Attribution analysis of epidemic spread based on multi-source data (Part 1)
This is the first time I write a blog, the purpose is to record my own learning process, so that I can review it in the future, and I can also communicate with you. If you have any ideas, you can leave a message, because I feel that this research has many limitations. The method may not be appropriate, welcome to correct me! The text begins below
introduction
The main purpose of this study is to analyze the attribution of new coronary pneumonia. In the current domestic and foreign research on the factors affecting the spread of the new crown pneumonia virus and the prediction of the spread trend, some scholars such as Bi Jia combined the population, medical care, and economic conditions to improve the SEIR model to evaluate and predict the evolution trend of public health emergencies. The improved SEIR The model fitting effect is good; scholars such as Liang Ze used the GWR model to study the impact of population migration and socioeconomic factors on the incidence of new coronary pneumonia during the outbreak, and found that the two have obvious spatial dependence characteristics with the incidence of new coronary pneumonia; Ma Yiwen et al. Scholars analyzed the impact of spatial characteristics such as community size, population mobility, and community quality on the development of the epidemic by studying the three major indicators of the community's physical environment, surrounding facilities, and resident population; Mugen Ujiie and other scholars evaluated the impact of temperature on the development of COVID-19 in Japan through Poisson regression analysis. -19 infectivity, found that there may be an association between low temperature and increased risk of COVID-19 infection, but further evaluation is needed at the global level; scholars such as Zoran Maria A[9] studied the spread of new coronavirus pneumonia in Milan, Italy The relationship between speed and fatality rate and surface air pollution, it is concluded that the spread of COVID-19 virus is positively correlated with surface ozone (O3) concentration and negatively correlated with surface nitrogen dioxide (NO2) concentration, warm season does not prevent COVID-19 19 for the dissemination of other conclusions. However, at this stage, domestic and foreign studies on the factors affecting the spatial and temporal spread of the epidemic are still not comprehensive. However, there are still insufficient researches at home and abroad on the factors affecting the spatio-temporal spread of the new crown pneumonia virus in the post-epidemic era. The so-called post-epidemic era does not mean that the virus will disappear completely and the epidemic will not break out, but a state of small-scale outbreaks with ups and downs. With the gradual control of the spread of the epidemic, my country is the first to enter the post-epidemic era.
After our country entered the post-epidemic era, the fight against the epidemic has also become normalized. However, the scientific community, including the World Health Organization, judges that the new crown pneumonia virus will not disappear and will coexist with human beings. In order to fit the current situation of domestic epidemic spread, to facilitate the normalization of epidemic prevention and control in the post-epidemic period, and to provide experience for other countries that are about to enter the post-epidemic era, this study takes Hebei Province as the research area, and takes Hebei Province’s outbreak in 2021 as the research area. January is the research time, constructing three major elements of meteorological factors, air quality factors, and urban travel intensity, and obtaining nine indicators including average temperature, humidity, wind direction angle, ozone concentration, air particle concentration, and urban travel intensity, using Poisson regression The analysis modeled and analyzed the reasons for the spread of the new coronavirus in the post-epidemic era in Hebei Province, and found the main influencing factors of the spread of the novel coronavirus pneumonia virus in the post-epidemic period in Hebei Province, providing new ideas for the normalization of epidemic prevention and control and early warning work in the post-epidemic period.
1. Data source
The epidemic data of Hebei Province from January 2 to 31, 2021 comes from the "Information Report on Confirmed Cases of Novel Coronavirus Pneumonia" issued by the health committees of various cities in Hebei Province. Case data include the cumulative number of confirmed cases, the number of new confirmed cases, etc.; Hebei Province’s meteorological data from January 2 to 31, 2021 comes from the China Meteorological Administration, including data such as average temperature, humidity, rainfall, and wind direction; Hebei Province Air quality data from January 2nd to 31st, 2021 includes 5 types of air pollutants including nitrogen dioxide (NO2), ozone (O3), inhalable particulate matter (PM10), fine particulate matter (PM2.5) and sulfur dioxide (SO2) data. The data comes from the national urban air quality real-time release platform of China National Environmental Monitoring Center, which updates air pollutant data every hour. This paper calculates the daily average concentration data of five types of air pollutants in various cities in Hebei Province; Hebei Province, January 2, 2021 -The data of urban travel intensity on the 31st, the data comes from Baidu Migration-Baidu Map Smart Eye Platform (https://qianxi.baidu.com/); the population density data of cities in Hebei Province in 2020 comes from the Michael Bauer Research Center in Germany, the research Every year, the center collates data released by the United Nations, the World Bank, etc., and publishes online map services with spatial data; the data of general hospitals in various cities in Hebei Province in 2019 comes from ArcGIS Online, a public cloud GIS platform for global users under Esri (http:// www.arcgisonline) published public data; Hebei Province's 2019 public management and public service land use, commercial service facility land use, road traffic facility land use, and public utility land use data comes from the "China Urban Construction Statistical Yearbook 2019".
2. Results Analysis
Let’s briefly describe the data this time, and update the results of the regression model next time.
2.1 Spatio-temporal diffusion characteristics of
the epidemic in Hebei
Province A total of 1,172 confirmed cases of COVID-19 were mainly from Shijiazhuang and Xingtai. The cumulative number of confirmed cases of COVID-19 in the two cities was 894 and 94 respectively. The cumulative number of confirmed cases of COVID-19 in other cities only accounted for 15.7% of the province. Therefore, the daily new confirmed cases of COVID-19 and the cumulative confirmed cases of COVID-19 in Shijiazhuang City were mapped (see Figure 1). According to Figure 1, the cumulative number of confirmed cases of COVID-19 in Hebei Province began to level off in late January, and the daily new confirmed cases of COVID-19 showed a first rise and then a slow decline, reaching a peak on January 15, with a maximum of 150 cases. On that day, the number of new confirmed cases of COVID-19 in Shijiazhuang City was 132 (see Figure 2). Observing Figure 1 and Figure 2 at the same time, it is found that the trend of change is basically the same, indicating that the main outbreak location of COVID-19 in Hebei Province is Shijiazhuang City.
As of January 31, 2021, Beijing time, no confirmed cases have been found in some cities such as Cangzhou City, Chengde City, and Handan City, indicating that Hebei Province's anti-epidemic measures have achieved excellent results. The outbreak was basically controlled in Shijiazhuang City and Xingtai City, and did not cause large-scale spread.
2.1.2 Spatial spread of epidemic situation in Hebei Province
In order to intuitively understand the detailed characteristics of the spatial pattern of the COVID-19 epidemic in Hebei Province, this study purposefully plotted the data on January 6, January 12, January 18, and January according to the overall change trend of the epidemic. The distribution map of the cumulative number of confirmed cases of COVID-19 in Hebei Province on the 24th is shown in the figure below. The study found that as of January 6, 2021, the overall epidemic situation is relatively mild, and the number of confirmed cases is mainly concentrated in Shijiazhuang City, Tangshan City, and Xingtai City, with the highest cumulative number of confirmed cases being 62; as of January 12, 2021, The cumulative number of confirmed cases in Hebei Province has increased significantly, especially in Shijiazhuang City in the southwest, where the cumulative number of confirmed cases has reached 333. Among them, the cumulative number of confirmed cases in Shijiazhuang, the provincial capital, was as high as 774, making it the hardest-hit area of the epidemic in Hebei Province. At this time, the epidemic situation in Xingtai City became more serious; Under favorable conditions such as fast and efficient detection methods and local people's correct protection against the COVID-19 epidemic, the spatial pattern of the epidemic distribution is basically stable. Compared with the spatial distribution on January 18, only Shijiazhuang City and Xingtai City have slightly different proportions of cumulative confirmed cases. Increase. Although the outbreak of the COVID-19 epidemic in Hebei Province is very serious, the data on the spatial scope, time, and number of people affected by the epidemic have been greatly reduced compared with the first outbreak of the COVID-19 epidemic in 2020. The epidemic is mainly controlled in Shijiazhuang City and Xingtai City and the remaining 9 cities have not been greatly affected, and the anti-epidemic measures have achieved great success.
2.2 Analysis of the characteristics of factors affecting the spatio-temporal spread of the epidemic in Hebei Province
2.2.1 Analysis of the characteristics of the
influence of time and space in Hebei Province Intensity, weather data (including temperature, humidity, and wind angle), and air quality data (including O3, SO2, NO2, PM10, and PM2.5).
(1) Temporal characteristics of travel intensity in the city
The results of intra-city travel intensity in various cities in Hebei Province from January 2 to 31, 2021 are shown in the figure below. Among them, Baoding City, Cangzhou City, Chengde City, Handan City, Hengshui City and other cities have seen relatively small changes, and the trend of change is basically the same, basically not affected by the epidemic. However, Shijiazhuang City and Xingtai City have relatively large changes, and the travel intensity is significantly lower than other cities. On January 6, the intensity of travel in Shijiazhuang City dropped sharply, and continued until January 24 before slowly increasing. The outbreak of the epidemic in Xingtai City was not as serious as that in Shijiazhuang City, so the intensity of travel within the city was higher than that in Shijiazhuang City. It began to decline on January 7 and began to rise slowly on January 12. Between January 5 and 8, the intensity of intra-city travel in various cities dropped sharply. Combining the weather distribution maps of each city, it can be seen that at this time, Hebei Province was affected by the cold air moving southward, and there was a province-wide cold wave. The decline has led to a decline in the travel intensity of the population in various cities. On January 11, Langfang City discovered the first case of infection with the new crown pneumonia virus in the city. On January 12, the intensity of Langfang City dropped sharply, and it did not gradually recover until January 18.
(2) Temporal characteristics of meteorological factors
The meteorological data of various cities in Hebei Province are shown in Figure X. Combined with the "Climate Disaster Monitoring Bulletin" issued by the Hebei Provincial Climate Center, it can be found that from January 5 to 7, 2021, Hebei Province was affected by the cold air from the polar region to the south. A provincial cold snap cools the weather. As of January 7, the average minimum temperature in Hebei Province was as low as -20.7°C, which was the coldest day since 1967. At the same time, we combined the data of the COVID-19 epidemic in Hebei Province and found that the inflection point of the outbreak of cumulative confirmed cases was also 1 From March 6th to 9th, it shows that meteorological factors have a certain impact on the spread of the COVID-19 epidemic in Hebei Province. From January 5th to 7th, 90% of Hebei province's entire province experienced a sharp drop in temperature, mainly concentrated in Tangshan, southwestern Qinhuangdao, Langfang, southeastern Zhangjiakou, northeastern Baoding, Xiong'an New District, northeastern Hengshui, and Cangzhou. Along with the cooling process, the humidity in Hebei Province also decreases, and the trend of change is roughly the same as that of temperature. Among them, the humidity in Shijiazhuang City, Xingtai City and Langfang City has a large range of changes. The humidity from January 5th to 13th is relatively low compared with other cities, and the humidity increases from January 13th to 16th. The humidity dropped sharply from January 16th to 18th, and the humidity rose sharply from January 19th to 26th, with large changes. The humidity in Handan City, Zhangjiakou City and Chengde City is relatively stable, showing small changes over time. During this period, the wind direction angles in various cities in Hebei Province also varied greatly. Among them, Baoding City, Handan City and other regions have larger changes in the daily average wind direction angle than other cities. Zhangjiakou City and Shijiazhuang City have a relatively stable range of daily average wind direction angle changes.
I will write it next time. There are still some influencing factors and characteristics that have not been analyzed, and the regression equation has not been displayed. These contents will be written next time. If someone who is destined to see this article, you can see if there is a problem with the analysis. If so Welcome everyone to criticize and correct, thank you in advance.