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ISDS 2018 Conference Abstracts

Spatial temporal cluster analysis to enhance 
awareness of disease re-emergence on a global scale
Forest M. Altherr*1, Maneesha Chitanvis1, Ashlynn Daughton1, 2, Geoffrey Fairchild1, 
William Rosenberger1, Nicholas Generous1, Nidhi Parikh1, Derek Aberle1, 
Nileena Velappan1, Emily Alipio Lyon1, Attelia Hollander1 and Alina Deshpande1
1Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, USA; 2University of Colorado, Boulder, Boulder, CO, USA

Objective
The application of spatial analysis to improve the awareness and 

use of surveillance data.

Introduction
The re-emergence of an infectious disease is dependent on social, 

political, behavioral, and disease-specific factors. Global disease 
surveillance is a requisite of early detection that facilitates coordinated 
interventions to these events. Novel informatics tools developed from 
publicly available data are constantly evolving with the incorporation 
of new data streams. Re-emerging Infectious Disease (RED) Alert is 
an open-source tool designed to help analysts develop a contextual 
framework when planning for future events, given what has occurred 
in the past. Geospatial methods assist researchers in making informed 
decisions by incorporating the power of place to better explain the 
relationships between variables.

Methods
Disease incidence and indicator data derived for the RED Alert 

project were analyzed for spatial associations. Using aggregate 
country-level data, spatial and spatiotemporal clusters were 
identified in ArcMap 10.5.1. The identified clusters were then used as  
the outcome for a series of binary logistic regression models to 
determine significant covariates that help explain global hotspots. 
These methods will continue to evolve and be incorporated into the 
RED Alert decision support ecosystem to provide analysts with a 
global perspective on potential re-emergence.

Results
Hotspots of high disease incidence in relation to neighboring 

countries were identified for measles, cholera, dengue, and yellow 
fever between 2000 and 2014. Disease-specific predictors were 
identified using aggregate estimates from World Bank indicator 
dataset. Data was imputed where possible to enhance the validity of 
the Gi * statistic for clustering. In the future, as data streams become 
more readily available, hotspot modeling at a finer resolution will 
help to improve the precision of spatial epidemiology.

Conclusions
Spatial methods enhance the capability of understanding complex 

population and disease relationships, which in turn improves 
surveillance and the ability to predict re-emergence. With tools like 
RED Alert, public health analysts can better prepare to respond 
rapidly to future re-emerging disease threats.

Keywords
Re-emergence; Surveillance; Spatial; Hotspot

*Forest M. Altherr
E-mail: faltherr@lanl.gov

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 10(1):e23, 2018