This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2394) Machine Learning (1401) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484)

Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2020.05.31.20118414v1, first published .
Dynamics and Development of the COVID-19 Epidemic in the United States: A Compartmental Model Enhanced With Deep Learning Techniques

Dynamics and Development of the COVID-19 Epidemic in the United States: A Compartmental Model Enhanced With Deep Learning Techniques

Dynamics and Development of the COVID-19 Epidemic in the United States: A Compartmental Model Enhanced With Deep Learning Techniques

Authors of this article:

Qi Deng1, 2, 3 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

Journals

  1. Mohimont L, Chemchem A, Alin F, Krajecki M, Steffenel L. Convolutional neural networks and temporal CNNs for COVID-19 forecasting in France. Applied Intelligence 2021;51(12):8784 View
  2. Caro J, Möller J, Santhirapala V, Gill H, Johnston J, El-Boghdadly K, Santhirapala R, Kelly P, McGuire A. Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories. Value in Health 2021;24(11):1570 View
  3. Khan J, Ullah F, Lee S. Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model. Chaos, Solitons & Fractals 2022;165:112818 View
  4. Guan J, Zhao Y, Wei Y, Shen S, You D, Zhang R, Lange T, Chen F. Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges. Medical Review 2022;2(1):89 View
  5. Öz Y. Analytical investigation of compartmental models and measure for reactions of governments. The European Physical Journal E 2022;45(8) View
  6. Balarezo J, Wang S, Gomez Chavez K, Al-Hourani A, Kandeepan S. Dynamics of Botnet Propagation in Software Defined Networks Using Epidemic Models. IEEE Access 2021;9:119406 View
  7. Zhang W, Liu S, Osgood N, Zhu H, Qian Y, Jia P. Using simulation modelling and systems science to help contain COVID‐19: A systematic review. Systems Research and Behavioral Science 2023;40(1):207 View
  8. Gatto A, Aloisi V, Accarino G, Immorlano F, Chiarelli M, Aloisio G. An Artificial Neural Network-Based Approach for Predicting the COVID-19 Daily Effective Reproduction Number Rt in Italy. AI 2022;3(1):146 View
  9. Chumachenko T, Chumachenko D. SIMULATION OF EPIDEMIC PROCESSES: A REVIEW OF MODERN METHODS, MODELS AND APPROACHES. Inter Collegas 2022;9(1):66 View
  10. Boaventura V, Grave M, Cerqueira-Silva T, Carreiro R, Pinheiro A, Coutinho A, Barral Netto M. Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil. JMIR Public Health and Surveillance 2023;9:e40036 View
  11. Deng Q. Modeling the Omicron Dynamics and Development in China with a Deep Learning Enhanced Compartmental Model. SSRN Electronic Journal 2022 View
  12. Yu L, Vijay M, Sunil J, Vincy V, Govindan V, Khan M, Ali S, Tamam N, Abdullaeva B. Hybrid deep learning model based smart IOT based monitoring system for Covid-19. Heliyon 2023;9(11):e21150 View
  13. Yin Z, Xiao H, McDonald S, Brusic V, Qiu T. Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID‐19. Journal of Medical Virology 2023;95(12) View