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Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak
BACKGROUND: Approximately 40 countries in Central and South America have experienced local vector-born transmission of Zika virus, resulting in nearly 300,000 total reported cases of Zika virus disease to date. Of the cases that have sought care thus far in the region, more than 70,000 have been rep...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4909981/ https://www.ncbi.nlm.nih.gov/pubmed/27251981 http://dx.doi.org/10.2196/publichealth.5814 |
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author | Majumder, Maimuna S Santillana, Mauricio Mekaru, Sumiko R McGinnis, Denise P Khan, Kamran Brownstein, John S |
author_facet | Majumder, Maimuna S Santillana, Mauricio Mekaru, Sumiko R McGinnis, Denise P Khan, Kamran Brownstein, John S |
author_sort | Majumder, Maimuna S |
collection | PubMed |
description | BACKGROUND: Approximately 40 countries in Central and South America have experienced local vector-born transmission of Zika virus, resulting in nearly 300,000 total reported cases of Zika virus disease to date. Of the cases that have sought care thus far in the region, more than 70,000 have been reported out of Colombia. OBJECTIVE: In this paper, we use nontraditional digital disease surveillance data via HealthMap and Google Trends to develop near real-time estimates for the basic (R(0)) and observed (R(obs)) reproductive numbers associated with Zika virus disease in Colombia. We then validate our results against traditional health care-based disease surveillance data. METHODS: Cumulative reported case counts of Zika virus disease in Colombia were acquired via the HealthMap digital disease surveillance system. Linear smoothing was conducted to adjust the shape of the HealthMap cumulative case curve using Google search data. Traditional surveillance data on Zika virus disease were obtained from weekly Instituto Nacional de Salud (INS) epidemiological bulletin publications. The Incidence Decay and Exponential Adjustment (IDEA) model was used to estimate R(0) and R(obs) for both data sources. RESULTS: Using the digital (smoothed HealthMap) data, we estimated a mean R(0) of 2.56 (range 1.42-3.83) and a mean R(obs) of 1.80 (range 1.42-2.30). The traditional (INS) data yielded a mean R(0) of 4.82 (range 2.34-8.32) and a mean R(obs) of 2.34 (range 1.60-3.31). CONCLUSIONS: Although modeling using the traditional (INS) data yielded higher R(0) estimates than the digital (smoothed HealthMap) data, modeled ranges for R(obs) were comparable across both data sources. As a result, the narrow range of possible case projections generated by the traditional (INS) data was largely encompassed by the wider range produced by the digital (smoothed HealthMap) data. Thus, in the absence of traditional surveillance data, digital surveillance data can yield similar estimates for key transmission parameters and should be utilized in other Zika virus-affected countries to assess outbreak dynamics in near real time. |
format | Online Article Text |
id | pubmed-4909981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-49099812016-06-28 Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak Majumder, Maimuna S Santillana, Mauricio Mekaru, Sumiko R McGinnis, Denise P Khan, Kamran Brownstein, John S JMIR Public Health Surveill Original Paper BACKGROUND: Approximately 40 countries in Central and South America have experienced local vector-born transmission of Zika virus, resulting in nearly 300,000 total reported cases of Zika virus disease to date. Of the cases that have sought care thus far in the region, more than 70,000 have been reported out of Colombia. OBJECTIVE: In this paper, we use nontraditional digital disease surveillance data via HealthMap and Google Trends to develop near real-time estimates for the basic (R(0)) and observed (R(obs)) reproductive numbers associated with Zika virus disease in Colombia. We then validate our results against traditional health care-based disease surveillance data. METHODS: Cumulative reported case counts of Zika virus disease in Colombia were acquired via the HealthMap digital disease surveillance system. Linear smoothing was conducted to adjust the shape of the HealthMap cumulative case curve using Google search data. Traditional surveillance data on Zika virus disease were obtained from weekly Instituto Nacional de Salud (INS) epidemiological bulletin publications. The Incidence Decay and Exponential Adjustment (IDEA) model was used to estimate R(0) and R(obs) for both data sources. RESULTS: Using the digital (smoothed HealthMap) data, we estimated a mean R(0) of 2.56 (range 1.42-3.83) and a mean R(obs) of 1.80 (range 1.42-2.30). The traditional (INS) data yielded a mean R(0) of 4.82 (range 2.34-8.32) and a mean R(obs) of 2.34 (range 1.60-3.31). CONCLUSIONS: Although modeling using the traditional (INS) data yielded higher R(0) estimates than the digital (smoothed HealthMap) data, modeled ranges for R(obs) were comparable across both data sources. As a result, the narrow range of possible case projections generated by the traditional (INS) data was largely encompassed by the wider range produced by the digital (smoothed HealthMap) data. Thus, in the absence of traditional surveillance data, digital surveillance data can yield similar estimates for key transmission parameters and should be utilized in other Zika virus-affected countries to assess outbreak dynamics in near real time. JMIR Publications 2016-06-01 /pmc/articles/PMC4909981/ /pubmed/27251981 http://dx.doi.org/10.2196/publichealth.5814 Text en ©Maimuna S Majumder, Mauricio Santillana, Sumiko R Mekaru, Denise P McGinnis, Kamran Khan, John S Brownstein. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 01.06.2016. https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/ (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Majumder, Maimuna S Santillana, Mauricio Mekaru, Sumiko R McGinnis, Denise P Khan, Kamran Brownstein, John S Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak |
title | Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak |
title_full | Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak |
title_fullStr | Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak |
title_full_unstemmed | Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak |
title_short | Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak |
title_sort | utilizing nontraditional data sources for near real-time estimation of transmission dynamics during the 2015-2016 colombian zika virus disease outbreak |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4909981/ https://www.ncbi.nlm.nih.gov/pubmed/27251981 http://dx.doi.org/10.2196/publichealth.5814 |
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