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Title: Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks

Abstract

Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verificationmore » and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [3]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Specifica Inc, Los Alamos, NM (United States). New Mexico Consortium Biological Lab.
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Virginia, Charlottesville, VA (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; Defense Threat Reduction Agency (DTRA)
OSTI Identifier:
1499355
Report Number(s):
LA-UR-18-28570
Journal ID: ISSN 2369-2960
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
JMIR Public Health and Surveillance
Additional Journal Information:
Journal Volume: 5; Journal Issue: 1; Journal ID: ISSN 2369-2960
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Biological Science; disease surveillance; web-based analytics; infectious disease epidemiology

Citation Formats

Velappan, Nileena, Daughton, Ashlynn Rae, Fairchild, Geoffrey, Rosenberger, William Earl, Generous, Nicholas, Chitanvis, Maneesha Elizabeth, Altherr, Forest Michael, Castro, Lauren A., Priedhorsky, Reid, Abeyta, Esteban Luis, Naranjo, Leslie A., Hollander, Attelia Dawn, Vuyisich, Grace, Lillo, Antonietta Maria, Cloyd, Emily Kathryn, Vaidya, Ashvini Rajendra, and Deshpande, Alina. Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks. United States: N. p., 2019. Web. doi:10.2196/12032.
Velappan, Nileena, Daughton, Ashlynn Rae, Fairchild, Geoffrey, Rosenberger, William Earl, Generous, Nicholas, Chitanvis, Maneesha Elizabeth, Altherr, Forest Michael, Castro, Lauren A., Priedhorsky, Reid, Abeyta, Esteban Luis, Naranjo, Leslie A., Hollander, Attelia Dawn, Vuyisich, Grace, Lillo, Antonietta Maria, Cloyd, Emily Kathryn, Vaidya, Ashvini Rajendra, & Deshpande, Alina. Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks. United States. doi:10.2196/12032.
Velappan, Nileena, Daughton, Ashlynn Rae, Fairchild, Geoffrey, Rosenberger, William Earl, Generous, Nicholas, Chitanvis, Maneesha Elizabeth, Altherr, Forest Michael, Castro, Lauren A., Priedhorsky, Reid, Abeyta, Esteban Luis, Naranjo, Leslie A., Hollander, Attelia Dawn, Vuyisich, Grace, Lillo, Antonietta Maria, Cloyd, Emily Kathryn, Vaidya, Ashvini Rajendra, and Deshpande, Alina. Mon . "Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks". United States. doi:10.2196/12032. https://www.osti.gov/servlets/purl/1499355.
@article{osti_1499355,
title = {Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks},
author = {Velappan, Nileena and Daughton, Ashlynn Rae and Fairchild, Geoffrey and Rosenberger, William Earl and Generous, Nicholas and Chitanvis, Maneesha Elizabeth and Altherr, Forest Michael and Castro, Lauren A. and Priedhorsky, Reid and Abeyta, Esteban Luis and Naranjo, Leslie A. and Hollander, Attelia Dawn and Vuyisich, Grace and Lillo, Antonietta Maria and Cloyd, Emily Kathryn and Vaidya, Ashvini Rajendra and Deshpande, Alina},
abstractNote = {Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.},
doi = {10.2196/12032},
journal = {JMIR Public Health and Surveillance},
number = 1,
volume = 5,
place = {United States},
year = {2019},
month = {2}
}

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