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Mathematical model-based oncomelania distribution influence factor identification and prediction method

A prediction method and technology of influencing factors, applied in the field of mathematical models, can solve the problems of losing time change information, not being able to incorporate time change into the method, and not being able to reflect the authenticity of oncomelania distribution data, so as to improve accuracy and facilitate analysis and interpretation , Improve the effect of fitting accuracy

Pending Publication Date: 2022-01-07
JIANGSU INST OF PARASITIC DISEASES
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Problems solved by technology

However, most of the current data analysis methods or models are based on spatial changes and cannot incorporate time changes into the method. Therefore, the research results will lose the relevant time change information and cannot reflect the authenticity of Oncomelania distribution data.

Method used

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  • Mathematical model-based oncomelania distribution influence factor identification and prediction method
  • Mathematical model-based oncomelania distribution influence factor identification and prediction method
  • Mathematical model-based oncomelania distribution influence factor identification and prediction method

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specific Embodiment approach

[0052] The present invention identifies and predicts the factors affecting the distribution of Oncomelania snails by constructing a spatio-temporal geographic weighted regression model, and its main links include:

[0053] 1. Based on the unit or platform, collect the snail situation data in the target area as the basic attribute data, mainly including: the spatial location data of the snail environment, the name of the snail environment, the area of ​​snails, the number of frames for checking snails, the number of frames for snails, the number of snails, Attribute information such as oncomelania density and environment type. The oncomelania density was used as the dependent variable of the regression model.

[0054] Obtain the environmental factors related to oncomelania breeding in the target area as the independent variables of the regression model, and the details are shown in the following table:

[0055]

[0056] In order to eliminate the influence of dimensions on t...

Embodiment

[0087] Collect and collect environmental data of snail reappearance along the river beach in Nanjing, Zhenjiang, and Yangzhou from 2016 to 2020, and use the following table to verify a mathematical model-based identification and prediction method for oncomelania distribution influencing factors proposed by the present invention.

[0088] 1. As shown in the table below, the environmental data of reappearing snails along the riverbanks in Nanjing, Zhenjiang and Yangzhou from 2016 to 2020 were collected, mainly including: the name of the environment with snails, the area with snails, the area of ​​positive snails, the number of boxes for checking snails, Attribute information such as the number of oncomelania, the number of positive oncomelania, the year of first appearance, and the year of last appearance, etc., counted a total of 221 environmental distribution points of oncomelania recurring in river beaches.

[0089] Obtain Sentinel 2 and LandSat8 satellite data from 2016 to 20...

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Abstract

The invention discloses a mathematical model-based oncomelania distribution influence factor identification and prediction method. The method comprises the following steps: establishing a space-time database of oncomelania distribution and attribute data; measuring units of time and space are balanced by using an elliptical coordinate system, and a space-time distance is defined by referring to a three-dimensional space Euclidean distance calculation formula; substituting the distance into a quadratic kernel function to construct a space-time weight matrix of each oncomelania distribution point, and selecting an optimal bandwidth value through an akaike information criterion; constructing a space-time geographically weighted regression (GTWR) model, obtaining related parameters, carrying out time and space change visualization processing on the parameters, and analyzing an internal rule to obtain a predicted value of oncomelania distribution. The invention has the greatest advantage that the time non-stationary characteristic of the oncomelania snail situation is integrated into a traditional oncomelania snail density prediction and analysis model, so that the fitting precision is improved, influence factors of oncomelania snail distribution in a research area are quantitatively analyzed, and the prediction precision can be remarkably improved.

Description

technical field [0001] The invention relates to the technical field of mathematical models, in particular to a method for identifying and predicting influencing factors of oncomelania distribution based on a mathematical model. Background technique [0002] Schistosomiasis is a disease closely related to biological, environmental, and socioeconomic factors. It has many transmission links and complex epidemic factors. It also has obvious susceptibility seasons, uncertain susceptibility environments, concentration of high-risk groups, infection modes, and residents. Production and lifestyle are closely related to other epidemiological characteristics. Hubei snail is the only intermediate host of Schistosoma japonicum, the breeding and distribution of this species directly affect the prevalence and transmission of schistosomiasis. The distribution of Oncomelania snails in Hubei has obvious regional characteristics. The current research believes that the factors affecting the r...

Claims

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Application Information

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IPC IPC(8): G06F16/9537G06F16/904G06Q10/04
CPCG06F16/9537G06F16/904G06Q10/04
Inventor 杨坤王喆蒋甜甜施亮刘璐
Owner JIANGSU INST OF PARASITIC DISEASES
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