Data-based infectious disease propagation rate and epidemic situation evaluation method

Abstract

A data-based infectious disease propagation rate and epidemic situation evaluation method provided by the invention comprises the following steps: firstly, assuming that infectious disease case growthconforms to a natural growth rate model, selecting model starting time, and collecting and counting case data; calculating an infectious disease growth rate and a propagation rate which change alongwith time according to the model; then, adopting an EMD (empirical mode decomposition) method or least square method to give the trend of the propagation rate; and finally, performing extrapolation byutilizing the propagation rate trend, and estimating the moment when the total number of infected persons reaches the maximum value (inflection points appear). The infectious disease propagation rateand epidemic situation evaluation method is simple and direct and is directly driven by statistical data, and the propagation rate dynamically changes along with epidemic situation development. Because the key parameter propagation rate is based on the direct observation data, the change of the propagation rate can be used for tracking the past epidemic situation evolution process according to the existing data, judging the current evolution situation of the epidemic situation or evaluating the epidemic situation prevention and control effect, and the change trend of the propagation rate canalso be used for estimating the future development situation of the epidemic situation.

Description

Technical field [0001] The present invention relates to the technical field of infectious disease transmission evaluation, in particular to a data-based method for evaluating the transmission rate and epidemic situation of infectious diseases. Background technique [0002] In recent years, there have been frequent outbreaks of large-scale infectious diseases, such as SARS in 2003, H1N1 avian influenza in 2005, Middle East Respiratory Syndrome (MERS) in 2012, and the new coronavirus (2019-nCoV) in 2019. ) Pneumonia, its consequences, in addition to direct casualties and huge medical expenses, the indirect impact on the economy, the harm to the people's psychology and social stability are very serious. [0003] The epidemiology community has been seeking more information from the perspective of dynamics to explore the development mechanism of the epidemic. The most critical parameter is the basic infection number R 0 . R 0 It refers to the number of second-generation cases produced ...

AI Technical Summary

Problems solved by technology

However R 0 Quantitative values ​​require too much information, and it is very difficult to estimate this parameter more accurately during the outbreak of infectious diseases, and the values ​​of different research groups vary greatly

Therefore, R 0 It is difficult to obtain by the method of actual measurement, but a guess value is obtained by artificially adjusting the results of the mathematical model, which greatly limits its practicability

Moreover, the dynamics of the spread of the epidemic may appear in different ways at different stages, so it is unreasonable to use a constant to describe the entire development process of the epidemic

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