BARRO, BLACKBURN – Redefining the Australian Anthrax Belt: Modeling the Ecological Niche and Predicting the Geographic Distribution of Bacillus anthracis

Alassane S. Barro, Mark Fegan , Barbara Moloney, Kelly Porter, Janine Muller, Simone Warner, Jason K. Blackburn

Article first published online: 09 JUN 2016 PLoS Neglected Tropical Diseases

DOI: 10.1371/journal.pntd.0004689

ABSTRACT: The ecology and distribution of B. anthracis in Australia is not well understood, despite the continued occurrence of anthrax outbreaks in the eastern states of the country. Efforts to estimate the spatial extent of the risk of disease have been limited to a qualitative definition of an anthrax belt extending from southeast Queensland through the centre of New South Wales and into northern Victoria. This definition of the anthrax belt does not consider the role of environmental conditions in the distribution of B. anthracis. Here, we used the genetic algorithm for rule-set prediction model system (GARP), historical anthrax outbreaks and environmental data to model the ecological niche of B. anthracis and predict its potential geographic distribution in Australia. Our models reveal the niche of B. anthracis in Australia is characterized by a narrow range of ecological conditions concentrated in two disjunct corridors. The most dominant corridor, used to redefine a new anthrax belt, parallels the Eastern Highlands and runs from north Victoria to central east Queensland through the centre of New South Wales. This study has redefined the anthrax belt in eastern Australia and provides insights about the ecological factors that limit the distribution of B. anthracis at the continental scale for Australia. The geographic distributions identified can help inform anthrax surveillance strategies by public and veterinary health agencies.

Read the full publication at PLoS Neglected Tropical Diseases

Image courtesy Alassane Barro
Image courtesy Alassane Barro

Geography Colloquium

Anthrax in Australia: Integrating Ecological Niche Modeling and Geographic Information Systems (GIS) for Assessing Risk of Transmission

Speaker: Mr. Alassane Barro

Graduate Student, Geography

Thursday, November 12, 2015

3:00-3:50 PM (Period 8)

Turlington Hall Room 3012

University of Florida

All are welcome to attend.

BARRO, KRACALIK, BLACKBURN – study compared three local cluster detection methods to identify local hotspots of human cutaneous anthrax transmission in the country of Georgia where cases have been steadily

Alassane S. Barro, Ian T. Kracalik, Lile Malania, Nikoloz Tsertsvadze,
Julietta Manvelyan, Paata Imnadze, Jason K. Blackburn

ABSTRACT: This study compared three local cluster detection methods to identify local hotspots of human cutaneous anthrax (HCA) transmission in the country of Georgia where cases have been steadily increasing since the dissolution of the Soviet Union. Recent reports have indicated that the disease has reached historical levels in 2012 highlighting the need for better informed policy recommendations and targeted control measures. The purpose of this paper was to identify spatial clusters of HCA to aid in the implementation of targeted public health interventions. At the same time, we compared the utility of different statistical tests in identifying hotspots. We used the Getis-Ord ðG*i ðdÞÞ, a multidirectional optimal ecotope-based algorithm (AMOEBA) e a cluster morphology statistic, and the spatial scan statistic in SaTScan™. Data on HCA cases from 2000 to 2012 at the community level were aggregated to an 8 8 km grid surface and population data from the Global Rural and Urban Mapping Project (GRUMP) were used to calculate local incidence. In general, there was agreement between tests in the locations of HCA hotspots. Significant local clusters of high HCA incidence were identified in the southern, eastern and western regions of Georgia. The G*i ðdÞ and spatial scan statistics appeared more sensitive but less specific than the AMOEBA algorithm. The scan statistic identified larger geographic areas as hotspots of transmission. In general, the spatial scan statistic and G*i ðdÞ performed well for spatial clusters with lower incidence rates, whereas AMOEBA was well suited for defining local spatial clusters of higher HCA incidence. In resource constrained areas, efficient allocation of public health interventions is crucial. Our findings identified hotspots
of HCA that can be used to target public health interventions such as livestock vaccination and
training on proper outbreak management. This paper illustrates the benefits of evaluating statistical
approaches for defining disease hotspots and highlights differences in these clustering approaches
applicable beyond public health studies.

© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND
license (

Barro_etal2015_AMOEBA (2)

VLUU L200 / Samsung L200


Country of Origin: Burkina Faso

Burkina Faso






Degree Program: PhD

Advisor: Dr. Jason Blackburn

Entered Program: Fall 2012

Dissertation Topic: Medical Geography

Research Interests: Spatial epidemiology, Spatial analysis, medical geography, GIS and Remote sensing.