@inproceedings{f4b113bf625f49ad9edb8cf7158da092,
title = "Analytics-oriented preventive care for communicable disease",
abstract = "Today, most sexually transmitted infections (STIs) can be cured. However, stigma around STIs-especially fears over negative social attitudes-prevents many people from getting tested or notifying their sexual partners that they are infected. The Government of Alberta invests considerable resources on strategies to improve the health of Albertans. However, the massive outbreak of epidemics like syphilis indicate there are huge gaps in the current approach. This paper proposes the development of a centralized information system to correlate the areas of edu-communication and epidemiology as well as employs techniques in predictive analytics to explore the correlation between certain risk factors and the change in the number of infectious syphilis cases in the province of Alberta. The performance of the resultant models provides a proof-of-concept for the preventive strategies proposed in the paper.",
keywords = "Communicable Diseases, Edu-communication, Machine Learning, Syphilis",
author = "Tang, {Liliana Quyen} and Rafael Pinto and Vivekanandan Kumar",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020 ; Conference date: 06-07-2020 Through 09-07-2020",
year = "2020",
month = jul,
doi = "10.1109/ICALT49669.2020.00049",
language = "English",
series = "Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020",
pages = "145--146",
editor = "Maiga Chang and Sampson, {Demetrios G} and Ronghuai Huang and Danial Hooshyar and Nian-Shing Chen and Kinshuk Kinshuk and Margus Pedaste",
booktitle = "Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020",
}
