The relationship between key natural and social factors and the transmission of novel coronavirus disease 2019 in China

Abstract Background Novel coronavirus disease 2019 (COVID-19) has become a global pandemic. This study aims to explore the relationship between key natural and social factors and the transmission of COVID-19 in China. Methods This study collected the number of confirmed cases of COVID-19 in 21 provinces and cities in China as of February 28, 2020. Three provinces were included in the sample: Hainan, Guizhou, and Qinghai. The 18 cities included Shanghai, Tianjin and so on. Key natural factors comprised monthly average temperatures in the January and February 2020 and spatial location as determined by longitude and latitude. Social factors were population density, Gross Domestic Product (GDP), number of medical institutions and health practitioners; as well as the per capita values for GDP, medical institutions, and health practitioners. Excel was used to collate the data and draw the temporal and spatial distribution map of the prevalence rate (PR) and the proportion of local infection (PLI). The influencing factors were analyzed by SPSS 21.0 statistical software, and the relationship between the dependent and independent variables was simulated by 11 models. Finally, we choose the exponential model according to the value of R2 and the applicability of the model. Results The temporal and spatial distribution of the PR varies across the 21 provinces and cities identified. The PR generally decreases with distance from Hubei, except in the case of Shenzhen City, where the converse is observed. The results of the exponential model simulation show that the monthly minimum, median, and maximum average temperatures in January and February, and the latitude and population density are significant and thus will affect the PLI. The corresponding values of R2 are 0.297, 0.322, 0.349, 0.290, 0.314, 0.339, 0.344, and 0.301. The effects of other factors were not statistically significant. Conclusions Among the selected key natural and social factors, higher temperatures may decrease the transmission of COVID-19. From this analysis, it is evident that if the temperature decreases by 1℃, the average PLI increases by 0.01. Further, it was established that locations at more northern latitudes had a higher PLI, and population density showed an inverse relationship with PLI.

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Temporal and Spatial Evolution Features of Precipitable Water in China during a Recent 65-Year Period (1951–2015)

Advances in Meteorology ◽

10.1155/2017/9156737 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Hao Wang ◽

Jianxin He

Keyword(s):

Spatial Distribution ◽

Water Vapor ◽

Transition Period ◽

Precipitable Water ◽

Distribution Characteristics ◽

Temporal And Spatial Distribution ◽

Spatial Distribution Characteristics ◽

Distribution Features ◽

Temporal And Spatial ◽

The Relationship

Water vapor in the atmosphere is not only an important greenhouse gas, but also an important factor that significantly affects the variations of global climate and water circulation. This study utilized the National Centers for Environmental Prediction (NCEP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) reanalysis data to probe the temporal and spatial distribution features of atmospheric precipitable water (PW) in China during a recent 65-year period (1951–2015), and the relationship between PW and actual precipitation was also studied. The temporal and spatial distribution characteristics of PW in China presented an overall decreasing spatial trend from the southeast to northwest direction. The spatial distribution pattern of the first eigenvector demonstrated that the PW in China shows nationwide variation features with a varying amount of PW across different regions. The year 1967 was further identified as an important transition period for the temporal and spatial distribution characteristics of the PW. We also found that the PW had inherent variability of around 30 years. Regarding the relationship with precipitation, PW was most closely correlated with precipitation in the northeastern region and the upper northwestern region in China. Different regions displayed different efficiencies for converting PW to precipitation. The conclusions are useful for understanding the long-term water vapor evolution and its potential effects on precipitation in China.

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Distribution characteristics of nitrogen and the related microbial community in the surface sediments of the Songhua River

RSC Advances ◽

10.1039/d1ra04903h ◽

2021 ◽

Vol 11 (43) ◽

pp. 26721-26731

Author(s):

Congyu Li ◽

Zhen Zhong ◽

Wenfu Wang ◽

Haiyan Wang ◽

Guokai Yan ◽

...

Keyword(s):

Spatial Distribution ◽

Microbial Community ◽

Surface Sediments ◽

River Sediments ◽

Distribution Characteristics ◽

Temporal And Spatial Distribution ◽

Songhua River ◽

Temporal And Spatial ◽

The Relationship ◽

The Songhua River

In this study, temporal and spatial distribution of nitrogen in the Songhua River sediments and distribution characteristics of related microbes as well as the relationship between them were investigated.

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Research on the Coordination Characteristics and Interaction Between Innovation-driven Development and Green Development of the Yangtze River Economic Belt in China

10.21203/rs.3.rs-673233/v1 ◽

2021 ◽

Author(s):

wei wang ◽

lei zhou ◽

wei chen ◽

chao Wu

Keyword(s):

Sustainable Development ◽

Spatial Distribution ◽

Yangtze River ◽

The Yangtze River ◽

Temporal And Spatial Distribution ◽

Development Index ◽

Green Development ◽

Regional Sustainable Development ◽

Temporal And Spatial ◽

The Relationship

Abstract Innovation-driven development and green development are both important ways to achieve regional sustainable development. Many studies have focused on innovation-driven dynamic factors and green development impact factors, yet most have paid little attention to the relationship between the two types of factors. This study considers the innovation-driven development and green development evaluation systems of 130 cities in the Yangtze River Economic Belt. Through expert group evaluation, the three dimensions of green production, green life and green ecology are selected to represent the green development index. Innovation input, innovation performance, and innovation potential reflect the innovation-driven development index. The entropy TOPSIS method is used to measure the innovation-driven development index and the green development index of 130 cities in the Yangtze River Economic Belt. Then, a coupling coordination evaluation model and a spatiotemporal heterogeneity analysis model are constructed to discuss the coupling coordination index of regional innovation-driven development and green development in the Yangtze River Economic Belt and to determine its temporal and spatial distribution characteristics. Finally, we choose a spatial panel regression model to explore the relationship between the innovation-driven development index and the green development index of the Yangtze River Economic Belt. The research results show that there is a significant difference between the innovation-driven development index and the green development index of the 130 cities in the Yangtze River Economic Belt in terms of the temporal and spatial distribution. The coordination index of the two has an imbalanced distribution feature, and there is a significant direct or indirect relationship between the two structural indicators in a mathematical sense. This study enhances the academic community's understanding of the interaction between innovation-driven development and green development, provides scientifically based support for green development, offers guidance for the implementation of innovation capabilities, and ultimately supports a policy design facilitating regional sustainable development.

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Temporal and Spatial Distribution of Clonal Complexes of Streptococcus pneumoniae Isolates Resistant to Multiple Classes of Antibiotics in Belgium, 1997 to 2004

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.00358-08 ◽

2008 ◽

Vol 52 (9) ◽

pp. 3216-3220 ◽

Cited By ~ 10

Author(s):

Heather Amrine-Madsen ◽

Johan Van Eldere ◽

Robertino M. Mera ◽

Linda A. Miller ◽

James A. Poupard ◽

...

Keyword(s):

Spatial Distribution ◽

Streptococcus Pneumoniae ◽

Pneumococcal Conjugate Vaccine ◽

Multilocus Sequence Typing ◽

Invasive Disease ◽

Temporal And Spatial Distribution ◽

Time Period ◽

Temporal And Spatial ◽

The Relationship ◽

Over Time

ABSTRACT We performed multilocus sequence typing on 203 invasive disease isolates of Streptococcus pneumoniae to assess the clonal compositions of isolates from two provinces in Belgium and to determine the relationship between clones and antibiotic nonsusceptibility, particularly nonsusceptibility to two or more classes of antibiotics. The frequency of multiclass nonsusceptibility (MCNS) was higher in the province of West Flanders (38%) than in Limburg (21%). This difference was largely attributable to five clonal complexes (CC156, CC81, CC143, CC193, and CC1848), which contained high proportions of isolates with MCNS (>47%) and which were circulating at higher frequencies in West Flanders. The S. pneumoniae population changed over time, as CC156 and CC81 declined in frequency from 1997 to 1999 to 2001 to 2004. Over the same time period, the frequency of pneumococcal conjugate vaccine 7 (PCV7) serotypes dropped from 69% to 41%. In contrast, the nonvaccine serotype 19A increased in frequency from 2.1% to 6.6%. None of these changes can be attributed to PCV7 vaccine, as it was not in use in Belgium during the time period studied. There was evidence that MCNS clones flowed from West Flanders to Limburg.

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