CN113724792B - Virus diffusion and climate factor relation analysis method based on correlation analysis - Google Patents
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Abstract
The invention discloses a correlation analysis-based virus diffusion and climate factor relation analysis method, which utilizes a multiple linear regression method to develop a series of verification and establish a multiple regression equation; and evaluating the relative importance of each climate factor on the influence of the newly added diagnostic person number and the correlation coefficient between each independent variable and dependent variable by using the Pearson correlation coefficient, searching the linear relationship, and judging the correlation between each observed variable. And judging the efficacy of the virus climate factor relation model by utilizing the correction decision coefficient, and determining the fitting degree of the multiple linear regression model and the real data of each country. The invention predicts and obtains the newly increased diagnosis number by means of the model, and can guide each country to make prevention and control measures with different strict grades. In addition, the climate factor prevention and control suggestion can be provided for the countries around the world, and positive measures are taken for prevention and control by aiming at factors suitable for virus survival such as temperature, humidity and the like.
Description
Technical Field
The invention belongs to the field of data processing, relates to a multiple linear regression model technology, and particularly relates to a method for analyzing the relationship between virus diffusion and climatic factors by using a multiple linear regression model.
Background
The outbreak of viruses can influence the lives of people worldwide, analyze the virus transmission rule, and support the implementation of virus prevention and control measures has urgent demands and important significance. The relationship between virus spread and climate factors was analyzed using a multiple linear regression model. Correlation analysis was performed based on the Novel Coronavirus 2019time series data on cases dataset published by john hopkins university system science and engineering Center (CSSE) and weather data of the weather network, chinese weather data network.
The multiple linear regression model is suitable for the condition that multiple variables influence single variables, and can accurately measure the correlation degree and regression fitting degree between the variables, so that the effect of the prediction model is improved. In the study, meteorological factors have influence on virus diffusion from multiple aspects, and the correlation degree between various meteorological factors and virus diffusion needs to be analyzed, so that the study selects a multiple linear regression model for analysis.
Some studies have been made on viral spread and climate factors, such as Zhu et al, by searchingThe method integrates the number of new daily cases and corresponding climate factor data of eight areas severely affected by viruses in four countries in south America, and confirms that the absolute humidity has high remarkable correlation with the new daily cases by using a multiple linear regression model. David et al propose to explore the linear and nonlinear relationship between annual average temperature compensation and diagnosed cases in the head city of brazil using the Generalized Additive Model (GAM), and found that the cumulative number of diagnosed cases per day was reduced by 4.8951% for every 1 ℃ increase in temperature. Kuldeep et al used the Sen's Slope and Man-Kendall test and regression Generalized Additive Model (GAM) to examine the effects of daily temperature and relative humidity inside indian countries on morbidity. Lowen, barreca andseveral studies have shown that ambient temperature has an important role in the survival and spread of viruses.
A large number of researches simultaneously support the effect of environmental temperature and humidity in transmission and infection, and samples selected by the researches are limited to local areas, so that the researches are promoted to explore the influence of environmental factors on viruses in the global scope, and the commonality of the viruses is clearly researched by the global scope data, so that the researches are more close to the real characteristics of the viruses.
Disclosure of Invention
Based on the analysis, the invention mainly adopts a multiple linear regression analysis method to analyze the relation between the number of newly increased people per day in each region and the climate factors of the region. The whole method mainly comprises two parts: model construction and correlation coefficient analysis. The invention hopes to guide the knowledge of virus characteristics through correlation coefficient analysis so as to control virus transmission in time.
In order to achieve the above purpose, the present invention adopts the following technical scheme: in order to better implement the whole method, python is chosen as the method writing language. The data processing stage uses Pandas to realize data set cleaning and data set division, and the model construction and training is mainly realized by Sklearn. Firstly, developing a series of verification by utilizing a multiple linear regression method, and establishing a multiple regression equation; and evaluating the relative importance of each climate factor on the influence of the newly added diagnostic person number and the correlation coefficient between each independent variable and dependent variable by using the Pearson correlation coefficient, searching the linear relationship, and judging the correlation between each observed variable. And judging the efficacy of the virus climate factor relation model by utilizing the correction decision coefficient, and determining the fitting degree of the multiple linear regression model and the real data of each country.
A method for analyzing the relation between virus diffusion and climate factors based on correlation analysis mainly comprises the following steps:
step 1, data source and experimental object:
the virus related data is derived from a published data set of the number of diagnosed people of related viruses published by the system science and engineering center of John Hopkins university and daily record data of global all-terrain weather stations collected by the Chinese weather data network. 65 countries with global cumulative diagnostic numbers exceeding 10000 from day 22 of 3 months to day 22 of 6 months were selected as study subjects.
Step 2, data collection and pretreatment:
the collected virus data is the number of confirmed diagnosis people in each country in a daily way, and the number of new diagnosis crowns in each country is obtained by subtracting the number of confirmed diagnosis people in each country in a daily way and the number of confirmed diagnosis people in the previous day. And selecting the month average high temperature, month average low temperature, sea level pressure, altitude, wind speed, rainfall, dew point temperature and relative humidity of each country as various climatic factor data. And for missing weather factor data of a certain day, taking an average value of data of two days before and after to fill. The climate factor data missing on consecutive dates is filled with 0, preventing the experimental results from being affected. Data were read as per 7: the scale of 3 is divided into training and test sets.
Step 3, constructing a multiple linear regression model:
taking the newly added number of diagnostic persons (New) as a dependent variable y, each climate factor comprises: the average high temperature (t_max), the month average low temperature (t_min), the sea level pressure (s_p), the wind speed (w_s), the altitude (EI), the Rainfall (RF), the dew point temperature (DP) and the relative Humidity (Humidity) are independent variables x1, x2, x3, x4, x5, x6, x7, x8, respectively. Beta 0 、β 1 、β 2 、β 3 、β 4 、β 5 、β 6 、β 7 、β 8 Unknown parameters which are corresponding independent variables; epsilon is referred to as the error term. The multiple linear regression model formula is shown in figure 1:
y＝β 0 +β 1 x 1 +β 2 x 2 +β 3 x 3 +β 4 x 4 +β 5 x 5 +β 6 x 6 +β 7 x 7 +β 8 x 8 +ε (1)
equation 1 represents the weighted sum of the newly added diagnostic population for each climate factor, and the weight of each climate factor is estimated by a linear regression method.
And 4, training a multiple linear regression model:
because the number of data samples is small, in order to ensure that certain data are reserved for testing on the premise of having enough data for model training, 70% of observation data are selected as training sets in the experiment, namely the number of newly increased daily diagnostic persons and eight types of climate factor data in the period of 22 days to 8 days of 3 months are selected as training data. The training set is input into a written program, and 65 national multiple linear regression coefficients are obtained through operation, so that a trained model is obtained.
Step 5, checking a model:
the predicted value of the number of newly added diagnostic persons per day is obtained by inputting the climate factor data of the test set into the constructed multiple linear regression model, and the correction decision coefficient is adoptedAnd (5) performing model performance judgment. The influence of the number of variables on the decision coefficient is suppressed by dividing the sum of squares of residuals by the degree of freedom thereof and dividing the sum of squares of total deviations by the degree of freedom thereof. The calculation formula is shown as the following formula 2:
SSE in equation 2 represents the sum of squares of residuals, SST represents the sum of squares of total deviations, n represents the number of variables, k represents the number of constraints, n-k-1 represents the degree of freedom of the sum of squares of residuals, n-1 isAnd the degree of freedom of the sum of squares of the total dispersion. Correction decision coefficient in 2A closer to 1 represents a higher degree of fitting to the relationship between variables, the more accurate the model effect. Consider that the representation model has a better fit when the decision coefficient is greater than 0.5.
Step 6, calculating the correlation coefficient between the independent variable and the dependent variable:
the Pearson correlation coefficient R is used for describing the correlation between two groups of different data, and when the development trend between the two groups of different data shows weak correlation, the absolute R is more than or equal to 0 and less than 0.3; when the medium correlation is presented between two different groups of data, the R is more than or equal to 0.3 and less than 0.6; when the development trend between the two different groups of data shows high correlation, R is more than or equal to 0.6 and less than or equal to 1. The calculation formula is shown in the formula 3:
x in 3 i Each value of X, namely X1, X2, X3, X4, X5, X6, X7, X8, i represents an integer greater than or equal to 1, is sequentially incremented, y i Each numerical value of y, i represents an integer of 1 or more, sequentially increases,mean value of the sum of x, +.>Represents the average of the sums of y. And calculating the correlation coefficient between each climate factor in different areas and the number of newly-increased diagnostic persons per day through a Pearson correlation coefficient formula, and providing data support for correlation strength analysis of the number of newly-increased diagnostic persons per country and each climate factor parameter.
The invention mainly comprises the following steps:
the research on virus transmission and climate factors at present is still remained in analyzing individual climate factors, and the influence of various climate factors on virus transmission is not clear. The invention uses a multiple linear regression model to analyze the relationship between the virus transmission of 65 countries and 8 climatic factors. And obtaining the climate factors with stronger correlation with the newly added diagnosed number through the pearson correlation coefficient. The model performance is verified by adopting the correction decision coefficient, and the correction decision coefficient of the multi-element linear regression model of two thirds countries in the sample is larger than 0.5, so that the fitting effect is good. And inputting climate factor parameters in the test set into a model, and predicting the newly-increased diagnosed number of people to be in accordance with actual data. And the number of predicted new cases is only related to the current parameter, so that compared with the method for directly predicting long sequences, the method effectively avoids error transfer.
The invention obtains that the virus has higher correlation with temperature and moderate degree, and can provide data for the global country in the aspect of virus prevention and control so as to support decision. The newly increased number of diagnostic people can be guided to make prevention and control actions with different strict grades by means of the model prediction. In addition, the climate factor prevention and control suggestion can be provided for the countries around the world, and positive measures are taken for prevention and control by aiming at factors suitable for virus survival such as temperature, humidity and the like.
Drawings
FIG. 1 is an overall block diagram of a correlation analysis-based method for studying the relationship between viral spread and climate factors in the present invention.
FIG. 2 is a graph of partial test data versus model predictive data for the present invention.
FIG. 3 is a graph showing the correlation of a portion of the climate factors of the present invention with the number of additional diagnostic persons.
Detailed Description
The invention will be described in further detail below with reference to specific embodiments and with reference to the accompanying drawings.
The invention provides a model of a relation between virus diffusion and climate factors based on correlation analysis, which specifically comprises the following steps:
the hardware equipment used in the invention comprises 1 PC machine and 1 NVIDIA GTX1650 display card;
step 1, data collection:
the published data set of the number of diagnosed people published by the system science and engineering center of John Hopkins university is downloaded and stored. Daily record data of all the global weather stations are collected and downloaded from the China weather data network.
Step 2, data preprocessing:
the collected virus data is the number of the confirmed diagnosis people in each country in a daily way, and the number of the confirmed diagnosis people in each country is obtained by subtracting the number of the confirmed diagnosis people in each country in the daily way from the number of the confirmed diagnosis people in the previous day. And selecting the month average high temperature, month average low temperature, sea level pressure, altitude, wind speed, rainfall, dew point temperature and relative humidity of each country as various climatic factor data. And for missing weather factor data of a certain day, taking an average value of data of two days before and after to fill. The climate factor data missing on consecutive dates is filled with 0, preventing the experimental results from being affected.
Step 3, data set division and training model:
the dataset was set at 7: the ratio of 3 is divided into a training set and a test set. That is, the data of the period from 3 months 22 days to 5 months 8 days of each country is a training set, and the data of the period from 5 months 9 days to 6 months 22 days is a test set.
And constructing a linear regression model by using a Python language, inputting a training set into the model, obtaining intercept and linear regression coefficients after training, and constructing and completing a multiple linear regression model. The expressive power of the current model on the data is checked by the test set.
And 4, checking a model:
the method comprises the steps of obtaining a predicted value of the number of newly added diagnostic persons every day by inputting test set climate factor data into a built multiple linear regression model, calling a score method of the multiple linear regression model to obtain 65 corrected decision coefficients of the models, determining the model with the corrected decision coefficient larger than 0.5 as a model with a good fitting effect, and selecting the model with the good fitting effect to calculate the correlation coefficients of independent variables and dependent variables.
Step 5, calculating the correlation coefficient between the independent variable and the dependent variable:
calculating the correlation coefficient between each climate factor and the newly increased number of diagnostic persons in different countries by using a Pearson correlation coefficient formula, and calculating the correlation coefficient between the newly increased number of diagnostic persons in each country in the period of 22 days of 3 months to 22 days of 6 months and eight climate factor parameters to obtain a correlation coefficient matrix between any two observation variables in each country.
The above embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, the scope of which is defined by the claims. Various modifications and equivalent arrangements of this invention will occur to those skilled in the art, and are intended to be within the spirit and scope of the invention.
Claims (3)
1. A method for analyzing the relation between virus diffusion and climate factors based on correlation analysis is characterized by comprising the following steps: comprising the following steps:
step 1, data source and experimental object:
the virus related data is derived from daily record data of global all-terrain weather stations collected by public data sets and weather data networks;
step 2, data collection and pretreatment:
the collected virus data is the number of the confirmed diagnosis of each country in each day, and the number of the new diagnosis of each country is obtained by subtracting the number of the confirmed diagnosis of each country in each day from the number of the confirmed diagnosis of the previous day in each day; selecting the month average high temperature, month average low temperature, sea level pressure, altitude, wind speed, rainfall, dew point temperature and relative humidity of each country as various climatic factor data; for missing weather factor data of a certain day, taking an average value of data of two days before and after to fill; the climate factor data with missing continuous dates is filled with 0, so that the experimental result is prevented from being influenced; data were read as per 7:3, dividing the ratio into a training set and a testing set;
step 3, constructing a multiple linear regression model:
taking the newly added number of diagnostic persons New as a dependent variable y, each climate factor comprises: the average high temperature t_max, the month average low temperature t_min, the sea level pressure S_P, the wind speed W_S, the altitude EI, the rainfall RF, the dew point temperature DP and the relative Humidity Humidi are independent variables x1, x2, x3, x4, x5, x6, x7 and x8 respectively; beta 0 、β 1 、β 2 、β 3 、β 4 、β 5 、β 6 、β 7 、β 8 Unknown parameters which are corresponding independent variables; epsilon is called the error term; the multiple linear regression model formula is shown in figure 1:
y＝β 0 +β 1 x 1 +β 2 x 2 +β 3 x 3 +β 4 x 4 +β 5 x 5 +β 6 x 6 +β 7 x 7 +β 8 x 8 +ε (1)
formula 1 shows that the number of newly added diagnosed people is the weighted sum of all the climate factors, and the weight of all the climate factors is estimated through a linear regression method;
and 4, training a multiple linear regression model:
selecting 70% of observation data as a training set, and newly adding the number of diagnosed people and eight types of climate factor data as training data every day; inputting the training set into a written program, and obtaining a multiple linear regression coefficient through operation, thereby obtaining a trained model;
step 5, checking a model:
the predicted value of the number of newly added diagnostic persons per day is obtained by inputting the climate factor data of the test set into the constructed multiple linear regression model, and the correction decision coefficient is adoptedPerforming model performance judgment; the influence of the variable number on the decision coefficient is restrained by dividing the sum of squares of residual errors by the degree of freedom of the variable number and dividing the sum of squares of total deviation by the degree of freedom of the variable number; the calculation formula is shown as the following formula 2:
SSE in formula 2 represents the sum of squares of residuals, SST represents the sum of squares of total deviations, n represents the number of variables, k represents the number of constraints, n-k-1 represents the degree of freedom of the sum of squares of residuals, and n-1 represents the degree of freedom of the sum of squares of total deviations; correction decision coefficient in 2The closer to 1 represents the higher the fitting degree of the relation between variables, the more accurate the model effect; considering that the representation model has a better fitting effect when the decision coefficient is larger than 0.5;
step 6, calculating the correlation coefficient between the independent variable and the dependent variable:
the Pearson correlation coefficient R is used for describing the correlation between two groups of different data, and when the development trend between the two groups of different data shows weak correlation, the absolute R is more than or equal to 0 and less than 0.3; when the medium correlation is presented between two different groups of data, the R is more than or equal to 0.3 and less than 0.6; when the development trend between the two different groups of data shows high correlation, R is more than or equal to 0.6 and less than or equal to 1; the calculation formula is shown in the formula 3:
x in 3 i Each value of X, namely X1, X2, X3, X4, X5, X6, X7, X8, i represents an integer greater than or equal to 1, is sequentially incremented, y i Each numerical value of y, i represents an integer of 1 or more, sequentially increases,the average value of the sum of x is indicated,represents the average of the sums of y; and calculating the correlation coefficient between each climate factor in different areas and the number of newly-increased diagnostic persons per day through a Pearson correlation coefficient formula, and providing data support for correlation strength analysis of the number of newly-increased diagnostic persons and each climate factor parameter.
2. The method for analyzing the relationship between virus diffusion and climate factors based on correlation analysis according to claim 1, wherein the method comprises the following steps: selecting Python as a method writing language; the data processing stage uses Pandas to realize data set cleaning and data set division, and the model construction and training is realized by Sklearn.
3. The method for analyzing the relationship between virus diffusion and climate factors based on correlation analysis according to claim 1, wherein the method comprises the following steps: and judging the efficacy of the virus climate factor relation model by using the correction decision coefficient, and determining the fitting degree of the multiple linear regression model and the real data of each country.
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| CN111090831A (en) * | 2019-11-21 | 2020-05-01 | 河海大学 | Lake area change key driving factor identification method |
| CN112164471A (en) * | 2020-09-17 | 2021-01-01 | 吉林大学 | New crown epidemic situation comprehensive evaluation method based on classification regression model |
| CN113192647A (en) * | 2021-05-06 | 2021-07-30 | 浙江工业大学 | New crown confirmed diagnosis people number prediction method and system based on multi-feature layered space-time characterization |
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| CN111090831A (en) * | 2019-11-21 | 2020-05-01 | 河海大学 | Lake area change key driving factor identification method |
| CN112164471A (en) * | 2020-09-17 | 2021-01-01 | 吉林大学 | New crown epidemic situation comprehensive evaluation method based on classification regression model |
| CN113192647A (en) * | 2021-05-06 | 2021-07-30 | 浙江工业大学 | New crown confirmed diagnosis people number prediction method and system based on multi-feature layered space-time characterization |
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