Evidence map›Paper›PMID 31069558›Full record

ArticleEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2019

Rapid mapping of the spatial and temporal intensity of influenza.

David J Muscatello, Robert Neil F Leong, Robin M Turner, Anthony T Newall

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Article in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

David J MuscatelloSchool of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, 2052, Australia. david.muscatello@unsw.edu.au.ORCID http://orcid.org/0000-0002-2391-4396
Robert Neil F LeongSchool of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, 2052, Australia.
Robin M TurnerSchool of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, 2052, Australia.
Anthony T NewallSchool of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, 2052, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surveillance of influenza epidemics is a priority for risk assessment and pandemic preparedness, yet representation of their spatiotemporal intensity remains limited. Using the epidemic of influenza type A in 2016 in Australia, we demonstrated a simple but statistically sound adaptive method of mapping epidemic evolution over space and time. Weekly counts of persons with laboratory confirmed influenza type A infections in Australia in 2016 were analysed by official national statistical region. Weekly standardised epidemic intensity was represented by a standard score (z-score) calculated using the standard deviation of below-median counts in the previous 52 weeks. A geographic information system (GIS) was used to present the epidemic progression. There were 79,628 notifications of influenza A infections included. Of these, 79,218 (99.5%) were allocated to a geographical area. The GIS maps indicated areas of elevated epidemic intensity across Australia by week and area that were consistent with the observed start, peak and decline of the epidemic when compared with counts aggregated at the state and territory level. This simple, adaptable approach could improve local level epidemic intelligence in a variety of settings and for other diseases. It may also facilitate increased understanding of geographic epidemic dynamics.

Indexed as

Epidemiological MonitoringSpatio-Temporal AnalysisAustraliaClinical Laboratory TechniquesData Interpretation, StatisticalDisease ProgressionEpidemicsGeographic Information SystemsGeographyHumansInfluenza A Virus, H1N1 SubtypeInfluenza, HumanPandemicsRegression AnalysisRisk AssessmentAustraliaEpidemic intelligenceEpidemicsGeographic information systemsInfluenza, humanLaboratoriesPandemicsRisk assessment

Identifiers

PMID31069558

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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.