Development of a Supervised Learning Algorithm for Detection of Potential Disease Reemergence: A Proof of Concept.

Autor: Chitanvis M; Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM., Daughton AR; Ashlynn R. Daughton, MPH, Nidhi Parikh, PhD, Geoffrey Fairchild, PhD, and William Rosenberger are Scientists, Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM., Altherr F; Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM., Parikh N; Ashlynn R. Daughton, MPH, Nidhi Parikh, PhD, Geoffrey Fairchild, PhD, and William Rosenberger are Scientists, Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM., Fairchild G; Ashlynn R. Daughton, MPH, Nidhi Parikh, PhD, Geoffrey Fairchild, PhD, and William Rosenberger are Scientists, Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM., Rosenberger W; Ashlynn R. Daughton, MPH, Nidhi Parikh, PhD, Geoffrey Fairchild, PhD, and William Rosenberger are Scientists, Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM., Velappan N; Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM., Hollander A; Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM., Alipio-Lyon E; Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM., Vuyisich G; Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM., Aberle D; Derek Aberle is a Software Developer, Applied Modern Physics, Physics Division, Los Alamos National Laboratory, Los Alamos, NM., Deshpande A; Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM.
Jazyk: angličtina
Zdroj: Health security [Health Secur] 2019 Jul/Aug; Vol. 17 (4), pp. 255-267.
DOI: 10.1089/hs.2019.0020
Abstrakt: 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.
Databáze: MEDLINE