Google Health Trends performance reflecting dengue incidence for the Brazilian states

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.

Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico

“…Regions with better broadband access tend to be those with lower incidence rates of dengue, however in this case the relationship is flat at low levels of broadband coverage (below 40 percent) and then turns negative and quadratic at higher levels of access. These results could suggest residents are more likely to search for information on dengue prevention measures consequently lowering transmission potential, or when suffering with symptoms may be more likely to seek medical advice, therefore breaking the transmission cycle; these results are consistent with findings by [ 56 , 57 ]. This result also could be an indicator of more advance and urbanized regions vs agricultural and less developed regions.…”

Using Google Health Trends to investigate COVID-19 incidence in Africa

“…We downloaded four English terms from the GHT application programming interface (API): ‘coronavirus’, ‘coronavirus symptoms’, ‘COVID19’, and ‘pandemic’. Although the four terms are conceptually related, they have the potential to capture a broad spectrum of information related to the disease (Asseo et al, 2020; Romero-Alvarez et al, 2020). We matched the relative search proportions of these words—which is the raw output provided by GHT (Romero-Alvarez et al, 2020)—with the weekly COVID-19 case incidence for the selected time period.…”

“…Finally, we included a predictor based on a standardized volatility index calculated using the standardized normalized case incidence data of each country as follows: in which n is the total number of observations and Y is the normalized case incidence per country. The average of the absolute difference (i.e., volatility) summarizes the case signal reflecting if it is relatively constant or fluctuates broadly from week to week (Romero-Alvarez et al, 2020). Overall, we explored a total of 72 potential explanatory variables (Table 1 and Supplementary Table 1).…”

“…GT inquiries yield a ranked score from 0 to 100 based on the highest frequency of searches for a term in a particular time period. GHT provides search counts from a relative proportion of a random sample of the overall Google search dataset for any particular term in a selected time interval (Romero-Alvarez et al, 2020). Both of these portals have limitations, such as possibly excluding certain groups (e.g., the elderly, rural residents, low income populations), a lack of detail on who is searching certain terms, and the underlying motivations of the searches (Arora et al, 2019).…”

Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

“…Windows of prediction include nowcasting (necessary since reported case counts often have a lag of 1-4 weeks) 19 , forecasting weekly cases 4-12 weeks into the future 20 , and several-months ahead categorical risk forecasting 21 . Many of these studies have exploited multiple data streams including socioeconomic drivers 9 , weather data 6 , satellite imagery 11 , topography (e.g., altitude), entomological factors 24 , Internet data (e.g., social media) 13,25 , and clinical surveillance data 19 . However, most of these studies have not evaluated the contribution of each data stream on the forecast, with a few exceptions that have evaluated the contribution of one data source 12 or a single type of data stream 20 .…”