scholarly journals Sentiment Classifier and Analysis for Epidemic Prediction

2018 ◽  
Author(s):  
Nimai Chand Das Adhikari ◽  
Vamshi Kumar Kurva ◽  
Suhas S ◽  
Jitendra Kumar Kushwaha ◽  
Ashish Kumar Nayak ◽  
...  
Keyword(s):  
Epidemic Prediction

Analysis and Management of COVID-19 Using Computational Intelligence Technologies

2021 ◽  
Vol 11 (6) ◽  
pp. 1642-1648
Author(s):  
Xiangmin Meng ◽  
Jie Zhang
Keyword(s):  
Computational Intelligence ◽  
Fuzzy System ◽  
World Economy ◽  
Support Vector ◽  
Medical Institutions ◽  
The World ◽  
Treatment Measures ◽  
Epidemic Prediction ◽  
Learning Machine ◽  
Intelligent Model

After the outbreak of COVID-19, the world economy and people’s health have been greatly challenged. What is the law of the spread of COVID-19, when will it reach its peak, and when will it be effectively controlled? These have all become major issues of common concern throughout China and the world. Based on this background, this article introduces a variety of classic computational intelligence technologies to predict the spread of COVID-19. Computational intelligence technology mainly includes support vector machine regression (SVR), Takagi-Sugeuo-Kang fuzzy system (TSK-FS), and extreme learning machine (ELM). Compare the predictions of the infection rate, mortality rate, and recovery rate of the COVID-19 epidemic in China by each intelligent model in 5 and 10 days, the effectiveness of the computational intelligence algorithm used in epidemic prediction is verified. Based on the prediction results, the patients are classified and managed. According to the time of illness, physical fitness and other factors, patients are divided into three categories: Severe, moderate, and mild. In the case of serious shortage of medical equipment and medical staff, auxiliary medical institutions take corresponding treatment measures for different patients.

scholarly journals Advancing Epidemic Prediction and Forecasting: A New US Government Initiative

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Jean-Paul Chretien ◽  
David Swedlow ◽  
Irene Eckstrand ◽  
Dylan George ◽  
Michael Johansson ◽  
...  
Keyword(s):  
Working Group ◽  
Science And Technology ◽  
Group Leaders ◽  
National Science ◽  
Us Government ◽  
Epidemic Prediction ◽  
Prediction And Forecasting ◽  
Government Initiative

The National Science and Technology Council, within the Executive Office of the President, established the Pandemic Prediction and Forecasting Science and Technology Working Group in 2013 to advance US Government epidemic prediction and forecasting capabilities. Working Group leaders will provide an overview of activities, and seek feedback on the Working Group direction from the ISDS community.

A Human Mobility Data Driven Hybrid GNN+RNN Based Model For Epidemic Prediction

2021 ◽  
Author(s):  
Shohaib Mahmud ◽  
Haiying Shen ◽  
Ying Natasha Zhang Foutz ◽  
Joshua Anton
Keyword(s):  
Human Mobility ◽  
Data Driven ◽  
Mobility Data ◽  
Epidemic Prediction

scholarly journals Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey

2020 ◽  
Author(s):  
Ying Mao ◽  
Susiyan Jiang ◽  
Daniel Nametz
Keyword(s):  
Clinical Diagnosis ◽  
Early Stage ◽  
Disease Outbreaks ◽  
Analytical Models ◽  
Contact Tracing ◽  
Policy Effectiveness ◽  
Cross Model ◽  
Related Data ◽  
Comprehensive Survey ◽  
Epidemic Prediction

The widely spread CoronaVirus Disease (COVID)- 19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

scholarly journals Regulatory Sandboxes: A Cure for mHealth Pilotitis? (Preprint)

2020 ◽  
Author(s):  
Abhishek Bhatia ◽  
Rahul Matthan ◽  
Tarun Khanna ◽  
Satchit Balsari
Keyword(s):  
Digital Health ◽  
Contact Tracing ◽  
User Needs ◽  
End User ◽  
Health Seeking ◽  
Testing Environment ◽  
The Past ◽  
Seeking Behavior ◽  
The Law ◽  
Epidemic Prediction

UNSTRUCTURED Mobile health (mHealth) and related digital health interventions in the past decade have not always scaled globally as anticipated earlier despite large investments by governments and philanthropic foundations. The implementation of digital health tools has suffered from 2 limitations: (1) the interventions commonly ignore the “law of amplification” that states that technology is most likely to succeed when it seeks to augment and not alter human behavior; and (2) end-user needs and clinical gaps are often poorly understood while designing solutions, contributing to a substantial decrease in usage, referred to as the “law of attrition” in eHealth. The COVID-19 pandemic has addressed the first of the 2 problems—technology solutions, such as telemedicine, that were struggling to find traction are now closely aligned with health-seeking behavior. The second problem (poorly designed solutions) persists, as demonstrated by a plethora of poorly designed epidemic prediction tools and digital contact-tracing apps, which were deployed at scale, around the world, with little validation. The pandemic has accelerated the Indian state’s desire to build the nation’s digital health ecosystem. We call for the inclusion of regulatory sandboxes, as successfully done in the fintech sector, to provide a real-world testing environment for mHealth solutions before deploying them at scale.

scholarly journals Japan's Covid mitigation strategy and its epidemic prediction

2021 ◽  
Author(s):  
Yasuharu Tokuda ◽  
Toshikazu Kuniya
Keyword(s):  
Curve Fitting ◽  
Urban Areas ◽  
Compartmental Model ◽  
Mitigation Strategy ◽  
State Of Emergency ◽  
Epidemic Curve ◽  
Epidemic Prediction ◽  
One Year ◽  
Reported Data ◽  
Original Virus

The COVID-19 epidemic curve in Japan was constructed based on daily reported data from January 14, 2020 until April 20, 2021. A SEIR compartmental model was used for the curve fitting by updating the estimation per wave. In the current vaccination pace of 1/1000, restrictions (state of emergency in Japan) would be repeated 4 times until the end of next March. In the case of 1/500, another round of restriction would be required in the summer 2021, after which the infection would be mitigated. In the case of 1/250, there would be no need for restriction after the current spring restriction. The scenario of completing the vaccination of 110 million people by the end of March 2020 corresponds to the case of 1/250 in this curve. When considering the likely spread of variant with greater infectiousness (here we assume 1.3 times greater than the original virus), 1/500 pace of vaccination would not be enough to contain it and need several series of restrictions. There are currently several variants of concern that are already spreading in urban areas in this country. In the new stage of the replacement of variants, if the vaccination pace could not be quadrupled from the current pace, Japan could not become a zero covid (zero corona) country at least one year.

scholarly journals Regulatory Sandboxes: A Cure for mHealth Pilotitis?

10.2196/21276 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e21276
Author(s):  
Abhishek Bhatia ◽  
Rahul Matthan ◽  
Tarun Khanna ◽  
Satchit Balsari
Keyword(s):  
Digital Health ◽  
Contact Tracing ◽  
User Needs ◽  
End User ◽  
Health Seeking ◽  
Testing Environment ◽  
The Past ◽  
Seeking Behavior ◽  
The Law ◽  
Epidemic Prediction

Mobile health (mHealth) and related digital health interventions in the past decade have not always scaled globally as anticipated earlier despite large investments by governments and philanthropic foundations. The implementation of digital health tools has suffered from 2 limitations: (1) the interventions commonly ignore the “law of amplification” that states that technology is most likely to succeed when it seeks to augment and not alter human behavior; and (2) end-user needs and clinical gaps are often poorly understood while designing solutions, contributing to a substantial decrease in usage, referred to as the “law of attrition” in eHealth. The COVID-19 pandemic has addressed the first of the 2 problems—technology solutions, such as telemedicine, that were struggling to find traction are now closely aligned with health-seeking behavior. The second problem (poorly designed solutions) persists, as demonstrated by a plethora of poorly designed epidemic prediction tools and digital contact-tracing apps, which were deployed at scale, around the world, with little validation. The pandemic has accelerated the Indian state’s desire to build the nation’s digital health ecosystem. We call for the inclusion of regulatory sandboxes, as successfully done in the fintech sector, to provide a real-world testing environment for mHealth solutions before deploying them at scale.

Sign in / Sign up

Export Citation Format

Share Document