SEIRDQ: A COVID-19 case projection modeling framework using ANN to model quarantine
Chandra, H.; Meng, X.; Margaryan, A.
Show abstract
We propose and implement a novel approach to model the evolution of COVID-19 pandemic and predict the daily COVID-19 cases (infected, recovered and dead). Our model builds on the classical SEIR-based framework by adding additional compartments to capture recovered, dead and quarantined cases. Quarantine impacts are modeled using an Artificial Neural Network (ANN), leveraging alternative data sources such as the Google mobility reports. Since our model captures the impact of lockdown policies through the quarantine functions we designed, it is able to model and predict future waves of COVID-19 cases. We also benchmark out-of-sample predictions from our model versus those from other popular COVID-19 case projection models.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deep reinforcement learning framework for controlling infectious disease outbreaks in the context of multi-jurisdictions 97%
- A Machine Learning Approach to Differentiate Between COVID-19 and Influenza Infection Using Synthetic Infection and Immune Response Data 96%
- Identifiability investigation of within-host models of acute virus infection 96%
Similar papers in this journal
- An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City 98%
- Estimation of heterogeneous instantaneous reproduction numbers with application to characterize SARS-CoV-2 transmission in Massachusetts counties 97%
- An ensemble n -sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA 97%
Similar papers in this journal
- Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy 97%
- Estimate of the rate of unreported COVID-19 cases during the first outbreak in Rio de Janeiro 96%
- Impact of School Reopening on Pandemic Spread: A Case Study using an Agent-Based Model for COVID-19 96%
Similar papers in this journal
- Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study 97%
- Estimation of COVID-19 recovery and decease periods in Canada using machine learning algorithms 97%
- Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.