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.
Highlights
This study aims to answer a fundamental question: given epidemiological data, how to develop an appropriate model and identify which parameters we can accurately infer that would, in turn, allow us to correctly project the states of interest such as daily cases, hospitalizations, and deaths
The transmission dynamics of pandemics are often modeled by ordinary differential equations, which normally involve many undetermined parameters needed to be estimated from data
We provide a general framework, which includes identifiability analysis, sensitivity analysis, model robustness analysis, and uncertainty quantification, to examine the relationship between the model dynamics, data, and parameters
Summary
This study aims to answer a fundamental question: given epidemiological data, how to develop an appropriate model and identify which parameters we can accurately infer that would, in turn, allow us to correctly project the states of interest such as daily cases, hospitalizations, and deaths. Mathematical modeling of COVID-19 using compartmental models described by ordinary differential equations (ODEs) such as susceptible–infectious–recovered (SIR) [1,2,3], modified SIR [1, 4,5,6], susceptible–exposed–infectious–recovered (SEIR) [7,8,9] and modified SEIR models [10, 11] has been used extensively in an attempt to capture the virus’ spread These types of lumped mechanistic models, unlike data-driven models, can explore future outcomes of the pandemic and evaluate the effects of various interventions. For a long-lasting pandemic, the model parameters change with time; the parameter identification problem becomes nontrivial given the fact that typically a limited amount of relevant data is available
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