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Infectious disease early warning method and system based on individual adaptive propagation network, and medium

A technology for spreading networks and infectious diseases, applied in the fields of coronavirus infectious disease early warning methods, systems and storage media, can solve problems such as adding adaptive behavior to propagation dynamics and inaccurate prediction of infectious diseases, so as to improve feature extraction capabilities and accuracy The effect of predicting and improving accuracy

Active Publication Date: 2020-07-31
CENT SOUTH UNIV
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Problems solved by technology

The relevant studies listed above did not include human adaptive behavior in the study of transmission dynamics, and the prediction of the actual spread of infectious diseases is not accurate enough

Method used

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  • Infectious disease early warning method and system based on individual adaptive propagation network, and medium
  • Infectious disease early warning method and system based on individual adaptive propagation network, and medium
  • Infectious disease early warning method and system based on individual adaptive propagation network, and medium

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Embodiment

[0082] The unified representation of multi-modal infectious disease data is the basis for constructing dynamic adaptive information networks and dissemination networks. The present invention is based on a deep learning method, and eliminates outliers in the data [13] ,Noise value [14] , redundant value [15] On the basis of , the fusion of different modal data into a unified vector can improve the integrity of individual representations.

[0083] The flow of the unified characterization method for multimodal infectious disease data in the present invention is as follows: figure 1 Shown:

[0084] 1) Encoding multimodal data. Convert different modalities into computer-recognizable formats, and adopt different encoding network structures for data of different modalities, for example: use CNN structure for images, RNN structure for text, etc.;

[0085]2) The data of each modality are mapped through different networks. By constructing the loss function, the data of different mo...

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Abstract

The invention discloses an infectious disease early warning method and system based on an individual adaptive propagation network, and a medium. The method comprises the following steps: carrying outunified representation on multi-modal infectious disease data; and constructing an interaction model based on the individual adaptive behavior information network and the infectious disease propagation network by utilizing the uniformly represented infectious disease data, analyzing a threshold value of the interaction model, and determining an action relationship between the individual adaptive behavior and the infectious disease propagation according to the threshold value. According to the method, data of different modes can be represented in a unified manner, individual adaptive behaviorscan be accurately described, and the propagation threshold of infectious diseases can be accurately predicted.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a coronavirus infectious disease early warning method, system and storage medium. Background technique [0002] Looking back at human history, infectious diseases have always been the greatest threat to human health. With the help of theoretical analysis and simulation, by establishing an infectious disease transmission model, predicting the development trend of infectious diseases, and formulating precise prevention and control measures and personalized coping strategies for specific scenarios and individualized groups, it is an optimal way to prevent and control infectious diseases. An important means of defeating infectious diseases. The study of infectious diseases based on dynamic models can be traced back to Bernoulli's analysis of the smallpox vaccine in 1760. [1] .In 1911, Ross (Nobel Prize winner) proposed a differential dynamic model when studying the dynamics o...

Claims

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Application Information

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IPC IPC(8): G16H50/80G06N3/04G06N3/08G06K9/62
CPCG16H50/80G06N3/084G06N3/045G06F18/25Y02A90/10
Inventor 郭克华陈翔任盛沈敏学黄勋黄志军
Owner CENT SOUTH UNIV
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