Literature DB >> 31069558

Rapid mapping of the spatial and temporal intensity of influenza.

David J Muscatello1, Robert Neil F Leong2, Robin M Turner2,3, Anthony T Newall2.   

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.

Entities:  

Keywords:  Australia; Epidemic intelligence; Epidemics; Geographic information systems; Influenza, human; Laboratories; Pandemics; Risk assessment

Mesh:

Year:  2019        PMID: 31069558     DOI: 10.1007/s10096-019-03554-7

Source DB:  PubMed          Journal:  Eur J Clin Microbiol Infect Dis        ISSN: 0934-9723            Impact factor:   3.267


  6 in total

1.  Pilot study to harmonize the reported influenza intensity levels within the Spanish Influenza Sentinel Surveillance System (SISSS) using the Moving Epidemic Method (MEM).

Authors:  M Bangert; H Gil; J Oliva; C Delgado; T Vega; S DE Mateo; A Larrauri
Journal:  Epidemiol Infect       Date:  2016-12-05       Impact factor: 4.434

2.  Influenza surveillance in Europe: comparing intensity levels calculated using the moving epidemic method.

Authors:  Tomás Vega; José E Lozano; Tamara Meerhoff; René Snacken; Julien Beauté; Pernille Jorgensen; Raúl Ortiz de Lejarazu; Lisa Domegan; Joël Mossong; Jens Nielsen; Rita Born; Amparo Larrauri; Caroline Brown
Journal:  Influenza Other Respir Viruses       Date:  2015-09       Impact factor: 4.380

3.  Translation of Real-Time Infectious Disease Modeling into Routine Public Health Practice.

Authors:  David J Muscatello; Abrar A Chughtai; Anita Heywood; Lauren M Gardner; David J Heslop; C Raina MacIntyre
Journal:  Emerg Infect Dis       Date:  2017-05       Impact factor: 6.883

4.  Improving regional influenza surveillance through a combination of automated outbreak detection methods: the 2015/16 season in France.

Authors:  Camille Pelat; Isabelle Bonmarin; Marc Ruello; Anne Fouillet; Céline Caserio-Schönemann; Daniel Levy-Bruhl; Yann Le Strat
Journal:  Euro Surveill       Date:  2017-08-10

5.  Inaccurate ascertainment of morbidity and mortality due to influenza in administrative databases: a population-based record linkage study.

Authors:  David J Muscatello; Janaki Amin; C Raina MacIntyre; Anthony T Newall; William D Rawlinson; Vitali Sintchenko; Robin Gilmour; Sarah Thackway
Journal:  PLoS One       Date:  2014-05-29       Impact factor: 3.240

6.  The significance of increased influenza notifications during spring and summer of 2010-11 in Australia.

Authors:  Heath A Kelly; Kristina A Grant; Ee Laine Tay; Lucinda Franklin; Aeron C Hurt
Journal:  Influenza Other Respir Viruses       Date:  2012-11-26       Impact factor: 4.380

  6 in total

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