Evidence map›Paper›PMID 22749467›Full record

ReviewSpatial and spatio-temporal epidemiology2010

Review of methods for space-time disease surveillance.

Colin Robertson, Trisalyn A Nelson, Ying C MacNab, Andrew B Lawson

Abstract readComparative StudyReview
In one paragraph

Review in Spatial and spatio-temporal epidemiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

54 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. A Systematic Review of Spatial and Spatio-temporal Analyses in Public Health Research in Korea.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2021
    Pooled it
  2. Bayesian mapping of mortality clusters.Biostatistics (Oxford, England) · 2025
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  13. Coupled models of genomic surveillance and evolving pandemics with applications for timely public health interventions.Proceedings of the National Academy of Sciences of the United States of America · 2023
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  19. Mathematical modeling in perspective of vector-borne viral infections: a review.Beni-Suef University journal of basic and applied sciences · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Colin RobertsonSpatial Pattern Analysis & Research (SPAR) Laboratory, Dept. of Geography, University of Victoria, P.O. Box 3060, Victoria, BC, Canada V8W 3R4. colinr23@gmail.com
Trisalyn A Nelson
Ying C MacNab
Andrew B Lawson

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A review of some methods for analysis of space-time disease surveillance data is presented. Increasingly, surveillance systems are capturing spatial and temporal data on disease and health outcomes in a variety of public health contexts. A vast and growing suite of methods exists for detection of outbreaks and trends in surveillance data and the selection of appropriate methods in a given surveillance context is not always clear. While most reviews of methods focus on algorithm performance, in practice, a variety of factors determine what methods are appropriate for surveillance. In this review, we focus on the role of contextual factors such as scale, scope, surveillance objective, disease characteristics, and technical issues in relation to commonly used approaches to surveillance. Methods are classified as testing-based or model-based approaches. Reviewing methods in the context of factors other than algorithm performance highlights important aspects of implementing and selecting appropriate disease surveillance methods.

Indexed as

Population SurveillanceSpace-Time ClusteringBayes TheoremCommunicable DiseasesDisease OutbreaksFemaleHealth SurveysHumansIncidenceLinear ModelsMaleSensitivity and SpecificitySpatial Analysis

Identifiers

PMID22749467
PMCPMC7185413

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.