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Google Health Trends performance reflecting dengue incidence for the Brazilian states.
Romero-Alvarez, Daniel; Parikh, Nidhi; Osthus, Dave; Martinez, Kaitlyn; Generous, Nicholas; Del Valle, Sara; Manore, Carrie A.
Afiliación
  • Romero-Alvarez D; Department of Ecology & Evolutionary Biology and Biodiversity Institute, University of Kansas, Lawrence, Kansas, USA. daromero88@gmail.com.
  • Parikh N; Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA. daromero88@gmail.com.
  • Osthus D; Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Martinez K; Statistical Sciences (CCS-6), Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Generous N; Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Del Valle S; Applied Math and Statistics, Colorado School of Mines, Golden, CO, USA.
  • Manore CA; National Security & Defense Program Office (GS-NSD), Los Alamos National Laboratory, Los Alamos, NM, USA.
BMC Infect Dis ; 20(1): 252, 2020 Mar 26.
Article en En | MEDLINE | ID: mdl-32228508
ABSTRACT

BACKGROUND:

Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags.

METHODS:

We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective.

RESULTS:

From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index.

CONCLUSIONS:

The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Dengue / Motor de Búsqueda Tipo de estudio: Incidence_studies / Prognostic_studies / Risk_factors_studies Límite: Animals / Humans País/Región como asunto: America do sul / Brasil Idioma: En Revista: BMC Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Dengue / Motor de Búsqueda Tipo de estudio: Incidence_studies / Prognostic_studies / Risk_factors_studies Límite: Animals / Humans País/Región como asunto: America do sul / Brasil Idioma: En Revista: BMC Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos