An Approach for Corona Spreads Forecast and Herd Immunity Prediction with the Help of Machine Learning

. In this article we present forecasts of the spread of COVID-19 virus, obtained by econometric and machine learning methods. Furthermore, by employing modelling method, we estimate effectiveness of preventive measures implemented by the government. Each of the models discussed in this article is modelling different characteristics of the COVID-19 epidemic’s trajectory: peak and end date, number of daily infections over different forecasting horizons, total number of infection cases. All these provide quite clear picture to the interested reader of the future threats posed by COVID-19. In terms of existing models and data, our research indicates that phenomenological models do well in forecasting the trend, duration and total infections of the COVID- 19 epidemic, but make serious mistakes in forecasting the number of daily infections. Machine learning models, deliver more accurate short –term forecast of daily infections, but due to data limitations, they struggle to make long-term forecasts. Compartmental models are the best choice for modelling the measures implemented by the government for preventing the spread of COVID-19 and determining optimal level of restrictions. These models show that until achieving herd immunity (i.e. without any epidemiological or government implemented measures), approximate number of people infected with COVID-19 would be 3 million, but due to preventive measures, expected total number of infections has reduced to several thousand (1555-3189) people. This unequivocally indicates the effectiveness of the preventive measures.

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Intelligent COVID-19 Forecasting, Diagnoses and Monitoring Systems: A Survey

10.36227/techrxiv.15172488 ◽

2021 ◽

Author(s):

Nasreen Anjum ◽

Amna Asif, ◽

Mehreen Kiran ◽

Fouzia Jabeen ◽

Zhaohui Yang ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Herd Immunity ◽

Machine Learning Algorithms ◽

Future Research ◽

Monitoring Systems ◽

Extensive Literature ◽

Awareness Campaigns ◽

Research Areas ◽

Wide Range

<div>To date, the novel Corona virus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent COVID-19 forecasting, diagnosing, and monitoring systems have been proposed to tackle the COVID-19 pandemic. In this article based on our extensive literature review, we provide a taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and monitoring systems. We review the available literature extensively under the proposed taxonomy and have analyzed a significantly wide range of machine learning algorithms and IoTs which can be used in predicting the spread of COVID-19 and in diagnosing and monitoring the infected individuals. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.</div>

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Intelligent COVID-19 Forecasting, Diagnoses and Monitoring Systems: A Survey

10.36227/techrxiv.15172488.v1 ◽

2021 ◽

Author(s):

Nasreen Anjum ◽

Amna Asif, ◽

Mehreen Kiran ◽

Fouzia Jabeen ◽

Zhaohui Yang ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Herd Immunity ◽

Machine Learning Algorithms ◽

Future Research ◽

Monitoring Systems ◽

Extensive Literature ◽

Awareness Campaigns ◽

Research Areas ◽

Wide Range

<div>To date, the novel Corona virus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent COVID-19 forecasting, diagnosing, and monitoring systems have been proposed to tackle the COVID-19 pandemic. In this article based on our extensive literature review, we provide a taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and monitoring systems. We review the available literature extensively under the proposed taxonomy and have analyzed a significantly wide range of machine learning algorithms and IoTs which can be used in predicting the spread of COVID-19 and in diagnosing and monitoring the infected individuals. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.</div>

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Twitter Speaks: An Analysis of Australian Twitter Users' Topics And Sentiments About COVID-19 Vaccination Using Machine Learning (Preprint)

10.2196/preprints.26953 ◽

2021 ◽

Author(s):

Stephen Wai Hang Kwok ◽

Sai Kumar Vadde ◽

Guanjin Wang

Keyword(s):

Machine Learning ◽

Public Opinion ◽

Infection Control ◽

Herd Immunity ◽

Control Measures ◽

The Novel ◽

The Public ◽

Infection Control Measures ◽

Twitter Users ◽

Novel Coronavirus

BACKGROUND The novel coronavirus disease (COVID-19) is one of the greatest threats to human beings in terms of healthcare, economy and society in recent history. Up to this moment, there are no signs of remission and there is no proven effective cure. The vaccine is the primary biomedical preventive measure against the novel coronavirus. However, the public bias or sentiments, as reflected on social media, may have a significant impact on the progress to achieve the herd immunity needed principally. OBJECTIVE This study aims to use machine learning methods to extract public topics and sentiments on the COVID-19 vaccination on Twitter. METHODS We collected 31,100 English tweets containing COVID-19 vaccine-related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed the tweets by visualizing the high-frequency word clouds and correlations between word tokens. We built the Latent Dirichlet Allocation (LDA) topic model to identify the commonly discussed topics from massive tweets. We also performed sentiment analysis to understand the overall sentiments and emotions on COVID-19 vaccination in Australian society. RESULTS Our analysis identified three LDA topics, including "Attitudes towards COVID-19 and its vaccination", "Advocating infection control measures against COVID-19", and "Misconceptions and complaints about COVID-19 control". In all tweets, nearly two-thirds of the sentiments were positive, and around one-third were negative in the public opinion about the COVID-19 vaccine. Among the eight basic emotions, "trust" and "anticipation" were the two prominent positive emotions, while "fear" was the top negative emotion in the tweets. CONCLUSIONS Our new findings indicate that some Australian Twitter users supported infection control measures against COVID-19 and would refute misinformation. However, the others who underestimated the risks and severity of COVID-19 would probably rationalize their position on the COVID-19 vaccine with certain conspiracy theories. It is also noticed that the level of positive sentiment in the public may not be enough to further a vaccination coverage which would be sufficient to achieve vaccination-induced herd immunity. Governments should explore the public opinion and sentiments towards COVID-19 and its vaccination and implement an effective vaccination promotion scheme besides supporting the development and clinical administration of COVID-19 vaccines.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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