Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

Lei Zhang1(), Min-ye Li2(), Chen Zhi2, Min Zhu1, Hui Ma2()

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Current Medical Science ›› 2024, Vol. 44 ›› Issue (2) : 273-280. DOI: 10.1007/s11596-024-2850-x
Review Article

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

  • Lei Zhang1(), Min-ye Li2(), Chen Zhi2, Min Zhu1, Hui Ma2()
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Abstract

Abstract

The global incidence of infectious diseases has increased in recent years, posing a significant threat to human health. Hospitals typically serve as frontline institutions for detecting infectious diseases. However, accurately identifying warning signals of infectious diseases in a timely manner, especially emerging infectious diseases, can be challenging. Consequently, there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals. This paper examines the role of medical data in the early identification of infectious diseases, explores early warning technologies for infectious disease recognition, and assesses monitoring and early warning mechanisms for infectious diseases. We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems, in compliance with national strategies to integrate clinical treatment and disease prevention. Furthermore, hospitals should establish institution-specific, clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

Keywords

infectious disease / disease prevention and control / medical data / warning signals

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Lei Zhang, Min-ye Li, Chen Zhi, Min Zhu, Hui Ma. Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention. Current Medical Science, 2024, 44(2): 273‒280 https://doi.org/10.1007/s11596-024-2850-x

References

[1]
Warpeha KM, Chen SH, Mullié C. Editorial: Emerging Infectious and Vector-Borne Diseases: A Global Challenge. Front Public Health, 2022,10:942950
[2]
Yang Y, Peng F, Wang R, et al. The deadly coronaviruses: the 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. J Autoimmun, 2020,109:102434
[3]
Li L, Liu Y, Wu P, et al. Influenza-associated excess respiratory mortality in China, 2010–15: a population-based study. Lancet Public Health, 2019,4:e473–e481
[4]
WHO. Weekly epidemiological update on COVID-19 - 29 March 2022. Available from: https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19—29-march-2022. [Accessed 29 Mar 2022]
[5]
Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 2020,395:497–506
[6]
Kim D. Exploratory study on the spatial relationship between emerging infectious diseases and urban characteristics: Cases from Korea. Sustain Cities Soc, 2021,66:102672
[7]
Zhao IY, Ma YX, Yu MWC, et al. Ethics, Integrity, and Retributions of Digital Detection Surveillance Systems for Infectious Diseases: Systematic Literature Review. Med Internet Res, 2021,23:e32328
[8]
Osthus D, Moran KR. Multiscale influenza forecasting. Nat Commun, 2021,12:2991
[9]
Yang CY, Chen RJ, Chou WL, et al. An integrated influenza surveillance framework based on national influenza-like illness incidence and multiple hospital electronic medical records for early prediction of influenza epidemics: design and evaluation. J Med Internet Res, 2019,21:e12341
[10]
Lewnard JA, Reingold AL. Emerging Challenges and Opportunities in Infectious Disease Epidemiology. Am J Epidemiol, 2019,188:873–882
[11]
Tao FB. Healing the schism between public health and medicine, promoting the integration of prevention and treatment. Chin J Prev Med, 2020,54(5):465–468
[12]
Tan X, Liu X, Shao H. Healthy China 2030: A Vision for Health Care — ScienceDirect. Value Health Reg Issues, 2017,12:112–114
[13]
Zhong NS, Zeng GQ. Pandemic planning in China: applying lessons from severe acute respiratory syndrome. Respirology, 2008,13(Suppl 1):S33–S35
[14]
Jacobson PD, Parmet WE. Public Health and Health Care: Integration, Disintegration, or Eclipse. J Law Med Ethics, 2018,46:940–951
[15]
Bradley S, David McKelvey S. General practitioners with a special interest in public health; at last a way to deliver public health in primary care. J Epidemiol Community Health, 2005,59:920–923
[16]
Gottschalk R, Grünewald T, Biederbick W. The goals and structure of the Permanent Working Group of Medical Competence and Treatment Centers for highly contagious, life-threatening diseases. Bundesgesundheitsbla, 2009,52:214–218
[17]
Stevenson Rowan M, Hogg W, Huston P. Integrating public health and primary care. Healthc Policy, 2007,3(1):e160–e181
[18]
Wang L, Wang Y, Jin S, et al. Emergence and control of infectious diseases in China. Lancet, 2008,37:1598–1605
[19]
Ma H, Zhu J, Liu J, et al. Hospital biosecurity capacitation: Analysis and recommendations for the prevention and control of COVID-19. J Biosaf Biosecur, 2020,2(1):5–9
[20]
Hao B, Sotudian S, Wang T, et al. Early prediction of level-of-care requirements in patients with COVID-19. Elife, 2020,9:e60519
[21]
Chen XH, Xu L, Ge L, et al. A Survey of Infectious Disease Transmission Data Visual Analysis. J Comput Aided Des Comput Graph (Chinese), 2020,32:1581–1593
[22]
Yip W, Fu H, Chen AT, et al. 10 years of health-care reform in China: progress and gaps in Universal Health Coverage. Lancet, 2019,394:1192–1204
[23]
Ye Y, Shi J, Zhu D, et al. Management of medical and health big data based on integrated learning-based health care system: A review and comparative analysis. Comput Meth Prog Bio, 2021,209:106293
[24]
Venna SR, Tavanaei A, Gottumukkala RN, et al. A novel data-driven model for real-time influenza forecasting. IEEE Access, 2019,7:7691–7701
[25]
Iregbu K, Dramowski A, Milton R, et al. Global health systems’ data science approach for Precision diagnosis of sepsis in early life. Lancet Infect Dis, 2022,22(5):e143–e152
[26]
Aiello AE, Renson A, Zivich PN. Social media- and Internet-based disease surveillance for public health. Annu Rev Publ Health, 2020,41:101–118
[27]
Winkelman TNA, Margolis KL, Waring S, et al. Minnesota Electronic Health Record Consortium COVID-19 Project: Informing Pandemic Response Through Statewide Collaboration Using Observational Data. Public Health Rep, 2022,137(2):263–271
[28]
Wang MH, Chen HK, Hsu MH, et al. Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System. J Med Internet Res, 2018,20(8):e10886
[29]
Jia P, Yang S. China needs a national intelligent syndromic surveillance system. Nat Med, 2020,26:990
[30]
Nousi C, Belogianni P, Koukaras P. Mining Data to Deal with Epidemics: Case Studies to Demonstrate Real World AI Applications. In: Lim CP, Vaidya A, Jain K, et al, editors. Handbook of Artificial Intelligence in Healthcare. Cham: Springer, 2022:287–312.
[31]
Jang B, Kim M, Kim I, et al. EagleEye: a worldwide disease-related topic extraction system using a deep learning based ranking algorithm and Internet-sourced data. Sensors, 2021,21:4665
[32]
Simonsen L, Gog JR, Olson D, et al. Infectious disease surveillance in the big data era: towards faster and locally relevant systems. J Infect Dis, 2016,214(suppl_4):S380–S385
[33]
Dong J, Wu H, Zhou D, et al. Application of big data and artificial intelligence in COVID-19 prevention, diagnosis, treatment and management decisions in China. J Med Syst, 2021,45(9):84
[34]
Wu J, Wang J, Nicholas S, et al. Application of big data technology for COVID-19 prevention and control in China: lessons and recommendations. J Med Internet Res, 2020,22(10):e21980
[35]
Fei XL, Jiang L, Chen PY, et al. Exploration of the Change Trend Analysis of the Epidemiological History of COVID-19 Patients Based on Natural Language Processing. China Digital Med, 2020,15(5):76–78
[36]
Zhang QP, Gao JX, Wu JT, et al. Data science approaches to confronting the COVID-19 pandemic: a narrative review. Philos Trans A Math Phys Eng Sci, 2022,380(2214):20210127
[37]
Southall E, Brett TS, Tildesley MJ, et al. Early warning signals of infectious disease transitions: a review. J R Soc Interface, 2021,18(182):20210555
[38]
Southall E, Tildesley MJ, Dyson L. Prospects for detecting early warning signals in discrete event sequence data: Application to epidemiological incidence data. PLoS Comput Biol, 2020,16(9):e1007836
[39]
Jia P, Yang SJ. Early warning of epidemics: towards a national intelligent syndromic surveillance system (NISSS) in China. BMJ Glob Health, 2020,5(10):e002925
[40]
Liu MY, Li QH, Zhang YJ, et al. Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015. Infect Dis Poverty, 2018,7(1):106
[41]
Yeng PK, Woldaregay AZ, Solvoll T, et al. Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development. JMIR Public Health Sur, 2020;6(2):e11512.
[42]
Sartorius B, Lawson AB, Pullan RL. Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England. Sci Rep, 2021,11(1):5378
[43]
Yang W, Li Z, Lan Y, et al. A nationwide web-based automated system for outbreak early detection and rapid response in China. Western Pac Surveill Response J, 2011,2(1):10–15
[44]
Yang YL, Hu X, Yuan JS. Design and application of AI early warning system for infectious diseases in hospital. China Med Equip (Chinese), 2020,17(5):162–164
[45]
Salathé M. Digital Pharmacovigilance and Disease Surveillance: Combining Traditional and Big-Data Systems for Better Public Health. J Infect Dis, 2016,214(suppl_4):S399–S403
[46]
Poirier C, Lavenu A, Bertaud V, et al. Real time influenza monitoring using hospital big data in combination with machine learning methods: comparison study. JMIR Public Health Sur, 2018,4(4):e11361
[47]
Wang JL, Chen T, Ren X, et al. The Exploration of Strategy for Intelligent Surveillance and Early Warning of Important Respiratory Infectious Diseases and Application of Effective Countermeasures. Chin J Virol, 2021,37(5):1175–1178
[48]
Kangbai JB, James PB, Mandoh SL, et al. Tracking Ebola through cellphone, Internet of Things and blockchain technology. Emerg Infect Dis 2018,1(2):14–16
[49]
Lampos V, Majumder MS, Yom-Tov E, et al. Tracking COVID-19 using online search. NPJ Digit Med, 2021,4(1):17
[50]
Mavragani A. Tracking COVID-19 in Europe: infodemiology approach. JMIR Public Health Sur, 2020,6(2):e18941
[51]
Ginsberg J, Mohebbi MH, Patel RS, et al. Detecting influenza epidemics using search engine query data. Nature, 2009,457(7232):1012–1014
[52]
Tang L, Bie B, Park SE, et al. Social media and outbreaks of emerging infectious diseases: A systematic review of literature. Am J Infect Control, 2018,46(9):962–972
[53]
Kaya M, ?etin-Kaya Y. Seamless computation offloading for mobile applications using an online learning algorithm. Computing, 2021,103(5):771–799
[54]
Salathé M. Digital pharmacovigilance and disease surveillance: combining traditional and big-data systems for better public health. J Infect Dis, 2016,214(suppl_4):S399–S403
[55]
Hao R, Liu Y, Shen W, et al. Surveillance of emerging infectious diseases for biosecurity. Sci China Life Sci, 2022,65(8):1504–1516
[56]
Ganesan S, Subramani D. Spatio-temporal predictive modeling framework for infectious disease spread. Sci Rep, 2021,11(1):6741
[57]
Wu J, Wang J, Nicholas S, et al. Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations. J Med Internet Res, 2020,22(10):e21980
[58]
Pastorino R, De Vito C, Migliara G, et al. Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. Eur J Public Health, 2019,29(Supplement_3):23–27
[59]
Kaur I, Behl T, Aleya L, et al. Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic. Environ Sci Pollut Res Int, 2021,28(30):40515–40532
[60]
Ouyang L, Yuan Y, Cao Y, et al. A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts. Inf Sci, 2021,570:124–143
[61]
Kamel Boulos MN, Wilson JT, Clauson KA. Geospatial blockchain: promises, challenges, and scenarios in health and healthcare. Int J Health Geogr, 2018,17(1):25
[62]
Cuan-Baltazar JY, Mu?oz-Perez MJ, Robledo-Vega C, et al. Misinformation of COVID-19 on the Internet: infodemiology study. JMIR Public Health Sur, 2020,6(2):e18444
[63]
Kogan NE, Clemente L, Liautaud P, et al. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time. Sci Adv, 2021,7(10):eabd6989
[64]
Tekola B, Myers L, Lubroth J, et al. International health threats and global early warning and response mechanisms. Rev Sci Tech, 2017,36(2):657–670
[65]
Yang WZ, Lan YJ, Lyu W, et al. Establishment of multipoint trigger and multi-channel surveillance mechanism for intelligent early warning of infectious diseases in China. Chin J Epidemiol, 2020,41(11):1753–1757
[66]
Zeng J, Huang J, Pan L. How to balance acute myocardial infarction and COVID-19: the protocols from Sichuan Provincial People’s Hospital. Intens Care Med, 2020,46(6):1111–1113
[67]
Ye C, Li Z, Fu Y, et al. SCM: a practical tool to implement hospital-based syndromic surveillance. BMC Res Notes, 2016,9:315
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