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.

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.

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.

基于SEIR和AI模型的COVID-19流行趋势预测数据，包括中国各省确诊的COVID-19病例数，中国各省的当地人口密度，中国各省的首都GDP，其他中国省份到武汉的距离，中国，在中国各省年平均气温

  年平均降雨量 在中国各省和迁移人口在武汉，中国

Context

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world. We rely on the ECDC as they collect and harmonize data from around the world which allows us to compare what is happening in different countries.

Content

This dataset has daily level information on the number of affected cases, deaths and recovery etc. from coronavirus. It also contains various other parameters like average life expectancy, population density, smocking population etc. which users can find useful in further prediction that they need to make.

The data is available from 31 Dec,2019.

Inspiration

Give people weekly data so that they can use it to make accurate predictions.

Regarding all Vaccination Data The date of Last Update is 4/21/2023. Additionally on 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. See this link for more information https://imap.maryland.gov/pages/covid-data Summary The cumulative number of COVID-19 vaccinations percent age group population: 16-17; 18-49; 50-64; 65 Plus. Description COVID-19 - Vaccination Percent Age Group Population data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet. COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county. Terms of Use The Spatial Data, and the information therein, (collectively the Data) is provided as is without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata. This map is for planning purposes only. MEMA does not guarantee the accuracy of any forecast or predictive elements.

Analysis of ‘MD COVID-19 - Vaccination Percent Age Group Population’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/831ca7df-1265-414c-9c54-2555d926e8c3 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Summary The cumulative number of COVID-19 vaccinations percent age group population: 16-17; 18-49; 50-64; 65 Plus.

Description COVID-19 - Vaccination Percent Age Group Population data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet.

COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

Terms of Use The Spatial Data, and the information therein, (collectively the Data) is provided as is without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata. This map is for planning purposes only. MEMA does not guarantee the accuracy of any forecast or predictive elements.

--- Original source retains full ownership of the source dataset ---

Summary The cumulative number of COVID-19 vaccinations percent age group population: 16-17; 18-49; 50-64; 65 Plus.

Description COVID-19 - Vaccination Percent Age Group Population data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet.

COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

Terms of Use The Spatial Data, and the information therein, (collectively the Data) is provided as is without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata. This map is for planning purposes only. MEMA does not guarantee the accuracy of any forecast or predictive elements.

1. Â Clarifying the effects of biodiversity on ecosystem stability in the context of global environmental change is crucial for maintaining ecosystem functions and services. Asynchronous changes between trophic levels over time (i.e., trophic community asynchrony) are expected to increase trophic mismatch and alter trophic interactions, which may consequently alter ecosystem stability. However, previous studies have often highlighted the stabilising mechanism of population asynchrony within a single trophic level, while rarely examining the mechanism of trophic community asynchrony between consumers and their food resources.
2. Â In this study, we analysed the effects of population asynchrony within and between trophic levels on community stability under the disturbances of climate warming, fishery decline, and de-eutrophication, based on an 18-year monthly monitoring dataset of 137 phytoplankton and 91 zooplankton in a subtropical lake.
3.  Our results showed that species diversity promo..., Study sites Lake Donghu (30°31′–30°36′ N, 114°21′–114°28′ E) is a subtropical shallow lake in the Yangtze River basin in Wuhan, Hubei province, China. Lake Donghu contains more than 10 connected sub-lakes, with a total surface area of approximately 33 km2 and a catchment area of approximately 187 km2. The mean depth of Lake Donghu is 2.21 m. Data obtained An 18-year monthly dataset for Lake Donghu from 2003 to 2020 was gathered from the Donghu Experimental Station, which belongs to the framework of the Chinese Ecosystem Research Network (CERN). Lake Guozhenghu (11.37 km2) and Lake Tanglinhu (5.78 km2) are the first and second largest sub-lakes of Lake Donghu. Stations I and II are located in the coastal and pelagic zones of Lake Guozhenghu, respectively, and Station III is located in the pelagic zone of Lake Tanglinhu. Monthly surveys (in the order of Station III, Station II, and Station I) were conducted on sunny days on approximately the 15th to reduce the potential impacts of the wea..., , # Population asynchrony within and between trophic levels have contrasting effects on consumer community stability in a subtropical lake

https://doi.org/10.5061/dryad.w3r228118

Description of the data and file structure

We have submitted our raw data (the_data_used_in_the_manuscript.xlsx).

the_data_used_in_the_manuscript

Station: Name of stations in the Lake Donghu during investigation, including Station I, Station II, and Station III.

Start_year&month: the start year&month of the one-year the moving window.

End_year&month: the end year&month of the one-year the moving window.

Water_temperature: the average water temperature (℃) in the moving window.

Fish_yield: the average fish yield (kg/ha) in the moving window.

Total_nitrogen: the average concentration of total nitrogen (TN, mg/L) in the moving window.

Total_phosphorus: the average concentration of total phosphorus (TP, mg/L) in the moving window.

Phyt...