Measles outbreak risk in Pakistan: exploring the potential of combining vaccination coverage and incidence data with novel data-streams to strengthen control
Epidemiology and Infection·
- DOI
- 10.1017/s0950268818001449
- PMID
- 29860954
- PMCID
- PMC6090714
- OpenAlex
- W2807224987
- Study type
- Journal article
- Publisher
- Cambridge University Press (CUP)
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The work is relevant to targeting vaccination efforts in Pakistan by attempting to identify districts before outbreaks start, and it highlights what additional data may be needed to improve outbreak prediction in vaccinated populations.
Structured evidence summary
Research question
The study examines how combining vaccination coverage, incidence data, and novel data streams (such as mobile phone-derived mobility) can be used to evaluate population immunity and predict measles outbreak timing in Pakistan.
Study design
A retrospective, data-driven analysis combining inferred spatial susceptibility patterns, reported measles incidence, and mobile phone-derived mobility data to predict district-level outbreak timing in Pakistan during 2012–2013.
Population and setting
Districts of Pakistan during a large 2012–2013 measles outbreak with over 30,000 reported cases, using mobility data from more than 40 million mobile phone subscribers.
Main findings
The authors report that some prediction of district-level epidemic timing was possible, but overall accuracy was low, indicating that the available data streams limited robust inference of outbreak timing.
Public-health relevance
The work is relevant to targeting vaccination efforts in Pakistan by attempting to identify districts before outbreaks start, and it highlights what additional data may be needed to improve outbreak prediction in vaccinated populations.
Important limitations
The abstract explicitly notes low prediction accuracy and key uncertainties in existing data streams that impede inference. The summary is limited to the supplied single-article abstract/metadata and requires the original paper for decision-grade interpretation.
GIDS interpretation
The paper relates to GIDS discoverability themes such as outbreak investigation, vaccination, and travel/mobility data integration for infectious disease context-setting, but no link to a live surveillance signal is made.
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.