Artificial Intelligence to Modernize Public Health Surveillance Restricted; Files Only

Abstract

Introduction 

During COVID-19, countries like the United States – with well-financed public health surveillance (PHS) – realized their systems had become overwhelmed and could not keep up with public health demands. With U.S. PHS siloed (i.e., based on disease and data abstracted from proprietary healthcare systems) it was impossible to address (re)emerging disease issues and healthcare complications. Awareness of these gaps led the global public health community to consider and reframe the modernization of PHS. 

Objective 

Show the important role of artificial intelligence (AI) and machine learning (ML) in modernizing PHS data sources, collection, case detection, prediction, analyses, reporting, and forecasting. 

Methods 

I performed a systematic literature review of published articles using PubMed™, Science Direct™, SCOPUS™, Web of Science™, Academic Search Complete and Institute of Electrical and Electronics Engineers (IEEE)™ from Jan 2000 – Jan 2022. Articles were imported, screened, and assessed for eligibility using Covidence™ systematic review software. Selection based on eligibility criteria (e.g., published in English, peer-reviewed, related to PHS, AI, ML, and their subsets) allowed 1,983 articles imported into Covidence™. After removing duplicates, 1,443 were screened in the first stage based on title and abstract, and a full-text review of 274 articles was conducted where 259 were eliminated; fifteen articles remained and were analyzed. 

Results 

Specific details such as author’s name, title, aim/objectives, data sources, AI methods used, key findings, publication year, and country of study were extracted. Most articles analyzed were from the United States (n = 6), South Korea (n=3) China (n=2), Canada (n = 2); France, Pakistan, United Kingdom, Italy, Singapore, and Philippines published one study each. Most of the articles were published in 2021(n=7). The synthesis of these articles resulted in the formulation of three themes: data sources and collection, prediction and forecasting, and detection. 

Discussion 

AI and ML should be applied in modernizing PHS for data sourcing, collection, case detection, prediction, analyses, reporting, and forecasting. Modernizing PHS will take funding plus scientific and political commitment; AI and ML can make it successful. 

Table of Contents

Chapter 1. Introduction....................................................................................1-4 

Chapter 2. Methods..........................................................................................5-6 

2.1 Information sources and search strategy………………………….…………...............5 

2.2 Study selection………………………………………………………....…....................….5 

2.3 Eligibility criteria…………………………………………………….….......................….5 

2.4 Data collection, and extraction……………………………………..............................6 

Chapter 3. Results ...........................................................................................7-15 

3.1 Distribution of studies by publication year and country…………………................ 7 

3.2 Data synthesis and charting Process…………………………...……….................…...8 

Chapter 4. Discussion.....................................................................................16-20 

4.1 Conclusion ...................................................................................................16 

4.1.1 Data sources and collection .........................................................................16 

4.1.2 Use of AI to Predict and Forecast infectious disease events ............................17 

4.1.3 The utility of AI in infectious disease Detection…………………….....................18 

4.2. Limitation………………………………………………………........…….…....................19 

4.3. Recommendation…………………………………………………...…..……....................19 

References......................................................................................................20-24

About this Master's Thesis

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School	Rollins School of Public Health
Department	Hubert Department of Global Health
Degree	M.P.H.
Submission	Master's Thesis
Language	English
Research Field	Artificial Intelligence Health Sciences, Public Health Education Health Sciences, Public Health
Palavra-chave	Infectious Disease Detection Public Health Surveillance Artificial Intelligence Modernizing Public Health Surveillance Infectious Disease Prediction and Forecasting
Committee Chair / Thesis Advisor	McNabb, Scott J.N., Emory University

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