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

Lateef babatunde amusa.

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

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

Hossana Twinomurinzi

Edith phalane.

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

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

Refilwe Nancy Phaswana-Mafuya

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.

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

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.

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.

Introduction

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*)

(“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 ).

Flow chart of the literature selection process.

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.

Annual growth of publications related to big data in infectious diseases research.

Main descriptive summary of the extracted bibliographic records from 1995 to 2022.

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.

Top 10 productive and influential publication sources ranked by H-index.

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).

Top 10 productive and influential authors ranked by H-index and total citations.

a Not 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.

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.

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%).

Top 20 productive countries by number of publications.

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%).

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.

Network of country collaborations (≥10 documents, 50 countries, 2 clusters).

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.

Network of cocited references.

Summary of the top 15 most cited references.

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

b Online platform of real-time COVID-19 cases in China.

c Internet searches include Google Trends and Baidu Index.

d Weibo is a China-based social media platform.

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.

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 ].

Summary of top 20 studies that addressed an infectious disease and utilized a big data source.

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.

Abbreviations

Conflicts of Interest: None declared.

Bibliometric analysis of the Doctor of Nursing Practice dissertations in the ProQuest Dissertations and Theses database

Affiliation.

• 1 School of Nursing, Peking Union Medical College, Beijing, China.
• PMID: 34468043
• DOI: 10.1111/jan.15006

Aims: To examine the distributed characteristics and explore the research themes of Doctor of Nursing Practice (DNP) dissertations during the past two decades.

Design: A descriptive statistical and visualization bibliometric analysis was conducted.

Methods: Doctor of Nursing Practice dissertations submitted between January 2005 and June 2021 were collected from the ProQuest Dissertations and Theses database. A descriptive statistical analysis was conducted to calculate the distribution of the DNP dissertations by granting institution and the published year of publications. The VOSviewer 1.6.13 was used to explore the bibliometric networks and research priorities of the DNP dissertations.

Results: A total of 4989 DNP dissertations from 90 universities were included in this study, all from the United States. The number of DNP dissertations showed an upward trend, with steady growth from 2005 to 2014 and rapid growth after 2015. The DNP studies focused on five areas: health care management in clinical nursing, advanced practice in nursing education and health education, public health problems, mental health care for adolescents and nurses and the older people care and long-term care.

Conclusion: Parallel to the numerical increase in DNP dissertations is a steady expansion in the range of research topics and scopes, which is aligned with specific specializations of the DNP. Many are interdisciplinary and employ techniques imported from the fields of public health, psychology and social sciences, resulting in nursing educators and practitioners continually broaden their subject perspectives.

Impact: Knowing where, when and why DNP research trends developed will help nursing educators to further develop DNP education and optimize DNP programs in the future, such as paying more attention to the nursing practice. Moreover, this study will inspire DNP students and researchers to expand their subject perspectives and broaden the research scope to solve nursing practice problems based on interdisciplinary theories and methods.

Keywords: Doctor of Nursing Practice; bibliometric analysis; dissertation; nursing; nursing education.

© 2021 John Wiley & Sons Ltd.

• Bibliometrics
• Education, Nursing*
• Education, Nursing, Graduate*
• Faculty, Nursing
• Nursing Research*
• Students, Nursing*
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A Bibliometric Analysis of the Theses and Dissertations on Information Literacy Published in the United States and Taiwan

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• Pao-Nuan Hsieh 4 ,
• Tao-Ming Chuang 5 &
• Mei-Ling Wang 6  

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 20))

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The purpose of this study is to explore the characteristics of the theses and dissertations on information literacy from 1988 to 2010 in the United States and Taiwan, such as publishing universities, paper growth, author/ advisor productivity, type of literacy, and research methods. The comparison of theses and dissertations on information literary research is made between those completed in the United States and Taiwan. This study investigates and maps the trends in information literacy research by applying bibliometric analysis to the 767 theses and dissertations in the field of information literacy in the United States and Taiwan. The study reveals that theses and dissertations on information literacy in Taiwan grew rapidly (502, 65.45%) and more were published than in the United States (265, 34.55%), although the first doctoral dissertation published in the United States was in 1988 while the first master thesis published in Taiwan was in 1996.The rates at which they dealt with information literacy, media literacy, and digital literacy were respectively 54.57%, 30.59%, and 14.84%, there are significant differences between the United States and Taiwan in the three types of literacy research. Furthermore, the type of methodology implemented in theses and dissertations in the United States is different from that used in Taiwan.

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Pao-Nuan Hsieh

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Graduate Institute of Library, Information and Archival Studies, National ChengChi University, Taipei, Taiwan

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Hsieh, PN., Chuang, TM., Wang, ML. (2013). A Bibliometric Analysis of the Theses and Dissertations on Information Literacy Published in the United States and Taiwan. In: Chang, RS., Jain, L., Peng, SL. (eds) Advances in Intelligent Systems and Applications - Volume 1. Smart Innovation, Systems and Technologies, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35452-6_35

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This passive seismic dataset was analyzed as part of the PhD Thesis by David Rampton, "A Comprehensive Geophysical Analysis to Determine Induced Fracture Distribution from a Hydraulic Fracturing Operation in the Marcellus Shale Formation", March 2014 and will be presented in an upcoming paper. This data was acquired in conjunction with a timelapse crosswell dataset, data also available on Kilthub. The entire dataset is available on NETL's data exchange EDX, but this paper only analyzed four stages. The raw data is included, along with contractor information, final results, and scripts used to generate intermediate and final results.

This research was supported in part by an appointment to the U.S. Department of Energy (DOE) Postgraduate Research Program at the National Energy Technology Laboratory administered by the Oak Ridge Institute for Science and Education.

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