Digital disease detection - harnessing the Web for public health surveillance

Detecting influenza epidemics using search engine query data

Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295

Using Internet Searches for Influenza Surveillance

Polgreen, P. M., Chen, Y., Pennock, D. M., & Nelson, F. D. (2008) · Clinical Infectious Diseases · doi:10.1086/593098 · Cited by 567

Infodemiology: tracking flu-related searches on the web for syndromic surveillance.

Eysenbach, G. (2006) · PubMed · Cited by 414

Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project

Brownstein, J. S., Freifeld, C. C., Reis, B. Y., & Mandl, K. D. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050151 · Cited by 413

ProMED-mail: An Early Warning System for Emerging Diseases

Yu, V. L., & Madoff, L. C. (2004) · Clinical Infectious Diseases · doi:10.1086/422003 · Cited by 269

Using Social Media and Internet Data for Public Health Surveillance: The Importance of Talking

Hartley, D. (2014) · Milbank Quarterly · doi:10.1111/1468-0009.12039 · Cited by 22

Characterizing the Time-Perspective of Nations with Search Engine Query Data

Noguchi, T., Stewart, N., Olivola, C. Y., Moat, H. S., & Preis, T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095209 · Cited by 22

Using Social Media Data in Routine Pharmacovigilance: A Pilot Study to Identify Safety Signals and Patient Perspectives

Bhattacharya, M., Snyder, S., Malin, M., Truffa, M. M., Marinic, S., Engelmann, R., & Raheja, R. (2017) · Pharmaceutical Medicine · doi:10.1007/s40290-017-0186-6 · Cited by 22

Global Search Trends of Oral Problems using Google Trends from 2004 to 2016: An Exploratory Analysis

Patthi, B. (2017) · JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH · doi:10.7860/jcdr/2017/26658.10564 · Cited by 27

Public awareness, emotional reactions and human mobility in response to the COVID-19 outbreak in China – a population-based ecological study

Li, Y., Zeng, Y., Liu, G., Lu, D., Yang, H., Ying, Z., Hu, Y., Qiu, J., Zhang, C., Fall, K., et al. (2020) · Psychological Medicine · doi:10.1017/s003329172000375x · Cited by 23

Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches

Campo, D. S., Gussler, J. W., Sue, A., Skums, P., & Khudyakov, Y. E. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243622 · Cited by 23

Effective Detection of the 2009 H1N1 Influenza Pandemic in U.S. Veterans Affairs Medical Centers Using a National Electronic Biosurveillance System

Schirmer, P., Lucero-Obusan, C. A., Oda, G., Lopez, J., & Holodniy, M. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009533 · Cited by 21

Crime, inequality and public health: a survey of emerging trends in urban data science

Luca, M., Campedelli, G. M., Centellegher, S., Tizzoni, M., & Lepri, B. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1124526 · Cited by 29

Artificial Intelligence in battling infectious diseases: A transformative role

Li, C., Ye, G., Jiang, Y., Wang, Z., Yu, H., & Yang, M. (2024) · Journal of Medical Virology · doi:10.1002/jmv.29355 · Cited by 24

Epidemiologic Research and Web 2.0—the User-driven Web

Lee, B. K. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181f5a75f · Cited by 21

Contact tracing apps for self-quarantine in South Korea: rethinking datafication and dataveillance in the COVID-19 age

Lee, C. S. (2021) · Online Information Review · doi:10.1108/oir-08-2020-0377 · Cited by 21

A novel method for detecting psychological stress at tweet level using neighborhood tweets

KVTKN, P., & Ramakrishnudu, T. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.08.015 · Cited by 21

Approaches to the Surveillance of Foodborne Disease: A Review of the Evidence

Ford, L., Miller, M., Cawthorne, A., Fearnley, E., & Kirk, M. (2015) · Foodborne Pathogens and Disease · doi:10.1089/fpd.2015.2013 · Cited by 21

Elucidating Transmission Patterns From Internet Reports: Ebola and Middle East Respiratory Syndrome as Case Studies

Chowell, G., Cleaton, J. M., & Viboud, C. G. (2016) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiw356 · Cited by 21

Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls

Idahor, C. O., Esomu, E.-J. O., Ogbonna, N., Momoh, Z., Ogbeide, O. A., Ikhu-Omoregbe, O., Adigwe, A., Erhabor, O. M., Osaghae, O., & Orons, N. (2025) · Cureus · doi:10.7759/cureus.93929 · Cited by 29

Computational Approaches to Influenza Surveillance: Beyond Timeliness

Nsoesie, E. O., & Brownstein, J. S. (2015) · Cell Host & Microbe · doi:10.1016/j.chom.2015.02.004 · Cited by 20

Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

Wang, S., Tong, Y., Fan, Y., Liu, H., Wu, J., Wang, Z., & Fang, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-94060-4 · Cited by 20

An Overview of Spatial Analysis of Emerging Infectious Diseases

Robertson, C., & NELSON, T. A. (2014) · The Professional Geographer · doi:10.1080/00330124.2014.907702 · Cited by 20

Analysis of public concerns about influenza vaccinations by mining a massive online question dataset in Japan

Nawa, N., Kogaki, S., Takahashi, K., Ishida, H., Baden, H., Katsuragi, S., Narita, J., Tanaka‐Taya, K., & Ozono, K. (2016) · Vaccine · doi:10.1016/j.vaccine.2016.01.008 · Cited by 21

Ethics, Medicine, and Information Technology

Goodman, K. W. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781139600330 · Cited by 40

Sensitivity of an international notification system for wildlife diseases: A case study using the OIE‐WAHIS data on tularemia

Fanelli, A., Awada, L., Caceres‐Soto, P., Díaz, F., Grillo, T., Gizo, I., Hamilton, K., Rolez, C. L., Melens, P., Morales, R., et al. (2022) · Zoonoses and Public Health · doi:10.1111/zph.12916 · Cited by 19

ISIS

Beckman, R., Bisset, K. R., Chen, J., Lewis, B. L., MARATHE, M. V., & Stretz, P. E. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623375 · Cited by 19

Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform

Yang, H., Li, S., Sun, L. N., Zhang, X., Hou, J., & Wang, Y. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.8078 · Cited by 19

Genomics in Epidemiology and Disease Surveillance: An Exploratory Analysis

Tiwari, S., Dhakal, T., Kim, B.-J., Jang, G., & Oh, Y. (2025) · Life · doi:10.3390/life15121848 · Cited by 18

Effects and challenges of the COVID-19 pandemic in supply chain management: a text analytics approach

Khodoomi, M. R., Seif, M., & Hanne, T. (2023) · Supply Chain Forum an International Journal · doi:10.1080/16258312.2023.2253523 · Cited by 18

Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics

Liu, A. B., Lee, D., Jalihal, A. P., Hanage, W. P., & Springer, M. (2023) · Nature Communications · doi:10.1038/s41467-023-44199-7 · Cited by 17

Eye-Opening Approach to Norovirus Surveillance

Hulth, A., Andersson, Y., Hedlund, K., & Andersson, M. (2010) · Emerging infectious diseases · doi:10.3201/eid1608.100093 · Cited by 18

Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study

Nsoesie, E. O., Butler, P., Ramakrishnan, N., Mekaru, S. R., & Brownstein, J. S. (2015) · Scientific Reports · doi:10.1038/srep09112 · Cited by 18

Tweeting back: predicting new cases of back pain with mass social media data

Lee, H., McAuley, J. H., Hübscher, M., Allen, H. G., Kamper, S. J., & Moseley, G. L. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv168 · Cited by 18

Seasonal-adjustment Based Feature Selection Method for Predicting Epidemic with Large-scale Search Engine Logs

Tran, T. Q., & Sakuma, J. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330766 · Cited by 18

Impact assessment of immunization and the COVID-19 pandemic on varicella across Europe using digital epidemiology methods: A descriptive study

Sabale, U., Jarmale, L., Murtagh, J., Pawaskar, M. D., & Benčina, G. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283465 · Cited by 17

Searching for Truth: Internet Search Patterns as a Method of Investigating Online Responses to a Russian Illicit Drug Policy Debate

Zheluk, A., Gillespie, J., & Quinn, C. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2270 · Cited by 17

An efficient Session_Weight load balancing and scheduling methodology for high-quality telehealth care service based on WebRTC

Van, L. N., Kim, J., Park, S. H., Kim, J., & Jang, J. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-016-1636-8 · Cited by 17

A novel web informatics approach for automated surveillance of cancer mortality trends

Tourassi, G. D., Yoon, H., & Xu, S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.03.027 · Cited by 15

Neglected Tropical Disease Control – The Case for Adaptive, Location-specific Solutions

Booth, M., & Clements, A. (2018) · Trends in Parasitology · doi:10.1016/j.pt.2018.02.001 · Cited by 17

A proposed framework to limit post-lockdown community transmission of COVID-19 in Africa

Ikoona, E. N., & Kitara, D. L. (2021) · Pan African Medical Journal · doi:10.11604/pamj.2021.38.303.24008 · Cited by 17

Indications of Depressive Symptoms During the COVID-19 Pandemic in Germany: Comparison of National Survey and Twitter Data

Cohrdes, C., Yenikent, S., Wu, J., Ghanem, B., Franco-Salvador, M., & Vogelgesang, F. (2021) · JMIR Mental Health · doi:10.2196/27140 · Cited by 17

The Perfect Storm of Information: Combining Traditional and Non-Traditional Data Sources for Public Health Situational Awareness During Hurricane Response

Bennett, K. J., Olsen, J. M., Harris, S., Mekaru, S. R., Livinski, A. A., & Brownstein, J. S. (2013) · PLoS Currents · doi:10.1371/currents.dis.d2800aa4e536b9d6849e966e91488003 · Cited by 17

Precise Localization of Homes and Activities: Detecting Drinking-While-Tweeting Patterns in Communities

Hossain, N., Hu, T., Feizi, R., White, A. M., Luo, J., & Kautz, H. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14797 · Cited by 16

Using google trends to assess for seasonal variation in knee injuries

Dewan, V., & Sur, H. (2018) · Journal of Arthroscopy and Joint Surgery · doi:10.1016/j.jajs.2018.02.002 · Cited by 16

Forecasting virus outbreaks with social media data via neural ordinary differential equations

Núñez, M., Barreiro, N. L., Barrio, R. A., & Rackauckas, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-37118-9 · Cited by 21

Web search activity data accurately predict population chronic disease risk in the USA

Nguyen, T., Tran, T., Luo, W., Gupta, S., Rana, S., Phung, D., Nichols, M. S., Millar, L., Venkatesh, S., & Allender, S. E. (2015) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2014-204523 · Cited by 16

Using network theory to identify the causes of disease outbreaks of unknown origin

Bogich, T. L., Funk, S., Malcolm, T. R., Chhun, N., Epstein, J. H., Chmura, A. A., Kilpatrick, A. M., Brownstein, J. S., Hutchison, O. C., Doyle‐Capitman, C., et al. (2013) · Journal of The Royal Society Interface · doi:10.1098/rsif.2012.0904 · Cited by 16

Measles, social media and surveillance in Baltimore City

Warren, K. E., & Wen, L. S. (2016) · Journal of Public Health · doi:10.1093/pubmed/fdw076 · Cited by 16

The Prediction of Hepatitis E through Ensemble Learning

Peng, T., Chen, X., Wan, M., Jin, L., Wang, X., Du, X., Ge, H., & Yang, X. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18010159 · Cited by 16

Using internet intelligence to manage biosecurity risks: a case study for aquatic animal health

Lyon, A., Grossel, G., Burgman, M. A., & Nunn, M. (2013) · Diversity and Distributions · doi:10.1111/ddi.12057 · Cited by 16

Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions

Robertson, C. (2015) · GeoJournal · doi:10.1007/s10708-015-9688-5 · Cited by 16

Addressing machine learning concept drift reveals declining vaccine sentiment during the COVID-19 pandemicPreprint

Müller, M., & Salathé, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.02197 · Cited by 31

How COVID-19 Has Influenced Public Interest in Antimicrobials, Antimicrobial Resistance and Related Preventive Measures: A Google Trends Analysis of Italian Data

Maugeri, A., Barchitta, M., Basile, G., & Agodi, A. (2022) · Antibiotics · doi:10.3390/antibiotics11030379 · Cited by 13

From infectious diseases to chronic diseases: the paradigm shift of spatial epidemiology in disease prevention and control

Hu, K., Li, C., Yang, X., Ou, S., Zhang, X., Xiao, D., & Yu, M. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1698964 · Cited by 15

Assessing the Continuum of Event-Based Biosurveillance Through an Operational Lens

Corley, C. D., Lancaster, M. J., Brigantic, R. T., Chung, J. S., Walters, R. A., Arthur, R. R., Bruckner-Lea, C. J., Calapristi, A. J., Dowling, G., Hartley, D., et al. (2012) · Biosecurity and Bioterrorism Biodefense Strategy Practice and Science · doi:10.1089/bsp.2011.0096 · Cited by 15

Mobile User Research

Consolvo, S., Bentley, F. R., Hekler, E. B., & Phatak, S. S. (2017) · Synthesis lectures on mobile and pervasive computing · doi:10.2200/s00763ed1v01y201703mpc012 · Cited by 14

Economic Recession and Obesity-Related Internet Search Behavior in Taiwan: Analysis of Google Trends Data

Wang, H.-W., & Chen, D. (2018) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.7314 · Cited by 16

Nonannual seasonality of influenza‐like illness in a tropical urban setting

Lam, H. M., Wesolowski, A., Hùng, N. T., Nguyen, T. D., Nhat, N. T. D., Todd, S., Vinh, D. N., Vy, N. H. T., Thao, T. T. N., Thanh, N. T. L., et al. (2018) · Influenza and Other Respiratory Viruses · doi:10.1111/irv.12595 · Cited by 13

Lay perceptions of emerging infectious diseases: a commentary

Washer, P. (2011) · Public Understanding of Science · doi:10.1177/0963662511406468 · Cited by 15

Enabling Rapid Classification of Social Media Communications During Crises

Imran, M., Mitra, P., & Srivastava, J. (2016) · International Journal of Information Systems for Crisis Response and Management · doi:10.4018/ijiscram.2016070101 · Cited by 15

Conflicting Online Health Information and Rational Decision Making

Yoon, H., Sohn, M., Choi, M., & Jung, M. (2017) · The Health Care Manager · doi:10.1097/hcm.0000000000000159 · Cited by 15

Summertime blues? A re-examination of the seasonality of web searches for restless legs and leg cramps

O’Keeffe, S. T. (2017) · Sleep Medicine · doi:10.1016/j.sleep.2017.06.017 · Cited by 14

Online Public Health Intelligence: Ethical Considerations at the Big Data Era

Mamiya, H., Shaban‐Nejad, A., & Buckeridge, D. L. (2017) · Lecture notes in social networks · doi:10.1007/978-3-319-68604-2_8 · Cited by 14

Social Media, Rumors, and Hurricane Warning Systems in Puerto Rico

Bui, L. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.321 · Cited by 14

Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach

Hemedan, A. A., Elaziz, M. A., Jiao, P., Alavi, A. H., Bahgat, M. M., Ostaszewski, M., Schneider, R., Ghazy, H. A., Ewees, A. A., & Lu, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-61853-y · Cited by 16

Teaching and Learning in Higher Education in the Time of COVID-19

Mkhize, N., Ndimande-Hlongwa, N., Ramrathan, L., & Smit, J. (2020) · CSALL Publishers (Pty) LtD eBooks · doi:10.29086/978-0-9869936-4-0/2020/aasbs03 · Cited by 14

A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization

Ji, Z., Xia, T., Han, M., & Xiao, J. Z. (2021) · doi:10.18653/v1/2021.acl-long.219 · Cited by 17

Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion

Jahja, M., Chin, A., & Tibshirani, R. J. (2022) · Statistical Science · doi:10.1214/22-sts856 · Cited by 14

Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach

Dolatabadi, E., Moyano, D., Bales, M., Spasojevic, S. S., Bhambhoria, R. V., Bhatti, J. A., Debnath, S., Hoell, N., Li, X., Leng, C., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/45767 · Cited by 14

Quantifying Network Dynamics and Information Flow Across Chinese Social Media During the African Ebola Outbreak

Feng, S., Hossain, L., Crawford, J. W., & Bossomaier, T. R. J. (2017) · Disaster Medicine and Public Health Preparedness · doi:10.1017/dmp.2017.29 · Cited by 14

Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization

Musa, I., Park, H. W., Munkhdalai, L., & Ryu, K. H. (2018) · Sustainability · doi:10.3390/su10103414 · Cited by 14

Communication About Hereditary Cancers on Social Media: A Content Analysis of Tweets About Hereditary Breast and Ovarian Cancer and Lynch Syndrome

Allen, C. G., Roberts, M. C., Andersen, B., & Khoury, M. J. (2018) · Journal of Cancer Education · doi:10.1007/s13187-018-1451-4 · Cited by 15

Characterizing HIV discussions and engagement on Twitter

Malik, A. N., Angi, A., Khan, M. L., & Nieminen, M. (2021) · Health and Technology · doi:10.1007/s12553-021-00577-z · Cited by 13

Spatial aggregation choice in the era of digital and administrative surveillance data

Lee, E. C., Arab, A., Colizza, V., & Bansal, S. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000039 · Cited by 13

Analyzing social media to characterize local HIV at-risk populations

Thangarajan, N., Green, N., Gupta, A., Little, S. J., & Weibel, N. (2015) · Wireless Health · doi:10.1145/2811780.2811923 · Cited by 13

Enablers of innovation in digital public health surveillance: lessons from Flutracking

Dalton, C. B. (2017) · International Health · doi:10.1093/inthealth/ihx009 · Cited by 13

Seasonality of Back Pain in Italy: An Infodemiology Study

Ciaffi, J., Melicòni, R., Landini, M. P., Mancarella, L., Brusi, V., Faldini, C., & Ursini, F. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18031325 · Cited by 13

Experiences and perspectives of individuals accessing CAR-T cell therapy: A qualitative analysis of online Reddit discussions

Jenei, K., Burgess, M., Peacock, S. J., & Raymakers, A. (2021) · Journal of Cancer Policy · doi:10.1016/j.jcpo.2021.100303 · Cited by 13

Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining

Mori, Y., Miyatake, N., Suzuki, H., Mori, Y., Okada, S., & Tanimoto, K. (2023) · Vaccines · doi:10.3390/vaccines11081327 · Cited by 13

Surveillance of an Online Social Network to Assess Population-level

Weitzman, E. R., Keleman, S., & Mandl, K. D. (2011) · Online Journal of Public Health Informatics · doi:10.5210/ojphi.v3i3.3797 · Cited by 13

Use of Internet Search Data to Monitor Rotavirus Vaccine Impact in the United States, United Kingdom, and Mexico

Shah, M. P., Lopman, B. A., Tate, J. E., Harris, J. P., Esparza‐Aguilar, M., Sánchez-Uribe, E., Richardson, V., Steiner, C., & Parashar, U. D. (2017) · Journal of the Pediatric Infectious Diseases Society · doi:10.1093/jpids/pix004 · Cited by 14

An Overview of Point-of-Care Technologies Enabling Next-Generation Healthcare Monitoring and Management

Vashist, S. K., & Luong, J. H. T. (2019) · Springer eBooks · doi:10.1007/978-3-030-11416-9_1 · Cited by 13

The Transformative Power of Language

(2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088 · Cited by 13

A modular, extensible approach to massive ecologically valid behavioral data

VanDam, M., & Palma, P. D. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1167-8 · Cited by 13

Quantifying the Search Behaviour of Different Demographics Using Google Correlate

Letchford, A., Preis, T., & Moat, H. S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149025 · Cited by 12

Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States

Reich, N. G., Wang, Y., Burns, M., Ergas, R., Cramer, E. Y., & Ray, E. L. (2023) · Epidemics · doi:10.1016/j.epidem.2023.100728 · Cited by 12

Using AquaticHealth.net to Detect Emerging Trends in Aquatic Animal Health

Lyon, A., Mooney, A., & Grossel, G. (2013) · Agriculture · doi:10.3390/agriculture3020299 · Cited by 12

From Value Assessment to Value Cocreation: Informing Clinical Decision-Making with Medical Claims Data

Thompson, S. M., Varvel, S. A., Sasinowski, M., & Burke, J. P. (2016) · Big Data · doi:10.1089/big.2015.0030 · Cited by 12

Artificial Intelligence and Telehealth may Provide Early Warning of Epidemics

Arslan, J., & Benke, K. K. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.556848 · Cited by 12

Social Media Health-Related Information Credibility and Reliability: An Integrated User Perceived Quality Assessment

Egala, S. B., Liang, D., & Boateng, D. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3225182 · Cited by 14

Variation in the fluorescence intensity of thermally-exposed bacterial bioaerosols

Jung, J. H., & Lee, J. E. (2013) · Journal of Aerosol Science · doi:10.1016/j.jaerosci.2013.07.008 · Cited by 12

Heat stroke detection system based in IoT

Antonio, P. O., Ceballos-Hernández, M. R., Vicente, R., Carolina, B., & Barzallo, B. (2017) · IEEE Ecuador Technical Chapters Meeting (ETCM) · doi:10.1109/etcm.2017.8247509 · Cited by 12

Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method

Dong, J., Dai, W., Liu, Y., Yu, L., & Wang, J. (2019) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622019500287 · Cited by 12

Online Searching and Social Media to Detect Alcohol Use Risk at Population Scale

Weitzman, E. R., Magane, K. M., Chen, P.-H., Amiri, H., Naimi, T. S., & Wisk, L. E. (2019) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2019.08.027 · Cited by 12

A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China

He, Z., Zhang, C. J. P., Huang, J. V., Zhai, J., Zhou, S., Chiu, J. W.-T., Sheng, J., Tsang, W., Akinwunmi, B. O., & Ming, W. (2020) · Journal of Medical Internet Research · doi:10.2196/21685 · Cited by 12

Cyber security and the unexplored threat to global health: a call for global norms

Bernard, R., Bowsher, G. M., & Sullivan, R. J. (2020) · Global Security Health Science and Policy · doi:10.1080/23779497.2020.1865182 · Cited by 12

Harnessing the Potential of Google Searches for Understanding Dynamics of Intimate Partner Violence Before and After the COVID-19 Outbreak

Köksal, S., Pesando, L. M., Rotondi, V., & Şanlıtürk, A. E. (2022) · European Journal of Population / Revue européenne de Démographie · doi:10.1007/s10680-022-09619-2 · Cited by 15

Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study

Khan, Y., Leung, G. J., Belanger, P., Gournis, E., Buckeridge, D. L., Liu, L., Li, Y., & Johnson, I. L. (2018) · Canadian Journal of Public Health · doi:10.17269/s41997-018-0059-0 · Cited by 11

Big Data : Do Google Searches Predict Unemployment?

Tuhkuri, J. (2015) · Työväentutkimus Vuosikirja · Cited by 11

Public engagement with health and climate change around the world: a Google Trends analysis

Dasandi, N., Mikhaylov, S., Pantera, D. K., & Romanello, M. B. (2025) · The Lancet Planetary Health · doi:10.1016/s2542-5196(25)00029-4 · Cited by 13

In search of art: rapid estimates of gallery and museum visits using Google Trends

Botta, F., Preis, T., & Moat, H. S. (2020) · EPJ Data Science · doi:10.1140/epjds/s13688-020-00232-z · Cited by 11

Global online public interest in monkeypox compared with COVID-19: Google trends in 2022

Martins‐Filho, P. R., Araújo, A. A. D. S., & Quintans‐Júnior, L. J. (2022) · Journal of Travel Medicine · doi:10.1093/jtm/taac104 · Cited by 15

Who Uses Telehealth? Setting a Usage Baseline for the Early Identification of Pandemic Influenza Activity

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