Alene KA, Moore HC, Clements ACA, Gilmour B, Barth. Spatiotemporal patterns of influenza in Western Australia. Public Health Pract (Oxf). 2025 Mar 15;9:100602
Background: Understanding the geospatial distribution of influenza infection and the risk factors associated with infection clustering can inform targeted preventive interventions. We conducted a geospatial analysis to investigate the spatial patterns and identify drivers of medically attended influenza infection across all age groups in Western Australia (WA).
Methods: Data for confirmed influenza cases were obtained from the WA Notifiable Infectious Diseases Database for the period 2017-2020. Data were also obtained for vaccination coverage, meteorological parameters, socioeconomic indicators, and healthcare access. Spatial clustering of influenza incidence was identified using Global Moran´s I and Getis-Ord statistic. Bayesian spatial models were used to identify factors associated with spatial clustering of infection.
Results: Of the 36,228 influenza cases reported, over half (18,773, 51·8 %) were in individuals aged between 15 and 64 years and more than three quarters (28,545, 78·9 %) in the Perth metropolitan region. The annual incidence rate ranged from 2·7 per 1000 population in individuals aged between 15 and 64 years to 5·2 per 1000 population in children <5 years of age. For all age groups, the lowest incidence (0·4 per 1000 population) and the highest incidence rate (8·8 per 1000 population) were reported during and pre-the COVID-19 pandemic respectively. The influenza incidence rate shows both seasonal and spatial variation. Spatial clustering was significantly associated with distance to the nearest health facility in minutes (B = -0·181; 95 %CrI: 0·279, -0·088) and annual mean temperature in degrees Celsius (B = 0·171; 95 %CrI: 0·015, 0·319).
Conclusions: Spatial clustering of influenza incidence was significantly associated with climatic conditions and healthcare access.
Methods: Data for confirmed influenza cases were obtained from the WA Notifiable Infectious Diseases Database for the period 2017-2020. Data were also obtained for vaccination coverage, meteorological parameters, socioeconomic indicators, and healthcare access. Spatial clustering of influenza incidence was identified using Global Moran´s I and Getis-Ord statistic. Bayesian spatial models were used to identify factors associated with spatial clustering of infection.
Results: Of the 36,228 influenza cases reported, over half (18,773, 51·8 %) were in individuals aged between 15 and 64 years and more than three quarters (28,545, 78·9 %) in the Perth metropolitan region. The annual incidence rate ranged from 2·7 per 1000 population in individuals aged between 15 and 64 years to 5·2 per 1000 population in children <5 years of age. For all age groups, the lowest incidence (0·4 per 1000 population) and the highest incidence rate (8·8 per 1000 population) were reported during and pre-the COVID-19 pandemic respectively. The influenza incidence rate shows both seasonal and spatial variation. Spatial clustering was significantly associated with distance to the nearest health facility in minutes (B = -0·181; 95 %CrI: 0·279, -0·088) and annual mean temperature in degrees Celsius (B = 0·171; 95 %CrI: 0·015, 0·319).
Conclusions: Spatial clustering of influenza incidence was significantly associated with climatic conditions and healthcare access.
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