Evidence map›Paper›PMID 32228508›Full record

ArticleBMC infectious diseases2020

Google Health Trends performance reflecting dengue incidence for the Brazilian states.

Daniel Romero-Alvarez, Nidhi Parikh, Dave Osthus, Kaitlyn Martinez, Nicholas Generous, Sara Del Valle, Carrie A Manore

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Daniel Romero-AlvarezDepartment of Ecology & Evolutionary Biology and Biodiversity Institute, University of Kansas, Lawrence, Kansas, USA. daromero88@gmail.com.
Nidhi ParikhInformation Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA.
Dave OsthusStatistical Sciences (CCS-6), Los Alamos National Laboratory, Los Alamos, NM, USA.
Kaitlyn MartinezInformation Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA.
Nicholas GenerousNational Security & Defense Program Office (GS-NSD), Los Alamos National Laboratory, Los Alamos, NM, USA.
Sara Del ValleInformation Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA.
Carrie A ManoreInformation Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA.

Funding

Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
Los Alamos National Laboratory Laboratory Directed Research Development ProgramNIGMS NIH HHS R01 GM130668NIGMS NIH HHS R01GM130668
6 · The paper itself

Abstract

backgroundDengue 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.

methodsWe 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.

resultsFrom 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.

conclusionsThe 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.

Indexed as

AedesAnimalsBrazilDengueEpidemicsHumansIncidenceSearch EngineBrazilDigital epidemiologyEpidemiologyGoogle health trendsInternet data streamsInternet penetrationVolatility

Identifiers

PMID32228508
PMCPMC7104526

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.