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ISDS 2018 Conference Abstracts

Re-emerging Infectious Disease (RED) Alert tool
Maneesha Chitanvis*1, Ashlynn Daughton1, 2, Forest M. Altherr1, Geoffrey Fairchild1, 
William Rosenberger1, Emily Alipio Lyon1, Attelia Hollander1, Nileena Velappan1, 
Derek Aberle1, Nidhi Parikh1 and Alina Deshpande1
1Biosecurity and Public Health, Los Alamos National Laboratory, Los Alamos, NM, USA; 2University of Colorado, Boulder, Boulder, 
CO, USA

Objective
Although relying on verbal definitions of “re-emergence”, 

descriptions that classify a “re-emergence” event as any significant 
recurrence of a disease that had previously been under public health 
control, and subjective interpretations of these events is currently the 
conventional practice, this has the potential to hinder effective public 
health responses. Defining re-emergence in this manner offers limited 
ability for ad hoc analysis of prevention and control measures and 
facilitates non-reproducible assessments of public health events of 
potentially high consequence. Re-emerging infectious disease alert 
(RED Alert) is a decision-support tool designed to address this issue 
by enhancing situational awareness by providing spatiotemporal 
context through disease incidence pattern analysis following an 
event that may represent a local (country-level) re-emergence. 
The tool’s analytics also provide users with the associated causes 
(socioeconomic indicators) related to the event, and guide hypothesis-
generation regarding the global scenario.

Introduction
Definitions of “re-emerging infectious diseases” typically 

encompass any disease occurrence that was a historic public health 
threat, declined dramatically, and has since presented itself again as a 
significant health problem. Examples include antimicrobial resistance 
leading to resurgence of tuberculosis, or measles re-appearing 
in previously protected communities. While the language of this 
verbal definition of “re-emergence” is sensitive enough to capture 
most epidemiologically relevant resurgences, its qualitative nature 
obfuscates the ability to quantitatively classify disease re-emergence 
events as such.

Methods
Our tool automatically computes historic disease incidence and 

performs trend analyses to help elucidate events which a user may 
considered a true re-emergence in a subset of pertinent infectious 
diseases (measles, cholera, yellow fever, and dengue). The tool 
outputs data visualizations that illustrate incidence trends in diverse 
and informative ways. Additionally, we categorize location and 
incidence-specific indicators for re-emergence to provide users with 
associated indicators as well as justifications and documentation to 
guide users’ next steps. Additionally, the tool also houses interactive 
maps to facilitate global hypothesis-generation.

Results
These outputs provide historic trend pattern analyses as well as 

contextualization of the user’s situation with similar locations. The 
tool also broadens users’ understanding of the given situation by 
providing related indicators of the likely re-emergence, as well as the 
ability to investigate re-emergence factors of global relevance through 
spatial analysis and data visualization.

Conclusions
The inability to categorically name a re-emergence event as such 

is due to lack of standardization and/or availability of reproducible, 
data-based evidence, and hinders timely and effective public health 

response and planning. While the tool will not explicitly call out a 
user scenario as categorically re-emergent or not, by providing users 
with context in both time and space, RED Alert aims to empower 
users with data and analytics in order to substantially enhance their 
contextual awareness; thus, better enabling them to formulate plans 
of action regarding re-emerging infectious disease threats at both the 
country and global level.

Keywords
Infectious Disease; Re-emergence; Pattern analysis; Contextual 
Awareness

Acknowledgments
This work was supported by the Defense Threat Reduction Agency’s Joint 
Science and Technology Office for Chemical and Biological Defense 
under project numbers CB10007. Los Alamos National Laboratory is 
operated by Los Alamos National Security, LLC for the Department of 
Energy under contract DE-AC52-06NA25396. The authors also wish to 
thank Bryan Lewis at the Biocomplexity Institute of Virginia Tech and 
Ramesh Krishnamurthy at the World Health Organization. LA-UR-17-
21884.

*Maneesha Chitanvis
E-mail: mchitanvis@lanl.gov

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 10(1):e58, 2018