Influenza trend prediction system based on particle filter model and influenza trend prediction method based on particle filter model

Abstract

The invention provides an influenza trend prediction system based on a particle filter model and an influenza trend prediction method based on the particle filter model. The influenza trend predictionsystem comprises a data source, a storage unit, a preprocessing unit, an analysis and processing unit and an output unit. The data source, a storage unit, the preprocessing unit, the analysis and processing unit and the output unit are connected in turn. The preprocessing unit comprises a data cleaning module and a data dimension reduction module. The data cleaning module and the data dimension reduction module are connected through data. The system and the method are applied to data prediction and can also act as the particle filter algorithm applied to engineering practice. Cleaning, denoising, normalization and dimension reduction are performed on the data captured online so that the calculation complexity can be greatly reduced.

Description

technical field [0001] The invention belongs to the field of public health, and in particular relates to an influenza trend prediction system and method based on a particle filter model. Background technique [0002] A physics-based epidemic disease transmission model, such as the SIRS model, mainly describes the proportional relationship between the population being infected by the virus and recovering from the infection. The assumptions of the SIRS model include a completely mixed population of different influenza strains and identical transmission behaviors. Therefore, the predictive power of current mechanistic models based on the timing or size of influenza outbreaks is mainly limited by factors such as spatial heterogeneity, preferential mixing of interpersonal network structure, and so on. [0003] In addition to the above-mentioned mechanism models, another type of technology mainly includes agent-based models, parametric statistical models, and empirical Bayesian f...

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Problems solved by technology

In addition to the interference in the transmission process, there are also uncertainties in the real-time measurement of influenza counts, such as underestimation of the asymptomatic population, delayed reporting, etc.

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