Data

Contract Tracing Dataset in Alfred Hospital

Monash University
Maggie Gendy (Aggregated by) Mehmet Yuce (Aggregated by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.26180/25793949.v1&rft.title=Contract Tracing Dataset in Alfred Hospital&rft.identifier=https://doi.org/10.26180/25793949.v1&rft.publisher=Monash University&rft.description=Contract tracing is one of the most effective methods for mitigating the spread of infectious diseases, and it plays a crucial role in reducing COVID-19 outbreak. Since the pandemic, there has been increased concern regarding people's health in hospital and office settings, as these spaces with limited air exchange provide a conductive medium for virus spread. For our research, we constructed a completely new real-life dataset that was collected during the pandemic's peak in a hospital infectious ward (Alfred Hospital, Victoria, Melbourne) utilizing a Bluetooth Low Energy (BLE) Internet of Things (IoT) system. The dataset was developed by tracking the movements of health care personnel inside the hospital ward over a 12-day period with their permission in a privacy-preserving methodology. The dataset's richness in parameters and features makes it a good test bed for future research in the field. Our objective is to assist the scientific community in simulating the real-life scenarios during pandemics to improve the public health community's ability to slow any infectious diseases.About the data types files:Type A: Room or ward entry (Proximity sensors)Type B: Room or ward exit (Proximity sensors)Type C: Person-person and person-place interactionsType D: Periodic RSSI valuesType E: Room or ward entry RSSI valueType F: Room or ward exit RSSI valueP.S: If anyone wishes to utilize this data, we kindly request citation of the respective publications.1-Gendy, M.E.G., Rathnayaka, A., Curtis, S.J., Stewardson, A.J. and Yuce, M.R., 2023. Future Prediction of Close Contacts in IoT-based Contact Tracing System using a New Real-Life Dataset. IEEE Journal of Biomedical and Health Informatics.2- Rathnayaka, A., Gendy, M.E.G., Wu, F., Al Mamun, M.A., Curtis, S.J., Bingham, G., Peleg, A.Y., Stewardson, A.J. and Yuce, M.R., 2023. An Autonomous IoT-based Contact Tracing Platform in a COVID-19 Patient Ward. IEEE Internet of Things Journal.&rft.creator=Maggie Gendy&rft.creator=Mehmet Yuce&rft.date=2024&rft_rights=CC-BY-4.0&rft_subject=Contact tracing&rft_subject=Infectious diseases&rft_subject=COVID-19 outbreak&rft_subject=Hospital / Office settings&rft_subject=Bluetooth Low Energy (BLE)&rft_subject=Internet of Things (IoT)&rft_subject=Real-life dataset&rft_subject=Data communications&rft_subject=Signal processing&rft_subject=Wireless communication systems and technologies (incl. microwave and millimetrewave)&rft_subject=Digital electronic devices&rft_subject=Electronic sensors&rft_subject=Electronics, sensors and digital hardware not elsewhere classified&rft.type=dataset&rft.language=English Access the data

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CC-BY-4.0

Full description

Contract tracing is one of the most effective methods for mitigating the spread of infectious diseases, and it plays a crucial role in reducing COVID-19 outbreak. Since the pandemic, there has been increased concern regarding people's health in hospital and office settings, as these spaces with limited air exchange provide a conductive medium for virus spread. For our research, we constructed a completely new real-life dataset that was collected during the pandemic's peak in a hospital infectious ward (Alfred Hospital, Victoria, Melbourne) utilizing a Bluetooth Low Energy (BLE) Internet of Things (IoT) system. The dataset was developed by tracking the movements of health care personnel inside the hospital ward over a 12-day period with their permission in a privacy-preserving methodology. The dataset's richness in parameters and features makes it a good test bed for future research in the field. Our objective is to assist the scientific community in simulating the real-life scenarios during pandemics to improve the public health community's ability to slow any infectious diseases.

About the data types files:

Type A: Room or ward entry (Proximity sensors)

Type B: Room or ward exit (Proximity sensors)

Type C: Person-person and person-place interactions

Type D: Periodic RSSI values

Type E: Room or ward entry RSSI value

Type F: Room or ward exit RSSI value

P.S: If anyone wishes to utilize this data, we kindly request citation of the respective publications.

1-Gendy, M.E.G., Rathnayaka, A., Curtis, S.J., Stewardson, A.J. and Yuce, M.R., 2023. Future Prediction of Close Contacts in IoT-based Contact Tracing System using a New Real-Life Dataset. IEEE Journal of Biomedical and Health Informatics.

2- Rathnayaka, A., Gendy, M.E.G., Wu, F., Al Mamun, M.A., Curtis, S.J., Bingham, G., Peleg, A.Y., Stewardson, A.J. and Yuce, M.R., 2023. An Autonomous IoT-based Contact Tracing Platform in a COVID-19 Patient Ward. IEEE Internet of Things Journal.

Issued: 2024-05-10

Created: 2024-05-10

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