ArticleEpidemiologia (Basel, Switzerland)2021
Data-Driven Deep-Learning Algorithm for Asymptomatic COVID-19 Model with Varying Mitigation Measures and Transmission Rate.
Article in Epidemiologia (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 24 citations in OpenAlex.
- Learning Model Parameter Dynamics in a Combination Therapy for Bladder Cancer from Sparse Biological Data.ArXiv · 2025Article
- Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs.ArXiv · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters.Infectious Disease Modelling · 2024Article
- A Physics-Informed Neural Network approach for compartmental epidemiological models.PLoS computational biology · 2024Article
- Data-Driven Deep Learning Neural Networks for Predicting the Number of Individuals Infected by COVID-19 Omicron Variant.Epidemiologia (Basel, Switzerland) · 2023Article
- A Trendline and Predictive Analysis of the First-Wave COVID-19 Infections in Malta.Epidemiologia (Basel, Switzerland) · 2023Article
- A physics-informed neural network to model COVID-19 infection and hospitalization scenarios.Advances in continuous and discrete models · 2022Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epidemiological models with constant parameters may not capture satisfactory infection patterns in the presence of pharmaceutical and non-pharmaceutical mitigation measures during a pandemic, since infectiousness is a function of time. In this paper, an Epidemiology-Informed Neural Network algorithm is introduced to learn the time-varying transmission rate for the COVID-19 pandemic in the presence of various mitigation scenarios. There are asymptomatic infectives, mostly unreported, and the proposed algorithm learns the proportion of the total infective individuals that are asymptomatic infectives. Using cumulative and daily reported cases of the symptomatic infectives, we simulate the impact of non-pharmaceutical mitigation measures such as early detection of infectives, contact tracing, and social distancing on the basic reproduction number. We demonstrate the effectiveness of vaccination on the transmission of COVID-19. The accuracy of the proposed algorithm is demonstrated using error metrics in the data-driven simulation for COVID-19 data of Italy, South Korea, the United Kingdom, and the United States.
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