Infectious disease trend prediction method and system using multi-baseline correction model

Abstract

The invention relates to an infectious disease trend prediction method and system using a multi-baseline correction model. The method comprises the following steps: acquiring infectious disease epidemic situation data; preprocessing the data; judging an epidemic situation development stage; predicting an interval value via an autoregressive baseline; calibrating an upper limit value by using an index baseline; calibrating a lower limit value by using a growth baseline; conducting calculating and calibrating, wherein the main baseline, the pessimistic condition auxiliary baseline and the optimistic condition auxiliary baseline are mutually calibrated; and outputting an prediction result. The method has the advantages that compared with an infectious disease professional model analysis method, the method is low in data requirement, only historical basic data such as time, country and accumulative definite diagnosis number are needed, the data requirement threshold of infectious disease prediction is greatly lowered, and epidemic situation prediction difficulty is obviously lowered; and compared with a time sequence model analysis method, infectious disease trend prediction based on multi-baseline calibration is adopted in the invention, so the influence of time sequence instability caused by human factors can be reduced, and an accurate short-term prediction result can be obtained.

Description

technical field [0001] The invention belongs to the field of prediction data processing systems, in particular to a method and system for predicting infectious disease trends using a multi-baseline correction model. Background technique [0002] In the 21st century, mankind is facing many kinds of threats, such as the gradual deterioration of the natural environment and climate, and the prevalence of various diseases. Regardless of the impact of the environment or climate on human society, it is gradually formed, and it is a gradual accumulation process, and its harm is often not sudden. On the contrary, the occurrence of infectious diseases is both gradual and sudden, and its harm is often more obvious. During the outbreak of infectious diseases, researchers from all over the world have carried out studies on the transmission speed, spatial scope, transmission routes, and dynamic mechanisms of infectious diseases. Among them, the mathematical modeling of the infectious di...

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Problems solved by technology

The non-stationary sequence has no constant central trend, and the sample mean and variance of the time series cannot be used to infer the distribution characteristics of random variables at each time point. Using the time series model to predict the development of the epidemic has encountered problems

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