An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness, Response, and Management: Design and Development Study

Journals

1. Olusanya O, White B, Melton C, Shaban-Nejad A. Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries. JMIR Formative Research 2022;6(5):e34363 View
2. Hubler A, Wakefield D, Makepeace L, Carnell M, Sharma A, Jiang B, Dove A, Garner W, Edmonston D, Little J, Ozdenerol E, Hanson R, Martin M, Shaban-Nejad A, Pisu M, Schwartz D. Independent Predictors for Hospitalization-Associated Radiation therapy Interruptions. Advances in Radiation Oncology 2022;7(6):101041 View
3. Shaban-Nejad A, Michalowski M, Bianco S, Brownstein J, Buckeridge D, Davis R. Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world. Experimental Biology and Medicine 2022;247(22):1969 View
4. Lee J, Jansen R, Sanderson K, Guerra F, Keller-Olaman S, Murti M, O’Sullivan T, Law M, Schwartz B, Bourns L, Khan Y. Public health emergency preparedness for infectious disease emergencies: a scoping review of recent evidence. BMC Public Health 2023;23(1) View
5. Brakefield W, Olusanya O, White B, Shaban-Nejad A. Social Determinants and Indicators of COVID-19 Among Marginalized Communities: A Scientific Review and Call to Action for Pandemic Response and Recovery. Disaster Medicine and Public Health Preparedness 2023;17 View
6. Brakefield W, Ammar N, Shaban-Nejad A. An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model. JMIR Formative Research 2022;6(7):e36055 View
7. Brakefield W, Olusanya O, Shaban-Nejad A. Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach. JMIR Public Health and Surveillance 2022;8(8):e37039 View
8. Jiao J, Chen Y, Azimian A. Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models. Computational Urban Science 2021;1(1) View
9. Schwartz D, Stewart A, Harris L, Ozdenerol E, Thomas F, Johnson K, Davis R, Shaban-Nejad A. The Memphis Pandemic Health Informatics System (MEMPHI-SYS)—Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance. Disaster Medicine and Public Health Preparedness 2023;17 View
10. Sheikhtaheri A, Tabatabaee Jabali S, Bitaraf E, TehraniYazdi A, Kabir A. A near real-time electronic health record-based COVID-19 surveillance system: An experience from a developing country. Health Information Management Journal 2024;53(2):145 View
11. Vo A, Tao Y, Li Y, Albarrak A. The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis. JMIR Public Health and Surveillance 2023;9:e44070 View
12. Öcek Z, Geise M, Volkmann A, Basili A, Klünder V, Coenen M. Strengthening the social resilience of people living at the intersection of precariousness and migration during pandemics: action recommendations developed in Munich, Germany. Frontiers in Public Health 2023;11 View
13. Rayo M, Faulkner D, Kline D, Thornhill IV T, Malloy S, Della Vella D, Morey D, Zhang N, Gonsalves G. Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study. JMIR Public Health and Surveillance 2023;9:e39754 View
14. Hirani R, Noruzi K, Khuram H, Hussaini A, Aifuwa E, Ely K, Lewis J, Gabr A, Smiley A, Tiwari R, Etienne M. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life 2024;14(5):557 View

Books/Policy Documents

1. Shaban-Nejad A, Michalowski M, Bianco S. AI for Disease Surveillance and Pandemic Intelligence. View
2. Shaban-Nejad A, Michalowski M, Bianco S. Multimodal AI in Healthcare. View
3. Agrawal A. Genomic Surveillance and Pandemic Preparedness. View