ISDS Annual Conference Proceedings 2019. This is an Open Access article distributed under the terms of the Creative Commons 

AttributionNoncommercial 4.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, 

distribution, and reproduction in any medium, provided the original work is properly cited. 

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 11(1): e370, 2019 

 

ISDS 2019 Conference Abstracts 

A Novel Method for Rapid Mapping of the Spatial Intensity of 
Influenza Epidemics 

David J. Muscatello1, Robert N. Leong1, Robin M. Turner2, Anthony T. Newall1 

1
School of Public Health and Community Medicine, UNSW  Sydney, UNSW  Sydney, New South W ales, Australia, 

2
University of Otago, Dunedin, New 

Zealand 

Objective 

Using the epidemic of influenza type A in 2016 in Australia, we demonstrated a simple but statistically sound adaptive method  of 
automatically representing the spatial intensity and evolution of an influenza epidemic that could be applied to a laboratory 
surveillance count data stream that does not have a denominator. 

Introduction 

Surveillance of influenza epidemics is a priority for risk assessment and pandemic preparedness. Mapping epidemics can be 

challenging because influenza infections are incompletely ascertained, ascertainment can vary spatially, and often a denominator 
is not available. Rapid, more refined geographic or spatial intelligence could facilitate better preparedness and response. 

Methods 

Weekly counts of persons with laboratory confirmed influenza type A infections in Australia in 2016 were analysed by 86 sub-
state geographical areas. Weekly standardised epidemic intensity was represented by a z-score calculated using the standard 
deviation of below-median counts in the previous 52 weeks. A geographic information system was used to present the epidemic 
progression. 

Results 

There were 79,628 notifications of influenza A infections included. Of these, 79,218 (99.5%) were allocated to a geographical area. 
The 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 weekly counts aggregated at the state and territory level. An exam ple 
is shown in Figure 1. 

Conclusions 

The methods could be automated to rapidly generate spatially varying epidemic intensity maps using a surveillance data stream. 
This could improve local level epidemic intelligence in a variety of settings and for other diseases. It may also increase our 
understanding of geographic epidemic dynamics. 

Acknowledgement 

We thank the Australian Department of Health for providing the influenza laboratory data used in the study.  

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ISDS Annual Conference Proceedings 2019. This is an Open Access article distributed under the terms of the Creative Commons 

AttributionNoncommercial 4.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, 

distribution, and reproduction in any medium, provided the original work is properly cited. 

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 11(1): e370, 2019 

 

ISDS 2019 Conference Abstracts 

 
Figure 1. Example map as at 9 September 2016, showing intensity by area during the peak week of the season. Insets show 
capital cities 

 

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