China recorded 5226 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, China reported 99256991 Coronavirus Cases. This dataset includes a chart with historical data for China Coronavirus Deaths.

Context

What Is COVID-19?

A coronavirus is a kind of common virus that causes an infection in your nose, sinuses, or upper throat. Most coronaviruses aren't dangerous.

COVID-19 is a disease that can cause what doctors call a respiratory tract infection. It can affect your upper respiratory tract (sinuses, nose, and throat) or lower respiratory tract (windpipe and lungs). It's caused by a coronavirus named SARS-CoV-2.

It spreads the same way other coronaviruses do, mainly through person-to-person contact. Infections range from mild to serious.

SARS-CoV-2 is one of seven types of coronavirus, including the ones that cause severe diseases like Middle East respiratory syndrome (MERS) and sudden acute respiratory syndrome (SARS). The other coronaviruses cause most of the colds that affect us during the year but aren’t a serious threat for otherwise healthy people.

In early 2020, after a December 2019 outbreak in China, the World Health Organization identified SARS-CoV-2 as a new type of coronavirus. The outbreak quickly spread around the world.

Is there more than one strain of SARS-CoV-2?

It’s normal for a virus to change, or mutate, as it infects people. A Chinese study of 103 COVID-19 cases suggests the virus that causes it has done just that. They found two strains, which they named L and S. The S type is older, but the L type was more common in early stages of the outbreak. They think one may cause more cases of the disease than the other, but they’re still working on what it all means.

How long will the coronavirus last?

It’s too soon to tell how long the pandemic will continue. It depends on many things, including researchers’ work to learn more about the virus, their search for a treatment and a vaccine, and the public’s efforts to slow the spread.

Dozens of vaccine candidates are in various stages of development and testing. This process usually takes years. Researchers are speeding it up as much as they can, but it still might take 12 to 18 months to find a vaccine that works and is safe.

Symptoms of COVID-19

The main symptoms include:

• Fever
• Coughing
• Shortness of breath
• Fatigue
• Chills, sometimes with shaking
• Body aches
• Headache
• Sore throat
• Loss of smell or taste
• Nausea
• Diarrhea

The virus can lead to pneumonia, respiratory failure, septic shock, and death. Many COVID-19 complications may be caused by a condition known as cytokine release syndrome or a cytokine storm. This is when an infection triggers your immune system to flood your bloodstream with inflammatory proteins called cytokines. They can kill tissue and damage your organs.

STAY HOME. STAY SAFE !

Content

ALL DATASETS HAVE BEEN CLEANED FOR DIRECT USE.

Total_World_covid-19.csv : This dataset contains the worldwide data country-wise such as total cases , total active, deaths, etc. along with testing data.

Total_India_covid-19.csv : This dataset contains India level data statewise such as confirmed cases , active cases, deaths, etc.

Total_US_covid-19.csv : This dataset contains India level data statewise such as confirmed cases , active cases, deaths, etc.

Daily_States_India.csv : This dataset contains daily statewise data of India such as daily confirmed , daily active , daily deaths and daily recovered.

Total_Maharshtra_covid-19.csv : This dataset contains Maharashtra's district wise data such as confirmed cases , active cases, deaths, etc.

Acknowledgements

1. World and US data has been collected from Worldometer . Thanks a lot.

2. India and State level along with Maharashtra district data has been collected from Covid19India. Special thanks to them for providing updated and such wonderful data .

Inspiration

1) What has been the Covid-19 trend across the world, Is it declining? Is it increasing? 2) Which countries have been able to sustain and control the virus spread? 3) How is India coping up with the virus? Have they been able to control it at the given cost of 2 months nationwide lockdown?

The donation and sale of vaccines are diplomatic tools that have impact well beyond health policies. May Chinese Covid-related vaccine diplomacy be understood beyond reactive terms vis-à-vis power disputes with the West, in particularly the United States? We then scrutinize the drivers of China’s vaccine diplomacy, assessing whether Beijing privileged the expansion of its diplomatic leverage in the Global South. By employing logit and tobit models in the analysis of a cross-sectional dataset covering 213 countries, we examine the probability of countries receiving vaccines from China. We find that low-income states, in particular, and middle-income ones and those with more Covid deaths were more likely to receive vaccines through either donations or purchases. For donations, states that integrate the Belt and Road Initiative (BRI) and/or oppose the United States at the United Nations General Assembly (UNGA) were also privileged. China’s vaccine diplomacy has therefore a twofold purpose. First, the expansion of the country’s soft power in the Global South. Second, the consolidation of the BRI bilateral ties and an anti-US allied network. Hence, current global health initiatives cannot be detached from debates on the contestation of the liberal international order (LIO) and China’s dual role as a responsible stakeholder and most successful emerging power that has the potential to challenge American hegemony. Moreover, the findings also suggest that bilateral donor-recipient flows may be less politicized than what prior works on development aid and health diplomacy have claimed.

Context

• A new coronavirus designated 2019-nCoV was first identified in Wuhan, the capital of China's Hubei province
• People developed pneumonia without a clear cause and for which existing vaccines or treatments were not effective
• The virus has shown evidence of human-to-human transmission
• Korea has defended well against coronavirus until summer, but it increased many confirmed cases from fall
• As of 24th Dec. approximately 53K cases have been confirmed, and daily around 1K cases are getting confirmed
• This datasets are prepared to cheer Korea up fighting against coronavirus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4837224%2Ff829b8bd45aacf4c63b17e0116cb52c9%2Fcover_photo.PNG?generation=1608792447857317&alt=media" alt="">

Content

• 3 files attached which are 1) COVID Korea Status 2) COVID Korea Demo 3) COVID Korea Geo

• 1) COVID Korea Status : General daily update . STATE_DT : standard date . STATE_TIME : standard time . DECIDE_CNT : confirmed cases . CLEAR_CNT : clear cases after hospitalization . EXAM_CNT : examination cases . DEATH_CNT : death counts . CARE_CNT : counts on care . RESUTL_NEG_CNT : negative results after examination . ACC_EXAM_CNT : accumulative examination counts . ACC_EXAM_COMP_CNT: accumulative examination completes count . ACC_DEF_RATE : accumulative confirmed rate . CREATE_DT : posted date and time . UPDATE_DT : updated date and time

• 2) COVID Korea Demo : Updates with demographic information . GUBUN : classified by gender and age . CONF_CASE : confirmed cases . CONF_CASE_RATE : confirmed case rate . DEATH : death counts . DEATH_RATE : death rate . CRITICAL_RATE : critical rate . CREATE_DT : created date and time . UPDATE_DT : updated date and time

• 3) COVID Korea Geo : Updates with geographic information
  . CREATE_DT : created date and time
  . DEATH_CNT : death counts
  . GUBUN : city name
  . GUBUN_CN : city name in Chinese
  . GUBUN_EN : city name in English
  . INC_DEC : increase/decrease vs. past day
  . ISOL_CLEAR_CNT : clear counts from isolation
  . QUR_RATE : confirmed rate per 100K people
  . STD_DAY : standard day
  . UPDATE_DT : updated date and time
  . DEF_CNT : confirmed cases
  . ISOL_ING_CNT : isolated cases
  . OVER_FLOW_CNT : confirmed cases from foreign countries
  . LOCAL_OCC_CNT : domestic confirmed cases

Acknowledgements

• http://www.kdca.go.kr/cdc/
• https://data.go.kr/index.do

If these are useful, I will frequently update. Thanks.

Context

COVID-19 is a novel coronavirus that emerged in China in 2019. However, Coronaviruses are zoonotic viruses that circulate amongst animals and spill ove9r to humans from time to time and have been causing illness ranging from mild symptoms to severe illness. On 7 January 2020, Chinese authorities confirmed COVID-19 and on 30 January 2020, the Director-General of WHO declared the COVID-19 outbreak a Public Health Emergency of International concern. On 8 March, Bangladesh has confirmed 3 laboratories tested coronavirus cases for the very first time. This Dataset file contains the data for analysing different cases of COVID-19 outbreak in Bangladesh. Date in a specific format, Daily new confirmed cases, Total confirmed cases, Daily new deaths, total deaths, Daily new recovered, Total recovered, Daily New Tests, Total Tests, and Active Cases are the vailable data format for this dataset.

Content

This dataset contains every single days data of COVID-19 outbreak in Bangladesh. From the first confirmed case of COVID-19, on 8 March 2020, it contains each confirmed, recovery, and death cases till date, This is a time-series dataset and this dataset will updated in a daily basis.

Acknowledgements

I would like to acknowldgwe the following organizations for their great efforts to make these data available for the greater community. Institute of Epidemiology, Disease Control and Research (IEDCR): https://www.iedcr.gov.bd/ DGHS:https://dghs.gov.bd/index.php/en/ Official Website of BD Government: http://www.corona.gov.bd/ WHO: https://www.who.int/countries/bgd/en/

Inspiration

As an academician and data science resercher, I feel this is an ample need for the greater data science community all over the world to understand and develop meaningful insights on the outbreak of COVID-19 in Bangladesh. Constructive suggestions and comments are highly appreciated.

BackgroundImpact of B-cell depletion following treatment with Bruton tyrosine kinase-inhibitors (BTKi) on the outcome of SARS-CoV-2 infection in chronic lymphocytic leukemia (CLL) patients remain controversial. We investigated the impact of BTKi on susceptibility and the severity of COVID-19 in Chinese patients with CLL during the first wave of COVID-19 (Omicron variant).MethodsCLL patients (n=171) visiting the Institute of Hematology, Peoples’ Hospital, China (November 15, 2022- January 20, 2023) were included in the study. Seventeen patients receiving BTKi and venetoclax with or without obinutuzumab were excluded. Data from 117 patients receiving treatment with BTKi were collected using a standardized questionnaire through telephone interviews. Thirty-four patients without CLL-specific treatment served as controls. The data was analysed using IBM SPSS Software version 21 and a P value of <0.05 was considered statistically significant.ResultsThe median age of patients was 67 years and majority were males (n=100). Treatment with BTKi was not associated with higher incidence of COVID-19 (74% [95% Confidence Interval (CI) 60%, 92%]) versus 74% (CI 48%, 100%) without any treatment (P=0.92). Hypoxemia was reported by 45% (32%, 61%) and 16% (4%, 41%) (P=0.01). BTKi was the only independent risk factor of hypoxemia (Hazard Ratio [HR], 4.22 [1.32, 13.50]; P = 0.02). Five (5.7%) patients with COVID-19 under BTKi required ICU admission; 4 of them died. No ICU admissions/deaths were observed in the control group.ConclusionIn Chinese patients with CLL and treated with BTKi experienced more severe lung disease and ICU admissions due to COVID-19 than patients without CLL therapy. Frequency of infections with SARS-CoV-2, however, was not different in patients with or without BTKi treatment.

Context

• Severe acute respiratory syndrome (SARS) is a viral respiratory disease of zoonotic origin caused by the SARS coronavirus (SARS-CoV).
• Between November 2002 and July 2003, an outbreak of SARS in southern China caused an eventual
• 8,098 cases, resulting in 774 deaths reported in
• 17 countries (9.6% fatality rate), with the majority of cases in mainland China and Hong Kong.
• No cases of SARS have been reported worldwide since 2004.
• In late 2017, Chinese scientists traced the virus through the intermediary of civets to cave-dwelling horseshoe bats in Yunnan province.
• More information https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndrome

Content

• sars_2003_complete_dataset_clean.csv - The file contains day by day no. from March to July 2003 across the world.
• summary_data_clean.csv - Final no.s from across the world

Acknowledgements / Data Source

https://www.who.int/csr/sars/country/en/

Collection methodology

https://github.com/imdevskp/sars-2003-outbreak-data-webscraping-code

Cover Photo

Photo from CDC website https://www.cdc.gov/dotw/sars/index.html#

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

The outbreak of the novel coronavirus disease 2019 (COVID-19) has had an unprecedented impact worldwide, and it is of great significance to predict the prognosis of patients for guiding clinical management. This study aimed to construct a nomogram to predict the prognosis of COVID-19 patients. Clinical records and laboratory results were retrospectively reviewed for 331 patients with laboratory-confirmed COVID-19 from Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital) and Third Affiliated Hospital of Sun Yat-sen University. All COVID-19 patients were followed up for 80 days, and the primary outcome was defined as patient death. Cases were randomly divided into training (n=199) and validation (n=132) groups. Based on baseline data, we used statistically significant prognostic factors to construct a nomogram and assessed its performance. The patients were divided into Death (n=23) and Survival (n=308) groups. Analysis of clinical characteristics showed that these patients presented with fever (n=271, 81.9%), diarrhea (n=20, 6.0%) and had comorbidities (n=89, 26.9.0%). Multivariate Cox regression analysis showed that age, UREA and LDH were independent risk factors for predicting 80-day survival of COVID-19 patients. We constructed a qualitative nomogram with high C-indexes (0.933 and 0.894 in the training and validation groups, respectively). The calibration curve for 80-day survival showed optimal agreement between the predicted and actual outcomes. Decision curve analysis revealed the high clinical net benefit of the nomogram. Overall, our nomogram could effectively predict the 80-day survival of COVID-19 patients and hence assist in providing optimal treatment and decreasing mortality rates.

Comparing the performance of the proposed hybrid and the base models on the malaria deaths in China before and during COVID-19 Pandemic.

ObjectiveTo predict mortality in severe patients with COVID-19 at admission to the intensive care unit (ICU) using thromboelastography (TEG).MethodsThis retrospective, two-center, observational study involved 87 patients with PCR-and chest CT-confirmed severe COVID-19 who were admitted to at Wuhan Huoshenshan Hospital and the 908th Hospital of Chinese PLA Logistic Support Force between February 2020 and February 2023. Clinic demographics, laboratory results, and outcomes were compared between those who survived and those who died during hospitalization.ResultsThromboelastography showed that of the 87 patients, 14 were in a hypercoagulable state, 25 were in a hypocoagulable state, and 48 were normal, based on the time to maximum amplitude (TMA). Patients who died showed significantly lower α angle, but significantly longer R-time, K-time and TMA than patients who survived. Random forest selection showed that K-time, TMA, prothrombin time (PT), international normalized ratio (INR), D-dimer, C-reactive protein (CRP), aspartate aminotransferase (AST), and total bilirubin (Tbil) were significant predictors. Multivariate logistic regression identified that TMA and CRP were independently associated with mortality. TMA had a greater predictive power than CRP levels based on time-dependent AUCs. Patients with TMA ≥ 26.4 min were at significantly higher risk of mortality (hazard ratio 3.99, 95% Confidence Interval, 1.92–8.27, p < 0.01).ConclusionTMA ≥26.4 min at admission to ICU may be an independent predictor of in-hospital mortality for patients with severe COVID-19.

Comparing the performance of the proposed hybrid model and GRU model on P. Falciparum cases before the COVID-19 pandemic.

BackgroundThe rapid development of coronavirus disease 2019 (COVID-19) pandemic has become a great threat to global health. Its mortality is associated with inflammation-related airway mucus hypersecretion and dysfunction of expectoration, and the subsequent mucus blockage of the bronchioles at critical stage is attributed to hypoxemia, complications, and even death. Traditional Chinese medicine (TCM) has rich experience in expectorant, including treatment of COVID-19 patients with airway mucus dysfunction, yet little is known about the mechanisms. This study is aiming to explore the potential biological basis of TCM herbal expectorant for treating COVID-19.ObjectiveTo get core herbs with high used frequency applications in the actions of expectoration by using association rule algorithm and to investigate the multitarget mechanisms of core herbs in expectorant formulae for COVID-19 therapies.MethodsForty prescriptions for expectorant were retrieved from TCM Formulae. The ingredient compounds and targets of core herbs were collected from the TCMSP database, Gene-Cards, and NCBI. The protein interaction network (PPI) was constructed by SRING, and the network analysis was done by Cytoscape software. Bioconductor was applied for functional enrichment analysis of targets.ResultsThe core herbs of expectorant could regulate core pathways (MAP kinase activity, cytokine receptor binding, G-protein-coupled receptor binding, etc.) via interactions of ingredients (glycyrol, citromitin, etc.) on mucin family to eliminate phlegm.ConclusionTCM herbal expectorant could regulate MAPK and cytokine-related pathways, thereby modulating Mucin-family to affect mucus generation and clearance and eventually retarding the deterioration of COVID-19 disease.