COH601

Bagattini, Â. M., Rosa, M., Simon, L. M., de Almeida, G. B., Ferreira, L. S., Borges, M. E., Kraenkel, R. A., Coutinho, R. M., Diniz Filho, J. A. F., & Camey, S. A. (2022). PP25 Brazilian Collaborative Network For COVID-19 Modeling: Successful Experience Of Using Real-Time Science To Support Evidence-Based Decision-Making. International Journal of Technology Assessment in Health Care, 38(S1), S48-S49. https://doi.org/10.1017/S0266462322001702
This article provides valuable insights into the successful implementation of a multidisciplinary collaborative network for COVID-19 modeling in Brazil, demonstrating how real-time science can be effectively used to inform evidence-based decision-making during a pandemic. Understanding the methods and results of this initiative can enhance the learner's knowledge of how data science, epidemiology, and policy-making can intersect to address public health crises, offering a practical example of collaborative problem-solving and strategic planning in a real-world context.

Federspiel, F., Mitchell, R., Asokan, A., Umana, C., & McCoy, D. (2023). Threats by artificial intelligence to human health and human existence. BMJ Global Health, 8, e010435. https://doi.org/10.1136/bmjgh-2022-010435
This paper provides a comprehensive overview of the potential threats posed by AI to human health and existence, making it useful for anyone interested in the ethical, social, and health implications of AI and AGI. Understanding these threats can help in advocating for effective regulation and safe use of AI.

MacIntyre CR, Chen X, Kunasekaran M, et al. Artificial intelligence in public health: the potential of epidemic early warning systems. Journal of International Medical Research. 2023;51(3). doi:10.1177/03000605231159335
This paper provides a comprehensive overview of the role of AI in epidemic surveillance, making it useful for anyone interested in understanding how AI can revolutionize disease prevention and control. It provides valuable insights into the current epidemic intelligence systems and emphasizes the need for widespread adoption of AI-driven open-source surveillance in public health.

Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(44). https://doi.org/10.1186/s40537-019-0206-3
Understanding the concept of uncertainty in big data analytics is crucial for anyone working in data-driven fields. This paper provides a thorough overview of the topic, making it a valuable resource for learners seeking to enhance their knowledge and skills in big data analytics and artificial intelligence.