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.

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.

Historical dataset of population level and growth rate for the Wuhan, China metro area from 1950 to 2025.

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: 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: Female: Hubei: Wuhan data was reported at 4,602.000 Person th in 2021. This records an increase from the previous number of 4,508.300 Person th for 2020. Population: Household Registration: Female: Hubei: Wuhan data is updated yearly, averaging 4,058.722 Person th from Dec 2007 (Median) to 2021, with 15 observations. The data reached an all-time high of 4,602.000 Person th in 2021 and a record low of 4,007.231 Person th in 2012. Population: Household Registration: Female: 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 Sex.

Population: Usual Residence: Rural: Hubei: Wuhan data was reported at 2,095.000 Person th in 2023. This records a decrease from the previous number of 2,107.600 Person th for 2022. Population: Usual Residence: Rural: Hubei: Wuhan data is updated yearly, averaging 2,140.900 Person th from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 2,187.461 Person th in 2019 and a record low of 1,933.818 Person th in 2020. Population: Usual Residence: Rural: 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: Usual Residence: By Residence.

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.

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.

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.

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.

Number of Household: Hubei: Wuhan data was reported at 3,419.600 Unit th in 2021. This records an increase from the previous number of 3,341.600 Unit th for 2020. Number of Household: Hubei: Wuhan data is updated yearly, averaging 2,111.850 Unit th from Dec 1978 (Median) to 2021, with 44 observations. The data reached an all-time high of 3,419.600 Unit th in 2021 and a record low of 1,242.800 Unit th in 1978. Number of Household: 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: No of Household.

Since the discovery of COVID-19 in Wuhan, China in 2019, close to seven million people have died from the infection. At the onset of the pandemic, many countries enacted stringent measures such as school and event closings in a bid to control and curtail the spread of the virus, leading to many within-household infections as people spent more time at home. This study develops an agent-based model (ABM) to gain insight into the impact of government COVID-19 mitigation guidelines and policy options on within-household and community COVID-19 infections in Gauteng, South Africa. Gauteng is the province in South Africa having the smallest land area, but it accounts for 25.8% of the country’s population. Agents are randomly assigned to cells on a square grid varying according to Gauteng’s population density and household size distribution. We found that the percentage of within-household infections is higher in communities with smaller population densities, with the reverse being true for communities with larger population densities. Furthermore, as the agents’ movement activation rate increases, community-related infections increase, especially in communities with small population densities. Our study found an interesting phenomenon, observed for the first time: the existence of a movement activation threshold where the percentage and number of outside household infections overtake the percentage and number of within household infections when the activation rate increases. Lastly, our simulation results captured the two epidemic peaks experienced in Gauteng from March 30, 2020 to June 22, 2021 while varying quarantine violation and movement activation rates. Thus, the developed ABM can be used to exploit the implications of COVID-19 mitigation guidelines and policy options on household transmission to provide interesting insights.

Since the discovery of COVID-19 in Wuhan, China in 2019, close to seven million people have died from the infection. At the onset of the pandemic, many countries enacted stringent measures such as school and event closings in a bid to control and curtail the spread of the virus, leading to many within-household infections as people spent more time at home. This study develops an agent-based model (ABM) to gain insight into the impact of government COVID-19 mitigation guidelines and policy options on within-household and community COVID-19 infections in Gauteng, South Africa. Gauteng is the province in South Africa having the smallest land area, but it accounts for 25.8% of the country’s population. Agents are randomly assigned to cells on a square grid varying according to Gauteng’s population density and household size distribution. We found that the percentage of within-household infections is higher in communities with smaller population densities, with the reverse being true for communities with larger population densities. Furthermore, as the agents’ movement activation rate increases, community-related infections increase, especially in communities with small population densities. Our study found an interesting phenomenon, observed for the first time: the existence of a movement activation threshold where the percentage and number of outside household infections overtake the percentage and number of within household infections when the activation rate increases. Lastly, our simulation results captured the two epidemic peaks experienced in Gauteng from March 30, 2020 to June 22, 2021 while varying quarantine violation and movement activation rates. Thus, the developed ABM can be used to exploit the implications of COVID-19 mitigation guidelines and policy options on household transmission to provide interesting insights.

The COVID-19 pandemic has had varying impacts across different regions, necessitating localised data-driven responses. SARS-CoV-2 was first identified in a person in Wuhan, China, in December 2019 and spread globally within three months. While there were similarities in the pandemic’s impact across regions, key differences motivated systematic quantitative analysis of diverse geographical data to inform responses. Malawi reported its first COVID-19 case on 2 April 2020 but had significantly less data than Global North countries to inform its response. Here, we present a modelling analysis of SARS-CoV-2 epidemiology and phylogenetics in Malawi between 2 April 2020 and 19 October 2022. We carried out this analysis using open-source tools and open data on confirmed cases, deaths, geography, demographics, and viral genomics. R was used for data visualisation, while Generalised Additive Models (GAMs) estimated incidence trends, growth rates, and doubling times. Phylogenetic analysis was conducted using IQ-TREE, TreeTime, and interactive tree of life. This analysis identifies five major COVID-19 waves in Malawi, driven by different lineages: (1) Early variants, (2) Beta, (3) Delta, (4) Omicron BA.1, and (5) Other Omicron. While the Alpha variant was present, it did not cause a major wave, likely due to competition from the more infectious Delta variant, since Alpha circulated in Malawi when Beta was phasing out and Delta emerging. Case Fatality Ratios were higher for Delta, and lower for Omicron, than for earlier lineages. Phylogeny reveals separation of the tree into major lineages as would be expected, and early emergence of Omicron, as is consistent with proximity to the likely origin of this variant. Both variant prevalence and overall rates of confirmed cases and confirmed deaths were highly geographically heterogeneous. We suggest that real-time analyses should be considered in Malawi and other countries, where similar computational and data resources are available.

人口:户籍:出生率:湖北:武汉在12-01-2022达8.410‰，相较于12-01-2021的8.970‰有所下降。人口:户籍:出生率:湖北:武汉数据按年更新，12-01-2007至12-01-2022期间平均值为10.720‰，共15份观测结果。该数据的历史最高值出现于12-01-2017，达15.570‰，而历史最低值则出现于12-01-2007，为7.700‰。CEIC提供的人口:户籍:出生率:湖北:武汉数据处于定期更新的状态，数据来源于武汉市统计局，数据归类于中国经济数据库的社会人口 – Table CN.GE: Population: Prefecture Level City: Household Registration: Natural Growth Rate。

人口:户籍:死亡率:湖北:武汉在12-01-2022达6.820‰，相较于12-01-2021的5.900‰有所增长。人口:户籍:死亡率:湖北:武汉数据按年更新，12-01-2007至12-01-2022期间平均值为5.720‰，共15份观测结果。该数据的历史最高值出现于12-01-2017，达11.620‰，而历史最低值则出现于12-01-2007，为4.030‰。CEIC提供的人口:户籍:死亡率:湖北:武汉数据处于定期更新的状态，数据来源于武汉市统计局，数据归类于中国经济数据库的社会人口 – Table CN.GE: Population: Prefecture Level City: Household Registration: Natural Growth Rate。

人口数:户籍:女性:湖北:武汉在12-01-2021达4,602.000千人，相较于12-01-2020的4,508.300千人有所增长。人口数:户籍:女性:湖北:武汉数据按年更新，12-01-2007至12-01-2021期间平均值为4,058.722千人，共15份观测结果。该数据的历史最高值出现于12-01-2021，达4,602.000千人，而历史最低值则出现于12-01-2012，为4,007.231千人。CEIC提供的人口数:户籍:女性:湖北:武汉数据处于定期更新的状态，数据来源于武汉市统计局，数据归类于中国经济数据库的社会人口 – Table CN.GE: Population: Prefecture Level City: Household Registration: By Sex。

人口数:户籍:城镇:湖北:武汉在12-01-2021达7,026.100千人，相较于12-01-2020的6,842.300千人有所增长。人口数:户籍:城镇:湖北:武汉数据按年更新，12-01-2015至12-01-2021期间平均值为6,469.279千人，共7份观测结果。该数据的历史最高值出现于12-01-2021，达7,026.100千人，而历史最低值则出现于12-01-2015，为5,857.052千人。CEIC提供的人口数:户籍:城镇:湖北:武汉数据处于定期更新的状态，数据来源于武汉市统计局，数据归类于中国经济数据库的社会人口 – Table CN.GE: Population: Prefecture Level City: Household Registration: By Residence。