ArticlePLoS computational biology2021
An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Data-driven modelling of IRCU patient flow during the COVID-19 pandemic.Computational and structural biotechnology journal · 2025Article
- Optimal time-dependent SUC model for COVID-19 pandemic in India.BMC infectious diseases · 2024Article
- Emergence of the reproduction matrix in epidemic forecasting.Journal of the Royal Society, Interface · 2024Article
- A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile.Journal of biological dynamics · 2023Article
- Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA.Mathematical biosciences and engineering : MBE · 2023Article
- Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation.Nonlinear dynamics · 2023Article
- Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela.Spatial and spatio-temporal epidemiology · 2022Article
- Modeling nonlocal behavior in epidemics via a reaction-diffusion system incorporating population movement along a network.Computer methods in applied mechanics and engineering · 2022Article
- An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.PLoS computational biology · 2022Article
- Data-driven causal model discovery and personalized prediction in Alzheimer's disease.NPJ digital medicine · 2022Article
- A vaccination model for COVID-19 in Gauteng, South Africa.Infectious Disease Modelling · 2022Article
- A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.Computers in biology and medicine · 2022Article
- An ensemblemedRxiv : the preprint server for health sciences · 2022Article
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5 authors.
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Abstract
Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and projection with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible-exposed-infectious-recovered (SEIR) model, including new compartments and model vaccination in order to project the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately project the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC's government's website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.
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