Infectious disease epidemic situation predicative analysis method based on nonlinear and coefficient variation predictive model

Abstract

The invention establishes a nonlinear and coefficient variation infectious disease predictive model aiming at epidemic diseases with viruses which have infectivity at a latent period and a period of onset, provides an epidemic situation control function directly related to the model, simulating and predicting effects of different control measures and different control degrees on the basis of prediction in consideration of the control measures, considers the epidemic situation control as a continuous change process, integrally simulating and predicting development and control of the epidemic situation and provides crucial quantitative information for decision-making departments to optimally decide and control the epidemic situation with the smallest cost. By adopting the invention, a relative error for simulating SARS (Severe Acute Respiratory Syndromes) in Beijing areas in 2003 is 0.98% and predictive results for influenza A virus subtype H1N1 in US and Japan are well matched with the actual epidemic situation development, a quantified control factor for preventing the influenza A virus subtype H1N1 at an initial development stage and controlling the spread of the epidemic situation is obtained and epidemic situation development conditions of different control intensities and different susceptible people are predicted.

Description

technical field [0001] The invention relates to the prediction and control of the epidemic situation of infectious diseases, and is especially suitable for the integrated simulation prediction of the development trend of the epidemic situation and the control effect, and belongs to the field of epidemic spread and control. Background technique [0002] In recent years, there have been frequent outbreaks of large-scale infectious diseases, including SARS in 2003, bird flu in 2005, hand, foot and mouth disease in 2008, and this year's H1N1 flu. The consequences, in addition to direct casualties and huge medical expenses In addition, the indirect impact on the economy and the harm to public psychology and social stability are very serious. [0003] The beginning, outbreak and control process of infectious diseases follow corresponding objective laws, and scientific prediction of its evolution is an important link for decision-making departments to correctly judge the situation ...

AI Technical Summary

Problems solved by technology

Most of the parameters of the conventional dynamic model are constant, and it is difficult to describe the control process of the development of the epidemic. The parameters of the stochastic dynamic model are difficult to determine. For a large-scale outbreak of the epidemic, the amount of simulation calculation is too large

Statistical models do not need to assume dynamic laws, and have unique advantages in the prediction of new infectious diseases with scarce information, but due to the uncertainty of the internal mechanism, it is difficult to guarantee the prediction accuracy

The spatio-temporal interaction model is also limited by the lack of dynamic mechanism of virus transmission

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