Covid-19 Dataset: Worldwide spread log including countries first case and first death

Ali, Hasmot; Hossain, Fahad; Hasan, Mehedi; Abujar, Sheikh

doi:10.1016/j.dib.2020.106173

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…We also provide the Stringency Index measure generated by the COVID-19 Government Response Tracker, 47 which is a 0-100 index based on various restrictions put in place by governments to control the pandemic (eg, closing schools and 'shelter in place' requirements). External data were sourced from the COVID-19 Government Response Tracker 47 and Ali et al 48 The information about potential vaccines also changed over the data collection period. In February 2020, the first major vaccine candidates, the Moderna and Oxford-AstraZeneca vaccines, were announced.…”

Section: Open Accessmentioning

confidence: 99%

Correlates of intended COVID-19 vaccine acceptance across time and countries: results from a series of cross-sectional surveys

et al. 2021

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ObjectiveDescribe demographical, social and psychological correlates of willingness to receive a COVID-19 vaccine.SettingSeries of online surveys undertaken between March and October 2020.ParticipantsA total of 25 separate national samples (matched to country population by age and sex) in 12 different countries were recruited through online panel providers (n=25 334).Primary outcome measuresReported willingness to receive a COVID-19 vaccination.ResultsReported willingness to receive a vaccine varied widely across samples, ranging from 63% to 88%. Multivariate logistic regression analyses reveal sex (female OR=0.59, 95% CI 0.55 to 0.64), trust in medical and scientific experts (OR=1.28, 95% CI 1.22 to 1.34) and worry about the COVID-19 virus (OR=1.47, 95% CI 1.41 to 1.53) as the strongest correlates of stated vaccine acceptance considering pooled data and the most consistent correlates across countries. In a subset of UK samples, we show that these effects are robust after controlling for attitudes towards vaccination in general.ConclusionsOur results indicate that the burden of trust largely rests on the shoulders of the scientific and medical community, with implications for how future COVID-19 vaccination information should be communicated to maximise uptake.

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Section: Open Accessmentioning

confidence: 99%

Correlates of intended COVID-19 vaccine acceptance across time and countries: results from a series of cross-sectional surveys

et al. 2021

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“…In Figure 4, the numbers of mass acute deaths in the United States are from February 29, 2020 through April 22, 2020 that correspond to the function (11) is plotted. The function (11) fulfills the hypotheses ( 6) and (7). This is a six parameter model that takes into account the uncertainty of the COVID-19 coronavirus in the population.…”

Section: A Dependent Mathematical Model-a With 6 Parameters and Resultsmentioning

confidence: 99%

“…In [99] an intelligent surveillance system is used to monitor people using deep CNN(Convolutional NN) models to prevent the spread of COVID-19. In [7], Ali et al, using the Machine Learning Applications Predict and Compare Spread Rates, In-fection Rates, and Mortality Rates with Other Pandemics, collected data on COVID-19 patients and, in particular, information on the first deaths in 186 countries from November 17, 2019, to May 16, 2020. In [91] Shinde et al conducted a study of forecasting techniques such as stochastic models and machine learning techniques by classifying real data.…”

Section: Literature Review Of Mathematical Modelsmentioning

confidence: 99%

The Prediction of the Lifetime of the COVID-19 Virus using Parameter-dependent New Mathematical Models

2021

Preprint

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This article is about a complex real-world human medical problem that people all over the world face, a major international public Health problem due to the new coronavirus disease 2019(COVID-19), a highly communicable infectious disease between humans. Spreads rapidly among humans of both sexes of all ages, in large masses in the cyclical manner(seasonally) causing disease in susceptible human Hosts affecting most of the organs in humans mainly lungs resulting in Severe Acute Respiratory Syndrome resulting in mass acute deaths. Acute deaths are more common with Comorbidities like Diabetes mellitus, Ischaemic heart disease, Liver disease, Kidney disease, Gut, etc. Now it is the major emergency international pandemic public health medical disease. On the face of the earth, there are large masses of infection and mass acute deaths due to COVID-19 virus infection and so the life of every individual is uncertain at any time. Because of the mass acute deaths from the COVID-19 virus infection, everyone in the world is scared. From now on, it is the responsibility of the researchers of all nations to bring hope to people. In this article, by predicting the lifetime of disease-causing virus, hope to the people is given, to better protect all people and speed up the immediate general pandemic preparedness within the lifespan of the virus. To accelerate actions to save people's lives, mathematical models will help make public health decisions and reduce mortality using the resources available during this time of the COVID-19 pandemic. In this article, to better protect people from disease preparedness for the virus and a general pandemic by predicting the lifetime of the disease-causing coronavirus, three new mathematical models which are dependent on parameters are proposed. The parameters in the model function model uncertainty of death due to the present international real-life problem caused by different strains of the COVID-19 virus. The first model is a model with six parameters and the second and third models are models with seven parameters respectively. These three models are the generalization of the three models of Phem . The errors due to the models of this article are minimized from the errors due to the models of Phem. These three models can predict the acute death count outside the data period and can predict the lifetime. To illustrate the applicability of the models a big data set of size 54 days starting from February 29, 2020, to April 22, 2020, of acute death counts of USA( United States of America) is considered. The main focus is on the USA due to the significant large mass of infection and large mass of acute death from the COVID-19 virus. As a result, everyone's life is uncertain about death at any time. Since it is a major international public health-related medical problem in humans, with an accuracy of 95% of confidence the results using three models are erected. The large mass of acute deaths due to the number of COVID-19 virus infections in the USA are fitted by the model functions of three mathematical models and a solution is found to an international problem. Based on the acute death rate, the lifetime of the COVID-19 virus is estimated to be 1484.76198616309920 days from the first day of acute death, February 29, 2020. In other words, there will be no mass acute deaths from the COVID-19 virus in the USA after April 2024 if the nation follows the guidelines of the WHO(World Health Organization) and the recommendations of the pathogen. And when the people and the government are very well prepared for this crisis then the spread of infection can be prevented, the people and government can be saved from the economic crisis, and many lives can be saved from mass acute deaths. A comparative study of all models is presented for different measures of errors. The acute death count of the USA outside the date of the data set of 54 days is predicted using three models. The data set misses some counts during the collection of data and it is identified. From the ratio of standard deviation and average acute deaths, it is predicted that the total acute death counts during 54 days will be 62,969. Using the standard deviation around the line of regression it is shown that in the data set a large count is missing during the collection of data of USA. Using the coefficient of determination it is predicted that the Model-C, provides 100% of fitness with the given data set and only 0.0% variation. All three models are suitable to fit the data set of acute death counts of the USA, but Model-C is the best and optimal among the three models. Tt is predicted from Model-A, Model-B, and Model-C the total acute death counts during 54 days will be 66537, 67085, and 68523 respectively. Since Model-C is the best and optimal model, the predicted total acute death counts during 54 days will be 68523. Finally, this article suggests various steps to help control the spread and severity of the new disease. The prediction of the lifetime and data count missing in the data set presented in this research article is entirely new and differs totally from all other articles in the literature. To accelerate actions to save people's lives, mathematical models will help make public health decisions and reduce mortality using the resources available during this time of the COVID-19 pandemic.

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“…Moreover, there was a high frequency of severe and lethal cases in elderly persons, mainly those with comorbidities such as diabetes and hypertension [ 9 ]. SARS-CoV-2 coronavirus propagated to several countries in February and March 2020 [ 10 , 11 , 12 , 13 ], and in the same year, the WHO declared a pandemic on March 11 [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Indirect Dispersion of SARS-CoV-2 Live-Attenuated Vaccine and Its Contribution to Herd Immunity

Pacheco-García

Serafín‐López

2023

Vaccines

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It has been 34 months since the beginning of the SARS-CoV-2 coronavirus pandemic, which causes the COVID-19 disease. In several countries, immunization has reached a proportion near what is required to reach herd immunity. Nevertheless, infections and re-infections have been observed even in vaccinated persons. That is because protection conferred by vaccines is not entirely effective against new virus variants. It is unknown how often booster vaccines will be necessary to maintain a good level of protective immunity. Furthermore, many individuals refuse vaccination, and in developing countries, a large proportion of the population has not yet been vaccinated. Some live-attenuated vaccines against SARS-CoV-2 are being developed. Here, we analyze the indirect dispersion of a live-attenuated virus from vaccinated individuals to their contacts and the contribution that this phenomenon could have to reaching Herd Immunity.

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Covid-19 Dataset: Worldwide spread log including countries first case and first death

Cited by 3 publications

References 8 publications

Correlates of intended COVID-19 vaccine acceptance across time and countries: results from a series of cross-sectional surveys

Correlates of intended COVID-19 vaccine acceptance across time and countries: results from a series of cross-sectional surveys

The Prediction of the Lifetime of the COVID-19 Virus using Parameter-dependent New Mathematical Models

Indirect Dispersion of SARS-CoV-2 Live-Attenuated Vaccine and Its Contribution to Herd Immunity

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