Forecasting and early-warning management system for epidemic diseases

Abstract

The invention discloses an epidemic prediction and early warning management system. The system comprises a data layer, a platform layer, a service layer and a display layer, wherein the data layer internally comprises data acquisition, data processing, data loading, data issuing, ETL data extraction, etiological and monitoring information data, epidemiological analysis data, laboratory data, environment and serum monitoring data and sequence data. According to the invention, the antigen variation condition of a new virus and the epidemic condition of the new virus in a group are predicted through a vaccine strain recommendation model; a series of potential vaccine strains are recommended in real time; a virus harmfulness prediction model is used for predicting the potential hazards of thevirus under an epidemic condition; a calculation model of the binding capacity between HA protein of a toxic strain and sialic acid receptors of different hosts is established through a toxic strain receptor affinity prediction model; and the emergency disposal and comprehensive prevention and control capacity of the new outburst epidemic virus is enhanced through the etiological and monitoring information data and the epidemiological analysis data.

Description

technical field [0001] The invention relates to the technical field of epidemic early warning, in particular to an epidemic forecast early warning management system. Background technique [0002] Since the 21st century, epidemics of infectious diseases, public health emergencies, and natural disasters have continued to appear in various countries around the world. The large-scale outbreak of atypical pneumonia in the world in 2003 brought great panic to people. Due to the lack of corresponding detection, prevention and early warning methods for the new SARS virus at that time, the collection of case information lacked sufficient timeliness, and it was impossible to Comprehensive and rapid provision of first-hand information for the government and various monitoring departments, resulting in a serious lag in decision-making on prevention and control. The A H1N1 outbreak in 2009 and the H7N9 avian influenza virus outbreak in 2013 have had a serious impact on people's daily li...

AI Technical Summary

Problems solved by technology

The large-scale outbreak of atypical pneumonia in the world in 2003 brought great panic to people. Due to the lack of corresponding detection, prevention and early warning methods for the new SARS virus at that time, the collection of case information lacked sufficient timeliness, and it was impossible to Comprehensive and rapid provision of first-hand information for the government and various monitoring departments, resulting in a serious lag in decision-making on prevention and control

[0003] The disadvantage of the existing technology is that the misreporting rate of the epidemic is high, and the prediction effect is not accurate: the factors affecting the epidemic trend of infectious diseases are complex and changeable. Since most of them use the data of the statutory infectious disease reporting system, there are few data on factors such as meteorology and environment. , there are integrity and reliability issues, lack of intelligent screening of vaccine strains

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