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Reviews (82) Scientometrics, Infometrics, and Altmetrics (81) Big Data (216) Innovative Methods in Public Health and Surveillance (305)

Published on in Vol 12 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42292, first published .
Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda

Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda

Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda

Authors of this article:

Lateef Babatunde Amusa1, 2 Author Orcid Image ;   Hossana Twinomurinzi1 Author Orcid Image ;   Edith Phalane3, 4 Author Orcid Image ;   Refilwe Nancy Phaswana-Mafuya3, 4 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

    Original Paper

    1Centre for Applied Data Science, University of Johannesburg, Johannesburg, South Africa

    2Department of Statistics, University of Ilorin, Ilorin, Nigeria

    3Pan African Centre for Epidemics Research (PACER) Extramural Unit, South African Medical Research Council/University of Johannesburg, Johannesburg, South Africa

    4Department of Environmental Health, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa

    Corresponding Author:

    Lateef Babatunde Amusa, PhD

    Centre for Applied Data Science

    University of Johannesburg

    Bunting Road Campus, Auckland Park

    Johannesburg, 2006

    South Africa

    Phone: 27 659904686

    Email: amusasuxes@gmail.com


    Background: Infectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling.

    Objective: The aim of this study was to synthesize research and identify hotspots of big data in infectious disease epidemiology.

    Methods: Bibliometric data from 3054 documents that satisfied the inclusion criteria retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents.

    Results: The bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. The analysis also placed US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes.

    Conclusions: Proposals for future studies are made based on these findings. This study will provide health care informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.

    Interact J Med Res 2023;12:e42292

    doi:10.2196/42292

    Keywords

    big data; bibliometrics; infectious disease; COVID-19; disease surveillance; disease; pandemic; data; surveillance; hotspot; epidemiology; social media; utility; electronic health records 


    Globally, the infectious disease burden continues to be substantial in countries with low and lower-middle income, while morbidity and mortality related to neglected tropical diseases and HIV infection, tuberculosis, and malaria remain high. Tuberculosis and malaria are endemic to many areas, imposing substantial but steady burdens. At the same time, other infections such as influenza fluctuate in pervasiveness and intensity, disrupting the developing and developed settings alike when an outbreak and epidemic occurs. Additionally, deaths have persisted over the 21st century due to emerging and reemerging infectious diseases compared with seasonal and endemic infections. This portrays a new era of infectious disease, defined by outbreaks of emerging, reemerging, and endemic pathogens that spread quickly with the help of global mobility and climate change [1].

    Moreover, the risk from infectious diseases is globally shared. While infectious diseases thrive in underresourced settings, inequalities and inequities in accessing health and health care create a favorable environment for infectious diseases to spread [2,3]. Addressing inequalities and inequities in accessing health care, and improving surveillance and monitoring of infectious diseases should be prioritized to minimize the emergence and spread of infections.

    Recent years have witnessed the rapid emergence of big data and data science research, propelled by the increasing availability of digital traces [4]. The growing availability of electronic records and passive data generated by social media, the internet, and other digital sources can be mined for pattern discoveries and knowledge extraction. Like most buzz words, big data has no straightforward meaning and its definition is evolving. Broadly, big data refers to a large volume of structured or unstructured data, with largeness itself associated with three major terms known as the “3 Vs”: volume (large quantity), velocity (coming in at unprecedented real-time speeds), and variety (increasing collection from different data sources). Additional characteristics of big data include veracity, validity, volatility, and value [5]. For epidemiology and infectious diseases research, this means that in the last decade, there has been a significant spike in the number of studies with considerable interest in using digital epidemiology and big data tools to enhance health systems in terms of disease surveillance, modeling, and evidence-based responses [4,6-8]. Digital epidemiology uses digital data or online sources to gain insight into disease dynamics and health equity, and to inform public health programs and policies [9,10].

    The success of infectious disease control relies heavily on surveillance systems tracking diseases, pathogens, and clinical outcomes [11]. However, conventional surveillance systems are known to frequently have severe time lags and limited spatial resolution; therefore, surveillance systems that are robust, local, and timely are critically needed. It is crucial to monitor and forecast emerging and reemerging infections [12] such as severe acute respiratory syndrome, pandemic influenza, Ebola, Zika, and drug-resistant pathogens, especially in resource-limited settings such as low-middle–income countries. Using big data to strengthen surveillance systems is critical for future pandemic preparedness. This approach provides big data streams that can be triangulated with spatial and temporal data. These big data streams include digital data sources such as mobile health apps, electronic health (or medical) records, social media, internet searches, mobile phone network data, and GPS mobile data. Many studies have demonstrated the usefulness of real-time data in health assessments [13-18]. Some of these studies have been used explicitly for the monitoring and forecasting of epidemics such as COVID-19 [19], Zika [13], Ebola [16], and influenza [14].

    The body of extant literature at the nexus of big data, epidemiology, and infectious diseases is rapidly growing. However, despite its growth and dispersion, there has been a limited synthesis of the applications. A previous study [20] performed a bibliometric analysis focusing on only HIV. A bibliometric analysis is a statistical or quantitative analysis of large-scale bibliographic metadata (or metrics of published studies) on a given topic. These quantitative analyses detect patterns, networks, and trends among the bibliographic metadata [21,22]. Thus, the aim of this study was to address the evolution of big data in epidemiology and infectious diseases to identify gaps and opportunities for further research. The study findings reveal interesting patterns and can inform trending research focus and future directions in big data–driven infectious diseases research.


    Study Design

    A bibliometric analysis was performed to understand and explore research on big data in infectious disease modeling and surveillance. The adopted bibliometric methodology involved three main phases: data collection, data analysis, and data visualization and reporting [23].

    Search Strategy

    Regarding data collection, which entails querying and exporting data from selected databases, we queried the Web of Science (WoS) core databases for publications using specific inclusion and exclusion criteria. Compared to other databases, the WoS has been shown to have better quality bibliometric information [23,24] and more excellent coverage of high-impact journals [25]. With the aid of domain knowledge experts from the fields of both big data and epidemiology, we iteratively developed a search strategy and selected the following search terms. The following search string queried all documents’ titles, abstracts, and keywords, and generated 3235 publications in the WoS collection:

    (Epidemic* OR “infectious disease*” OR “Disease surveillance” OR “disease transmission” OR “disease outbreak*” OR (“communicable disease*” NOT “non-communicable disease”) OR syndemic* OR HIV OR AIDS OR “human immunodeficiency virus” OR coronavirus* OR SARS-CoV-2 OR COVID-19 OR Influenza OR flu OR Zika OR Ebola OR MERS OR “Middle East respiratory syndrome” OR Tuberculosis OR “Monkey Pox” OR “Dengue virus” OR Hepatitis*)

    AND

    (“BIG DATA” OR “web mining” OR “opinion mining” OR “Google Trend*” OR “Google search*” OR “Google quer*” OR “Internet search*” OR “Internet quer*” OR “search engine quer*” OR “Digital traces” OR “electronic health records” OR “Digital epidemiology”)

    Screening Strategy

    Documents not written in English and not peer-reviewed, including editorial materials, letters, meeting abstracts, news items, book reviews, and retracted publications, were removed from the data set given the focus on bibliometric analysis, leaving 3054 documents for the analytic sample (Figure 1).

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    Figure 1. Flow chart of the literature selection process.

    Analysis

    The 3054 bibliographic data were exported into the R package bibliometrix [23] for analysis. This package was specifically used to conduct performance analysis and science mapping of big data in infectious disease epidemiology. Performance mapping evaluates the production and impact of research constituents, including authors, institutions, countries, and journals. Science mapping examines the relationships between the research constituents by analyzing the topic’s conceptual, intellectual, and social structure.

    There are several metrics available for bibliometric analysis. In this study, the primary metrics used for evaluating productivity and influence were the H-index and M-index. The H-index represents the number of published papers h, such that the citation number is at least h [26]. The H-index can be computed for different bibliometric units of analysis: authors, journals, institutions, and countries. The M-index simply adjusts the H-index for the academic age (ie, the number of years since the researcher’s first publication). Other utilized performance analysis metrics were obtained from yearly research output and citation counts. These metrics also contribute to identifying the main themes and the key actors in the research area.

    In terms of science mapping, network maps were constructed for some selected bibliographic units of analysis [27]. These networks exhibit frequency distributions of the involved bibliographic data over time. For instance, international collaborations can be explored by assessing same-country publications. A cocitation network analysis was also used to analyze publication references. In addition, using the Louvain clustering algorithm and a greedy optimization technique [28], a co-occurrence analysis was used to understand the conceptual structure of the research area. The basic purpose of co-occurrence analysis is to investigate the link between keywords based on the number of times they appear together in a publication. Notable research topics and over-time trends were detected by generating clusters for author-provided keywords [29]. VOSviewer [30] was used to construct the network visualizations. Each network node represents a research constituent (eg, author, country, institution, article, document source, keyword). The node’s size is proportional to the occurrence frequency of the relevant parameters. The degree of association is represented by the thickness of the link between nodes, and the various colors reflect distinct clusters.


    Descriptive Summary

    The bibliographic data set comprises 3054 documents from 1600 sources, 14,351 authors, and 121,726 references. From the 3054 documents, 2666 (87.30%) were original research articles and the remaining 388 (12.70%) were review papers. The research output before 2009 was relatively low. The annual publication output during the 27 years (1995-2022) grew steadily, with a yearly growth rate of 26.5%. The publication growth increased steeply between 2013 and 2020 (Figure 2). Table 1 presents the summary statistics of the primary characteristics of these 3054 publications, including the time span and information about documents and authors.

    As shown in Table 2, the most productive and influential sources publishing on topics related to big data and infectious diseases epidemiology were Journal of Medical Internet Research and PLoS One (H-index=18), followed by IEEE Access (H-index=13). In terms of productivity, Journal of Medical Internet Research produced a slightly higher number of publications (n=61) than the next best journal PLoS One (n=56). PLoS One had the highest number of total citations at 1893.

    As shown in Table 3, the most productive and influential author was Zhang Y (H-index=17), followed by Li X (H-index=13) and Wang J (H-index=12). Wang L had the highest total citations (n=1072), which was substantially higher than the next most impactful author Wang J (total citations=861).

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    Figure 2. Annual growth of publications related to big data in infectious diseases research.
    Table 1. Main descriptive summary of the extracted bibliographic records from 1995 to 2022.
    DescriptionResults
    Time span (years)1995-2022
    Sources, n1600
    Documents, n3054
    Annual growth rate, %26.52
    Document average age (years)2.86
    Average citations per document, n18.52
    References, n121,726
    Authors, n

    		Total14,351

    		Single-authored documents225
    Author collaborations

    		Single-authored documents, n236

    		Coauthors per document, n5.55

    		International coauthorships, %28.04
    Table 2. Top 10 productive and influential publication sources ranked by H-index.
    JournalAim and scopeH-indexM-indexTotal citations, nPublications, nPublication year
    Journal of Medical Internet ResearchDigital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research181.131705612007
    PLoS OneMultidisciplinary181.391893562010
    IEEE AccessMultidisciplinary, comprising all IEEE fields of interest, emphasizing applications-oriented and interdisciplinary articles131.63983322015
    Scientific ReportsPublishes from all areas of the natural sciences, psychology, medicine, and engineering131.63389232015
    Journal of the American Medical Informatics AssociationBiomedical and health informatics, including clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy120.86569332009
    BMJ OpenMedical journal considering papers in clinical medicine, public health, and epidemiology111.10310322013
    JMIR Public Health & SurveillanceMultidisciplinary journal with a unique focus on the intersection of innovation and technology in public health112.20724232018
    International Journal of Medical InformaticsMedical informatics, including information systems and computer-aided medical support decision systems110.65450162006
    International Journal of Infectious DiseasesOriginal clinical and laboratory-based research, together with reports of clinical trials, reviews, and some case reports dealing with the epidemiology, clinical diagnosis, treatment, and control of infectious diseases100.91530172012
    BMC Medical Informatics & Decision MakingRelating to the design, development, implementation, and evaluation of health information technologies and decision-making for human health100.91208152012
    Table 3. Top 10 productive and influential authors ranked by H-index and total citations.
    AuthorH-indexM-indexTotal citations, nPublications, nPublication year
    Zhang Y17—a77635—
    Li X13—54435—
    Wang J121.33861242014
    Wang L12—107222—
    Wang Y101.25342212015
    Li Z101.67366142017
    Brownstein JS100.77748112010
    Wang Z91.00427182014
    Zhang W91.13556122015
    Zhang X91.29371122016

    aNot available.

    The aim and scope of the top 10 most influential journals, as listed in Table 2, is to publish medical research, medical informatics, or multidisciplinary studies. It can thus be inferred that major future breakthroughs regarding big data in infectious diseases epidemiology will likely appear in these journals.

    Figure 3 displays the top 20 most productive institutions. Institutional contributions were assessed by affiliations with at least one author in the publication. Except for the University of California, the top three institutions, which account for 21.3% of the number of publications in the top 20, were medical schools: Harvard Medical School (7.9%) and Icahn School of Medicine at Mount Sinai (6.4%). The other institutions, each accounting for more than 6% of the total, included Columbia University and Oxford University in the top 5, whereas others in the top 20 are research universities: London School of Hygiene and Tropical Medicine focuses on global and public health, Taipei Medical University is medical-based, and Huazhong University of Science and Technology is focused on science and technology. The United States produced the majority of the top 10 most productive institutions, which were in the top 5.

    The 20 most productive countries (Figure 4) are led by the United States and China, accounting for more than half (57.3%) of the total publication output. The United States alone accounted for 41.1% of the productivity in this field. The other countries in the top five were the United Kingdom (9.4%), India (4.4%), and Canada (3.3%).

    Computer science was the most productive research domain in the bibliographic collection (Figure 5), accounting for 17.6% of the top 10 subject areas. In order of productivity, the other research subjects in the top 5 were public environmental and occupational health (11.4%), health care services (9.6%), medical informatics (9.0%), and engineering (8.8%).

    ‎
    Figure 3. Top 20 institutions by number of publications. CALIF: California; HARVARD MED SCH: Harvard Medical School; ICAHN SCH MED MT SINAI: Icahn School of Medicine at Mount Sinai; LONDON SCH HYG AND TROP MED: London School of Hygiene & Tropical Medicine; PENN: Pennsylvania; UNIV: University.
    ‎
    Figure 4. Top 20 productive countries by number of publications.
    ‎
    Figure 5. Top 10 key subject areas by number of publications.

    Two major clusters of countries represent the collaboration patterns of the most productive countries (Figure 6). The network was set to include only countries with at least 10 documents, resulting in 50 productive countries. The clustering results demonstrated a demarcation of European countries from the others. For instance, cluster 1 (red) represented most countries from Europe, with England, Germany, and Spain being the core countries. Non-European countries constituted the second cluster (green). The United States and China were the core countries of this group.

    Regarding collaboration strength, the United States, with a total link strength of 570, featured the highest number of partners (48), accounting for almost all 50 countries in the network (96%). China, which distantly followed the United States, featured 38 partners and a total link strength of 304. This implies that collaboration is mainly regional.

    Figure 7 shows a network map of cocited references in this research area, wherein the node’s size represents the citation strength of the individual studies. The network was set to include only studies with at least 25 citations, resulting in 37 studies. Ginsberg et al [31] published the most highly cited article (185 citations). This 13-year-old study presented a method that used Google search queries to track flu-like illnesses in a population. The second most cited study by Eysenbach [9] introduced the concept of infodemiology, the science of using the internet (eg, social media, search engines, blogs, and websites) to inform public health and public policy. Table 4 further summarizes the top 15 most cited references, including the title, year of publication, number of citations, type of disease, and data source.

    The 37 studies in the network map of cocited references produced four thematic clusters (Figure 7); disease monitoring and surveillance (cluster 1), utility of electronic health (or medical) records (cluster 2), methodology framework for infodemiology tools (cluster 3), and machine learning and deep learning methods (cluster 4) were the main topics discussed.

    Keyword co-occurrence analysis serves as a supplement to enrich the understanding of the thematic clusters derived from the reference cocitation analysis and helps identify the core topics and contents [29]. As shown in Figure 8, the co-occurrence network displayed 100 relevant keywords after assigning a selection threshold of 10 for the number of keyword occurrences. The top 5 most frequently used keywords were COVID-19, big data, machine learning, coronavirus, and electronic health records.

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    Figure 6. Network of country collaborations (≥10 documents, 50 countries, 2 clusters).
    ‎
    Figure 7. Network of cocited references.
    Table 4. Summary of the top 15 most cited references.
    ReferenceCitations, nDiseaseData source
    Ginsberg et al [31]185InfluenzaGoogle Trends
    Eysenbach [9]74InfluenzaNAa
    Nuti et al [32]69NAGoogle Trends
    Lazer et al [33]67InfluenzaGoogle Flu
    Carneiro and Mylonaki [34]54NAGoogle Trends
    Zhou et al [35]49COVID-19Electronic health records
    Dong et al [36]49COVID-19Twitter feeds and DXYb
    Polgreen et al [37]48InfluenzaYahoo searches
    Mavragani and Ochoa [38]43NAGoogle Trends
    Huang et al [39]42COVID-19Electronic medical records
    Eysenbach [40]41InfluenzaGoogle Trends
    Wu et al [41]34COVID-19Electronic medical records
    Li et al [42]33COVID-19Internet searchesc and Weibo indexd
    Santillana et al [43]31InfluenzaTwitter and Google Trends
    Signorini et al [44]30InfluenzaTwitter

    aNA: not applicable (eg, a review paper, no particular disease or data source for a case study).

    bOnline platform of real-time COVID-19 cases in China.

    cInternet searches include Google Trends and Baidu Index.

    dWeibo is a China-based social media platform.

    ‎
    Figure 8. Co-occurrence networks of author keywords.

    The 100 author-derived keywords produced four clusters from the coword analysis (Figure 8). Cluster 1 (yellow-green) is related to public health and infectious diseases, with top keywords such as COVID-19, SARS-CoV-2, epidemiology, and epidemics. Cluster 2 (green) is related to electronic storage and delivery of health care, with top keywords including electronic health records, clinical decision support, primary care, epidemiology, and telemedicine. Cluster 3 (blue) involves infodemiology tools, with top keywords including coronavirus, google trends, social media, infodemiology, and surveillance. Cluster 4 (red) is more coherent and broadly related to big data and artificial intelligence, including top keywords big data, machine learning, artificial intelligence, deep learning, and big data analytics.

    Systematic Review of the Top 20 Papers

    Further filtering of the top 20 papers was performed to determine if they met the following criteria: (1) addressed at least one infectious disease and (2) utilized a big data source. A review of these 20 papers (summarized in Table 5) was then performed. These selected studies were mainly characterized by papers that utilized novel data sources, including internet search engine data (Google Trends: n=11; Baidu or Weibo index: n=2; Yahoo: n=1) and social media data (Twitter: n=5). Other data sources included electronic health or medical records (n=3) and Tencent migration data (n=1). The most frequently studied diseases were COVID-19 (n=10) [35,36,39,42,45-50], followed by influenza (n=8) [37,40,43,44,51-54]. Only one study considered the Zika virus [55], and another considered the trio of meningitis, legionella pneumonia, and Ebola [56].

    Table 5. Summary of top 20 studies that addressed an infectious disease and utilized a big data source.
    RankStudyResearch objective and key findingsInfectious disease(s)Big data source
    1Polgreen et al [37]Used internet search engine data for infectious diseases epidemiology and examined the relationship between Yahoo search queries for influenza and actual influenza occurrence. They estimated linear models, using searches with 1-10–week lead times as explanatory variables to predict the percentage of cultures positive for flu and deaths attributable to pneumonia and influenza in the United States. The fitted models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P<.001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P<.001).

    		InfluenzaInternet search engine
    2Walker et al [45]The research explored internet activity related to loss of smell in the United States and seven European countries. Spearman rank correlation was used to assess the relationship between loss-of-smell relative search volumes (RSVs), with the daily confirmed cases of COVID-19 and deaths. Strong and significant correlations (P<.05) between daily RSVs related to loss of smell, daily COVID-19 cases, and deaths were found, ranging from 0.633 to 0.952.COVID-19Google Trends
    3Effenberger et al [46]Studied correlations between RSVs and the official COVID-19 cases reported by the European Centre for Disease Control (ECDC) for some selected countries. They opted for time-lag correlation analysis and observed a time lag of –11.5 days being the highest correlation across all investigated countries.COVID-19Google Trends
    4Ayyoubzadeh et al [47]Opted for machine/deep learning with Google Trends data. Linear regression and long short-term memory (LSTM) models were used to estimate COVID-19 cases. They found that the linear regression model had the smaller root mean square error (RMSE) and was the better predictive model. They also found the most predictive factors of the model to be search terms of handwashing, hand sanitizer, and antiseptic topics.COVID-19Google Trends
    5Husnayain et al [48]Considered smaller spatial coverages in their Google Trends analysis. They retrieved data from specific locations and subregions in Taiwan nationwide using defined search terms related to the coronavirus, handwashing, and face masks. Their findings suggest high to moderate correlations between RSVs and COVID-19 cases in Taipei (lag –3), New Taipei (lag –2), Taoyuan (lag –2), Tainan (lag –1), Taichung (lag 0), and Kaohsiung (lag 0).COVID-19Google Trends
    6Eysenbach [40]Found a strong correlation (Pearson r=0.91) between the number of clicks on a keyword-triggered link in Google with epidemiological data from Canada’s flu season of 2004-2005.InfluenzaGoogle Trends
    7Yang et al [51]To improve the existing Google Flu Trends (GFT), they proposed an influenza tracking model, ARGO (AutoRegression with Google search data), that uses publicly available online search data. Besides having a rigorous statistical foundation, ARGO outperforms the latest GFT version. Not only does ARGO incorporate seasonality in influenza epidemics but it also captures changes in online search behavior over time.InfluenzaARGO
    8Cook et al [52]Evaluated the accuracy of each US GFT model by comparing weekly estimates of influenza-like illness (ILI) activity with the US Outpatient Influenza-like Illness Surveillance Network (ILINet). They calculated the correlation and RMSE between model estimates and ILINet for four seasons: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall. Both models’ estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during the pre-H1N1 phase. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively).InfluenzaGoogle Trends
    9Yuan et al [53]Used Baidu, a popular Chinese search index, to model and monitor influenza activity in China. A comprehensive technique was presented for (1) keyword selection, (2) keyword filtering, (3) index composition, and (4) modeling and detection of influenza activity in China. Sequential time series for the selected composite keyword index was significantly correlated with official Chinese influenza cases. Further, 1-month-ahead prediction of flu cases had a considerably small prediction error (mean absolute percent error<11%).InfluenzaBaidu search index
    10Dong et al [36]Used DXY, an online platform of the Chinese medical community, as a primary data source to develop an online interactive dashboard. The dashboard is hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, United States. They monitored various Twitter feeds, online news services, and direct communication sent through the dashboard to identify new cases.COVID-19DXY, Twitter feeds, online news services
    11Signorini et al [44]To explore public concerns regarding rapidly evolving H1N1 activity, used Twitter data and support vector regression to show that estimates of ILI accurately tracked reported disease levels. They retrieved a large sample of public tweets that matched a set of flu-related search terms.InfluenzaTwitter
    12Chew and Eyesenbach [54]Proposed and evaluated a complementary infoveillance approach using Twitter during the 2009 H1N1 pandemic. They performed a content analysis of tweets and validated Twitter as a real-time content and sentiment tracking tool. Infovigil, an infoveillance technology, was used to record more than 2 million Twitter postings with the terms “swine flu” and “H1N1.” According to content analysis, resource-related posts were most commonly shared (52.6%). Misinformation occurred in 4.5% of the cases. News websites were the most popular sources (23.2%), while government and health agencies were linked only 1.5% of the time.InfluenzaTwitter
    13Zhou et al [35]Used logistic regression models to explore the risk factors associated with in-hospital deaths. They utilized a retrospective, multicenter cohort study that included all adult inpatients with laboratory-confirmed COVID-19.COVID-19Electronic health (medical) records
    14Huang et al [39]This descriptive study detailed descriptive statistics of clinical features of patients infected with COVD-19 in China as extracted from electronic medical records.COVID-19Electronic health (medical) records
    15Williamson et al [49]Developed OpenSAFELY, a secure health analytics platform that maintains patient data in the current data center of a significant provider of primary care electronic health records and serves 40% of all patients in England. OpenSAFELY was used to examine factors associated with COVID-19–related deaths; 10,926 COVID-19–related deaths were pseudonymously linked to primary care records of 17,278,392 persons.COVID-19Electronic health (medical) records
    16Wu et al [50]Used data on monthly airline bookings from the Official Aviation Guide and data on human mobility across more than 300 prefecture-level Chinese cities from the Tencent database. The reports released by the Chinese Center for Disease Control and Prevention provided information on confirmed cases. A susceptible-exposed-infectious-recovered (SEIR) model was used to simulate the epidemics in China’s main cities. They concluded that, with a lag time of roughly 1-2 weeks behind the Wuhan outbreak, epidemics were already expanding exponentially in several large cities throughout China, assuming the transmissibility of SARS-CoV-2 was identical domestically and over time.COVID-19Tencent Migration data
    17Santillana et al [43]Presented an ensemble-based machine learning method that leverages data from various sources, including Google searches, Twitter microblogs, and near real-time hospital visit records, to provide nowcast and forecast estimates of influenza activity in the United States. Their method combines multiple ILI activity estimates, generated independently with each data source, into a single prediction of ILI. Evaluation of the predictive ability of their method suggests that it outperforms every prediction using each data source independently. Additionally, it generated estimates 2 and 3 weeks ahead of time with comparable accuracy to real-time forecasts from an autoregressive model and predictions 1 week ahead of GFT’s real-time estimates.InfluenzaGoogle searches, Twitter microblogs, and near real-time hospital visit records
    18Li et al [42]Evaluated the predictive value of search data from Google Trends and two Chinese social media platforms, Weibo index and Baidu index, for the COVID-19 epidemic in China. They observed that the peak of internet searches and social media data about the COVID-19 outbreak occurred 10-14 days earlier than the peak of daily incidences in China. Internet searches and social media data were highly correlated (r>0.89) with daily incidences.COVID-19Google Trends, Weibo index, Baidu index
    19Cervellin et al [56]Compared the reliability of Google Trends in different clinical settings for common diseases with lower media coverage and for less common diseases attracting major media coverage. They carried out a Google Trends search using the keywords “renal colic,” “epistaxis,” and “mushroom poisoning.” Additionally, a second search was carried out for three clinical conditions (ie, “meningitis,” “Legionella pneumophila pneumonia,” and “Ebola fever”). No correlation was observed between Google Trends and epidemiology of renal colics, epistaxis, and mushroom poisoning. When searching the term “mushroom” alone, the Google Trends search generated a seasonal pattern, almost overlapping with the epidemiological profile.Meningitis, Legionella pneumonia, and EbolaGoogle Trends
    20Teng et al [55]Developed a dynamic forecasting model for Zika virus (ZIKV) based on Google Trends. A strong correlation was found between Zika-related Google Trends and the cumulative numbers of reported cases (confirmed, suspected, and total cases P<.001). Further, an autoregressive integrated moving average (ARIMA) model (0,1,3) was fitted for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the ARIMA model, which used the online search data as the external regressor to enhance the forecasting model, is quite similar to the actual data during the Zika virus epidemic.ZikaGoogle Trends

    Principal Findings

    Novel big data streams have created interesting opportunities for infectious disease monitoring and control. The review of the top 20 papers suggests the domination of high-volume electronic health records and digital traces such as internet searches and social media. Of note is the relatively increased use of Google Trends. Most studies used Google Trends data by correlating them with official data on disease occurrence, spread, and outbreaks. Some of these studies further adopted nowcasting for disease surveillance. However, using Google Trends for forecasts and predictions in infectious diseases epidemiology fills a gap in the extant literature. Few studies have gone as far as predicting incidents and occurrences, even though data on reported cases of various health concerns and the associated Google Trends data have been correlated in many studies. Predicting the future is hard; hence, more reliable and efficient methodologies are needed for forecasting infectious disease outbreaks.

    There are a few drawbacks to digital trace data that should be considered. Many of these data streams miss demographic information such as age and gender, which is essential in almost any epidemiological study. Besides, they represent a growing but still limited population segment, with infants unfeatured and fewer older adults than younger people. Geographic heterogeneity in coverage exists, with underrepresentation in developing countries, although these biases tend to fade and are arguably less pronounced than those found in traditional global surveillance systems. Further, the retrieved data are subject to spatial and temporal uncertainty. Accordingly, hybrid systems that supplement rather than replace conventional surveillance systems as well as improve prospects for accurate infectious disease models and forecasts should be developed.

    Most studies, except for those in the United States and China, were conducted in the European context. Thus, more studies need to test the utility of these big data streams for infectious disease epidemiology in the context of more countries, especially in Africa. Future research questions should ask if any cross-cultural differences between countries affect the adoption and use of big data in infectious disease epidemiology.

    The vast majority of infectious diseases have a global distribution. Apart from the coronavirus, influenza, Zika, and Ebola virus outbreaks that are featured in our review, the utility of these big data sources for more infectious diseases should be studied.

    Limitations

    A few limitations were inherent in our study. First, like any bibliometric study, we are limited by the search terms and database used. This study utilized English publications from the WoS core collection; therefore, relevant publications may have been missed. However, our choice of WoS was informed by its greater coverage of high-impact journals. Second, some studies may have been published after we concluded document extraction. Accordingly, this study does not claim to be exhaustive but rather extensive.

    Future Research Agenda and Conclusions

    The bibliometric study identified the United States and China as research leaders in this field, with most affiliations from the Harvard Medical School and the University of California. Top authors were Zhang Yi and Li Xingwang. Journal of Medical Internet Research and PLoS One are the most productive and influential journals in this field. Internet searches and social media data are the most utilized data sources. COVID-19 and influenza were the most studied infectious diseases. The main research themes in this area of research were disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning. Most research papers on big data in infectious diseases epidemiology were published in outlets related to computer science, public health, and health care services.

    Opportunities for future research are revealed directly from the results of this study. Integrating multiple surveillance platforms, including big data tools, are critical to better understanding pathogen spread. It is also paramount for the research needs to align with a global view of disease risk. The risk of infectious disease is globally shared in an increasingly connected world. The COVID-19 pandemic, including the rapid global circulation of evolved strains, has emphasized the need for an interdisciplinary, collaborative, global framework for infectious disease research and control. There is a need to empower epidemiologists and public health scientists to leverage insights from big data for infectious disease prevention and control.

    Conflicts of Interest

    None declared.

    1. Baker RE, Mahmud AS, Miller IF, Rajeev M, Rasambainarivo F, Rice BL, et al. Infectious disease in an era of global change. Nat Rev Microbiol 2022 Apr 13;20(4):193-205 [FREE Full text] [CrossRef] [Medline]
    2. Hotez PJ. Globalists versus nationalists: Bridging the divide through blue marble health. PLoS Negl Trop Dis 2019 Jul 11;13(7):e0007156 [FREE Full text] [CrossRef] [Medline]
    3. Metcalf CJE, Tatem A, Bjornstad ON, Lessler J, O'Reilly K, Takahashi S, et al. Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa. Epidemiol Infect 2015 May;143(7):1457-1466 [FREE Full text] [CrossRef] [Medline]
    4. Kasson PM. Infectious disease research in the era of big data. Annu Rev Biomed Data Sci 2020 Jul 20;3(1):43-59. [CrossRef]
    5. Big data. Wikipedia.   URL: https://en.wikipedia.org/w/index.php?title=Big_data&oldid=1089502025 [accessed 2022-05-26]
    6. Hay SI, George DB, Moyes CL, Brownstein JS. Big data opportunities for global infectious disease surveillance. PLoS Med 2013 Apr 2;10(4):e1001413 [FREE Full text] [CrossRef] [Medline]
    7. Milinovich GJ, Soares Magalhães RJ, Hu W. Role of big data in the early detection of Ebola and other emerging infectious diseases. Lancet Glob Health 2015 Jan;3(1):e20-e21 [FREE Full text] [CrossRef] [Medline]
    8. Wong ZS, Zhou J, Zhang Q. Artificial intelligence for infectious disease big data analytics. Infect Dis Health 2019 Feb;24(1):44-48. [CrossRef] [Medline]
    9. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res 2009 Mar 27;11(1):e11 [FREE Full text] [CrossRef] [Medline]
    10. Salathé M. Digital epidemiology: what is it, and where is it going? Life Sci Soc Policy 2018 Jan 04;14(1):1 [FREE Full text] [CrossRef] [Medline]
    11. Thacker SB, Stroup DF. Future directions for comprehensive public health surveillance and health information systems in the United States. Am J Epidemiol 1994 Sep 01;140(5):383-397. [CrossRef] [Medline]
    12. Woolhouse MEJ, Rambaut A, Kellam P. Lessons from Ebola: Improving infectious disease surveillance to inform outbreak management. Sci Transl Med 2015 Sep 30;7(307):307rv5 [FREE Full text] [CrossRef] [Medline]
    13. Farhadloo M, Winneg K, Chan MS, Hall Jamieson K, Albarracin D. Associations of topics of discussion on Twitter with survey measures of attitudes, knowledge, and behaviors related to Zika: probabilistic study in the United States. JMIR Public Health Surveill 2018 Feb 09;4(1):e16 [FREE Full text] [CrossRef] [Medline]
    14. Lu FS, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, et al. Accurate influenza monitoring and forecasting using novel internet data streams: a case study in the Boston metropolis. JMIR Public Health Surveill 2018 Jan 09;4(1):e4 [FREE Full text] [CrossRef] [Medline]
    15. Mavragani A, Sampri A, Sypsa K, Tsagarakis KP. Integrating smart health in the US health care system: infodemiology study of asthma monitoring in the Google era. JMIR Public Health Surveill 2018 Mar 12;4(1):e24 [FREE Full text] [CrossRef] [Medline]
    16. van Lent LG, Sungur H, Kunneman FA, van de Velde B, Das E. Too far to care? Measuring public attention and fear for Ebola using Twitter. J Med Internet Res 2017 Jun 13;19(6):e193 [FREE Full text] [CrossRef] [Medline]
    17. Wongkoblap A, Vadillo MA, Curcin V. Researching mental health disorders in the era of social media: systematic review. J Med Internet Res 2017 Jun 29;19(6):e228 [FREE Full text] [CrossRef] [Medline]
    18. Xu C, Yang H, Sun L, Cao X, Hou Y, Cai Q, et al. Detecting lung cancer trends by leveraging real-world and internet-based data: infodemiology study. J Med Internet Res 2020 Mar 12;22(3):e16184 [FREE Full text] [CrossRef] [Medline]
    19. Mavragani A, Gkillas K. COVID-19 predictability in the United States using Google Trends time series. Sci Rep 2020 Nov 26;10(1):20693. [CrossRef] [Medline]
    20. Liang C, Qiao S, Olatosi B, Lyu T, Li X. Emergence and evolution of big data science in HIV research: Bibliometric analysis of federally sponsored studies 2000-2019. Int J Med Inform 2021 Oct;154:104558 [FREE Full text] [CrossRef] [Medline]
    21. Hassan NR, Loebbecke C. Engaging scientometrics in information systems. J Inf Technol 2017 Mar 01;32(1):85-109. [CrossRef]
    22. Chen C, Song M. Visualizing a field of research: a methodology of systematic scientometric reviews. PLoS One 2019 Oct 31;14(10):e0223994 [FREE Full text] [CrossRef] [Medline]
    23. Aria M, Cuccurullo C. bibliometrix : An R-tool for comprehensive science mapping analysis. J Informetr 2017 Nov;11(4):959-975. [CrossRef]
    24. Zhang Y, Huang Y, Porter AL, Zhang G, Lu J. Discovering and forecasting interactions in big data research: a learning-enhanced bibliometric study. Technol Forecast Soc Change 2019 Sep;146:795-807. [CrossRef]
    25. Harzing A, Alakangas S. Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison. Scientometrics 2015 Nov 26;106(2):787-804. [CrossRef]
    26. Hirsch JE. An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics 2010 Mar 13;85(3):741-754. [CrossRef]
    27. Batagelj V, Cerinšek M. On bibliographic networks. Scientometrics 2013 Jan 20;96(3):845-864. [CrossRef]
    28. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008;10:P10008. [CrossRef]
    29. Su H, Lee P. Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight. Scientometrics 2010 Jun 22;85(1):65-79. [CrossRef]
    30. van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010 Aug 31;84(2):523-538 [FREE Full text] [CrossRef] [Medline]
    31. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2009 Feb 19;457(7232):1012-1014. [CrossRef] [Medline]
    32. Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, et al. The use of google trends in health care research: a systematic review. PLoS One 2014 Oct 22;9(10):e109583 [FREE Full text] [CrossRef] [Medline]
    33. Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google Flu: traps in big data analysis. Science 2014 Mar 14;343(6176):1203-1205. [CrossRef] [Medline]
    34. Carneiro H, Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis 2009 Nov 15;49(10):1557-1564. [CrossRef] [Medline]
    35. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020 Mar 28;395(10229):1054-1062 [FREE Full text] [CrossRef] [Medline]
    36. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020 May;20(5):533-534 [FREE Full text] [CrossRef] [Medline]
    37. Polgreen P, Chen Y, Pennock D, Nelson F. Using internet searches for influenza surveillance. Clin Infect Dis 2008 Dec 01;47(11):1443-1448. [CrossRef] [Medline]
    38. Mavragani A, Ochoa G. Google trends in infodemiology and infoveillance: methodology framework. JMIR Public Health Surveill 2019 May 29;5(2):e13439 [FREE Full text] [CrossRef] [Medline]
    39. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020 Feb 15;395(10223):497-506 [FREE Full text] [CrossRef] [Medline]
    40. Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc 2006;2006:244-248 [FREE Full text] [Medline]
    41. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020 Apr 07;323(13):1239-1242. [CrossRef] [Medline]
    42. Li C, Chen L, Chen X, Zhang M, Pang C, Chen H. Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Euro Surveill 2020 Mar;25(10):2000199 [FREE Full text] [CrossRef] [Medline]
    43. Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput Biol 2015 Oct 29;11(10):e1004513 [FREE Full text] [CrossRef] [Medline]
    44. Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One 2011 May 04;6(5):e19467 [FREE Full text] [CrossRef] [Medline]
    45. Walker A, Hopkins C, Surda P. Use of Google Trends to investigate loss-of-smell-related searches during the COVID-19 outbreak. Int Forum Allergy Rhinol 2020 Jul 15;10(7):839-847 [FREE Full text] [CrossRef] [Medline]
    46. Effenberger M, Kronbichler A, Shin JI, Mayer G, Tilg H, Perco P. Association of the COVID-19 pandemic with Internet Search Volumes: A Google Trends Analysis. Int J Infect Dis 2020 Jun;95:192-197 [FREE Full text] [CrossRef] [Medline]
    47. Ayyoubzadeh SM, Ayyoubzadeh SM, Zahedi H, Ahmadi M, R Niakan Kalhori S. Predicting COVID-19 incidence through analysis of Google Trends data in Iran: data mining and deep learning pilot study. JMIR Public Health Surveill 2020 Apr 14;6(2):e18828 [FREE Full text] [CrossRef] [Medline]
    48. Husnayain A, Fuad A, Su EC. Applications of Google Search Trends for risk communication in infectious disease management: a case study of the COVID-19 outbreak in Taiwan. Int J Infect Dis 2020 Jun;95:221-223 [FREE Full text] [CrossRef] [Medline]
    49. Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020 Aug 08;584(7821):430-436 [FREE Full text] [CrossRef] [Medline]
    50. Wu J, Leung K, Leung G. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 2020 Feb 29;395(10225):689-697 [FREE Full text] [CrossRef] [Medline]
    51. Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using Google search data via ARGO. Proc Natl Acad Sci U S A 2015 Nov 24;112(47):14473-14478 [FREE Full text] [CrossRef] [Medline]
    52. Cook S, Conrad C, Fowlkes AL, Mohebbi MH. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One 2011 Aug 19;6(8):e23610 [FREE Full text] [CrossRef] [Medline]
    53. Yuan Q, Nsoesie EO, Lv B, Peng G, Chunara R, Brownstein JS. Monitoring influenza epidemics in china with search query from baidu. PLoS One 2013 May 30;8(5):e64323 [FREE Full text] [CrossRef] [Medline]
    54. Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One 2010 Nov 29;5(11):e14118 [FREE Full text] [CrossRef] [Medline]
    55. Teng Y, Bi D, Xie G, Jin Y, Huang Y, Lin B, et al. Dynamic forecasting of Zika epidemics using Google Trends. PLoS One 2017 Jan 6;12(1):e0165085 [FREE Full text] [CrossRef] [Medline]
    56. Cervellin G, Comelli I, Lippi G. Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings. J Epidemiol Glob Health 2017 Sep;7(3):185-189 [FREE Full text] [CrossRef] [Medline]

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    WoS: Web of Science

    Edited by A Mavragani; submitted 30.08.22; peer-reviewed by Z Wang, P Berchialla; comments to author 10.10.22; revised version received 21.10.22; accepted 29.11.22; published 31.03.23

    Copyright

    ©Lateef Babatunde Amusa, Hossana Twinomurinzi, Edith Phalane, Refilwe Nancy Phaswana-Mafuya. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 31.03.2023.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Interactive Journal of Medical Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.i-jmr.org/, as well as this copyright and license information must be included.