Fine-Grained Infectious Disease Simulation Model
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Solution Overview
Problem
Existing epidemic prediction or simulation tools, based on compartment models like SEIR, fail to distinguish between urban and village spread modes and cannot quantitatively analyze factors such as traffic flow, making it difficult to simulate the spread of infectious diseases from multiple dimensions like time and space.
Innovation Solution
A fine-grained infectious disease simulation model is constructed by obtaining population movement flow between target regions, dividing time and space into intervals, and building a simulation model that accounts for population movement, time modes, and spatial nodes, using equations that represent susceptible, exposed, infected, and removed individuals based on infection and removal rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If compartment models based on SEIR are used, then the model structure is simple, but the model cannot distinguish different spread modes in city and village and cannot quantitatively analyze factors affecting epidemic spread
Solution Approach 1:
The patent divides the study area into multiple spatial nodes (e.g., districts, counties) and time periods to create a fine-grained simulation framework. This segmentation allows the model to capture different spread modes in urban and rural areas separately, while maintaining a manageable structure through modular design of each spatial node's epidemic dynamics
Solution Approach 2:
The patent introduces spatial dimension by adding geographic location information to the traditional SEIR compartments, creating spatially explicit epidemic states. It also incorporates temporal dimension by dividing time into discrete periods, enabling the model to analyze epidemic spread patterns across both space and time dimensions simultaneously
2Measurement precision
If fine-grained spatial and temporal division is implemented, then the simulation precision is improved, but the model complexity and computational burden increase
Solution Approach 1:
The patent segments the epidemic simulation into independent spatial nodes and time periods, where each node follows the same SEIR framework but with location-specific parameters. This segmentation enables fine-grained simulation while reducing overall complexity through reuse of standardized modular components across different regions
Solution Approach 2:
The patent adjusts model parameters (infection rate, recovery rate, transmission probability) according to spatial and temporal variations rather than using fixed uniform parameters. This allows the model to capture local epidemic characteristics and temporal patterns while maintaining the same basic model structure, thereby improving precision without proportionally increasing complexity
3Measurement precision
If population movement flow is incorporated, then the epidemic spread simulation becomes more accurate, but the data processing complexity increases
Solution Approach 1:
The patent uses population movement flow data as an intermediary variable that connects different spatial nodes. Instead of directly modeling complex human behavior patterns, the model uses aggregated movement flow statistics (e.g., number of people moving from one region to another) as input parameters, simplifying the data processing while capturing essential spread dynamics
Solution Approach 2:
The patent performs preliminary data processing by pre-calculating population movement flows and storing them as input data before running the epidemic simulation. This preliminary action separates data preparation from the main simulation computation, reducing the complexity of real-time data processing during simulation execution
Data Source
AI summary
A construction method of a fine-grained infectious disease simulation model is disclosed. The construction method includes: obtaining a population movement flow between multiple target regions within a predetermined time period; dividing the predetermined time period into multiple time periods based on a time mode; dividing the multiple target regions into multiple spatial nodes based on a spatial mode; and constructing a simulation model according to the population movement flow, the multiple time periods, and the multiple spatial nodes. By the method, the dynamic modeling of the development of infectious diseases is completed; the sub model modeling under different time modes is completed; and the fine-grained modeling under different spatial modes is completed.