TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence
Artificial Intelligence (AI) technologies have enabled researchers to develop tools to monitor real-world events and user behavior using social media platforms. Twitter is particularly useful for gathering invaluable information related to diseases and public health to build real-time disease survei...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10858732/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859639045849088 |
|---|---|
| author | Bilal Tahir Muhammad Amir Mehmood |
| author_facet | Bilal Tahir Muhammad Amir Mehmood |
| author_sort | Bilal Tahir |
| collection | DOAJ |
| description | Artificial Intelligence (AI) technologies have enabled researchers to develop tools to monitor real-world events and user behavior using social media platforms. Twitter is particularly useful for gathering invaluable information related to diseases and public health to build real-time disease surveillance systems. Such systems offer a cost-effective and efficient alternative to the passive, expensive, and time-consuming process of using data from healthcare organizations and hospitals. In this paper, we propose a novel system of TepiSense to automatically perform disease surveillance of epidemic-prone diseases. Our system classifies tweets related to diseases and further identifies ‘indication’ tweets that highlight the presence of patients. Our system consists of four distinct modules of pre-processor, feature extractor, classifier, and evaluator. TepiSense compares the performance of 3 feature extraction techniques, 9 machine/deep learning models, and 3 Large Language Models (LLMs). To test the performance of our system, we build a dataset of Twitter Epidemic Surveillance Corpus (TESC) containing 23.9K English and 13K labelled Urdu tweets related to six diseases: COVID19, hepatitis, malaria, flu, dengue, and HIV/AIDS. Our results show that mBERT LLM achieves the highest F-measure values of 0.96 and 0.83 for topic and indication tweets classification, respectively. Furthermore, we compute the correlation of signals generated by our system with real-world cases to test the efficacy on COVID19 disease. We notice that real-world cases have a correlation of 0.58-0.63 with the indication category tweets. Finally, we develop an interactive and user-friendly dashboard to disseminate the analytics of our system. Overall, our system offers a powerful tool for real-time disease surveillance using social media with potential implications for public health policy and decision-making. |
| format | Article |
| id | doaj-art-bd29c09b61c44e74b2b2f7de73eed897 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bd29c09b61c44e74b2b2f7de73eed8972025-02-11T00:01:17ZengIEEEIEEE Access2169-35362025-01-0113238162383210.1109/ACCESS.2025.353716810858732TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial IntelligenceBilal Tahir0https://orcid.org/0000-0002-4907-0988Muhammad Amir Mehmood1https://orcid.org/0000-0002-6652-5104Al-Khawarizmi Institute of Computer Science, University of Engineering and Technology, Lahore, PakistanFaculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaArtificial Intelligence (AI) technologies have enabled researchers to develop tools to monitor real-world events and user behavior using social media platforms. Twitter is particularly useful for gathering invaluable information related to diseases and public health to build real-time disease surveillance systems. Such systems offer a cost-effective and efficient alternative to the passive, expensive, and time-consuming process of using data from healthcare organizations and hospitals. In this paper, we propose a novel system of TepiSense to automatically perform disease surveillance of epidemic-prone diseases. Our system classifies tweets related to diseases and further identifies ‘indication’ tweets that highlight the presence of patients. Our system consists of four distinct modules of pre-processor, feature extractor, classifier, and evaluator. TepiSense compares the performance of 3 feature extraction techniques, 9 machine/deep learning models, and 3 Large Language Models (LLMs). To test the performance of our system, we build a dataset of Twitter Epidemic Surveillance Corpus (TESC) containing 23.9K English and 13K labelled Urdu tweets related to six diseases: COVID19, hepatitis, malaria, flu, dengue, and HIV/AIDS. Our results show that mBERT LLM achieves the highest F-measure values of 0.96 and 0.83 for topic and indication tweets classification, respectively. Furthermore, we compute the correlation of signals generated by our system with real-world cases to test the efficacy on COVID19 disease. We notice that real-world cases have a correlation of 0.58-0.63 with the indication category tweets. Finally, we develop an interactive and user-friendly dashboard to disseminate the analytics of our system. Overall, our system offers a powerful tool for real-time disease surveillance using social media with potential implications for public health policy and decision-making.https://ieeexplore.ieee.org/document/10858732/Natural language processingepidemic intelligencepublic healthdata miningsmart citye-health |
| spellingShingle | Bilal Tahir Muhammad Amir Mehmood TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence IEEE Access Natural language processing epidemic intelligence public health data mining smart city e-health |
| title | TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence |
| title_full | TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence |
| title_fullStr | TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence |
| title_full_unstemmed | TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence |
| title_short | TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence |
| title_sort | tepisense a social computing based real time epidemic surveillance system using artificial intelligence |
| topic | Natural language processing epidemic intelligence public health data mining smart city e-health |
| url | https://ieeexplore.ieee.org/document/10858732/ |
| work_keys_str_mv | AT bilaltahir tepisenseasocialcomputingbasedrealtimeepidemicsurveillancesystemusingartificialintelligence AT muhammadamirmehmood tepisenseasocialcomputingbasedrealtimeepidemicsurveillancesystemusingartificialintelligence |