ReviewInternational journal of health geographics2010
Review of software for space-time disease surveillance.
Review in International journal of health geographics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis 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
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visualization and analytics tools for infectious disease epidemiology: a systematic review.Journal of biomedical informatics · 2014Pooled it
- Assessing spatial variability in observed infectious disease spread in a prospective time-space series.International journal of health geographics · 2025Article
- Investigating the spatiotemporal characteristics and medical response during the initial COVID-19 epidemic in six Chinese cities.Scientific reports · 2024Article
- An Approach to Identifying Spatial Variability in Observed Infectious Disease Spread in a Prospective Time-Space Series with Applications to COVID-19 and Dengue Incidence.Research square · 2024Article
- Spatio-temporal distributions and determinants of diarrhea among under-five children in Ethiopia.Frontiers in public health · 2024Article
- Soil-transmitted helminthiasis in mainland China from 2016 to 2020: a population-based study.The Lancet regional health. Western Pacific · 2023Article
- Preponderance of vaccine-preventable diseases hotspots in northern Ghana: a spatial and space-time clustering analysis from 2010 to 2014.BMC public health · 2022Article
- The development and validation of a dashboard prototype for real-time suicide mortality data.Frontiers in digital health · 2022Article
- Comparing the space-time patterns of high-risk areas in different waves of COVID-19 in Hong Kong.Transactions in GIS : TG · 2021Article
- Detection of COVID-19 case clusters in Québec, May-October 2020.Canadian journal of public health = Revue canadienne de sante publique · 2021Article
- Identifying the space-time patterns of COVID-19 risk and their associations with different built environment features in Hong Kong.The Science of the total environment · 2021Article
- Spatial and spatio-temporal analysis of malaria cases in Zimbabwe.Infectious diseases of poverty · 2020Article
- Spatial-temporal analysis of tuberculosis in Chongqing, China 2011-2018.BMC infectious diseases · 2020Article
- Spatiotemporal analyses of foot and mouth disease outbreaks in cattle farms in Chiang Mai and Lamphun, Thailand.BMC veterinary research · 2020Article
- Geostatistical predictive modeling for asthma and chronic obstructive pulmonary disease using socioeconomic and environmental determinants.Environmental monitoring and assessment · 2019Article
- An algorithm applied to national surveillance data for the early detection of major dengue outbreaks in Cambodia.PloS one · 2019Article
- Geographic distribution of echinococcosis in Tibetan region of Sichuan Province, China.Infectious diseases of poverty · 2018Article
- Identification and characterization of areas of high and low risk for asymptomatic malaria infections at sub-village level in Ratanakiri, Cambodia.Malaria journal · 2018Article
- Spatial, temporal, and spatiotemporal analysis of under-five diarrhea in Southern Ethiopia.Tropical medicine and health · 2018Article
- Space and space-time distributions of dengue in a hyper-endemic urban space: the case of Girardot, Colombia.BMC infectious diseases · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disease surveillance makes use of information technology at almost every stage of the process, from data collection and collation, through to analysis and dissemination. Automated data collection systems enable near-real time analysis of incoming data. This context places a heavy burden on software used for space-time surveillance. In this paper, we review software programs capable of space-time disease surveillance analysis, and outline some of their salient features, shortcomings, and usability. Programs with space-time methods were selected for inclusion, limiting our review to ClusterSeer, SaTScan, GeoSurveillance and the Surveillance package for R. We structure the review around stages of analysis: preprocessing, analysis, technical issues, and output. Simulated data were used to review each of the software packages. SaTScan was found to be the best equipped package for use in an automated surveillance system. ClusterSeer is more suited to data exploration, and learning about the different methods of statistical surveillance.
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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.