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Epidemic disease intervention method based on detection and contact tracking on sequential network

A time series network, epidemic technology, applied in epidemic warning systems, computer-aided medical procedures, medical simulations, etc., can solve the problems of lack of mathematical modeling and theoretical analysis, unable to capture dynamic information, etc. Effect

Active Publication Date: 2021-09-03
SHANGHAI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Among the existing technologies and modeling methods, one mainly intervenes in the epidemic in the static time-accumulation network, but cannot capture the dynamic information of the time-series network evolution; the other mainly intervenes in the epidemic spread through real data analysis, and lacks Mathematical modeling and theoretical analysis to verify that detection and tracing technology can effectively control the spread of epidemics

Method used

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  • Epidemic disease intervention method based on detection and contact tracking on sequential network
  • Epidemic disease intervention method based on detection and contact tracking on sequential network
  • Epidemic disease intervention method based on detection and contact tracking on sequential network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0097] In this example, the classic SEIRD model is used as the benchmark model to study the impact on the spread of the epidemic when testing and tracking are implemented simultaneously. When both detection and tracking are implemented, the above matrix simplifies to:

[0098]

[0099] When the epidemic can spread in the network, the maximum eigenvalue must be greater than zero, therefore, the critical threshold λ of the epidemic c for:

[0100]

[0101] The node states in this embodiment include: individual states include susceptible state (S), infected state (I), latent state (E), recovered state (R), dead state (D), isolated S state (Q S ), the isolated E state (Q E ), the isolated I state (Q I ) and hospitalized state (H). The individual status of the sudden epidemic based on detection and contact tracing is fully considered, which can provide guidance on how to respond to and control the spread of the sudden epidemic among the population.

Embodiment 2

[0103] In this example, consider a study examining the effect of an intervention on the spread of an epidemic when a Complete Tracking (CCT) is implemented. When implementing full tracing, its Jacobian matrix is ​​expressed as:

[0104]

[0105] Likewise, the critical threshold for an epidemic for:

[0106]

[0107] This example provides a theoretical threshold when the epidemic is fully tracked, which can be used to study the impact of detection interventions on the spread of the epidemic.

Embodiment 3

[0109] In this example, consider studying the impact of tracking interventions on the spread of an epidemic when complete detection (CD) is implemented. When implementing full detection, its Jacobian matrix is ​​expressed as:

[0110]

[0111] Likewise, the critical threshold for an epidemic for:

[0112]

[0113] This example provides a theoretical threshold when the epidemic is fully detected, which can be used to study the impact of contact tracing interventions on the spread of the epidemic.

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Abstract

The invention discloses an epidemic disease intervention method based on detection and contact tracking on a sequential network, and the method comprises the steps: firstly, constructing a susceptibility-latency-infection-recovery-death-hospitalization (SEIRDH) model based on detection and contact tracking according to a mean field theory; secondly, establishing an evolution rule of the sequential network; then, according to an evolution rule of the sequential network, simulating a dynamic evolution process of epidemic propagation; then, counting the proportion of recovery nodes and dead nodes in the network in a steady state; and finally, under detection, contact tracking and isolation measures, theoretically deriving a critical threshold lambda c of epidemic propagation. According to the method, intervention measures such as detection and contact tracking are considered on a sequential network, a kinetic equation of the SEIRDH model based on detection and contact tracking is derived through a mean field theory, a critical threshold lambda c of the kinetic equation is theoretically solved, and guidance can be provided for coping with Covid-19 or other sudden epidemic diseases outbreak in the future.

Description

technical field [0001] The invention relates to the technical field of controlling epidemic spread, in particular to an epidemic intervention method based on detection and contact tracing on a time series network. Background technique [0002] In the field of controlling the spread of epidemics, mathematical modeling and theoretical analysis are usually used to understand the dynamics of epidemic spread. In this regard, the classic transmission dynamics models are the susceptibility-infection-recovery (SIR) model and susceptibility-infection-susceptibility (SIS) model and their variants, which play a key role in dealing with novel epidemics. In addition to this, testing and contact tracing technology is the key to controlling the spread of epidemics and can provide a cost-effective solution to control the spread of epidemics. [0003] Among the existing technologies and modeling methods, one mainly intervenes in the epidemic in the static time-accumulation network, but cann...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/80G16H50/50
CPCG16H50/80G16H50/50Y02A90/10
Inventor 王冰洪潇韩越兴李卫民
Owner SHANGHAI UNIV
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