Assistant Professor, Public Health
Assistant Professor Atina Husnayain
Atina Husnayain is an Assistant Professor at the Department of Public Health, Monash University, Indonesia. She holds a Ph.D. in biomedical informatics (2022) from the Graduate Institute of Biomedical Informatics (GIBI), College of Medical Science and Technology, Taipei Medical University. Previously, she obtained her master's degree in Public Health (2018) from Universitas Gadjah Mada and her bachelor's degree in Public Health (2015) from Universitas Airlangga.
Her research focused on digital epidemiology, information technology-based surveillance, spatial epidemiology, and regression machine learning. During her Ph.D., she worked on infodemiology studies in applying search engine query data for COVID-19 time series prediction. In her research, she collaborated with Professors from Taipei Medical University, Taiwan, MSUIligan Institute of Technology, Philippines, and Soongsil University, Republic of Korea. Studies were funded by research grants from the Taiwan Ministry of Science and Technology, the Taiwan Ministry of Education, and the Korean government and were published in the International Journal of Infectious Disease (IF: 12.074), Journal of Medical Internet Research (IF: 7.08), PLoS ONE (IF: 3.752), JMIR Medical Informatics (IF: 3.43), and International Journal of Environmental Research and Public Health (IF: 3.39).
Before pursuing her Ph.D., she was a research assistant and teaching assistant at the Department of Biostatistics, Epidemiology, and Population Health, and also at the Department of Health Policy, Universitas Gadjah Mada, Indonesia. She taught courses related to epidemiology, biostatistics, spatial epidemiology, and research methodology. Besides, she was also involved in various research activities including designing information systems and spatial analysis of dengue in Yogyakarta and analyzing BPJS sample data.
Rahmanti AR, Chien CH, Nursetyo AA, Husnayain A, Wiratama BS, Fuad A, Yang HC, Li YJ. Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout. Comput Methods Programs Biomed. 2022 Jun;221:106838. doi: 10.1016/j.cmpb.2022.106838. Epub 2022 Apr 27. PMID: 35567863; PMCID: PMC9045866.
Husnayain A, Shim E, Fuad A, Su EC. Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study. J Med Internet Res. 2021 Dec 22;23(12):e34178. doi: 10.2196/34178. PMID: 34762064; PMCID: PMC8698803.
Husnayain A, Chuang TW, Fuad A, Su EC. High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA. Int J Infect Dis. 2021 Aug;109:269-278. doi: 10.1016/j.ijid.2021.07.031. Epub 2021 Jul 14. PMID: 34273513; PMCID: PMC8922685.
Galido A, Ecleo JJ, Husnayain A, Chia-Yu Su E. Exploring online search behavior for COVID-19 preventive measures: The Philippine case. PLoS One. 2021 Apr 8;16(4):e0249810. doi: 10.1371/journal.pone.0249810. PMID: 33831076; PMCID: PMC8031411.
Husnayain A, Ekadinata N, Sulistiawan D, Chia-Yu Su E. Multimorbidity Patterns of Chronic Diseases among Indonesians: Insights from Indonesian National Health Insurance (INHI) Sample Data. Int J Environ Res Public Health. 2020 Nov 30;17(23):8900. doi: 10.3390/ijerph17238900. PMID: 33266273; PMCID: PMC7731032.
Sufriyana H, Husnayain A, Chen YL, Kuo CY, Singh O, Yeh TY, Wu YW, Su EC. Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis. JMIR Med Inform. 2020 Nov 17;8(11):e16503. doi: 10.2196/16503. PMID: 33200995; PMCID: PMC7708089.
Husnayain A, Shim E, Fuad A, Su EC. Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study. J Med Internet Res. 2020 Sep 29;22(9):e19788. doi: 10.2196/19788. PMID: 32931446; PMCID: PMC7527166.
Husnayain A, Fuad A, Laksono IS, Su EC. Improving Dengue Surveillance System with Administrative Claim Data in Indonesia: Opportunities and Challenges. Stud Health Technol Inform. 2020 Jun 16;270:853-857. doi: 10.3233/SHTI200282. PMID: 32570503.
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. doi: 10.1016/j.ijid.2020.03.021. Epub 2020 Mar 12. PMID: 32173572; PMCID: PMC7270523.
Husnayain A, Fuad A, Lazuardi L. Correlation between Google Trends on dengue fever and national surveillance report in Indonesia. Glob Health Action. 2019;12(1):1552652. doi: 10.1080/16549716.2018.1552652. PMID: 31154985; PMCID: PMC6327938.
- Digital Health Innovation
- Professionals Practice Development
- Introductory Biostatistics
- Quantitative Research Method
- Infectious Diseases: Epidemiology and Prevention
- Digital epidemiology
- Information technology-based surveillance
- Spatial epidemiology
- Regression machine learning
Menggunakan Google Trends untuk dukung sistem monitoring demam berdarah, bagaimana caranya? The conversation indonesia Januari 24, 2019.
- Student Research Paper Award from Taipei Medical University (2020-2021)
- Taipei Medical University Scholarship - Type A (2019-2021)
- Indonesia Endowment Fund for Education - Master Degree Scholarship (2016-2018)