iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices
The emergence of pandemics poses a persistent threat to both global health and economic stability. While zoonotic spillovers and local outbreaks may not be fully preventable, early detection of infections in individuals before they spread to communities can make a major difference in containing an i...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-01-01
|
| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914824000340 |
| _version_ | 1797237993160835072 |
|---|---|
| author | Muhammad Sajid Riaz Maria Shaukat Tabish Saeed Aneeqa Ijaz Haneya Naeem Qureshi Iryna Posokhova Ismail Sadiq Ali Rizwan Ali Imran |
| author_facet | Muhammad Sajid Riaz Maria Shaukat Tabish Saeed Aneeqa Ijaz Haneya Naeem Qureshi Iryna Posokhova Ismail Sadiq Ali Rizwan Ali Imran |
| author_sort | Muhammad Sajid Riaz |
| collection | DOAJ |
| description | The emergence of pandemics poses a persistent threat to both global health and economic stability. While zoonotic spillovers and local outbreaks may not be fully preventable, early detection of infections in individuals before they spread to communities can make a major difference in containing an infectious disease and stopping it from becoming an epidemic and then a pandemic.In this paper, we propose a novel Artificial Intelligence (AI)-based pandemic prediction framework called iPREDICT—a concept framework designed to leverage the power of AI and crowd-sensed data for accurate and timely pandemic prediction. The core idea of iPREDICT is to leverage the deluge of data that can be harnessed from connected and wearable biosensing devices. iPREDICT system then works by monitoring anomalies in the biomarkers at the individual level and correlating them with similar anomalies observed in other members of the community. Using AI-based anomaly detection in conjunction with analysis of the spatiotemporal growth of the correlated anomalies, iPREDICT thus can potentially detect and monitor the emergence of a local outbreak in near real-time to predict a potential pandemic.However, not every outbreak has the potential to become a pandemic. We illustrate how tools like graph neural networks can be leveraged to determine optimal thresholds as a function of a large number of demographical, social, and geographical factors that determine the spatiotemporal spread of an outbreak, thus quantifying the risk of it becoming an epidemic or pandemic.We also identify essential technological and social challenges that require attention to transform iPREDICT from an idea into a globally deployable solution for future pandemic prediction and management. To provide deeper insights into iPREDICT design challenges and trigger research towards possible solutions we present a COVID-19 based case study. The results signify the impact of variation in biosensing hardware, data sampling rate, and compression rate on the performance of AI models that underpin various components of the iPREDICT system. |
| first_indexed | 2024-04-24T17:28:34Z |
| format | Article |
| id | doaj.art-ce4ffa4db24f44d4ae60a2f712bb589b |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-04-24T17:28:34Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-ce4ffa4db24f44d4ae60a2f712bb589b2024-03-28T06:38:18ZengElsevierInformatics in Medicine Unlocked2352-91482024-01-0146101478iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devicesMuhammad Sajid Riaz0Maria Shaukat1Tabish Saeed2Aneeqa Ijaz3Haneya Naeem Qureshi4Iryna Posokhova5Ismail Sadiq6Ali Rizwan7Ali Imran8AI4Networks Research Center, School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, USA; Corresponding author.AI4Networks Research Center, School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, USAAI4Networks Research Center, School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, USAAI4Networks Research Center, School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, USAAI4Networks Research Center, School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, USAHudson College of Public Health, University of Oklahoma, USAJames Watt School of Engineering, University of Glasgow, Scotland, UKJames Watt School of Engineering, University of Glasgow, Scotland, UKAI4Networks Research Center, School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, USA; James Watt School of Engineering, University of Glasgow, Scotland, UKThe emergence of pandemics poses a persistent threat to both global health and economic stability. While zoonotic spillovers and local outbreaks may not be fully preventable, early detection of infections in individuals before they spread to communities can make a major difference in containing an infectious disease and stopping it from becoming an epidemic and then a pandemic.In this paper, we propose a novel Artificial Intelligence (AI)-based pandemic prediction framework called iPREDICT—a concept framework designed to leverage the power of AI and crowd-sensed data for accurate and timely pandemic prediction. The core idea of iPREDICT is to leverage the deluge of data that can be harnessed from connected and wearable biosensing devices. iPREDICT system then works by monitoring anomalies in the biomarkers at the individual level and correlating them with similar anomalies observed in other members of the community. Using AI-based anomaly detection in conjunction with analysis of the spatiotemporal growth of the correlated anomalies, iPREDICT thus can potentially detect and monitor the emergence of a local outbreak in near real-time to predict a potential pandemic.However, not every outbreak has the potential to become a pandemic. We illustrate how tools like graph neural networks can be leveraged to determine optimal thresholds as a function of a large number of demographical, social, and geographical factors that determine the spatiotemporal spread of an outbreak, thus quantifying the risk of it becoming an epidemic or pandemic.We also identify essential technological and social challenges that require attention to transform iPREDICT from an idea into a globally deployable solution for future pandemic prediction and management. To provide deeper insights into iPREDICT design challenges and trigger research towards possible solutions we present a COVID-19 based case study. The results signify the impact of variation in biosensing hardware, data sampling rate, and compression rate on the performance of AI models that underpin various components of the iPREDICT system.http://www.sciencedirect.com/science/article/pii/S2352914824000340Pandemic predictionWearable biosensorsBiosensing devicesAI for healthcarePandemic prognosticsPathogen spread |
| spellingShingle | Muhammad Sajid Riaz Maria Shaukat Tabish Saeed Aneeqa Ijaz Haneya Naeem Qureshi Iryna Posokhova Ismail Sadiq Ali Rizwan Ali Imran iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices Informatics in Medicine Unlocked Pandemic prediction Wearable biosensors Biosensing devices AI for healthcare Pandemic prognostics Pathogen spread |
| title | iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices |
| title_full | iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices |
| title_fullStr | iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices |
| title_full_unstemmed | iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices |
| title_short | iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices |
| title_sort | ipredict ai enabled proactive pandemic prediction using biosensing wearable devices |
| topic | Pandemic prediction Wearable biosensors Biosensing devices AI for healthcare Pandemic prognostics Pathogen spread |
| url | http://www.sciencedirect.com/science/article/pii/S2352914824000340 |
| work_keys_str_mv | AT muhammadsajidriaz ipredictaienabledproactivepandemicpredictionusingbiosensingwearabledevices AT mariashaukat ipredictaienabledproactivepandemicpredictionusingbiosensingwearabledevices AT tabishsaeed ipredictaienabledproactivepandemicpredictionusingbiosensingwearabledevices AT aneeqaijaz ipredictaienabledproactivepandemicpredictionusingbiosensingwearabledevices AT haneyanaeemqureshi ipredictaienabledproactivepandemicpredictionusingbiosensingwearabledevices AT irynaposokhova ipredictaienabledproactivepandemicpredictionusingbiosensingwearabledevices AT ismailsadiq ipredictaienabledproactivepandemicpredictionusingbiosensingwearabledevices AT alirizwan ipredictaienabledproactivepandemicpredictionusingbiosensingwearabledevices AT aliimran ipredictaienabledproactivepandemicpredictionusingbiosensingwearabledevices |