SynthesisInternational journal of health geographics2018
A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks.
Synthesis in International journal of health geographics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Spatial Distribution and Genetic Characterization of Pepper Yellow Leaf Curl Virus Infecting Chili (Capsicum annuum) in Lampung Province, Indonesia.The plant pathology journal · 2026Article
- Strategies to support implementation of infectious disease decision support systems: a scoping review.BMC medical informatics and decision making · 2026Article
- Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk.Health care management science · 2025Article
- Mapping of stakeholders in avian influenza surveillance in Canada.One health outlook · 2025Article
- Geo-Temporal Variation in the Antimicrobial Resistance ofAntibiotics (Basel, Switzerland) · 2025Article
- Considering best practice standards for routine whole-genome sequencing for TB care and control.IJTLD open · 2024Article
- Zoonotic outbreak risk prediction with long short-term memory models: a case study with schistosomiasis, echinococcosis, and leptospirosis.BMC infectious diseases · 2024Article
- A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases.Veterinary research · 2024Article
- Building a pathway to One Health surveillance and response in Asian countries.Science in One Health · 2024Review
- Correction to: A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks.International journal of health geographics · 2021Article
- GIS as an Epidemiological Tool to Monitor the Spatial-Temporal Distribution of Tuberculosis in Large Game in a High-Risk Area in Portugal.Animals : an open access journal from MDPI · 2021Article
- Pandemics: Historically Slow "Learning Curve" Leading to Biomedical Informatics and Vaccine Breakthroughs.Yearbook of medical informatics · 2021Article
- Suggesting a framework for preparedness against the pandemic outbreak based on medical informatics solutions: a thematic analysis.The International journal of health planning and management · 2021Article
- Evaluation of the malaria reporting system supported by the District Health Information System 2 in Solomon Islands.Malaria journal · 2020Article
- Review
- User perceptions and preferences for GeoHealth tools: A qualitative focus group study of non-expert and expert users.Digital healthArticle
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
backgroundZoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks.
methodsA systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation.
resultsFor this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques.
conclusionsThe characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.
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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.