Infectious disease prediction method and system based on hidden Markov model, and medium

Abstract

The invention relates to an infectious disease prediction method and system based on a hidden Markov model, and a medium. The method comprises the following steps: S1, generating a meteorological and hydrological observation sequence and an infectious disease outbreak state sequence; S2, preprocessing the meteorological and hydrological observation sequence to generate a training sample set, and dividing samples in the training sample set into a plurality of sample feature intervals according to sample features; S3, calculating an observation probability matrix and an outbreak state transition probability matrix according to the sample features, the sample feature intervals and the infectious disease outbreak state sequence; S4, generating a hidden Markov model according to the meteorological and hydrological observation sequence, the infectious disease outbreak state sequence, the observation probability matrix, the outbreak state transition probability matrix and the initial state probability distribution; and S5, predicting a meteorological and hydrological sequence at a certain moment in the future according to the historical meteorological and hydrological observation sequence, and substituting the predicted meteorological and hydrological sequence into the hidden Markov model to predict the infectious disease outbreak state. The method has the characteristics of high calculation speed, high accuracy and easy acquisition of sample data.

Description

technical field [0001] The invention relates to an infectious disease prediction method, system and medium based on a hidden Markov model, and belongs to the technical field of artificial intelligence and smart medical care. Background technique [0002] The outbreak of infectious diseases has had a certain impact on the stability of social order and human health. Based on the analysis of the epidemic law of infectious diseases, using scientific methods to predict the epidemic trend of infectious diseases can effectively prevent and control infectious diseases. Infectious disease prediction methods are mainly divided into qualitative prediction and quantitative prediction. In order to ensure the accuracy of the prediction results, it is necessary to choose reasonably according to the prediction purpose, epidemiological characteristics and data characteristics of the predicted infectious disease. Whether the incidence rate will increase or decrease in the future, you can cho...

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Problems solved by technology

At present, there are many mathematical models used in the research of infectious disease prediction. One type is a differential equation model based on dynamics. This model is suitable for infectious diseases in the natural development process. Under the effect of artificial prevention and control, the predicted value is very different from the actual situation. The first category is multiple regression analysis, artificial neural network model and wavelet model combined with factors affecting the incidence rate. These methods have high requirements for the representativeness of the training samples in the process of use, so different regions and diseases Both time and time models need to be adjusted according to the specific situation. Due to the complexity of its analysis, the promotion and application of such methods are limited.

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