Infectious Disease Outbreak Prediction and Dashboard
Sukanya Krishna,Nikhil Dhanankam,Joyanta Jyoti Mondal · Team Infectious Diseace Dashboards
Submitted to AI Policy Hackathon at Johns Hopkins University. Sprint projects are early-stage work by participants, not Apart Research publications.
Our project developed an interactive dashboard to monitor, visualize, and analyze infectious disease outbreaks worldwide. It consolidates historical data from sources like WHO, OWID, and CDC for diseases including COVID-19, Polio, Malaria, Cholera, HIV/AIDS, Tuberculosis, and Smallpox. Users can filter data by country, time period, and disease type to gain insights into past trends and potential upcoming outbreaks. The platform provides statistical summaries, trend analyses, and future trend predictions using statistical and deep learning techniques like FB Prohphet , LSTM,Linear Regression, Polynomial Regression,Random Forset and Temporal Fusion Transformers
Reviews
Clear execution and solution. Novelty/Innovation is taken off due to many other similar examples. In general, it has potential to expand on
Cite this project
@misc{krishna2024infectious,
title = {{Infectious Disease Outbreak Prediction and Dashboard}},
author = {Sukanya Krishna and Nikhil Dhanankam and Joyanta Jyoti Mondal},
year = {2024},
month = oct,
note = {Submitted to AI Policy Hackathon at Johns Hopkins University, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/infectious-disease-outbreak-prediction-and-dashboard}},
url = {https://apartresearch.com/sprints/projects/infectious-disease-outbreak-prediction-and-dashboard}
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