Epidemic situation predicting and early warning method of infectious diseases

Abstract

The invention discloses an epidemic situation predicting and early warning method of infectious diseases, particularly a network early warning system of relevant public health events of infectious diseases in countries, provinces, cities (regions) and districts (counties). The method comprises the steps of integrating adverse reaction information data which are collected by the server side, calculating and determining the early warning threshold of public health events according to the quantities of the symptoms of adverse reactions on the basis of the operation of a time sequence analysis statistic model and automatically carrying out early warning in real time at any time. The method comprises the following specific steps of: (1) integrating information from different channels; (2) executing a path I: analyzing tasks; and (3) executing a path II: predicting and early warning. Once the quantity of monitoring data meets the traditional basic requirements of a tome sequence analysis method for considering the periodicity, the seasonal nature, long term tend and the like of diseases, the conclusion having relative-high value can be obtained to play important roles of early finding epidemic situations, taking measures as soon as possible, preventing the spread of the epidemic situations and preliminarily establishing an epidemic situation monitoring and early warning system and a working mechanism of the province.

Description

technical field [0001] The invention is a network early warning system for public health events related to infectious diseases at the national, provincial, city (region), and district (county) levels, and belongs to the technical field of epidemic situation prediction and early warning of infectious diseases. Background technique [0002] In order to help the government comprehensively, scientifically and timely provide effective early warning of public health emergencies, this system integrates information from different channels, and integrates the theoretical research results of infectious disease-related public health event prediction and early warning technologies and methods applied to public health surveillance. [0003] The medical department of each unit uploads information and data such as the basic situation of the unit, the basic situation of the employees of the unit, and the case of the epidemic report to the data center accurately and in real time. Provincial...

AI Technical Summary

Problems solved by technology

[0005] Through the analysis, it can be seen that the sales volume of over-the-counter drugs and the classification of drugs in the unit’s medical department are difficult to fully cover the outbreak of infectious diseases among the unit’s employees (school students), and the monitoring data of employees’ (students) absenteeism due to illness can only be classified in detail. In order to accurately reflect the epidemic situation of infectious diseases on campus, because the time series of current monitoring data is often interrupted (there are winter and summer vacations in between), it is still impossible to conduct a very comprehensive analysis of the periodicity and seasonality of infectious diseases reflected in the data. Persuasive analysis and forecasting, so fewer analysis options are available

Moreover, it is still not possible to analyze the data uploaded to the database through analysis methods such as time series analysis, and it is not possible to detect epidemics on a larger scale with hierarchical early warning

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