Full Terms & Conditions of access and use can be found at
Global Health Action
ISSN: 1654-9716 (Print) 1654-9880 (Online) Journal homepage: http://www.tandfonline.com/loi/zgha20
Correlation between Google Trends on dengue
fever and national surveillance report in Indonesia
Atina Husnayain, Anis Fuad & Lutfan Lazuardi
To cite this article: Atina Husnayain, Anis Fuad & Lutfan Lazuardi (2019) Correlation between
Google Trends on dengue fever and national surveillance report in Indonesia, Global Health Action,
12:1, 1552652, DOI: 10.1080/16549716.2018.1552652
To link to this article: https://doi.org/10.1080/16549716.2018.1552652
© 2019 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Published online: 08 Jan 2019.
Submit your article to this journal
View Crossmark data
Correlation between Google Trends on dengue fever and national
surveillance report in Indonesia
, Anis Fuad
and Lutfan Lazuardi
E-Health Division, Center for Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada,
Department of Biostatistics, Epidemiology, and Population Health, Faculty of Medicine, Public Health and
Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia;
Department of Health Policy Management, Faculty of Medicine, Public
Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
Background: Digital traces are rapidly used for health monitoring purposes in recent years.
This approach is growing as the consequence of increased use of mobile phone, Internet, and
machine learning. Many studies reported the use of Google Trends data as a potential data
source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and
the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia.
No study was performed to measure the correlation between country wide official dengue
reports and Google Trends data in Indonesia.
Objective: This study aims to measure the correlation between Google Trends data on
dengue fever and the Indonesian national surveillance report.
Methods: This research was a quantitative study using time series data (2012–2016). Two sets
of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time
lag correlations were also used to measure the correlation between those data.
Results: Moving Average analysis showed that Google Trends data have a linear time series
pattern with official dengue report. Pearson correlation indicated high correlation for three
defined search terms with R-value range from 0.921 to 0.937 (p≤0.05, overall period) which
showed increasing trend in epidemic periods (2015–2016). Time lag correlation also indicated
that Google Trends data can potentially be used for an early warning system and novel tool
to monitor public reaction before the increase of dengue cases and during the outbreak.
Conclusions: Google Trends data have a linear time series pattern and statistically correlated
with annual official dengue reports. Identification of information-seeking behavior is needed
to support the use of Google Trends for disease surveillance in Indonesia.
Received 4 September 2018
Accepted 21 November 2018
Stig Wall, Umeå University,
Google Trends; information
health-related purposes in the past few years. Digital
epidemiology is a new field that uses digital traces to
explore the patterns of disease and health dynamics in
a population. The definition of digital epidemiology
according to Salathe is:‘Digital epidemiology is epi-
demiology that uses data that was generated outside the
public health system, i.e. with data that was not generated
with the primary purpose of doing epidemiology.’
As the Internet penetration becomes more wide-
spread, with increased mobile phone usage, and the
growing artificial intelligence of machine learning, the
field of digital epidemiology provides a promising
approach to assist traditional surveillance systems [1,2].
This approach potentially fills the gap in conventional
surveillance systems in developing countries that often
suffer from underreporting, limited timeliness, and the
lack of sufficient budget for physical needs, facilities, and
infrastructures [3–6]. Data provided by conventional
surveillance system often required weeks or months to
In Indonesia, regulation by Ministry of Health
requested hospitals to report any new dengue cases
to district health office within 24 hours after confirmed
diagnosis . However no single application was avail-
able to capture the data electronically. Consequently,
each district has its own database structure of dengue
cases. Data from districts are submitted monthly to
province and national level. Reports at province and
national level are aggregated on number of cases by
districts, age group. Top-down feedbacks are provided
by the sub-directorate of Vector-Borne Diseases and
Zoonoses under the Directorate General of Disease
Prevention and Control in the Ministry of Health of
the Republic of Indonesia. This circumstances poten-
tially caused the delay in response and indicated the
need for an alternative data source to depict the den-
gue cases in near real-time.
Among the digital traces that are increasingly stu-
died for epidemiology are those recorded in search
CONTACT Lutfan Lazuardi firstname.lastname@example.org Department of Health Policy Management, Faculty of Medicine, Public Health and
Nursing, Universitas Gadjah Mada, 55281 Yogyakarta, Indonesia
GLOBAL HEALTH ACTION
2019, VOL. 12, 1552652
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
engines . These data provide the information-
seeking patterns using specified search terms in
defined locations during a specific time period.
Digital recorded data provided by Google are dis-
played on Google Trends’website (https://trends.goo
gle.com/trends/). Many studies proved that Google
Trends data correlated well with traditional surveil-
lance data [8–14]. Those researches reveal the poten-
tial use of Google Trends data that can be obtained
earlier, more easily, and at little cost compared with
conventional reporting systems. On the other hand,
some studies reported a weak potential use of Google
Trends data finding that they are more influenced by
media clamor than truly actual epidemiological bur-
The increasing Internet penetration in Indonesia
that has attained 54.7% and the huge utilization of
Google (98%) indicate the potential use of Google
Trends in Indonesia [17,18] This study was designed
to validate the use of Google Trends data as an alter-
native or complement data source for dengue surveil-
lance in Indonesia. No study was performed to
measure the correlation between country-wide offi-
cial dengue reports and Google Trends data in
Indonesia. This is the first study to measure the
correlation between Google Trends data on dengue
fever and the Indonesian national surveillance report
at a national level.
This research was a quantitative study using time
series data ranging from 2012 until 2016. The frame-
work in this study is adapted from a previous study
related to validation of Google Trends data at the
national level . We used official dengue reports
from the Department of Arbovirus, Health Ministry,
Indonesia and Google search volumes related to den-
gue in Indonesia from Google Trends. Official den-
gue reports were used as a gold standard to validate
the Google Trends data.
Cases with confirmed status of dengue from labora-
tory tests that were reported in official dengue reports
from 34 provinces in Indonesia are available on
a monthly basis. Data cleaning was performed for
those data to examine the completeness of data.
Missing values from five provinces including
Lampung, North Sulawesi, West Sulawesi, Papua, and
West Papua are filled in using the Amelia Package in
RStudio. This approach used multiple imputation and
frequently used to overcome missing values in time
series data . Multiple imputation used a Bayesian
approach to replace missing values with predictive
distribution based on the observed data .
Complete official dengue reports then were trans-
formed to the same interval of relative search volume
(RSV) in Google Trends data, in order to compare
the official dengue reports and Google Trends data in
a single graphical form. This approach is also used in
a previous study to transform the official dengue
reports in interval data which range from 0 to 100
. By using those approach, 0 is defined as the
absence of dengue case and 100 is defined as the
highest incidence of dengue cases during 2012 until
We compared the normalized dengue cases with
dengue Google search volume for the same data
period. Dengue Google search volume is described
as how often a defined search term is used by
Indonesians to search online information related to
dengue in Google. Data were downloaded in comma-
separated values (CSV) file from Google Trends’
website (https://trends.google.com/trends/) and are
available on a weekly basis. Data were obtained
using 19 search terms related to disease definition,
symptom, treatment, and vector of disease which is
listed in Table 1. Search terms were collected from
Google Trends (search terms listing the most fre-
quently used) and Google Correlate (search terms
which have a similar pattern with the search term
‘demam berdarah dengue’).
Obtained data from Google Trends then were
transformed from weekly period to monthly period
using mean. This method was also used in previous
study , in order to compare two sets of data in
a single graphical form using a line chart in Microsoft
Excel. Graphs which have relatively similar linearity
of pattern then can be visualized using moving aver-
age analysis. Moving average analysis was used to
measure the pattern similarity between official den-
gue reports and Google Trends data in more detailed
ways. Pattern similarity includes the linearity of pat-
tern, similarity of leap, and similarity of dengue out-
break per period in Indonesia.
Pearson correlation was performed for search
terms with the highest pattern of similarity with
official dengue reports. The correlation strength was
defined as a correlation coefficient R-value of 0.7
(p≤0.05). We also performed Time lag correlation
analysis with significance level at p≤0.05 for search
terms with the highest correlation. Time lag correla-
tion is used to compute the correlation between time
lag variables and official dengue case history.
Statistical analysis was conducted using Stata ver-
Results of data analysis in Figure 1 show the time
series of dengue cases in Indonesia from 2012 until
2016. There were four peaks of dengue cases with the
highest peak in February 2016 which involved 32,117
dengue cases. Figure 1 shows the dengue outbreaks
per period in Indonesia which tended to increase
2A. HUSNAYAIN ET AL.
between October to January and then spiked to
a peak in January or March.
Time series of dengue cases then were visualized in
single graphical form with Google Trends data.
Moving average graph from official dengue reports
and Google Trends data which has relative similarity
in linearity of pattern is shown in Figure 2. Search
terms such as ‘gejala demam berdarah’,‘demam ber-
darah’, and ‘dbd’seem to be in-line with official
dengue reports. Information seeking using search
term ‘demam berdarah’increased in point 9 (22.6);
23 (27.2); 34 (26.8); and 46 (24.6). Search term ‘dbd’
increased in point 10 (11.6); 22 (13.4); 33 (15.5); and
46 (16.2), followed by search term ‘gejala demam
berdarah’which increased in point 11 (15.5); 23
(18.3); 33 (20); and 48 (21.4). Compared with official
dengue reports which increased in point 11 (17.2); 25
(20); 34 (24.3); and 47 (18), search terms ‘gejala
demam berdarah’,‘demam berdarah’, and ‘dbd’are
increased in 1 until 3 points before the increase of
dengue cases. There were 4 peaks in the last 5 years
which are visualized by official dengue reports and
the 3 search terms from Google Trends in point 15,
27, 39, and 51. Figure 2 also shows that information
seeking using the search term, ‘demam berdarah’
tended to have higher value than official dengue
reports, different from the search terms, ‘gejala
demam berdarah’, and ‘dbd’which tend to have
lower values than official dengue reports.
Results of Pearson correlation in Table 2 show high
correlation (R-value≥0.7 and p≤0.05) between official
dengue reports and the Google Trends data. Correlations
from the three search terms in the overall time period
range from 0.921 to 0.937. The search term ‘gejala
demam berdarah’has the highest R-value in the overall
increased in the epidemic period (2015–2016) and search
terms ‘gejala demam berdarah’and ‘demam berdarah’
seem to have stable R-value. Results of Time lag correla-
tion in Table 3 show high correlation (R-value≥0.7 and
p≤0.05) between official dengue reports and Google
Trends data a month earlier which have R-value ranging
from 0.755 to 0.773. Information seeking using the
search term ‘gejala demam berdarah’in the month
prior shows the highest correlation with official dengue
reports (R-value = 0.773; p≤0.05).
Validation using moving average analysis showed
that Google Trends data have a linear time series
pattern correlated with official dengue reports. This
finding is relevant to previous research by Cho .
Information seeking using the search term ‘gejala
demam berdarah’,‘demam berdarah’, and ‘dbd’fol-
lowed the dengue outbreak period in Indonesia from
October to January and the peak in January to
March during the epidemic years 2015 and 2016.
Table 1. List of search terms.
Num Category Search Term Description Source
1. Disease definition ‘demam berdarah’,‘dengue’,‘dengue fever’,
‘fever’,‘penyakit demam’,‘penyakit demam
Terms are used to identify searching pattern
related to disease definition in bahasa
‘demam berdarah dengue pdf’,‘dengue
hemorrhagic fever’,‘dhf’,‘demam berdarah
2. Symptom ‘berdarah’,‘demam’Terms are used to identify searching pattern
related to dengue symptom in bahasa
‘gejala demam berdarah dengue’,‘gejala demam
3. Treatment ‘obat demam berdarah dengue’Terms are used to identify searching pattern
related to dengue treatment in bahasa
4. Vector of disease ‘aedes’,‘aedes aegypti’,‘aegypti’Terms are used to identify searching pattern
related to dengue vector in bahasa Indonesia
Figure 1. Time series of dengue cases in Indonesia (2012–2016).
GLOBAL HEALTH ACTION 3
Validation using Pearson correlation shows high
correlation (R-value ≥0.7 and p≤0.05) between
official dengue reports and Google Trends data.
This finding is relevant to previous studies by
Althouse, Chan, Cho, Gluskin, Castro, Strauss and
Teng [8–14]. Those publications showed R-values
ranging from 0.33 to 0.94. Researches in tropical
countries [10,11,13,14] showed the high correlation
(R-value ranging from 0.82 to 0.94) between official
surveillance data with Google Trends data that seem
to be similar with the result of this study. In com-
parison, the R-value in this research is relatively
high (R-values ranging from 0.921 to 0.937).
Figure 2. Moving average of dengue cases and information seeking using search term ‘gejala demam berdarah’,‘demam
berdarah’, and ‘dbd’in Indonesia (2012–2016).
DOI for Dataset: 10.17632/x855pphhx9.1
Table 2. Result of pearson correlation.
Overall period 0.937* 0.931* 0.921*
2012 0.936* 0.918* 0.862*
2013 0.847* 0.850* 0.719*
2014 0.844* 0.814* 0.570
2015 0.921* 0.929* 0.918*
2016 0.954* 0.966* 0.950*
*significant in p≤0.05.
Table 3. Result of time lag correlation.
rp-value r p-value r p-value
−3 0.264* 0.047 0.283* 0.033 0.356* 0.007
−2 0.517* <0.001 0.526* <0.001 0.567* <0.001
−1 0.773* <0.001 0.755* <0.001 0.767* <0.001
0 0.937* <0.001 0.931* <0.001 0.921* <0.001
*significant in p ≤0.05
4A. HUSNAYAIN ET AL.
The high correlation between official dengue
reports and Google Trends data in this study is dif-
ferent from Alicino, Cervellin, and Ellery’s research
finding [15,16,21]. Those researches found
a disassociation between Google Trends data and
disease occurrence and also found that Google
Trends data is more influenced by media coverage
than actual epidemiological burden. Thus, the poten-
tial use of Google Trends data depends on media
coverage, Internet penetration, and utilization of
mobile phone. Apart from those research findings,
research by Chan found that information seeking
related to dengue tended to be less influenced by
media coverage .
According to the 2 steps of validation, moving
average analysis and Pearson correlation, Google
Trends data is well correlated with official dengue
reports. This research successfully proved that
Google Trends data is potentially useful as
a complement data source for disease surveillance in
Indonesia where Internet penetration attained 54.7%
(2017). One previous study suggested that Google
Trends is better suited in developed countries with
large Internet penetration .
Three search terms with linear time series pattern
and high correlation with official dengue case were
drawn from disease definitions and symptom cate-
gory. This research finding is relevant to research
from Althouse, Chan, Cho, and Kang [8–10,23].
Search terms which are generally used by netizens
have higher correlation with official data [10,23].
This finding also are demonstrated in this study.
Search terms which generally are used by
Indonesian netizens such as ‘gejala demam berdarah’,
‘demam berdarah’, and ‘dbd’have higher correlation
with official dengue reports than the search term
‘demam berdarah dengue’, even though that search
term is a standard disease definition for dengue in
Bahasa Indonesia. Different from the search term
‘gejala demam berdarah’and ‘demam berdarah’that
had generally stable R-values as shown in Table 2, the
search term ‘dbd’had a fluctuating R-value. The
search terms ‘gejala demam berdarah’and ‘demam
berdarah’are specific search terms that have specified
result from query (‘gejala demam berdarah’has
104,000 results and ‘demam berdarah’has 2,560,000
results, whereas the search term ‘dbd’has a broad
query with 20,400,000 results).
Different from previous research [8–10,23], search
terms in this research were collected from Google
Trends (search terms used most frequently) and
Google Correlate (search terms which have a similar
pattern with the search term ‘demam berdarah dengue’).
According to the results of the Pearson correlation in
Table 2, and list of search terms in Table 1, Google
Trends and Google Correlate successfully describe the
keyword or search term utilization by Indonesians.
Nevertheless, the accuracy of keyword or search term
identification depends on information-seeking beha-
vior which are influenced by media trends, outbreak
news briefs, disease occurrence, and Internet penetra-
tion [8,9,10,11,13,15,16,23]. Information-seeking beha-
vior also is influenced by individual variables such as
age, sex, level of education, cultural aspect, language,
social class, marital status, level of healthcare utilization,
and level of stress [23,25,26,27]. An additional factor
that drives the information-seeking behavior is keyword
suggestion in Google. According to Wen and Sun ,
keyword suggestion is generated from previous query
using content-based re-ranking. Thus, keyword or
search term utilization also depends on previous
query. In summary, the condition, distribution, and
dynamic of factors that influences the information-
seeking behavior may vary among national wide and
change over time. Therefore, identification of factors
that influence the information-seeking behavior is
needed to support the use of Google Trends for disease
surveillance in Indonesia.
Increasing of information seeking in 1 until 3
points before the increase of dengue cases as shown
in Figure 2 and high correlation of lag-1 in Time lag
correlation indicate the initial potential use of Google
Trends data as an early warning system. Some pre-
vious studies showed the potential use of Google
Trends data as an early warning system in countries
with a weak surveillance system [8,9,11,13,28]. This
finding also indicates the potential use of Google
Trends as novel tool to monitor public reaction
before the increase of dengue cases and during the
outbreak. Google Trends is potentially used to cap-
ture the public reaction in terms of worries, knowl-
edge needs, and gaps which can be obtained earlier,
more easily and at little cost [9,10,23,29].
With an assumption that information seeking
related to dengue tended to be less influenced by
media coverage , Google Trends can be potentially
used to capture knowledge needs, and gaps between
available information and needed information. Gaps
can be identified using search term or keyword utili-
zation by Indonesians in Google Trends and Google
Correlate before the increase of dengue cases and
during the outbreak. Identified gaps then can be
used to determine the topic or information which is
published on health official website and news chan-
nel. As the three most frequently accessed news chan-
nel in Indonesia, Tribunnews.com, Detik.com, and
Liputan6.com could potentially be used to dissemi-
nate the needed information .
Early warning system according to World Health
Organization is ‘timely surveillance systems that col-
lect information on epidemic-prone diseases in order
to trigger prompt public health interventions’.
Approaches in utilization of Google Trends for early
warning systems and as a monitoring tool for public
GLOBAL HEALTH ACTION 5
reaction are intended to assist traditional surveillance
systems in order to increase public health response to
dengue in Indonesia. A study in Yogyakarta munici-
pality reported that on average it takes 12 days to
submit the report from hospital to the district health
office . Given that no standardized electronic
dengue surveillance system, each district develop the
system differently with limited interoperability.
Consequently, provincial health offices and the
Ministry of Health lacks of disaggregated data for
In this concern, Google Trends is a prospective
tool to overcome the timeliness problem in conven-
tional surveillance system which requires weeks or
months to collect the data. In principle, this study
offers opportunities to complement the existing sur-
veillance system especially in terms of early warning.
However, some works need to be done. These include
overcome the noise to increase the quality of data,
combine with other data source including social
media and online news data, then create the algo-
rithm to produce early warning systems. Google
Trends also can be used to reveal what people search
in a defined time and location. Otherwise, utilization
of Google Trends as a monitoring tool for public
reaction can be used immediately. Google Trends
data can be used to monitor the public interest and
the most commonly searched topic.
According to Bragazzi , Google Trends can be
used to monitor the public interest and the most
commonly searched topic, while Google Trends can-
not be used to disclose the individual characteristics
as done by conventional surveillance system.
Therefore, Google Trends does not have the capacity
to replace the existing surveillance systems  but
can serve to supplement and complement them.
Implementation of Google Trends in Indonesia still
poses some challenges related to Internet penetration
and information-seeking behavior. As an island coun-
try, Indonesia has to encounter the discrepancy of
infrastructure and level of literacy which may vary
widely nationally. Those factors can affect the
Internet utilization and information-seeking behavior
in all regions in Indonesia. Google Trends could be
used easily in a region with high Internet penetration
and high dengue incidence. Nevertheless, how to
implement the Google Trends in a region with high
dengue incidence but low Internet penetration still
remains challenging. Future studies need to validate
the utilization of Google Trends data in regions with
high dengue incidence and compare it among regions
with high and low Internet penetration in Indonesia.
Googling for health or disease-related information
does not always reflect the individual’s health condi-
tion. Likewise, searching for dengue information
could also be performed by those with the infected
disease in various point during the incubation period,
other related disease with similar symptoms or even
by healthy people . Research by Indriani revealed
the similarity of spatio-temporal patterns of dengue
and chikungunya in Yogyakarta . This issue poses
a critical challenge for any Google Trends research.
Thus, involving other variables that potentially influ-
encing the googling behavior is needed to weigh the
relative search volume and then improve the correla-
tion analysis. Beside information-seeking behavior,
other researchers could consider Internet penetration
rate by geographic areas as a weighting variable for
Google Trends data in order to increase the quality of
Google Trends data have a linear time series pattern
and are statistically correlated with official dengue
report. Identification of information-seeking behavior
is needed to support the use of Google Trends for
disease surveillance in Indonesia.
We gratefully give thanks to the sub-directorate of Vector-
Borne Diseases and Zoonosis under the Directorate
General of Disease Prevention and Control within the
Ministry of Health Republic of Indonesia for providing
the official dengue surveillance data from all 34 provinces
All authors contributed to the writing process. Atina
Husnayain contributed to writing the first draft including
study design, analysis, and interpretation of data. Anis
Fuad provided the research conception and revised draft
critically for important intellectual content. Lutfan
Lazuardi also played a part in ensuring the research accu-
racy and revised the final draft before submission.
No potential conflict of interest was reported by the
Ethics and consent
This research has been approved by the Medical and
Health Research Ethics Committee of Faculty of
Medicine, Public Health and Nursing, Universitas
Gadjah Mada with the reference number of KE/FK/
This work was supported by the Faculty of Medicine,
Public Health and Nursing, Universitas Gadjah Mada
[Grant number: UPPM/221/M/05/04/05.17].
6A. HUSNAYAIN ET AL.
This study has demonstrated that Google Trends data on
dengue has a high potential to complement the national
level surveillance data in Indonesia. In a dengue endemic
country with 54.7% Internet penetration, Google Trends
data could be adopted as an early warning system and
monitoring tool of public responses to communicable dis-
eases. Further validation studies at sub-national level are
necessary since the district level surveillance system is
highly influenced by the decentralization policy and inter-
ests of local leadership.
Atina Husnayain http://orcid.org/0000-0003-3002-8728
Anis Fuad http://orcid.org/0000-0003-2303-5903
Lutfan Lazuardi http://orcid.org/0000-0001-5146-8162
 Salathé M. Digital epidemiology: what is it, and where
is it going? J Life Sci Soc Policy. 2018;14:1–5.
 Salathé M, Bengtsson L, Bodnar TJ, et al. Digital
epidemiology. PLoS Comput Biol. 2012;8:1–5.
 Runge-Ranzinger S, McCall PJ, Kroeger A, et al.
Dengue disease surveillance: an updated systematic
literature review. Trop Med Int Heal.
 Das S, Sarfraz A, Jaiswal N, et al. Impediments of
reporting dengue cases in India. J Infect Public
Health [Internet]. 2017;10:494–498.
 Sitepu FY, Suprayogi A, Pramono D. Evaluasi dan
Implementasi Sistem Surveilans Demam Berdarah
Dengue (DBD) di Kota Singkawang, Kalimantan
Barat, 2010. J Litbang Pengendali Penyakit
Bersumber Binatang Banjarnegara [Internet].
2012;8:5–10. Available from: http://ejournal.litbang.
 Stahl H, Butenschoen VM, Tran HT, et al. Cost of
dengue outbreaks: literature review and country case
studies. BMC Public Health. 2013;13:1–11.
 Indonesian Health Ministry. Health ministry
regulation. 1501 Indonesia; 2010.
 Althouse BM, Ng YY, Cummings DAT. Prediction of
dengue incidence using search query surveillance.
PLoS Negl Trop Dis. 2011;5:1–7.
 Chan EH, Sahai V, Conrad C, et al. Using web search
query data to monitor dengue epidemics: a new model
for neglected tropical disease surveillance. PLoS Negl
Trop Dis. 2011;5.
 Cho S, Sohn CH, Jo MW, et al. Correlation between
national influenza surveillance data and Google
Trends in South Korea. PLoS One. 2013;8:e81422.
 Gluskin RT, Johansson MA, Santillana M, et al.
Evaluation of internet-based dengue query data:
Google dengue trends. PLoS Negl Trop Dis.
 Castro JS, Torres J, Oletta J, et al. Google Trend tool
as a predictor of chikungunya and zika epidemic in
a environment with little epidemiological data,
a Venezuelan case. Int J Infect Dis [Internet].
 Strauss R, Castro JS, Reintjes R, et al. Google dengue
trends: an indicator of epidemic behavior. The
Venezuelan case. Int J Infect Dis [Internet].
 Teng Y, Bi D, Xie G, et al. Dynamic forecasting of
Zika epidemics using Google Trends. PLoS One.
 Alicino C, Bragazzi NL, Faccio V, et al. Assessing
Ebola-related web search behaviour: insights and
implications from an analytical study of Google
Trends-based query volumes. Infect Dis Poverty
 Cervellin G, Comelli I, Lippi G. Is Google Trends
a reliable tool for digital epidemiology? Insights from
different clinical settings. J Epidemiol Glob Health.
 APJII. Penetrasi & Perilaku Pengguna Internet
Indonesia 2017 [Internet]. Penetrasi Perilaku
Pengguna Internet Indones. 2017. Jakarta; 2017.
Available from: https://web.kominfo.go.id/sites/
 StatCounter Global Stats. Search engine market share
in Indonesia [Internet]. 2017 [cited 2017 Apr 29].
Available from: http://gs.statcounter.com/search-
 Zhang Z. Multiple imputation for time series data
with Amelia package. Ann Transl Med [Internet].
2016;4:56. Available from: http://www.ncbi.nlm.nih.
 Sterne JAC, White IR, Carlin JB, et al. Multiple impu-
tation for missing data in epidemiological and clinical
research: potential and pitfalls. BMJ [Internet].
2009;338:1–12. Available from: https://www.bmj.com/
 Ellery PJ, Vaughn W, Ellery J, et al. Understanding
internet health search patterns: an early exploration
into the usefulness of Google Trends. J Commun
 Carneiro HA, Mylonakis E. Google Trends: a
web-based tool for real-time surveillance of disease
outbreaks. Clin Ifectious Dis. 2009;49:1557–1564.
 Kang M, Zhong H, He J, et al. Using Google Trends
for influenza surveillance in South China. PLoS One.
 Wen F, Sun J Google Patents: dynamic keyword sug-
gestion and image-search re-ranking [Internet].
Google Patents Dyn. keyword Suggest. image-search
re-ranking. 2010 [cited 2018 Feb 26]. Available from:
 Beck F, Richard J-B, Nguyen-Thanh V, et al. Use of the
internet as a health information resource among French
young adults: results from a nationally representative
survey. J Med Internet Res [Internet]. 2014;16:e128.
Available from: http://www.jmir.org/2014/5/e128/
 Nölke L, Mensing M, Krämer A, et al. Sociodemographic
and health-(care-)related characteristics of online health
information seekers: A cross-sectional German study.
BMC Public Health. 2015;15:1–12.
 Oh YS, Song NK. Investigating relationships between
health-related problems and online health information
seeking. J Comput Informatics, Nurs. 2017;35:29–35.
 Seo D-W, Shin S-Y. Methods using social media and
search queries to predict infectious disease outbreaks.
Healthc Inform Res [Internet]. 2017;23:343–348.
Available from: http://www.ncbi.nlm.nih.gov/
GLOBAL HEALTH ACTION 7
 Adawi M, Bragazzi NL, Watad A, et al. Discrepancies
between classic and digital epidemiology in searching
for the mayaro virus: preliminary qualitative and
quantitative analysis of Google Trends. J Med
Internet Res. 2017;3:1–11.
 Alexa. Top sites in Indonesia [Internet]. Top Sites
Indones. 2018 [cited 2018 Apr 2]. p. 1–13. Available
 World Health Organization. Emergencies prepared-
ness, response early warning systems [Internet]. 2016
[cited 2018 Nov 12]. p. 11–13. Available from: http://
 Cahyono AD, Satoto TBT, Lazuardi L. Kemanfaatan
pelaporan berbasis sms dan aplikasi surveilans demam
berdarah berbasis web di kota yogyakarta. Yogyakarta:
Universitas Gadjah Mada; 2018.
 Bragazzi NL, Barberis I, Rosselli R, et al. How often
people google for vaccination: qualitative and quanti-
tative insights from a systematic search of the
web-based activities using Google Trends. J Hum
Vaccines Immunother. 2018;13:1–20.
 Milinovich GJ, Williams GM, Clements ACA, et al.
Internet-based surveillance systems for monitoring
emerging infectious diseases. Lancet Infect Dis
 Indriani C, Fuad A, Kusnanto H. Spatial-temporal
pattern comparison between chikungunya outbreak
and dengue hemmorhagic fever incidence at Kota
Yogyakarta 2008. Ber Kedokt Masy. 2011;27:41–50.
8A. HUSNAYAIN ET AL.