Spatial Statistics and Influencing Factors of the Epidemic of Novel Coronavirus Pneumonia 2019 in Hubei Province, China

Abstract An in-depth understanding of spatiotemporal dynamic characteristics of infectious diseases could be helpful to an epidemic prevention and control. Based on the novel coronavirus pneumonia (NCP) data published on official websites, GIS spatial statistics and Pearson correlation methods were used to analyze the spatial autocorrelation and influencing factors of the 2019 NCP epidemic from January 30, 2020 to February 18, 2020. The results of the study showed that: (1) During the study period, Hubei Province was the only significant cluster area and hot spot of the cumulative cases confirmed with the NCP infection in China on the provincial scale; (2) The epidemic of the NCP infection in China on the prefecture-city scale had a very significant global spatial autocorrelation, and Wuhan had always been the significant hot spot and cluster city of the cumulative cases confirmed with the NCP infection in the whole country; (3) The cumulative cases confirmed with the NCP infection in Hubei Province on the county scale had a very significant global spatial autocorrelation, and the county-level districts under the jurisdiction of Wuhan and its neighboring Huangzhou district in Huanggang City were the significant hot spots and spatial clusters of the cumulative cases confirmed with the NCP infection; (4) Based on Pearson correlation analysis, the number of the accumulative cases confirmed with the NCP infection in Hubei Province on the prefecture-city scale and also on the county scale had very significant and positive correlations (p < 0.01) with the four indexes of population of registration population, resident population, regional GDP and total retail sales of consumer goods, respectively, during the study period; (5) The number of the cumulative cases confirmed with the NCP infection in Hubei Province on the prefecture-city scale also had a very significant and positive correlation (p < 0.01) with Baidu migration index and population density, respectively, but not with land area, whereas that in Hubei Province on the county scale had a significant and positive correlation (p < 0.05) with land area, but not with population density from January 30, 2020 to February 18, 2020. It is found that the NCP epidemic in Hubei Province has the distinctive characteristics of significantly centralized outbreak, significantly spatial autocorrelation and complex influencing factors and that the spatial scale has a significant effect on the global spatial autocorrelation of the NCP epidemic. The findings help to deepen the understanding of spatial distribution patterns and transmission trends of the NCP epidemic and may also benefit scientific prevention and control of epidemics such as NCP 2019.

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Spatial statistics and influencing factors of the novel coronavirus pneumonia 2019 epidemic in Hubei Province, China

10.21203/rs.3.rs-16858/v2 ◽

2020 ◽

Cited By ~ 2

Author(s):

Yongzhu Xiong ◽

Yunpeng Wang ◽

Feng Chen ◽

Mingyong Zhu

Keyword(s):

Spatial Autocorrelation ◽

Influencing Factors ◽

Pearson Correlation ◽

Hubei Province ◽

County Level ◽

The Novel ◽

Land Area ◽

Novel Coronavirus ◽

And Control ◽

Global Spatial Autocorrelation

Abstract An in-depth understanding of the spatiotemporal dynamic characteristics of infectious diseases could be helpful for epidemic prevention and control. Based on the novel coronavirus pneumonia (NCP) data published on official websites, GIS spatial statistics and Pearson correlation methods were used to analyze the spatial autocorrelation and influencing factors of the 2019 NCP epidemic from January 30, 2020 to February 18, 2020. The following results were obtained. (1) During the study period, Hubei Province was the only significant cluster area and hotspot of cumulative confirmed cases of NCP infection at the provincial level in China. (2) The NCP epidemic in China had a very significant global spatial autocorrelation at the prefecture-city level, and Wuhan was the significant hotspot and cluster city for cumulative confirmed NCP cases in the whole country. (3) The cumulative confirmed NCP cases had a very significant global spatial autocorrelation at the county level in Hubei Province, and the county-level districts under the jurisdiction of Wuhan and neighboring Huangzhou district in Huanggang City were the significant hotspots and spatial clusters of cumulative confirmed NCP cases. (4) Based on Pearson correlation analysis, the number of cumulative confirmed NCP cases in Hubei Province had very significant and positive correlations (p<0.01) at the prefecture-city and the county levels with four population indexes (registered population, resident population, regional GDP and total retail sales of consumer goods) during the study period. (5) The number of the cumulative confirmed NCP cases in Hubei Province also had a very significant and positive correlation (p<0.01) on the prefecture-city scale with the Baidu migration index and population density but not with land area, whereas that in Hubei Province had a significant and positive correlation (p<0.05) at the county level with land area but not with population density from January 30, 2020, to February 18, 2020. It was found that the NCP epidemic in Hubei Province had distinctive characteristics of a significant centralized outbreak, significant spatial autocorrelation and complex influencing factors and that the spatial scale had a significant effect on the global spatial autocorrelation of the NCP epidemic. The findings help to deepen the understanding of spatial distribution patterns and transmission trends of the NCP epidemic and may also benefit scientific prevention and control of epidemics such as COVID-19.

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Highland of COVID-19 outside Hubei: epidemic characteristics, control and projections of Wenzhou, China

10.1101/2020.02.25.20024398 ◽

2020 ◽

Cited By ~ 3

Author(s):

Liangde Xu ◽

Jian Yuan ◽

Yaru Zhang ◽

Guosi Zhang ◽

Fan Lu ◽

...

Keyword(s):

Health Care Workers ◽

Hubei Province ◽

Transmission Model ◽

Unknown Aetiology ◽

Expected Number ◽

Coastal City ◽

Care Workers ◽

Novel Coronavirus ◽

And Control ◽

Epidemiologic Characteristics

In late December 2019, Chinese authorities reported a cluster of pneumonia cases of unknown aetiology in Wuhan, China1. A novel strain of coronavirus named Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was isolated and identified on 2 January 2020 2. Human-to-human transmission have been confirmed by a study of a family cluster and have occurred in health-care workers 3,4. Until 10 February 2020, 42638 cases of 2019 novel coronavirus disease (COVID-19) have been confirmed in China, of which 31728 came from Hubei Province (Figure). Wenzhou, as a southeast coastal city with the most cases outside Hubei Province, its policy control and epidemic projections have certain references for national and worldwide epidemic prevention and control. We described the epidemiologic characteristics of COVID-19 in Wenzhou and made a transmission model to predict the expected number of cases in the coming days.

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Differential Equation Analysis on COVID-19

Annals of Veterinary Science ◽

10.31829/2691-5502/avs2020-3(1)-113 ◽

2020 ◽

pp. 1-15

Keyword(s):

Hot Spot ◽

Sufficient Information ◽

The Novel ◽

Effective Response ◽

The People ◽

Equation Analysis ◽

The World ◽

The Government ◽

Novel Coronavirus ◽

And Control

An infectious disease caused by a novel coronavirus called COVID-19 has raged across the world since December 2019. The novel coronavirus first appeared in Wuhan, China, and quickly spread to Asia and now many countries around the world are affected by the epidemic. The deaths of many patients, including medical staff, caused social panic, media attention, and high attention from governments and world organizations. Today, with the joint efforts of the government, the doctors and all walks of life, the epidemic in Hubei Province has been brought under control, preventing its spread from affecting the lives of the people. Because of its rapid spread and serious consequences, this sudden novel coronary pneumonia epidemic has become an important social hot spot event. Through the analysis of the novel coronary pneumonia epidemic situation, we can also have a better understanding of sudden infectious diseases in the future, so that we can take more effective response measures, establish a truly predictable and provide reliable and sufficient information for prevention and control model.

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Prediction of Epidemic Spread of the 2019 Novel Coronavirus Driven by Spring Festival Transportation in China: A Population-Based Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17051679 ◽

2020 ◽

Vol 17 (5) ◽

pp. 1679 ◽

Cited By ~ 40

Author(s):

Changyu Fan ◽

Linping Liu ◽

Wei Guo ◽

Anuo Yang ◽

Chenchen Ye ◽

...

Keyword(s):

Nuclear Family ◽

Population Based ◽

Hubei Province ◽

Social Characteristics ◽

Doing Business ◽

Spring Festival ◽

Quasi Experimental ◽

Highly Correlated ◽

Novel Coronavirus ◽

And Control

After the 2019 novel coronavirus (2019-nCoV) outbreak, we estimated the distribution and scale of more than 5 million migrants residing in Wuhan after they returned to their hometown communities in Hubei Province or other provinces at the end of 2019 by using the data from the 2013–2018 China Migrants Dynamic Survey (CMDS). We found that the distribution of Wuhan’s migrants is centred in Hubei Province (approximately 75%) at a provincial level, gradually decreasing in the surrounding provinces in layers, with obvious spatial characteristics of circle layers and echelons. The scale of Wuhan’s migrants, whose origins in Hubei Province give rise to a gradient reduction from east to west within the province, and account for 66% of Wuhan’s total migrants, are from the surrounding prefectural-level cities of Wuhan. The distribution comprises 94 districts and counties in Hubei Province, and the cumulative percentage of the top 30 districts and counties exceeds 80%. Wuhan’s migrants have a large proportion of middle-aged and high-risk individuals. Their social characteristics include nuclear family migration (84%), migration with families of 3–4 members (71%), a rural household registration (85%), and working or doing business (84%) as the main reason for migration. Using a quasi-experimental analysis framework, we found that the size of Wuhan’s migrants was highly correlated with the daily number of confirmed cases. Furthermore, we compared the epidemic situation in different regions and found that the number of confirmed cases in some provinces and cities in Hubei Province may be underestimated, while the epidemic situation in some regions has increased rapidly. The results are conducive to monitoring the epidemic prevention and control in various regions.

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Epidemic Situation of Novel Coronavirus Pneumonia in China mainland

10.1101/2020.02.17.20024034 ◽

2020 ◽

Cited By ~ 3

Author(s):

Youbin Liu ◽

Liming Gong ◽

Baohong Li

Keyword(s):

Prevention And Control ◽

Turning Point ◽

Hubei Province ◽

Wuhan City ◽

Novel Coronavirus ◽

China Mainland ◽

And Control

AbstractObjectiveAnalyze the occurrence of novel coronavirus pneumonia(NCP) in China mainland, explore the epidemiological rules, and evaluate the effect of prevention and control.MethodsFrom December 1, 2019 to March 4, 2020, Analysis of 80,409 confirmed cases of NCP in China mainland.ResultsFrom December 1, 2019 to March 4, 2020, a total of 80,409 cases of NCP were confirmed in China mainland, a total of 67,466 cases were confirmed in Hubei Province, a total of 49,671 cases were confirmed in Wuhan city. From December 1, 2019 to March 4, 2020, a total of 3,012 cases of NCP deaths in China mainland, the mortality was 3.75% (3012/80,409); A total of 52045 cases of cured in China mainland; The turning point of the epidemic have been reached since February 18.2020 in China mainland; The spread index of NCP gradually declined since January 27. 2020, and the extinction index of NCP rose little by little since January 29, 2020.ConclusionFrom December 1, 2019 to March 4, 2020, NCP is under control, and the trend of the epidemic will eventually disappear; The turning point of an epidemic that I’ve created is a great indicator that can calculate the turning date of an outbreak and provide a basis for scientific prevention.

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Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17113903 ◽

2020 ◽

Vol 17 (11) ◽

pp. 3903 ◽

Cited By ~ 8

Author(s):

Yongzhu Xiong ◽

Yunpeng Wang ◽

Feng Chen ◽

Mingyong Zhu

Keyword(s):

Spatial Autocorrelation ◽

Influencing Factors ◽

Multiple Scales ◽

Open Data ◽

Rank Correlation ◽

Hubei Province ◽

Spatiotemporal Patterns ◽

County Level ◽

Negatively Associated ◽

Spearman’S Rank Correlation

The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people’s lives and socio-economic development. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman’s rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspots and cluster/outlier areas were observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemics at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers.

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A deterministic epidemic model for the emergence of COVID-19 in China

10.1101/2020.03.08.20032854 ◽

2020 ◽

Cited By ~ 3

Author(s):

Meng Wang ◽

Jingtao Qi

Keyword(s):

Prevention And Control ◽

Reproduction Number ◽

Chinese Mainland ◽

Hubei Province ◽

Control Measures ◽

The Novel ◽

Health Authorities ◽

Prevention And Control Measures ◽

Novel Coronavirus ◽

And Control

AbstractCoronavirus disease (COVID-19) broke out in Wuhan, Hubei province, China, in December 2019 and soon after Chinese health authorities took unprecedented prevention and control measures to curb the spreading of the novel coronavirus-related pneumonia. We develop a mathematical model based on daily updates of reported cases to study the evolution of the epidemic. With the model, on 95% confidence level, we estimate the basic reproduction number, R0 = 2.82 ± 0.11, time between March 19 and March 21 when the effective reproduction number becoming less than one, the epidemic ending after April 2 and the total number of confirmed cases approaching 14408 ± 429 on the Chinese mainland excluding Hubei province.

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Current updates on COVID-19 - A review

International Journal of Novel Trends in Pharmaceutical Sciences ◽

10.26452/ijntps.v10i1.1141 ◽

2020 ◽

Vol 10 (1) ◽

pp. 1-8

Author(s):

Ghotekar D S ◽

Vishal N Kushare ◽

Sagar V Ghotekar

Keyword(s):

World Health Organization ◽

Common Cold ◽

Respiratory Diseases ◽

Gastrointestinal Diseases ◽

Hubei Province ◽

World Health ◽

The World ◽

The Common ◽

Novel Coronavirus ◽

Health Organization

Coronaviruses are a family of viruses that cause illness such as respiratory diseases or gastrointestinal diseases. Respiratory diseases can range from the common cold to more severe diseases. A novel coronavirus outbreak was first documented in Wuhan, Hubei Province, China in December 2019. The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) a pandemic. A global coordinated effort is needed to stop the further spread of the virus. A novel coronavirus (nCoV) is a new strain that has not been identified in humans previously. Once scientists determine exactly what coronavirus it is, they give it a name (as in the case of COVID-19, the virus causing it is SARS-CoV-2).

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