Dot map cartograms for detection of infectious disease outbreaks: an application to Q fever, the Netherlands and pertussis, Germany
Eurosurveillance·
- DOI
- 10.2807/1560-7917.es.2017.22.26.30562
- PMID
- 28681721
- PMCID
- PMC5779165
- OpenAlex
- W2730699851
- Study type
- Journal article
- Publisher
- European Centre for Disease Control and Prevention (ECDC)
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Dot map cartograms may support outbreak detection and visualization by public health professionals, facilitating informed decisions for investigation and control activities.
Structured evidence summary
Research question
The study evaluates whether dot map cartograms can improve detection and visualization of infectious disease outbreaks compared to standard dot maps and incidence maps.
Study design
The authors compared dot map cartograms with standard dot maps and incidence maps across four criteria, applying the methods to two example datasets: Q fever in the Netherlands and pertussis in Germany.
Population and setting
The method was applied to Q fever cases in the Netherlands and pertussis cases in Germany.
Main findings
Dot map cartograms displayed both incidence and absolute case counts, revealed potential source locations for Q fever and high-incidence clusters for pertussis, and were insensitive to spatial scale choices unlike incidence maps. The method ensured case privacy through spatial distortion, though this reduced the recognizability of specific locations.
Public-health relevance
Dot map cartograms may support outbreak detection and visualization by public health professionals, facilitating informed decisions for investigation and control activities.
Important limitations
The spatial distortion that protects case privacy reduces the ability to recognize specific geographic locations. This summary is limited to the supplied single-article abstract and metadata; the original paper is required for decision-grade interpretation.
GIDS interpretation
This article was linked to Q fever and pertussis as example datasets for methodological comparison, and to outbreak investigation and surveillance topics, reflecting its focus on cartographic methods for visualizing disease events.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 14 auditable classifier relationships to diseases, places, topics, and study design.