The statistic shows the population of Wuhan in China from 1980 to 2010, with forecasts up until 2035. In 2010, the population of Wuhan had amounted to about **** million inhabitants and was forecasted to exceed *********** by 2035.

Population: Census: Hubei: Wuhan data was reported at 12,326.518 Person th in 12-01-2020. This records an increase from the previous number of 9,785.392 Person th for 12-01-2010. Population: Census: Hubei: Wuhan data is updated decadal, averaging 9,785.392 Person th from Dec 2000 (Median) to 12-01-2020, with 3 observations. The data reached an all-time high of 12,326.518 Person th in 12-01-2020 and a record low of 8,312.700 Person th in 12-01-2000. Population: Census: Hubei: Wuhan data remains active status in CEIC and is reported by Wuhan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: By Census.

Population: Household Registration: Birth Rate: Hubei: Wuhan data was reported at 8.410 ‰ in 2022. This records a decrease from the previous number of 8.970 ‰ for 2021. Population: Household Registration: Birth Rate: Hubei: Wuhan data is updated yearly, averaging 10.720 ‰ from Dec 2007 (Median) to 2022, with 15 observations. The data reached an all-time high of 15.570 ‰ in 2017 and a record low of 7.700 ‰ in 2007. Population: Household Registration: Birth Rate: Hubei: Wuhan data remains active status in CEIC and is reported by Wuhan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Household Registration: Natural Growth Rate.

Population: Usual Residence: Urbanization Rate: Hubei: Wuhan data was reported at 84.788 % in 2023. This records an increase from the previous number of 84.660 % for 2022. Population: Usual Residence: Urbanization Rate: Hubei: Wuhan data is updated yearly, averaging 82.400 % from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 84.788 % in 2023 and a record low of 79.770 % in 2016. Population: Usual Residence: Urbanization Rate: Hubei: Wuhan data remains active status in CEIC and is reported by Wuhan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Urbanization Rate.

Additional file 1. The first file’s name is Data_source.xlsx. It is a table that lists the official COVID-19 websites of provincial and municipal health commissions for every city in China. It includes province, city and source websites in both Chinese and English. Information of COVID-19 patients are collected from these websites.

Population: Household Registration: Death Rate: Hubei: Wuhan data was reported at 6.820 ‰ in 2022. This records an increase from the previous number of 5.900 ‰ for 2021. Population: Household Registration: Death Rate: Hubei: Wuhan data is updated yearly, averaging 5.720 ‰ from Dec 2007 (Median) to 2022, with 15 observations. The data reached an all-time high of 11.620 ‰ in 2017 and a record low of 4.030 ‰ in 2007. Population: Household Registration: Death Rate: Hubei: Wuhan data remains active status in CEIC and is reported by Wuhan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Household Registration: Natural Growth Rate.

Population: Hubei: Wuhan: Household Registration data was reported at 9,495.183 Person th in 2023. This records an increase from the previous number of 9,444.168 Person th for 2022. Population: Hubei: Wuhan: Household Registration data is updated yearly, averaging 7,537.100 Person th from Dec 1978 (Median) to 2023, with 46 observations. The data reached an all-time high of 9,495.183 Person th in 2023 and a record low of 5,482.900 Person th in 1978. Population: Hubei: Wuhan: Household Registration data remains active status in CEIC and is reported by Wuhan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City.

Data and Code for Prediction of the COVID-19 Epidemic Trends Based on SEIR and AI Models.Data include the number of confirmed cases of COVID-19, local population density, capital GDP, distance to Wuhan, average annual temperature, average annual rainfall of Chinese provinces (Except for Hong Kong, Macao and Taiwan) and migration population in Wuhan. Code include SEIR, DNN, RNN for prediction.

As of June 6, 2022, the novel coronavirus SARS-CoV-2 that originated in Wuhan, the capital of Hubei province in China, had infected over 2.1 million people and killed 14,612 in the country. Hong Kong is currently the region with the highest active cases in China.

From Wuhan to the rest of China

In late December 2019, health authorities in Wuhan detected several pneumonia cases of unknown cause. Most of these patients had links to the Huanan Seafood Market. With Chinese New Year approaching, millions of Chinese migrant workers travelled back to their hometowns for the celebration. Before the start of the travel ban on January 23, around five million people had left Wuhan. By the end of January, the number of infections had surged to over ten thousand. The death toll from the virus exceeded that of the SARS outbreak a few days later. On February 12, thousands more cases were confirmed in Wuhan after an improvement to the diagnosis method, resulting in another sudden surge of confirmed cases. On March 31, 2020, the National Health Commission (NHC) in China announced that it would begin reporting the infection number of symptom-free individuals who tested positive for coronavirus. On April 17, 2020, health authorities in Wuhan revised its death toll, adding 50 percent more fatalities. After quarantine measures were implemented, the country reported no new local coronavirus COVID-19 transmissions for the first time on March 18, 2020.

The overloaded healthcare system

In Wuhan, 28 hospitals were designated to treat coronavirus patients, but the outbreak continued to test China’s disease control system and most of the hospitals were soon fully occupied. To combat the virus, the government announced plans to build a new hospital swiftly. On February 3, 2020, Huoshenshan Hospital was opened to provide an additional 1,300 beds. Due to an extreme shortage of health-care professionals in Wuhan, thousands of medical staff from all over China came voluntarily to the epicenter to offer their support. After no new deaths reported for first time, China lifted ten-week lockdown on Wuhan on April 8, 2020. Daily life was returning slowly back to normal in the country.

In 2021, around **** million people were estimated to be living in the urban area of Shanghai. Shanghai was the largest city in China in 2021, followed by Beijing, with around **** million inhabitants. The rise of the new first-tier cities The past decades have seen widespread and rapid urbanization and demographic transition in China. While the four first-tier megacities, namely Beijing, Shanghai, Guangzhou, and Shenzhen, are still highly attractive to people and companies due to their strong ability to synergize the competitive economic and social resources, some lower-tier cities are already facing declining populations, especially those in the northeastern region. Below the original four first-tier cities, 15 quickly developing cities are sharing the cake of the moving population with improving business vitality and GDP growth potential. These new first-tier cities are either municipalities directly under the central government, such as Chongqing and Tianjin, or regional central cities and provincial capitals, like Chengdu and Wuhan, or open coastal cities in the economically developed eastern regions. From urbanization to metropolitanization As more and more Chinese people migrate to large cities for better opportunities and quality of life, the ongoing urbanization has further evolved into metropolitanization. Among those metropolitans, Shenzhen's population exceeded **** million in 2020, a nearly ** percent increase from a decade ago, compared to eight percent in the already densely populated Shanghai. However, with people rushing into the big-four cities, the cost of housing, and other living standards, are soaring. As of 2020, the average sales price for residential real estate in Shenzhen exceeded ****** yuan per square meter. As a result, the fast-growing and more cost-effective new first-tier cities would be more appealing in the coming years. Furthermore, Shanghai and Beijing have set plans to control the size of their population to ** and ** million, respectively, before 2035.

In December 2019, novel coronavirus disease (COVID-19) hit Wuhan, Hubei Province, China and spread to the rest of China and overseas. The emergence of this virus coincided with the Spring Festival Travel Rush in China. It is possible to estimate the total number of COVID-19 cases in Wuhan, by 23 January 2020, given the cases reported in other cities/regions and population flow data between Wuhan and these cities/regions. We built a model to estimate the total number of COVID-19 cases in Wuhan by 23 January 2020, based on the number of cases detected outside Wuhan city in China, with the assumption that cases exported from Wuhan were less likely underreported in other cities/regions. We employed population flow data from different sources between Wuhan and other cities/regions by 23 January 2020. The number of total cases in Wuhan was determined by the maximum log likelihood estimation and Akaike Information Criterion (AIC) weight. We estimated 8 679 (95% CI: 7 701, 9 732) as total COVID-19 cases in Wuhan by 23 January 2020, based on combined source of data from Tencent and Baidu. Sources of population flow data impact the estimates of the total number of COVID-19 cases in Wuhan before city lockdown. We should make a comprehensive analysis based on different sources of data to overcome the bias from different sources.

This is the first wave data COVID-19 and Social Network Panel Study, Wuhan subsample (CovNetps-Wuhan). CovNetps-Wuhan is an online survey with a stratified probabilistic sample representing the college population in Wuhan City. The data collection procedure is reported below. Prior to sampling, the number of students in each higher educational institution (HEI) located in Wuhan is retrieved from the school’s official website. Then, survey invitations are sent out through mainstream social media platforms of the HEIs and student unions (such as their official Wechat public accounts, authorized QQ group chats, and micro-blogs). The survey takes about 13 minutes, and each participant is rewarded 5 CNY as an economic incentive until the expected number of respondents from their corresponding HEI is fulfilled. In addition, all HEIs are classified into 5 types based on the five-tier system developed by the Ministry of Education, which ranks the HEIs from the most selective, national key universities to vocational colleges. For schools with insufficient participants, the remaining quota is replaced by students from similar HEIs of the same tier. Finally, we obtain a sample of 2047 students from 45 out of the 54 eligible HEIs, who named 6134 contacts in their networks (i.e., alters). CovNetps-Wuhan is collected between 30 March and 26 May 2020, launched at the end of the first wave outbreak in China to capture the consequences of the whole process of exposure. Ethics clearance of data collection has been granted by East China University of Science and Technology. For detailed information, visit our website at www.cov-netps.com/home.

Population: Hubei: Wuhan: Usual Residence data was reported at 13,774.000 Person th in 2023. This records an increase from the previous number of 13,739.000 Person th for 2022. Population: Hubei: Wuhan: Usual Residence data is updated yearly, averaging 8,093.050 Person th from Dec 1978 (Median) to 2023, with 46 observations. The data reached an all-time high of 13,774.000 Person th in 2023 and a record low of 5,551.000 Person th in 1978. Population: Hubei: Wuhan: Usual Residence data remains active status in CEIC and is reported by Wuhan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Wuhan. name, office head of government, Mayor, image, Area, date founded, Elevation, Country, administrative division, continent, latitude, waterbody, longitude, Website, population, Demonym

ObjectiveFrom January 23rd, 2020, lock-down measures were adopted in Wuhan, China to stop the spread of COVID-19. However, due to the approach of the Spring Festival and the nature of COVID-19, more than 6 million permanent and temporary residents of Wuhan (who were potential carriers or spreaders of the virus), left the city before the lock-down measures were implemented. This study aims to explore whether and how the population inflow from Wuhan city impacted residents' confidence in controlling COVID-19 outbreaks at the destination cities.Study design and settingBased on questionnaire data and migration big data, a multiple regression model was developed to quantify the impact of the population inflow from Wuhan city on the sense of confidence of residents in controlling the COVID-19 outbreak at the destination cities. Scenarios were considered that varied residents' expected month for controlling COVID-19 outbreak at the destination cities, residents' confidence in controlling COVID-19 outbreak at the destination cities, and the overall indicators for the sense of confidence of residents in controlling COVID-19. A marginal effect analysis was also conducted to calculate the probability of change in residents' confidence in controlling the COVID-19 outbreak with per unit change in the population inflow from Wuhan city.ResultsThe impact of population inflow from Wuhan city on residents' expected month for controlling COVID-19 outbreak at the destination cities was positive and significant at the 1% level, while that on residents' confidence in controlling COVID-19 at the destination cities was negative and significant at the 1% level. Robustness checks, which included modifying the sample range and replacing measurement indicators of the population inflow from Wuhan city, demonstrated these findings were robust and credible. When the population inflow from Wuhan city increased by one additional unit, the probabilities of the variables “February” and “March” decreased significantly by 0.1023 and 0.1602, respectively, while the probabilities of “April,” “May,” “June,” “July,” “before the end of 2020,” and “unknown” significantly increased by 0.0470, 0.0856, 0.0333, 0.0080, 0.0046, and 0.0840, respectively. Similarly, when the population inflow from Wuhan city increased by one additional unit, the probability of the variable “extremely confident” decreased by 0.1973. Furthermore, the probabilities of the variables “confident,” “neutral,” and “unconfident” significantly increased by 0.1392, 0.0224, and 0.0320, respectively.ConclusionThe population inflow from Wuhan city played a negative role in the sense of confidence of residents in controlling COVID-19 in the destination cities. The higher the population inflow from Wuhan city, the longer the residents' expected month for controlling COVID-19 outbreak at the destination cities became, and the weaker the residents' confidence in controlling the COVID-19 outbreak at the destination cities.

Vaccination is essential to controlling the pandemic of coronavirus disease 2019 (COVID-19). People living with HIV (PLWH) were considered more vulnerable to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection compared with the general population. Therefore, it is urgent to protect PLWH from SARS-CoV-2 infection. For PLWH, vaccine hesitancy could be more common and may compromise vaccine coverage. Our study aimed to investigate the willingness to receive the COVID-19 vaccination among PLWH and associated factors. A cross-sectional online survey was performed among PLWH and the general population from 4 April to 18 April 2021 in Wuhan, China. The multivariable logistic regression was used to analyze associated factors for COVID-19 vaccination willingness among PLWH. A total of 556 PLWH and 570 individuals from the general population were enrolled. The COVID-19 vaccine willingness among PLWH was 60.8%, which was relatively lower than that in the general population (80.9%) (P < 0.001). The results of multivariable analysis indicated that PLWH with comorbidities (OR = 2.07, 95% CI: 1.25–3.45), those who had idea about PLWH would be more serious if they were infected with SARS-CoV-2 (OR = 1.67, 95% CI: 1.11–2.51) and those who thought their antiretroviral therapy (ART) would be affected by COVID-19 epidemic (OR = 2.04, 95% CI: 1.22–3.42) had higher willingness to receive COVID-19 vaccination. PLWH who had a monthly income over 5,000 RMB (OR = 0.64, 95% CI: 0.45–0.92) and had a sex orientation as non-homosexual (OR = 0.67, 95% CI: 0.47–0.96) were associated with lower willingness for COVID-19 vaccination. Our findings showed that the PLWH had a lower willingness for COVID-19 vaccination compared with the general population in Wuhan. Targeted interventions such as health education should be conducted to increase the willingness for COVID-19 vaccination among PLWH, thus enhancing COVID-19 vaccine uptake among PLWH.

Population: Household Registration: Urban: Hubei: Wuhan data was reported at 7,026.100 Person th in 2021. This records an increase from the previous number of 6,842.300 Person th for 2020. Population: Household Registration: Urban: Hubei: Wuhan data is updated yearly, averaging 6,469.279 Person th from Dec 2015 (Median) to 2021, with 7 observations. The data reached an all-time high of 7,026.100 Person th in 2021 and a record low of 5,857.052 Person th in 2015. Population: Household Registration: Urban: Hubei: Wuhan data remains active status in CEIC and is reported by Wuhan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Household Registration: By Residence.

Population: Household Registration: Natural Change: Hubei: Wuhan data was reported at 15.000 Person th in 2022. This records a decrease from the previous number of 28.000 Person th for 2021. Population: Household Registration: Natural Change: Hubei: Wuhan data is updated yearly, averaging 33.380 Person th from Dec 2007 (Median) to 2022, with 15 observations. The data reached an all-time high of 71.511 Person th in 2018 and a record low of 13.281 Person th in 2010. Population: Household Registration: Natural Change: Hubei: Wuhan data remains active status in CEIC and is reported by Wuhan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Household Registration: Natural Change.

Social media check-in data as a geo-tagged information source have been used for revealing spatio-temporal patterns in the field of social and urban study, such as human behavior or public issues. This paper investigates a case study and presents a new method of representing the mobility of people within a city from check-in data. By dividing the data in a temporal sequence, this study examines the overall mobility in the case study city through the gradient/difference of population density with a series of time after computing the population density from the check-in data using an incorporated Thiessen polygon method. By classifying check-in data with their geo-tags into several groups according to travel purposes, and partitioning the data according to administrative district boundaries, various moving patterns for those travel purposes in those administrative districts are identified by scrutinizing a series of spatial geometries of a weighted standard deviational ellipse (WSDE). Through deep analyses of those data by the adopted approaches, the general pattern of mobility in the case city, such as people moving to the central urban area from the suburb from 4 am to 8 am, is ascertained, and different characteristics of movement in those districts are also depicted. Furthermore, it can tell that in which district less movement is likely for a certain purpose (e.g., for dinner or entertainment). This study has demonstrated the availability of the proposed methodology and check-in data for investigating intra-urban human mobility.