stage, in which predictions and inferences were made based on the identified spatial dependences and associated covariates. In this step-wise process, six key research questions are formulated, and the answers to those questions should lead researchers to select one or more methods from four broad categories of SATs: (T1) visualization and descriptive analysis; (T2) spatial/spatiotemporal dependence and pattern recognition; (T3) spatial smoothing and interpolation; and (T4) geographic correlation studies (i.e., spatial modeling and regression). The SATs described here include both those used for decades and also other relatively new tools. Through this framework review, we intend to facilitate the choice among available SATs and promote their interdisciplinary use to support improving human, animal, and ecosystem health.
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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.