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Epidemic propagation network modeling and inference of based on autonomic computing

A technology for spreading networks and epidemics, applied in computing, special data processing applications, instruments, etc., and can solve problems such as difficult to expand models, limited epidemic monitoring data, and difficulties in spreading network inference methods.

Active Publication Date: 2013-02-27
JILIN UNIV
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AI Technical Summary

Problems solved by technology

② Existing propagation network inference methods assume that the observed propagation data is sufficient, and time series data corresponding to multiple propagation processes are required to more accurately infer the propagation network structure
However, in practical applications, the available epidemiological surveillance data are usually very limited
③ Epidemic surveillance data has multi-scale characteristics in space and time, which bring difficulties to existing propagation network inference methods
④ Existing propagation network inference methods are mainly aimed at the process of information dissemination. Most of them use cascade and threshold models to model the dissemination mechanism of information, and it is difficult to extend to models such as SIR, SIS or SEIR, and these models are very suitable for describing the spread and outbreak process

Method used

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  • Epidemic propagation network modeling and inference of based on autonomic computing
  • Epidemic propagation network modeling and inference of based on autonomic computing
  • Epidemic propagation network modeling and inference of based on autonomic computing

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0058] Example 1: Model Validation Based on Real Data

[0059] Test the plausibility of the above epidemic propagation network model. The experimental data used the real monitoring data of the 2009 H1N1 swine flu outbreak in Hong Kong, which recorded the changes in the cumulative cases in Hong Kong from May 2009 to March 2010. Figure 4 As shown by the red solid line, H1N1 patients appeared between May 1 and June 12, but large-scale transmission did not begin, and the number of infected people increased rapidly after June 12. In the middle and late stages of the H1N1 outbreak, the Hong Kong government implemented measures such as patient isolation and vaccination to control the spread of H1N1, and the number of new cases gradually decreased. Since the epidemic transmission model provided by the present invention does not consider the impact of human control measures on epidemic transmission, the data of the first three months with less human intervention during the H1N1 outbr...

example 2

[0063] Example 2 Outbreak trend and model parameter estimation

[0064] To test the parameter estimation method of the D-AOC system, the specific steps are as follows: ①Given a set of system parameters θ={A, θ 1 , θ 2 , θ 3 , θ 4 , θ 5}, where θ i =i , gamma i , τ i>, 1≤i≤5; ② Run the D-AOC system to simulate the spread of the epidemic based on θ, and record the change of the number of infected cases over time during this process, as the observed epidemic monitoring data D; ③ Based on D, use image 3 The method in estimates the parameters and epidemic trends ④Comparing the actual value with the estimated value, analyzing the estimated situation of parameters and outbreak trend. Figure 5 As shown in (a), the bar graph represents the epidemic surveillance data D (time series of new cases) obtained through the above step ②, and the red solid line represents the outbreak trend estimated through the above step ③ (time series of new case estimates). Estimated outbrea...

example 3

[0068] Example 3 Impact of Missing Data on Outbreak Trend Estimation

[0069] The actually available monitoring data may be missing to varying degrees. Although the missing data is not visible, the impact on the spread of the epidemic actually exists. After estimating the outbreak trend of the epidemic based on the missing surveillance data, the missing transmission data can be guessed from the estimated data. In this experiment, we study the impact of missing data in different situations on the estimation of the outbreak trend. First, we run the D-AOC system with the parameters in Table 1 to simulate the spread of the epidemic, and use different time scales to sample from it to obtain the observed monitoring data; based on Estimate parameters and outbreak trends from the monitoring data obtained by sampling; according to the formula Calculate the deviation between the estimated outbreak trend and the actual monitoring data, and then analyze the impact of missing data in dif...

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Abstract

Epidemic propagation network modeling and inference based on autonomic computing comprise the following step: inferring a propagation network structure and biological parameters associated with epidemic from epidemic monitoring data by adopting a multi-autonomous modeled epidemic propagation network and an epidemic propagation process as well as monte carlo simulation and negative feedback mechanism. The invention provides a new method for monitoring and prevention and treatment of epidemics. Compared with the existing method, the method has the following major advantages that (1) an epidemic propagation network modeling method with wider adaptation range is provided; (2) an effective epidemic propagation network inference method is provided, and the epidemic propagation network structure and the biological parameter associated with the epidemic can be inferred from the epidemic monitoring data; and (3) the method provided by the invention can be used for epidemic risk assessment and validity verification of an epidemic prevention and treatment strategy.

Description

technical field [0001] The invention belongs to the intersection field of information technology and public health, especially relates to the fields of epidemic control, complex network, data mining and the like. Background technique [0002] Every outbreak of an epidemic will bring huge losses to human society. Establishing a theoretical model to understand and simulate the spread and outbreak process of the epidemic, establishing a monitoring system to collect the spread data of the epidemic, and then conducting accurate risk assessment based on the model and monitoring data, helping decision makers to formulate corresponding prevention and emergency plans, is an effective control A new way to spread and break out the epidemic and minimize the loss of life and property. [0003] The spreading process of an epidemic is jointly determined by the epidemic model and the spreading network. The medical field has a long history of research on epidemics, and a variety of mathema...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 杨博
Owner JILIN UNIV
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