Literature DB >> 33733158

Forecasting and Evaluating Multiple Interventions for COVID-19 Worldwide.

Zixin Hu1,2, Qiyang Ge3, Shudi Li4, Eric Boerwinkle4, Li Jin1,2, Momiao Xiong4.   

Abstract

As the Covid-19 pandemic surges around the world, questions arise about the number of global cases at the pandemic's peak, the length of the pandemic before receding, and the timing of intervention strategies to significantly stop the spread of Covid-19. We have developed artificial intelligence (AI)-inspired methods for modeling the transmission dynamics of the epidemics and evaluating interventions to curb the spread and impact of COVID-19. The developed methods were applied to the surveillance data of cumulative and new COVID-19 cases and deaths reported by WHO as of March 16th, 2020. Both the timing and the degree of intervention were evaluated. The average error of five-step ahead forecasting was 2.5%. The total peak number of cumulative cases, new cases, and the maximum number of cumulative cases in the world with complete intervention implemented 4 weeks later than the beginning date (March 16th, 2020) reached 75,249,909, 10,086,085, and 255,392,154, respectively. However, the total peak number of cumulative cases, new cases, and the maximum number of cumulative cases in the world with complete intervention after 1 week were reduced to 951,799, 108,853 and 1,530,276, respectively. Duration time of the COVID-19 spread was reduced from 356 days to 232 days between later and earlier interventions. We observed that delaying intervention for 1 month caused the maximum number of cumulative cases reduce by -166.89 times that of earlier complete intervention, and the number of deaths increased from 53,560 to 8,938,725. Earlier and complete intervention is necessary to stem the tide of COVID-19 infection.
Copyright © 2020 Hu, Ge, Li, Boerwinkle, Jin and Xiong.

Entities:  

Keywords:  COVID-19; artificial intelligence; auto-encoder; forecasting; time series; transmission dynamics

Year:  2020        PMID: 33733158      PMCID: PMC7861333          DOI: 10.3389/frai.2020.00041

Source DB:  PubMed          Journal:  Front Artif Intell        ISSN: 2624-8212


  9 in total

1.  Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model.

Authors:  Sebastian Funk; Anton Camacho; Adam J Kucharski; Rosalind M Eggo; W John Edmunds
Journal:  Epidemics       Date:  2016-12-16       Impact factor: 4.396

2.  Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak.

Authors:  Shi Zhao; Salihu S Musa; Qianying Lin; Jinjun Ran; Guangpu Yang; Weiming Wang; Yijun Lou; Lin Yang; Daozhou Gao; Daihai He; Maggie H Wang
Journal:  J Clin Med       Date:  2020-02-01       Impact factor: 4.241

3.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia.

Authors:  Qun Li; Xuhua Guan; Peng Wu; Xiaoye Wang; Lei Zhou; Yeqing Tong; Ruiqi Ren; Kathy S M Leung; Eric H Y Lau; Jessica Y Wong; Xuesen Xing; Nijuan Xiang; Yang Wu; Chao Li; Qi Chen; Dan Li; Tian Liu; Jing Zhao; Man Liu; Wenxiao Tu; Chuding Chen; Lianmei Jin; Rui Yang; Qi Wang; Suhua Zhou; Rui Wang; Hui Liu; Yinbo Luo; Yuan Liu; Ge Shao; Huan Li; Zhongfa Tao; Yang Yang; Zhiqiang Deng; Boxi Liu; Zhitao Ma; Yanping Zhang; Guoqing Shi; Tommy T Y Lam; Joseph T Wu; George F Gao; Benjamin J Cowling; Bo Yang; Gabriel M Leung; Zijian Feng
Journal:  N Engl J Med       Date:  2020-01-29       Impact factor: 176.079

4.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study.

Authors:  Joseph T Wu; Kathy Leung; Gabriel M Leung
Journal:  Lancet       Date:  2020-01-31       Impact factor: 79.321

5.  Reporting, Epidemic Growth, and Reproduction Numbers for the 2019 Novel Coronavirus (2019-nCoV) Epidemic.

Authors:  Ashleigh R Tuite; David N Fisman
Journal:  Ann Intern Med       Date:  2020-02-05       Impact factor: 25.391

6.  Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions.

Authors:  Zifeng Yang; Zhiqi Zeng; Ke Wang; Sook-San Wong; Wenhua Liang; Mark Zanin; Peng Liu; Xudong Cao; Zhongqiang Gao; Zhitong Mai; Jingyi Liang; Xiaoqing Liu; Shiyue Li; Yimin Li; Feng Ye; Weijie Guan; Yifan Yang; Fei Li; Shengmei Luo; Yuqi Xie; Bin Liu; Zhoulang Wang; Shaobo Zhang; Yaonan Wang; Nanshan Zhong; Jianxing He
Journal:  J Thorac Dis       Date:  2020-03       Impact factor: 3.005

7.  Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2).

Authors:  Ruiyun Li; Sen Pei; Bin Chen; Yimeng Song; Tao Zhang; Wan Yang; Jeffrey Shaman
Journal:  Science       Date:  2020-03-16       Impact factor: 47.728

8.  An open challenge to advance probabilistic forecasting for dengue epidemics.

Authors:  Michael A Johansson; Karyn M Apfeldorf; Scott Dobson; Jason Devita; Anna L Buczak; Benjamin Baugher; Linda J Moniz; Thomas Bagley; Steven M Babin; Erhan Guven; Teresa K Yamana; Jeffrey Shaman; Terry Moschou; Nick Lothian; Aaron Lane; Grant Osborne; Gao Jiang; Logan C Brooks; David C Farrow; Sangwon Hyun; Ryan J Tibshirani; Roni Rosenfeld; Justin Lessler; Nicholas G Reich; Derek A T Cummings; Stephen A Lauer; Sean M Moore; Hannah E Clapham; Rachel Lowe; Trevor C Bailey; Markel García-Díez; Marilia Sá Carvalho; Xavier Rodó; Tridip Sardar; Richard Paul; Evan L Ray; Krzysztof Sakrejda; Alexandria C Brown; Xi Meng; Osonde Osoba; Raffaele Vardavas; David Manheim; Melinda Moore; Dhananjai M Rao; Travis C Porco; Sarah Ackley; Fengchen Liu; Lee Worden; Matteo Convertino; Yang Liu; Abraham Reddy; Eloy Ortiz; Jorge Rivero; Humberto Brito; Alicia Juarrero; Leah R Johnson; Robert B Gramacy; Jeremy M Cohen; Erin A Mordecai; Courtney C Murdock; Jason R Rohr; Sadie J Ryan; Anna M Stewart-Ibarra; Daniel P Weikel; Antarpreet Jutla; Rakibul Khan; Marissa Poultney; Rita R Colwell; Brenda Rivera-García; Christopher M Barker; Jesse E Bell; Matthew Biggerstaff; David Swerdlow; Luis Mier-Y-Teran-Romero; Brett M Forshey; Juli Trtanj; Jason Asher; Matt Clay; Harold S Margolis; Andrew M Hebbeler; Dylan George; Jean-Paul Chretien
Journal:  Proc Natl Acad Sci U S A       Date:  2019-11-11       Impact factor: 11.205

9.  Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts.

Authors:  Joel Hellewell; Sam Abbott; Amy Gimma; Nikos I Bosse; Christopher I Jarvis; Timothy W Russell; James D Munday; Adam J Kucharski; W John Edmunds; Sebastian Funk; Rosalind M Eggo
Journal:  Lancet Glob Health       Date:  2020-02-28       Impact factor: 26.763

  9 in total
  21 in total

1.  Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts.

Authors:  Quoc-Viet Pham; Dinh C Nguyen; Thien Huynh-The; Won-Joo Hwang; Pubudu N Pathirana
Journal:  IEEE Access       Date:  2020-07-15       Impact factor: 3.367

2.  A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic.

Authors:  Muhammad Nazrul Islam; Toki Tahmid Inan; Suzzana Rafi; Syeda Sabrina Akter; Iqbal H Sarker; A K M Najmul Islam
Journal:  IEEE Trans Artif Intell       Date:  2021-03-01

3.  Toward Combatting COVID-19: A Risk Assessment System.

Authors:  Qianlong Wang; Yifan Guo; Tianxi Ji; Xufei Wang; Bingfang Hu; Pan Li
Journal:  IEEE Internet Things J       Date:  2021-03-31       Impact factor: 10.238

4.  Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic.

Authors:  Choujun Zhan; Yufan Zheng; Haijun Zhang; Quansi Wen
Journal:  IEEE Internet Things J       Date:  2021-03-17       Impact factor: 10.238

Review 5.  Review on the COVID-19 pandemic prevention and control system based on AI.

Authors:  Junfei Yi; Hui Zhang; Jianxu Mao; Yurong Chen; Hang Zhong; Yaonan Wang
Journal:  Eng Appl Artif Intell       Date:  2022-07-11       Impact factor: 7.802

6.  The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges.

Authors:  Amir Ahmad; Sunita Garhwal; Santosh Kumar Ray; Gagan Kumar; Sharaf Jameel Malebary; Omar Mohammed Barukab
Journal:  Arch Comput Methods Eng       Date:  2020-08-04       Impact factor: 7.302

7.  Dynamics identification and forecasting of COVID-19 by switching Kalman filters.

Authors:  Xiaoshu Zeng; Roger Ghanem
Journal:  Comput Mech       Date:  2020-08-29       Impact factor: 4.014

Review 8.  The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype.

Authors:  Musa Abdulkareem; Steffen E Petersen
Journal:  Front Artif Intell       Date:  2021-05-14

9.  Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves.

Authors:  László Róbert Kolozsvári; Tamás Bérczes; András Hajdu; Rudolf Gesztelyi; Attila Tiba; Imre Varga; Ala'a B Al-Tammemi; Gergő József Szőllősi; Szilvia Harsányi; Szabolcs Garbóczy; Judit Zsuga
Journal:  Inform Med Unlocked       Date:  2021-08-08

10.  Insights Into Co-Morbidity and Other Risk Factors Related to COVID-19 Within Ontario, Canada.

Authors:  Brett Snider; Bhumi Patel; Edward McBean
Journal:  Front Artif Intell       Date:  2021-06-10
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