Keys Assignments¶
Assignment 1: Internship Description¶
Project Description¶
This summer, I have the privilege of working in Dr. Swetnam's lab, delving into the fascinating intersection of AI and scientific research. Dr. Swetnam, a Research Associate Professor of Geoinformatics and Joint faculty member in the School of Natural Resources and Environment, is actively involved in the CyVerse initiative, which promotes open science. Among his various projects, he is currently exploring the applications of AI in scientific fields. My specific focus this summer revolves around the utilization of AI tools such as Chat GPT and Google Bard to examine their impact on data analysis in the context of epidemic-related data (e.g., Covid-19, Cholera). The goal is to develop a reusable Python code that can effectively generate heat maps for hotspot mapping. By addressing the transformative influence of AI on scientific endeavors, we aim to foster a more accessible and collaborative scientific community. In essence, this research journey embraces the promising prospects of AI, forging new paths in scientific inquiry while cultivating a more inclusive approach to knowledge discovery.
Assignment 2: Introduction to your Research¶
Purpose:¶
As artificial intelligence (AI) continues to exert a growing influence on our society and scientific endeavors, it becomes imperative that we adapt to its presence. The way we interact with data must evolve, and it is crucial to harness the potential of these new tools effectively. This project focuses on utilizing AI tools to analyze epidemic-related data, specifically data on COVID-19 and Cholera, and exploring their efficiency in understanding and mitigating these diseases. The research undertaken aims to facilitate the widespread integration of AI in the field of data science, accompanied by the development of a reusable program. One of the key objectives is to create a Python code capable of evaluating specialized data and generating heat maps, which are graphical representations that visually highlight areas of concentration or origin for epidemic outbreaks.
Previous Research:¶
As AI continues to gain influence, particularly in the field of data science, it offers numerous implications. These include accelerating the discovery of relationships within complex data sets and automating repetitive tasks, leading to more efficient and accurate data analysis (Brookings, 2023; Our World in Data, 2022). Heatmaps are visual representations that utilize colors to display the intensity or density of certain data points on a geographical or grid-based layout (NIEHS, n.d.). In the context of epidemic-related data analysis, heatmaps can be used to identify areas of concentration or origin for diseases. For example, Michael Worobey and his team utilized heatmaps to analyze the genetic diversity of COVID-19 and identified the Huanan Seafood Market in Wuhan as a potential origin (Zimmer et al., 2020). This approach is reminiscent of the work of John Snow, a pioneer in epidemiology, who famously used a heatmap to track the cases of cholera during the 1854 outbreak in London, ultimately identifying contaminated water as the source (BBC, n.d.).
Need For Study:¶
By harnessing the promising prospects of AI, this research journey not only pioneers novel paths in scientific inquiry but also fosters a more inclusive approach to knowledge discovery. Through the development of this Python code, the project strives to implement the regular use of AI in the data science field while creating a reusable program that is open for anyone to use. In essence, it embraces the potential of AI, forging new paths in scientific inquiry and data analysis, all while cultivating a more inclusive and effective approach to understanding and combating epidemics like COVID-19 and cholera.
Problem Statement:¶
Research Question: How can AI tools be effectively utilized to analyze epidemic-related data and improve our understanding and response to diseases like COVID-19 and cholera?
Hypotheisis: The utilization of AI tools, specifically Chat GPT and Google Bard, will enhance our understanding and mitigation of diseases like COVID-19 and cholera, facilitating the integration of AI in data science.
References¶
References: - Brookings. (2023). How artificial intelligence is transforming the world. Retrieved from https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/ - Our World in Data. (2022). The brief history of artificial intelligence: The world has changed. Retrieved from https://ourworldindata.org/brief-history-of-ai - National Institute of Environmental Health Sciences (NIEHS). (n.d.). Data visualization: Heatmaps. Retrieved from https://tools.niehs.nih.gov/cehsweb/assets/fileassets/datavisualization/heatmap.pdf - Worobey, M. (2021). The Origins of COVID-19. Scientific American, 325(1), 46-53. - now, J. (1855). On the Mode of Communication of Cholera (2nd ed.). John Churchill.
Assignment 3: Materials and Methods¶
As artificial intelligence (AI) continues to exert a growing influence on our society and scientific endeavors, it becomes imperative that we adapt to its presence. The way we interact with data must evolve, and it is crucial to harness the potential of these new tools effectively. This project focuses on utilizing AI tools to analyze epidemic-related data, specifically data on COVID-19 and Cholera, and exploring their efficiency in understanding and mitigating these diseases. The research undertaken aims to facilitate the widespread integration of AI in the field of data science, accompanied by the development of a reusable program. One of the key objectives is to create a Python code capable of evaluating specialized data and generating heat maps, which are graphical representations that visually highlight areas of concentration or origin for epidemic outbreaks. As AI continues to gain influence, particularly in the field of data science, it offers numerous implications. These include accelerating the discovery of relationships within complex data sets and automating repetitive tasks, leading to more efficient and accurate data analysis (Brookings, 2023; Our World in Data, 2022). Heatmaps are visual representations that utilize colors to display the intensity or density of certain data points on a geographical or grid-based layout (NIEHS, n.d.). In the context of epidemic-related data analysis, heatmaps can be used to identify areas of concentration or origin for diseases. For example, Michael Worobey and his team utilized heatmaps to analyze the genetic diversity of COVID-19 and identified the Huanan Seafood Market in Wuhan as a potential origin (Zimmer et al., 2020). This approach is reminiscent of the work of John Snow, a pioneer in epidemiology, who famously used a heatmap to track the cases of cholera during the 1854 outbreak in London, ultimately identifying contaminated water as the source (BBC, n.d.). By harnessing the promising prospects of AI, this research journey not only pioneers novel paths in scientific inquiry but also fosters a more inclusive approach to knowledge discovery. Through the development of this Python code, the project strives to implement the regular use of AI in the data science field while creating a reusable program that is open for anyone to use. In essence, it embraces the potential of AI, forging new paths in scientific inquiry and data analysis, all while cultivating a more inclusive and effective approach to understanding and combating epidemics like COVID-19 and cholera. References: - Brookings. (2023). How artificial intelligence is transforming the world. Retrieved from https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/ - Our World in Data. (2022). The brief history of artificial intelligence: The world has changed. Retrieved from https://ourworldindata.org/brief-history-of-ai - National Institute of Environmental Health Sciences (NIEHS). (n.d.). Data visualization: Heatmaps. Retrieved from https://tools.niehs.nih.gov/cehsweb/assets/fileassets/datavisualization/heatmap.pdf • Worobey, M. (2021). The Origins of COVID-19. Scientific American, 325(1), 46-53. • Snow, J. (1855). On the Mode of Communication of Cholera (2nd ed.). John Churchill.
Assignment 4: Results¶
A heatmap depicting the origins of COVID-19 based on Michael Worobey’s original study. The code was made with the help of AI
A heatmap depicting John Snow’s 1854 Cholera outbreak in London. The code was created with the help of AI.
Assignment 5 Conclusion and Discussion¶
The successful integration of AI tools in this research project opens exciting opportunities for future exploration in data science.
AI's transformative potential in epidemic analysis makes it a promising avenue for making data analysis more accessible and efficient. By optimizing AI algorithms and expanding the scope of analyzed datasets, we can enhance public health and epidemiological research.
AI's continued advancement enables real-time analysis of vast amounts of epidemic data, uncovering valuable insights and patterns. Collaborative efforts and inclusive scientific communities can thrive with the integration of AI in data science.
This revolutionizes our approach to public health, empowering us to respond effectively to emerging health crises and proactively prevent epidemics.
Assignment 6 Title and Short Abstract¶
Title¶
Utilizing Artificial Intelligence in Epidemic Heat Mapping and Data Visualization
Short Abstract¶
My project focused on the utilization of artificial intelligence (AI) tools, specifically Chat GPT to analyze epidemic-related data, with emphasis on COVID-19 and Cholera. The goal was to develop a reusable Python code capable of generating heat maps to identify areas of concentration or origin for epidemic outbreaks. The project employed a three-step process: using Chat GPT to generate Python code, publishing the code on GitHub for collaboration, and executing the code using Jupyter Notebooks for data analysis. The project aimed to integrate AI into the field of data science and foster a more accessible and collaborative scientific community. By embracing the potential of AI, the research sought to advance scientific inquiry, enhance data analysis, and promote an inclusive approach to understanding and combating epidemics.