Blockchain-based early warning system for infectious diseases: Integrating risk metrics for complex networks
Abstract
The emergence of infectious diseases poses a significant threat to public health and requires prompt action to minimize their impact. Traditional infectious disease early warning systems (EDWS) face numerous challenges, including delays in reporting, data privacy concerns, and lack of transparency. Blockchain technology provides a promising solution to these challenges by offering a decentralized, secure, and transparent platform for data sharing and analysis. This paper analyzes cases, medical resources, pathological characteristics, and their connections using hospital data. It proposes an infectious disease risk measurement algorithm based on complex network modeling and integrates the risk measurement algorithm to construct a blockchain-based infectious disease risk early warning and data sharing system. The solution enables the safe storing and sharing of data using blockchain technology; performs distributed global model training via federated learning; and enables rapid and accurate infectious disease risk early warning by integrating smart contracts with physician expertise. Automatic reporting enhances infectious disease risk early warning and reporting.
Keywords
Full Text:
PDFReferences
1. Xiong L, Hu P, Wang H. Establishment of epidemic early warning index system and optimization of infectious disease model: Analysis on monitoring data of public health emergencies. International Journal of Disaster Risk Reduction 2021; 65: 102547. doi: 10.1016/j.ijdrr.2021.102547
2. Goh TN. Six sigma in industry: Some observations after twenty-five years. Quality and Reliability Engineering International 2011; 27(2): 221–227. doi: 10.1002/qre.1093
3. Tang W, Liao H, Marley G, et al. The changing patterns of coronavirus disease 2019 (COVID-19) in China: A tempogeographic analysis of the severe acute respiratory syndrome coronavirus 2 epidemic. Clinical Infectious Diseases 2020; 71(15): 818–824. doi: 10.1093/cid/ciaa423
4. Henrickson M. Kiwis and COVID-19: The Aotearoa New Zealand response to the global pandemic. The International Journal of Community and Social Development 2020; 2(2): 121–133. doi: 10.1177/2516602620932558
5. Lan Y, Zhou D, Zhang H, et al. Development of early warning models. In: Yang W (editor). Early Warning for Infectious Disease Outbreak. Academic Press; 2017. pp. 35–74.
6. Yang W. Early Warning of Infectious Disease Theory and Practice. Academic Press; 2000.
7. Buckeridge DL, Okhmatovskaia A, Tu S, et al. Understanding detection performance in public health surveillance: Modeling aberrancy-detection algorithms. Journal of the American Medical Informatics Association 2008; 15(6): 760–769. doi: 10.1197/jamia.M2799
8. Jia P, Yang S. Early warning of epidemics: Towards a national intelligent syndromic surveillance system (NISSS) in China. British Medical Journal Global Health 2020; 5(10): e002925. doi: 10.1136/bmjgh-2020-002925
9. Sellick JA. The use of statistical process control charts in hospital epidemiology. Infection Control and Hospital Epidemiology 1993; 14(11): 649–656. doi: 10.1086/646659
10. Baker AW, Nehls N, Ilieş I, et al. Use of optimised dual statistical process control charts for early detection of surgical site infection outbreaks. British Medical Journal Quality and Safety 2020; 29(6): 517–520. doi: 10.1136/bmjqs-2019-010586
11. Howard L. Statistical process control: A quantitative approach to ensuring quality. The Admitting Management Journal 1990; 15(3): 6–7.
12. Tao H, Zain JM, Band SB, et al. SDN-assisted technique for traffic control and information execution in vehicular adhoc networks. Computers and Electrical Engineering 2022; 102: 108108. doi: 10.1016/j.compeleceng.2022.108108
13. Hai T, Said NM, Zain JM, et al. ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location. Journal of Building Engineering 2022; 57: 104914. doi: 10.1016/j.jobe.2022.104914
14. Hai T, Abidi A, Zain JM, et al. Assessment of using solar system enhanced with MWCNT in PCM-enhanced building to decrease thermal energy usage in ejector cooling system. Journal of Building Engineering 2022; 55: 104697. doi: 10.1016/j.jobe.2022.104697
15. Hai T, Abidi A, Wang L, et al. Thermal analysis of building benefits from PCM and heat recovery-installing PCM to boost energy consumption reduction. Journal of Building Engineering 2022; 58: 104982. doi: 10.1016/j.jobe.2022.104982
16. Hai T, El-Shafay AS, Zain JM, et al. The effect of triangular phase change material rods in the air conditioning duct on the amount of energy required for a residential building. Journal of Building Engineering 2022; 52: 104330. doi: 10.1016/j.jobe.2022.104330
17. Zhang H, Li Z, Lai S, et al. Evaluation of the performance of a dengue outbreak detection tool for China. PLoS One 2014; 9(8): e106144. doi: 10.1371/journal.pone.0106144
18. Hutwagner L, Thompson W, Seeman GM, et al. The bioterrorism preparedness and response early aberration reporting system (EARS). Journal of Urban Health 2003; 80(1): i89–i96. doi: 10.1007/PL00022319
19. Li Z, Lai S, Zhang H, et al. Hand, foot and mouth disease in China: Evaluating an automated system for the detection of outbreaks. Bulletin of the World Health Organization 2014; 92: 656–663. doi: 10.2471/BLT.13.130666
20. Unkel S, Farrington CP, Garthwaite PH, et al. Statistical methods for the prospective detection of infectious disease outbreaks: A review. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2012; 175(1): 49–82. doi: 10.1111/j.1467-985X.2011.00714.x
21. Takahashi K, Shimadzu H. Detecting multiple spatial disease clusters: Information criterion and scan statistic approach. International Journal of Health Geographics 2020; 19(1): 1–11. doi: 10.1186/s12942-020-00228-y
22. Shariati M, Mesgari T, Kasraee M, et al. Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020). Journal of Environmental Health Science and Engineering 2020; 18(2): 1499–1507. doi: 10.1007/s40201-020-00565-x
23. Saffary T, Adegboye OA, Gayawan E, et al. Analysis of COVID-19 cases’ spatial dependence in US counties reveals health inequalities. Frontiers in Public Health 2020; 8: 579190. doi: 10.3389/fpubh.2020.579190
24. Kulldorff M. Spatial scan statistics: Models, calculations, and applications. In: Glaz J, Balakrishnan N (editors). Scan Statistics and Applications. Birkhäuser; 1999. pp. 303–322.
25. Yang W, Li Z, Lan Y, et al. A nationwide web-based automated system for outbreak early detection and rapid response in China. Western Pacific Surveillance and Response Journal 2011; 2(1): 10–15. doi:10.5365/WPSAR.2010.1.1.009
26. Kulldorff M, Heffernan R, Hartman J, et al. A space-time permutation scan statistic for outbreak disease detection. Public Library of Science Medicine 2005; 2(3): e59. doi: 10.1371/journal.pmed.0020059
27. Hohl A, Delmelle EM, Desjardins MR, et al. Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States. Spatial and Spatio–temporal Epidemiology 2020; 34: 100354. doi: 10.1016/j.sste.2020.100354
28. Takahashi K, Kulldorff M, Tango T, et al. A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring. International Journal of Health Geographics 2008; 7(1): 1–14. doi: 10.1186/1476-072X-7-14
29. Kraemer MU, Reiner RC, Brady OJ, et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nature Microbiology 2019; 4(5): 854–863. doi: 10.1038/s41564-019-0376-y
30. Odhiambo JN, Kalinda C, Macharia PM, et al. Spatial and spatio-temporal methods for mapping malaria risk: A systematic review. British Medical Journal Global Health 2020; 5(10): e002919. doi: 10.1136/bmjgh-2020-002919
31. Siettos CI, Russo L. Mathematical modeling of infectious disease dynamics. Virulence 2013; 4(4): 295–306. doi: 10.4161/viru.24041
32. Yang W, Lan Y, Li Z. Review and prospects of infectious disease early warning research. Chinese Journal of Preventive Medicine 2014; 48(4): 244–247.
33. Wu J, Wang J, Nicholas S, et al. Application of big data technology for COVID-19 prevention and control in China: lessons and recommendations. Journal of Medical Internet Research 2020: 22(10): e21980. doi: 10.2196/21980
34. Liu L, Luan RS, Yin F, et al. Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model. Epidemiology and Infection 2016; 144(1): 144–151. doi: 10.1017/S0950268815001144
35. Yang E, Park HW, Choi YH, et al. A simulation-based study on the comparison of statistical and time series forecasting methods for early detection of infectious disease outbreaks. International Journal of Environmental Research and Public Health 2018; 15(5): 966. doi: 10.3390/ijerph15050966
36. Carneiro HA, Mylonakis E. Google trends: A web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases 2009; 49(10): 1557–1564. doi: 10.1086/630200
37. Jia W, Wan Y, Li Y, et al. Integrating multiple data sources and learning models to predict infectious diseases in China. American Medical Informatics Association Joint Summits on Translational Science Proceedings 2019; 2019: 680–685.
38. Ceballos-Arroyo AM, Maldonado-Perez D, Mesa-Yepes H, et al. Towards a machine learning-based approach to forecasting Dengue virus outbreaks in Colombian cities: A case-study: Medellin, Antioquia. In: 15th International Symposium on Medical Information Processing and Analysis; 6–8 November 2019; Medellín, Colombia. pp. 349–359.
39. Hu S, Li J, Liu F, et al. Establishment and response of SARS surveillance and precaution system for early-stage symptoms in Hunan province. Disease Surveillance 2005; 20(7): 344–347. doi: 10.3784/j.issn.1003-9961.2005.7.344
40. Oeschger TM, McCloskey DS, Buchmann RM, et al. Early warning diagnostics for emerging infectious diseases in developing into late-stage pandemics. Accounts of Chemical Research 2021; 54(19): 3656–3666. doi: 10.1021/acs.accounts.1c00383
41. Hai T, Alsharif S, Dhahad HA, et al. The evolutionary artificial intelligence-based algorithm to find the minimum GHG emission via the integrated energy system using the MSW as fuel in a waste heat recovery plant. Sustainable Energy Technologies and Assessments 2022; 53: 102531. doi: 10.1016/j.jobe.2022.104330
42. Hai T, Dhahad HA, Attia EA, et al. Proposal 3E analysis and multi-objective optimization of a new biomass-based energy system based on the organic cycle and ejector for the generation of sustainable power, heat, and cold. Sustainable Energy Technologies and Assessments 2022; 53: 102551. doi: 10.1016/j.seta.2022.102551
43. Hai T, Zhou J, Srividhya SR, et al. BVFLEMR: An integrated federated learning and blockchain technology for cloud-based medical records recommendation system. Journal of Cloud Computing 2022; 11(1): 22. doi: 10.21203/rs.3.rs-1750276/v1
44. Peters KE, Walters CC, Moldowan JM. The Biomarker Guide: Volume 1, Biomarkers and Isotopes in the Environment and Human History, 2nd ed. Cambridge University Press; 2007.
45. Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks. Physical Review Letters 2001; 86(14): 3200. doi: 10.1103/PhysRevLett.86.3200
46. Moreno Y, Pastor-Satorras R, Vespignani A. Epidemic outbreaks in complex heterogeneous networks. European Physical Journal B 2002; 26: 521–529. doi: 10.1140/epjb/e20020122
47. Li CH, Tsai CC, Yang SY. Analysis of epidemic spreading of an SIRS model in complex heterogeneous networks. Communications in Nonlinear Science and Numerical Simulation 2014; 19(4): 1042–1054. doi: 10.1016/j.cnsns.2013.08.033
48. Yang Z, Zhang J, Gao S, et al. Complex contact network of patients at the beginning of an epidemic outbreak: An analysis based on 1218 COVID-19 cases in China. International Journal of Environmental Research and Public Health 2022; 19(2): 689. doi: 10.3390/ijerph19020689
49. Christakis NA, Fowler JH. Social network sensors for early detection of contagious outbreaks. Public Library of Science One 2010; 5(9): e12948. doi: 10.1371/journal.pone.0012948
50. Holme P, Saramäki J. Temporal networks. Physics Reports 2012; 519(3): 97–125. doi: 10.1016/j.physrep.2012.03.001
51. Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: Challenges, methods, and future directions. arXiv: 1908.07873. doi: 10. .48550/arXiv.1908.07873
52. Kim H, Park J, Bennis M, et al. Blockchained on-device federated learning. Institute of Electrical and Electronics Engineers Communications Letters 2019; 24(6): 1279–1283. doi: 10.1109/LCOMM.2019.2921755
DOI: https://doi.org/10.32629/jai.v7i4.651
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Yimin Cai, Jie Ma, Jasni Mohamad Zain, Yisong Wang, Maoxing Zheng
License URL: https://creativecommons.org/licenses/by-nc/4.0/