Development of a supervised learning algorithm for detection of potential disease reemergence: A proof of concept

Maneesha Chitanvis; Ashlynn R. Daughton; Forest Altherr; Nidhi Parikh; Geoffrey Fairchild; William Rosenberger; Nileena Velappan; Attelia Hollander; Emily Alipio-Lyon; Grace Vuyisich; Derek Aberle; Alina Deshpande

doi:10.1089/hs.2019.0020

Development of a supervised learning algorithm for detection of potential disease reemergence: A proof of concept

Maneesha Chitanvis, Ashlynn R. Daughton, Forest Altherr, Nidhi Parikh, Geoffrey Fairchild, William Rosenberger, Nileena Velappan, Attelia Hollander, Emily Alipio-Lyon, Grace Vuyisich, Derek Aberle, Alina Deshpande

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a reemergent event. This practice affects reproducible assessments of high-consequence public health events and disease response prioritization. This in turn can lead to misallocation of resources. More important, early recognition of reemergence facilitates effective mitigation. We used a supervised machine learning approach to detect potential disease reemergence. We demonstrate the feasibility of applying a machine learning classifier to identify reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence. Through this study, we offer a structured means of identifying potential reemergence using a data-driven approach.

Original language	English
Pages (from-to)	255-267
Number of pages	13
Journal	Health Security
Volume	17
Issue number	4
DOIs	https://doi.org/10.1089/hs.2019.0020
State	Published - Jul 1 2019
Externally published	Yes

Funding

This work was funded by the Defense Threat Reduction Agency (DTRA) (Grant # 10027). LA-UR: 18-24581. Dr. Ramesh Krishnamurthy, along with other team members of the World Health Organization’s Department of Information, Evidence, and Research in the Health Systems and Innovation Cluster, provided subject matter expertise on disease reemergence as a global phenomenon, as well as detailed insight in the formative stages of the study. Dr. Bryan Lewis and James Schlitt, Biocomplexity Institute, University of Virginia, were important contributors to understanding the spatial components of disease reemergence. Dr. Reid Priedhorsky and Dr. Carrie Manore provided critical feedback on initial drafts of this article.

Keywords

Data fusion
Health informatics
Infectious diseases
Machine learning
Random forest
Reemergence

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10.1089/hs.2019.0020

Cite this

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title = "Development of a supervised learning algorithm for detection of potential disease reemergence: A proof of concept",

abstract = "Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a reemergent event. This practice affects reproducible assessments of high-consequence public health events and disease response prioritization. This in turn can lead to misallocation of resources. More important, early recognition of reemergence facilitates effective mitigation. We used a supervised machine learning approach to detect potential disease reemergence. We demonstrate the feasibility of applying a machine learning classifier to identify reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence. Through this study, we offer a structured means of identifying potential reemergence using a data-driven approach.",

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doi = "10.1089/hs.2019.0020",

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AU - Chitanvis, Maneesha

AU - Daughton, Ashlynn R.

AU - Altherr, Forest

AU - Parikh, Nidhi

AU - Fairchild, Geoffrey

AU - Rosenberger, William

AU - Velappan, Nileena

AU - Hollander, Attelia

AU - Alipio-Lyon, Emily

AU - Vuyisich, Grace

AU - Aberle, Derek

AU - Deshpande, Alina

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AB - Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a reemergent event. This practice affects reproducible assessments of high-consequence public health events and disease response prioritization. This in turn can lead to misallocation of resources. More important, early recognition of reemergence facilitates effective mitigation. We used a supervised machine learning approach to detect potential disease reemergence. We demonstrate the feasibility of applying a machine learning classifier to identify reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence. Through this study, we offer a structured means of identifying potential reemergence using a data-driven approach.

KW - Data fusion

KW - Health informatics

KW - Infectious diseases

KW - Machine learning

KW - Random forest

KW - Reemergence

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ER -

Development of a supervised learning algorithm for detection of potential disease reemergence: A proof of concept

Abstract

Funding

Keywords

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