Abstract:
ObjectiveTo detect the epidemic of infectious disease in time through early warning of infectious diseases and make rapid response to it.
MethodsDuring 2006–2016, VB＋ VC language was used to compile the auxiliary software for disease cluster screening, Early warning thresholds of different infectious diseases were set and the early warning model of infectious disease cluster at township and collective unit level was established based on the epidemiologic characteristics of infectious diseases in Guizhou province. Since 2017, the provincial infectious diseases big data center and the “Infectious disease surveillance data analysis and auxiliary decision-making system in Guizhou province” have been established. Based on artificial intelligence technology, autonomous learning of all cases of infectious diseases in history through machine, an automatic and intelligent early warning model for infectious disease surveillance was established.
ResultsCompared with the screening support software compiled with VB＋VC language, the infectious disease surveillance data analysis and auxiliary decision-making system in Guizhou was more effective in the detection of abnormal increase of disease reporting and the clusters of infectious diseases in medical institutions in time. It realized the whole process of early warning information response of CDCs at all levels. The development of disease cluster could be effectively controlled. The timely response rate of early warning increased from 61.17% to 97.59%. The timely disposal rate increased from 63.67% to 98.51%. The number of reported outbreaks and public health emergencies decreased from 343 to 83.
ConclusionThe application of big data analysis and artificial intelligence technology in infectious disease surveillance system can greatly improve the accuracy and sensitivity of infectious disease surveillance, which play an important role in the early warning of infectious diseases and reduction of the risk of infectious disease outbreak.
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