Convergent Intelligence for Pandemic Preparedness: A Narrative Review of Integrated Digital Biosurveillance Systems Utilizing Pre-Diagnostic Data Streams
Abstract
Background: The early detection of infectious disease outbreaks is a critical public health imperative. Traditional surveillance, reliant on confirmed laboratory reports, often introduces a critical delay. The concept of the biosurveillance continuum advocates for the real-time integration of pre-diagnostic data streams from across the healthcare ecosystem to provide earlier warning. Aim: This narrative review systematically synthesizes the literature on integrated systems that combine syndromic Emergency Medical Services (EMS) data, over-the-counter (OTC) pharmacy sales, radiology findings, and nursing home reports for the early detection of infectious disease outbreaks. Methods: A systematic search was conducted across PubMed/MEDLINE, CINAHL, Scopus, and IEEE Xplore for studies published between 2010-2024. A narrative synthesis was performed, analyzing system architectures, detection performance, and implementation challenges. Results: Each data stream offers unique benefits for public health monitoring. EMS data ensures geographic and temporal insight into illnesses like influenza-like illness (ILI). Over-the-counter (OTC) sales data reflect symptom onset across populations. NLP analysis of radiology reports can detect pneumonia cluster patterns before definitive diagnosis. Nursing home data provides vital surveillance for high-risk groups. Effective integration of these sources necessitates Health Informatics platforms for data aggregation and visualization, supported by robust epidemiological frameworks. Conclusion: An integrated biosurveillance system utilizing EMS, pharmacy, radiology, and nursing home data can improve early outbreak detection. However, challenges such as data standardization, interoperability, privacy, and collaboration must be addressed to maximize its effectiveness. Investing in these systems is crucial for pandemic preparedness.
Full text article
References
Andersson, T., Bjelkmar, P., Hulth, A., Lindh, J., Stenmark, S., & Widerström, M. (2014). Syndromic surveillance for local outbreak detection and awareness: evaluating outbreak signals of acute gastroenteritis in telephone triage, web-based queries and over-the-counter pharmacy sales. Epidemiology & Infection, 142(2), 303-313. doi:10.1017/S0950268813001088
Buckeridge, D. L. (2007). Outbreak detection through automated surveillance: a review of the determinants of detection. Journal of biomedical informatics, 40(4), 370-379. https://doi.org/10.1016/j.jbi.2006.09.003
Cassell, K., Zipfel, C. M., Bansal, S., & Weinberger, D. M. (2022). Trends in non-COVID-19 hospitalizations prior to and during the COVID-19 pandemic period, United States, 2017–2021. Nature Communications, 13(1), 5930. https://doi.org/10.1038/s41467-022-33686-y
Chretien, J. P., Rivers, C. M., & Johansson, M. A. (2016). Make data sharing routine to prepare for public health emergencies. PLoS medicine, 13(8), e1002109. https://doi.org/10.1371/journal.pmed.1002109
Duijster, J. W., Doreleijers, S. D., Pilot, E., van der Hoek, W., Kommer, G. J., van der Sande, M. A., ... & van Asten, L. C. (2020). Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review. European journal of public health, 30(4), 639-647. https://doi.org/10.1093/eurpub/ckz177
Dixon, B. E., McGowan, J. J., & Grannis, S. J. (2011, October). Electronic laboratory data quality and the value of a health information exchange to support public health reporting processes. In AMIA annual symposium proceedings (Vol. 2011, p. 322).
Dugas, A. F., Jalalpour, M., Gel, Y., Levin, S., Torcaso, F., Igusa, T., & Rothman, R. E. (2013). Influenza forecasting with Google flu trends. PloS one, 8(2), e56176. https://doi.org/10.1371/journal.pone.0056176
Elkefi, S., & Asan, O. (2022). Digital twins for managing health care systems: rapid literature review. Journal of medical Internet research, 24(8), e37641. https://doi.org/10.2196/37641
Ferraro, C. F., Findlater, L., Morbey, R., Hughes, H. E., Harcourt, S., Hughes, T. C., ... & Smith, G. E. (2021). Describing the indirect impact of COVID-19 on healthcare utilisation using syndromic surveillance systems. BMC Public Health, 21(1), 2019. https://doi.org/10.1186/s12889-021-12117-5
Grannis, S. J., Stevens, K. C., & Merriwether, R. (2010). Leveraging health information exchange to support public health situational awareness: the Indiana experience. Online journal of public health informatics, 2(2), ojphi-v2i2.
Hswen, Y., Brownstein, J. S., Liu, J., & Hawkins, J. B. (2017). Use of a digital health application for influenza surveillance in China. American journal of public health, 107(7), 1130-1136. https://doi.org/10.2105/AJPH.2017.303767
Hughes, M. M., Groenewold, M. R., Lessem, S. E., Xu, K., & Ussery, E. N. (2020). RE, Wiegand, X. Qin, T. Do, D. Thomas, D., S. Tsai, A. Davidson, J. Latash, S. Eckel, J. Collins, M. Ojo, L. McHugh, W. Li, J. Chen, J. Chan, JM Wortham, S. Reagan-Steiner, JT Lee, SC Reddy, DT Kuhar, SL Burrer, MJ Stuckey,“Update: Characteristics of Health Care Personnel with COVID-19-United States, 1364-1368.
Hulth, A., Rydevik, G., & Linde, A. (2009). Web queries as a source for syndromic surveillance. PloS one, 4(2), e4378. https://doi.org/10.1371/journal.pone.0004378
Ising, A., Proescholdbell, S., Harmon, K. J., Sachdeva, N., Marshall, S. W., & Waller, A. E. (2016). Use of syndromic surveillance data to monitor poisonings and drug overdoses in state and local public health agencies. Injury prevention, 22(Suppl 1), i43-i49. https://doi.org/10.1136/injuryprev-2015-041821n
Johansson, M. A., Reich, N. G., Hota, A., Brownstein, J. S., & Santillana, M. (2016). Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Scientific reports, 6(1), 33707. https://doi.org/10.1038/srep33707
Khan, Y., O’Sullivan, T., Brown, A., Tracey, S., Gibson, J., Généreux, M., ... & Schwartz, B. (2018). Public health emergency preparedness: a framework to promote resilience. BMC public health, 18(1), 1344. https://doi.org/10.1186/s12889-018-6250-7
Kleinman, K. P., & Abrams, A. M. (2006). Assessing surveillance using sensitivity, specificity and timeliness. Statistical methods in medical research, 15(5), 445-464. https://doi.org/10.1177/0962280206071641
Kohli, M., Prevedello, L. M., Filice, R. W., & Geis, J. R. (2017). Implementing machine learning in radiology practice and research. American journal of roentgenology, 208(4), 754-760. https://doi.org/10.2214/AJR.16.17224
Liang, H., Tsui, B. Y., Ni, H., Valentim, C. C., Baxter, S. L., Liu, G., ... & Xia, H. (2019). Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature medicine, 25(3), 433-438. https://doi.org/10.1038/s41591-018-0335-9
Mathes, R. W., Lall, R., Levin-Rector, A., Sell, J., Paladini, M., Konty, K. J., ... & Weiss, D. (2017). Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system. PloS one, 12(9), e0184419. https://doi.org/10.1371/journal.pone.0184419
M'ikanatha, N. M., Lynfield, R., Julian, K. G., Van Beneden, C. A., & Valk, H. D. (2013). Infectious disease surveillance: a cornerstone for prevention and control. Infectious disease surveillance, 1-20. https://doi.org/10.1002/9781118543504.ch1
McMichael, T. M., Currie, D. W., Clark, S., Pogosjans, S., Kay, M., Schwartz, N. G., ... & Duchin, J. S. (2020). Epidemiology of COVID-19 in a long-term care facility in King County, Washington. New England Journal of Medicine, 382(21), 2005-2011. DOI: 10.1056/NEJMoa2005412
Mei, X., Lee, H. C., Diao, K. Y., Huang, M., Lin, B., Liu, C., ... & Yang, Y. (2020). Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature medicine, 26(8), 1224-1228. https://doi.org/10.1038/s41591-020-0931-3
Menni, C., Valdes, A. M., Freidin, M. B., Sudre, C. H., Nguyen, L. H., Drew, D. A., ... & Spector, T. D. (2020). Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature medicine, 26(7), 1037-1040. https://doi.org/10.1038/s41591-020-0916-2
Muchaal, P. K., Parker, S., Meganath, K., Landry, L., & Aramini, J. (2015). Evaluation of a national pharmacy‐based syndromic surveillance system. Canada Communicable Disease Report, 41(9), 203. https://doi.org/10.14745/ccdr.v41i09a01
Nuzzo, J. B., Meyer, D., Snyder, M., Ravi, S. J., Lapascu, A., Souleles, J., ... & Bishai, D. (2019). What makes health systems resilient against infectious disease outbreaks and natural hazards? Results from a scoping review. BMC public health, 19(1), 1310. https://doi.org/10.1186/s12889-019-7707-z
Ramay, B. M., Jara, J., Moreno, M. P., Lupo, P., Serrano, C., Alvis, J. P., ... & Kaydos-Daniels, S. C. (2022). Self-medication and ILI etiologies among individuals presenting at pharmacies with influenza-like illness: Guatemala City, 2018 influenza season. BMC Public Health, 22(1), 1541. https://doi.org/10.1186/s12889-022-13962-8
Rosenkötter, N., Ziemann, A., Riesgo, L. G. C., Gillet, J. B., Vergeiner, G., Krafft, T., & Brand, H. (2013). Validity and timeliness of syndromic influenza surveillance during the autumn/winter wave of A (H1N1) influenza 2009: results of emergency medical dispatch, ambulance and emergency department data from three European regions. BMC Public Health, 13(1), 905. https://doi.org/10.1186/1471-2458-13-905
Rumbold, J. M. M., & Pierscionek, B. (2017). The effect of the general data protection regulation on medical research. Journal of medical Internet research, 19(2), e47. https://doi.org/10.2196/jmir.7108
Shah, R., Della Porta, A., Leung, S., Samuels-Kalow, M., Schoenfeld, E. M., Richardson, L. D., & Lin, M. P. (2021). A scoping review of current social emergency medicine research. Western Journal of Emergency Medicine, 22(6), 1360. https://doi.org/10.5811/westjem.2021.4.51518
Soltan, A. A., Yang, J., Pattanshetty, R., Novak, A., Yang, Y., Rohanian, O., ... & Clifton, D. A. (2021). Real-world evaluation of AI-driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions. medRxiv, 2021-08. https://doi.org/10.1101/2021.08.24.21262376
Stone, N. D., Ashraf, M. S., Calder, J., Crnich, C. J., Crossley, K., Drinka, P. J., ... & Bradley, S. F. (2012). Surveillance definitions of infections in long-term care facilities: revisiting the McGeer criteria. Infection Control & Hospital Epidemiology, 33(10), 965-977. doi:10.1086/667743
Sugishita, Y., Sugawara, T., Ohkusa, Y., Ishikawa, T., Yoshida, M., & Endo, H. (2020). Syndromic surveillance using ambulance transfer data in Tokyo, Japan. Journal of Infection and Chemotherapy, 26(1), 8-12. https://doi.org/10.1016/j.jiac.2019.09.011
Syrowatka, A., Kuznetsova, M., Alsubai, A., Beckman, A. L., Bain, P. A., Craig, K. J. T., ... & Bates, D. W. (2021). Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases. NPJ digital medicine, 4(1), 96. https://doi.org/10.1038/s41746-021-00459-8
Thrupp, L., Bradley, S., Smith, P., Simor, A., Gantz, N., Crossley, K., ... & SHEA Long-Term-Care Committee. (2004). Tuberculosis prevention and control in long-term–care facilities for older adults. Infection Control & Hospital Epidemiology, 25(12), 1097-1108. doi:10.1086/502350
Wagner, M. M., Tsui, F. C., Espino, J. U., Dato, V. M., Sitting, D. F., Caruana, R. A., ... & Fridsma, D. B. (2001). The emerging science of very early detection of disease outbreaks. Journal of public health management and practice, 7(6), 51-59.
Wedd, J., Basu, M., Curtis, L. M., Smith, K., Lo, D. J., Serper, M., ... & Patzer, R. E. (2019). Racial, ethnic, and socioeconomic disparities in web-based patient portal usage among kidney and liver transplant recipients: cross-sectional study. Journal of medical Internet research, 21(4), e11864. https://doi.org/10.2196/11864
Wong, G., Greenhalgh, T., Westhorp, G., Buckingham, J., & Pawson, R. (2013). RAMESES publication standards: Meta‐narrative reviews. Journal of advanced nursing, 69(5), 987-1004. https://doi.org/10.1111/jan.12092
Yamana, T. K., Kandula, S., & Shaman, J. (2017). Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States. PLoS computational biology, 13(11), e1005801. https://doi.org/10.1371/journal.pcbi.1005801
Zeng, D., Cao, Z., & Neill, D. B. (2021). Artificial intelligence–enabled public health surveillance—from local detection to global epidemic monitoring and control. In Artificial intelligence in medicine (pp. 437-453). Academic Press. https://doi.org/10.1016/B978-0-12-821259-2.00022-3
Authors
Copyright (c) 2024 Khaloud Abdullah Fahad Alnassar, Nora Yahya Hussain Humadi, Taleb Huraymis Almutairi, Amal Mohamad Almadani, Nooh Thawab Almotiri, Abdulrahman Ali Nasser Alharbi, Ahmed Mueibid Mohammed Alharbi, Abdulaziz Khalaf Ghareeb Aldhaferi, Ahmed Mohammed Osaykir Alrashdi, Fahad Nazal Khalaf Alalawi, Dowahim Abdullah Aldosrai, Badr Mohsen Samir Al-Bashri Al-Harbi, Mohammed Shaya Alshmmari, Maha Sultan Majed Alotaibi
This work is licensed under a Creative Commons Attribution 4.0 International License.