This paper is in the following e-collection/theme issue:

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Infodemiology and Infoveillance (1349)

Published on in Vol 2, No 1 (2016): Jan-Jun

Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak

Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak

Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak

Authors of this article:

Maimuna S Majumder1, 2 Author Orcid Image ;   Mauricio Santillana1, 3, 4 Author Orcid Image ;   Sumiko R Mekaru1, 5 Author Orcid Image ;   Denise P McGinnis1 Author Orcid Image ;   Kamran Khan6, 7 Author Orcid Image ;   John S Brownstein1, 4 Author Orcid Image
Article Authors Cited by (76) Tweetations (31) Metrics

Journals

  1. Agarwal V, Zhang L, Zhu J, Fang S, Cheng T, Hong C, Shah N. Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis. Journal of Medical Internet Research 2016;18(9):e251 View
  2. Li J, Xu Q, Cuomo R, Purushothaman V, Mackey T. Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study. JMIR Public Health and Surveillance 2020;6(2):e18700 View
  3. Gong X, Han Y, Hou M, Guo R. Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index. JMIR Public Health and Surveillance 2020;6(4):e23098 View
  4. Gianfredi V, Bragazzi N, Nucci D, Martini M, Rosselli R, Minelli L, Moretti M. Harnessing Big Data for Communicable Tropical and Sub-Tropical Disorders: Implications From a Systematic Review of the Literature. Frontiers in Public Health 2018;6 View
  5. Liu Y, Lillepold K, Semenza J, Tozan Y, Quam M, Rocklöv J. Reviewing estimates of the basic reproduction number for dengue, Zika and chikungunya across global climate zones. Environmental Research 2020;182:109114 View
  6. 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. Eurosurveillance 2020;25(10) View
  7. McGough S, Brownstein J, Hawkins J, Santillana M, Althouse B. Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. PLOS Neglected Tropical Diseases 2017;11(1):e0005295 View
  8. Hsieh Y. Temporal patterns and geographic heterogeneity of Zika virus (ZIKV) outbreaks in French Polynesia and Central America. PeerJ 2017;5:e3015 View
  9. Alzahrani E, Ahmad W, Altaf Khan M, Malebary S. Optimal Control Strategies of Zika Virus Model with Mutant. Communications in Nonlinear Science and Numerical Simulation 2021;93:105532 View
  10. L. D, González-Parra G, Benincasa T. Mathematical modeling and numerical simulations of Zika in Colombia considering mutation. Mathematics and Computers in Simulation 2019;163:1 View
  11. Kobres P, Chretien J, Johansson M, Morgan J, Whung P, Mukundan H, Del Valle S, Forshey B, Quandelacy T, Biggerstaff M, Viboud C, Pollett S, Pimenta P. A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern. PLOS Neglected Tropical Diseases 2019;13(10):e0007451 View
  12. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  13. Manore C, Ostfeld R, Agusto F, Gaff H, LaDeau S, Scarpino S. Defining the Risk of Zika and Chikungunya Virus Transmission in Human Population Centers of the Eastern United States. PLOS Neglected Tropical Diseases 2017;11(1):e0005255 View
  14. Miller M, Banerjee T, Muppalla R, Romine W, Sheth A. What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention. JMIR Public Health and Surveillance 2017;3(2):e38 View
  15. Moon H, Lee G. Evaluation of Korean-Language COVID-19–Related Medical Information on YouTube: Cross-Sectional Infodemiology Study. Journal of Medical Internet Research 2020;22(8):e20775 View
  16. Chang Y, Chiang W, Wang W, Lin C, Hung L, Tsai Y, Suen J, Chen Y. Google Trends-based non-English language query data and epidemic diseases: a cross-sectional study of the popular search behaviour in Taiwan. BMJ Open 2020;10(7):e034156 View
  17. Aiken E, McGough S, Majumder M, Wachtel G, Nguyen A, Viboud C, Santillana M, Pulliam J. Real-time estimation of disease activity in emerging outbreaks using internet search information. PLOS Computational Biology 2020;16(8):e1008117 View
  18. Vayena E, Dzenowagis J, Brownstein J, Sheikh A. Policy implications of big data in the health sector. Bulletin of the World Health Organization 2018;96(1):66 View
  19. Anaya J, Rodríguez Y, Monsalve D, Vega D, Ojeda E, González-Bravo D, Rodríguez-Jiménez M, Pinto-Díaz C, Chaparro P, Gunturiz M, Ansari A, Gershwin M, Molano-González N, Ramírez-Santana C, Acosta-Ampudia Y. A comprehensive analysis and immunobiology of autoimmune neurological syndromes during the Zika virus outbreak in Cúcuta, Colombia. Journal of Autoimmunity 2017;77:123 View
  20. Fitzgibbon W, Morgan J, Webb G. An outbreak vector-host epidemic model with spatial structure: the 2015–2016 Zika outbreak in Rio De Janeiro. Theoretical Biology and Medical Modelling 2017;14(1) View
  21. Mavragani A, Ochoa G, Tsagarakis K. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270 View
  22. Masri S, Jia J, Li C, Zhou G, Lee M, Yan G, Wu J. Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic. BMC Public Health 2019;19(1) View
  23. Teng Y, Bi D, Xie G, Jin Y, Huang Y, Lin B, An X, Feng D, Tong Y, Paul R. Dynamic Forecasting of Zika Epidemics Using Google Trends. PLOS ONE 2017;12(1):e0165085 View
  24. Barros J, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. Journal of Medical Internet Research 2020;22(3):e13680 View
  25. Wang L, Zhao H, Oliva S, Zhu H. Modeling the transmission and control of Zika in Brazil. Scientific Reports 2017;7(1) View
  26. Ling R, Lee J. Disease Monitoring and Health Campaign Evaluation Using Google Search Activities for HIV and AIDS, Stroke, Colorectal Cancer, and Marijuana Use in Canada: A Retrospective Observational Study. JMIR Public Health and Surveillance 2016;2(2):e156 View
  27. Keegan L, Lessler J, Johansson M. Quantifying Zika: Advancing the Epidemiology of Zika With Quantitative Models. The Journal of Infectious Diseases 2017;216(suppl_10):S884 View
  28. Ospina J, Hincapie‐Palacio D, Ochoa J, Molina A, Rúa G, Pájaro D, Arrubla M, Almanza R, Paredes M, Mubayi A. Stratifying the potential local transmission of Zika in municipalities of Antioquia, Colombia. Tropical Medicine & International Health 2017;22(10):1249 View
  29. Bragazzi N, Alicino C, Trucchi C, Paganino C, Barberis I, Martini M, Sticchi L, Trinka E, Brigo F, Ansaldi F, Icardi G, Orsi A, Olson D. Global reaction to the recent outbreaks of Zika virus: Insights from a Big Data analysis. PLOS ONE 2017;12(9):e0185263 View
  30. Ayyoubzadeh S, Ayyoubzadeh S, 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 and Surveillance 2020;6(2):e18828 View
  31. Gardy J, Loman N. Towards a genomics-informed, real-time, global pathogen surveillance system. Nature Reviews Genetics 2018;19(1):9 View
  32. Chesnut M, Muñoz L, Harris G, Freeman D, Gama L, Pardo C, Pamies D. In vitro and in silico Models to Study Mosquito-Borne Flavivirus Neuropathogenesis, Prevention, and Treatment. Frontiers in Cellular and Infection Microbiology 2019;9 View
  33. Arora N, Banerjee A, Narasu M. Zika Outbreak Aftermath: Status, Progress, Concerns and New Insights. Future Virology 2018;13(8):539 View
  34. Jones R, Kulkarni M, Davidson T, Talbot B, Samy A. Arbovirus vectors of epidemiological concern in the Americas: A scoping review of entomological studies on Zika, dengue and chikungunya virus vectors. PLOS ONE 2020;15(2):e0220753 View
  35. Higgins T, Wu A, Sharma D, Illing E, Rubel K, Ting J. Correlations of Online Search Engine Trends With Coronavirus Disease (COVID-19) Incidence: Infodemiology Study. JMIR Public Health and Surveillance 2020;6(2):e19702 View
  36. Hou Z, Du F, Zhou X, Jiang H, Martin S, Larson H, Lin L. Cross-Country Comparison of Public Awareness, Rumors, and Behavioral Responses to the COVID-19 Epidemic: Infodemiology Study. Journal of Medical Internet Research 2020;22(8):e21143 View
  37. Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. Journal of Big Data 2018;5(1) View
  38. Mendivelso Duarte F, Robayo García A, Rodríguez Bedoya M, Suárez Rángel G. Notificación de defectos congénitos por brote del virus del Zika en Colombia, 2015-2017. Revista Panamericana de Salud Pública 2019;43:1 View
  39. Akhtar M, Kraemer M, Gardner L. A dynamic neural network model for predicting risk of Zika in real time. BMC Medicine 2019;17(1) View
  40. He D, Gao D, Lou Y, Zhao S, Ruan S. A comparison study of Zika virus outbreaks in French Polynesia, Colombia and the State of Bahia in Brazil. Scientific Reports 2017;7(1) View
  41. Majumder M, Rose S. Vaccine Deployment and Ebola Transmission Dynamics Estimation in Eastern DR Congo. SSRN Electronic Journal 2018 View
  42. Majumder M, Nguyen C, Cohn E, Hswen Y, Mekaru S, Brownstein J. Vaccine compliance and the 2016 Arkansas mumps outbreak. The Lancet Infectious Diseases 2017;17(4):361 View
  43. Nasrinpour H, Reimer A, Friesen M, McLeod R. Data Preparation for West Nile Virus Agent-Based Modelling: Protocol for Processing Bird Population Estimates and Incorporating ArcMap in AnyLogic. JMIR Research Protocols 2017;6(7):e138 View
  44. Smith B. A novel IDEA: The impact of serial interval on a modified-Incidence Decay and Exponential Adjustment (m-IDEA) model for projections of daily COVID-19 cases. Infectious Disease Modelling 2020;5:346 View
  45. Peng Y, Li C, Rong Y, Chen X, Chen H. Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning. Journal of Global Health 2020;10(2) View
  46. Yu Y. Big Data Technology in Museum Exhibition Digitization. Journal of Physics: Conference Series 2020;1648(4):042044 View
  47. Kurian S, Bhatti A, Alvi M, Ting H, Storlie C, Wilson P, Shah N, Liu H, Bydon M. Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis. Mayo Clinic Proceedings 2020;95(11):2370 View
  48. Kogan N, Clemente L, Liautaud P, Kaashoek J, Link N, Nguyen A, Lu F, Huybers P, Resch B, Havas C, Petutschnig A, Davis J, Chinazzi M, Mustafa B, Hanage W, Vespignani A, Santillana M. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time. Science Advances 2021;7(10) View
  49. R Niakan Kalhori S, Bahaadinbeigy K, Deldar K, Gholamzadeh M, Hajesmaeel-Gohari S, Ayyoubzadeh S. Digital Health Solutions to Control the COVID-19 Pandemic in Countries With High Disease Prevalence: Literature Review. Journal of Medical Internet Research 2021;23(3):e19473 View
  50. Smith B, Bancej C, Fazil A, Mullah M, Yan P, Zhang S. The performance of phenomenological models in providing near-term Canadian case projections in the midst of the COVID-19 pandemic: March – April, 2020. Epidemics 2021;35:100457 View
  51. Nsoesie E, Oladeji O, Abah A, Ndeffo-Mbah M. Forecasting influenza-like illness trends in Cameroon using Google Search Data. Scientific Reports 2021;11(1) View
  52. de Freitas C, Amorim M, Machado H, Leão Teles E, Baptista M, Renedo A, Provoost V, Silva S. Public and patient involvement in health data governance (DATAGov): protocol of a people-centred, mixed-methods study on data use and sharing for rare diseases care and research. BMJ Open 2021;11(3):e044289 View
  53. Lima Y, Pinheiro W, Barbosa C, Magalhães M, Chaves M, de Souza J, Rodrigues S, Xexéo G. Development of an Index for the Inspection of Aedes aegypti Breeding Sites in Brazil: Multi-criteria Analysis. JMIR Public Health and Surveillance 2021;7(5):e19502 View
  54. Yiu C, Macon-Cooney B, Fingerhut H. A research and policy agenda for the post-pandemic world. Future Healthcare Journal 2021;8(2):e198 View
  55. Kiang M, Chen J, Krieger N, Buckee C, Alexander M, Baker J, Buckner R, Coombs G, Rich-Edwards J, Carlson K, Onnela J. Sociodemographic characteristics of missing data in digital phenotyping. Scientific Reports 2021;11(1) View
  56. Majumder M, Rose S. A generalizable data assembly algorithm for infectious disease outbreaks. JAMIA Open 2021;4(3) View
  57. Saegner T, Austys D. Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review. International Journal of Environmental Research and Public Health 2022;19(19):12394 View
  58. Lan H, Sha D, Malarvizhi A, Liu Y, Li Y, Meister N, Liu Q, Wang Z, Yang J, Yang C. COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records. IEEE Access 2021;9:84783 View
  59. Ganser I, Thiébaut R, Buckeridge D. Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis. JMIR Public Health and Surveillance 2022;8(10):e36211 View
  60. Satpathy P, Kumar S, Prasad P. Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India. Disaster Medicine and Public Health Preparedness 2023;17 View
  61. Majumder M, Cusick M, Rose S. Measuring concordance of data sources used for infectious disease research in the USA: a retrospective data analysis. BMJ Open 2023;13(2):e065751 View
  62. TİRGİL M, ÇULHA E, DEMİRCİ Ş. Google arama motoru Türkiye’de Covid-19 salgınının yayılımının izlenmesinde ve tahmininde kullanılabilir mi?. Mersin Üniversitesi Sağlık Bilimleri Dergisi 2021;14(3):520 View
  63. Ben S, Xin J, Chen S, Jiang Y, Yuan Q, Su L, Christiani D, Zhang Z, Du M, Wang M. Global internet search trends related to gastrointestinal symptoms predict regional COVID-19 outbreaks. Journal of Infection 2022;84(1):56 View
  64. MacIntyre C, Chen X, Kunasekaran M, Quigley A, Lim S, Stone H, Paik H, Yao L, Heslop D, Wei W, Sarmiento I, Gurdasani D. Artificial intelligence in public health: the potential of epidemic early warning systems. Journal of International Medical Research 2023;51(3) View
  65. Zafar Z, Khan M, Inc M, Akgül A, Asiri M, Riaz M. The analysis of a new fractional model to the Zika virus infection with mutant. Heliyon 2024;10(1):e23390 View
  66. wei S, Lin S, wenjing Z, Shaoxia S, Yuejie Y, Yujie H, Shu Z, Zhong L, Ti L. The prediction of influenza-like illness using national influenza surveillance data and Baidu query data. BMC Public Health 2024;24(1) View
  67. El Morr C, Ozdemir D, Asdaah Y, Saab A, El-Lahib Y, Sokhn E. AI-based epidemic and pandemic early warning systems: A systematic scoping review. Health Informatics Journal 2024;30(3) View
  68. Lieberthal B, Allan B, De Urioste-Stone S, Mackay A, Soliman A, Wang S, Gardner A, Morrison A. The effects of seasonal human mobility and Aedes aegypti habitat suitability on Zika virus epidemic severity in Colombia. PLOS Neglected Tropical Diseases 2024;18(11):e0012571 View

Books/Policy Documents

  1. da Silva D, Goncalves G, dos Santos S, Pugliese V, Navas J, de Barros Santana R, Queiroz F, Dias L, da Cunha A, Tasinaffo P. Information Technology – New Generations. View
  2. Morley M, Majumder M, Gallanis T, Wilson J. Leveraging Data Science for Global Health. View
  3. Waring O, Majumder M. Leveraging Data Science for Global Health. View
  4. Saifullah M, Adnan M, Arshad M, Waqas M, Mehmood A. Challenges in Agro-Climate and Ecosystem. View
  5. Gilbert J, Niu J, de Montigny S, Ng V, Rees E. AI for Disease Surveillance and Pandemic Intelligence. View
  6. Birthare P, Raja M, Ramachandran G, Hargreaves C, Birthare S. Structural and Functional Aspects of Biocomputing Systems for Data Processing. View