%0 Journal Article
%@ 2369-2960
%I JMIR Publications
%V 2
%N 1
%P e30
%T Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak
%A Majumder,Maimuna S
%A Santillana,Mauricio
%A Mekaru,Sumiko R
%A McGinnis,Denise P
%A Khan,Kamran
%A Brownstein,John S
%+ Computational Epidemiology Group, Division of Emergency Medicine, Boston Children’s Hospital, 1 Autumn Street, Boston, MA, 02215, United States, 1 9784603677, maimuna@mit.edu
%K Zika virus disease
%K digital disease surveillance
%K mathematical modeling
%K reproductive number
%K transmission dynamics
%D 2016
%7 01.06.2016
%9 Original Paper
%J JMIR Public Health Surveill
%G English
%X 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 (R0) and observed (Robs) 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 R0 and Robs for both data sources. Results: Using the digital (smoothed HealthMap) data, we estimated a mean R0 of 2.56 (range 1.42-3.83) and a mean Robs of 1.80 (range 1.42-2.30). The traditional (INS) data yielded a mean R0 of 4.82 (range 2.34-8.32) and a mean Robs of 2.34 (range 1.60-3.31). Conclusions: Although modeling using the traditional (INS) data yielded higher R0 estimates than the digital (smoothed HealthMap) data, modeled ranges for Robs 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. 
%M 27251981
%R 10.2196/publichealth.5814
%U http://publichealth.jmir.org/2016/1/e30/
%U https://doi.org/10.2196/publichealth.5814
%U http://www.ncbi.nlm.nih.gov/pubmed/27251981