ArticleIEEE access : practical innovations, open solutions2021
A Generalized Mechanistic Model for Assessing and Forecasting the Spread of the COVID-19 Pandemic.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- An analytical approach to evaluate the impact of age demographics in a pandemic.Stochastic environmental research and risk assessment : research journal · 2023Article
- Bounding pandemic spread by heat spread.Journal of engineering mathematics · 2023Article
- Effect of flight connectivity on the introduction and evolution of the COVID-19 outbreak in Canadian provinces and territories.Journal of travel medicine · 2022Article
- Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model.International journal of environmental research and public health · 2022Article
- Parameter estimation of the COVID-19 transmission model using an improved quantum-behaved particle swarm optimization algorithm.Digital signal processing · 2022Article
- Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019.ISA transactions · 2022Article
- An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19.Neural processing letters · 2022Article
- Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy.Infectious Disease Modelling · 2022Article
- Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States.IEEE open journal of engineering in medicine and biology · 2021Article
- Optimization in the Context of COVID-19 Prediction and Control: A Literature Review.IEEE access : practical innovations, open solutions · 2021Article
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5 authors.
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Abstract
Since early 2020, the world has been afflicted with an unprecedented global pandemic. The SARS-CoV-19 (COVID-19) has levied massive economic and public health costs across many countries. Due to its virulence, the pathogen is rapidly propagating throughout the world in such a way that makes it incredibly challenging for officials to contain its spread. Therefore, there is a pressing need for national and local authorities to have tools that aid in their ability to assess and extrapolate the future trends of the spread of COVID-19, so they may make rational and informed decisions that minimize public harm. Mechanistic models are prominent mathematical tools that are used to characterize epidemics. In this paper, we propose a generalized mechanistic model with eight states characterizing the COVID-19 pandemic evolution from a susceptible state to discharged states while passing by quarantined and hospitalized states. The parameters of the model are determined by solving a fitting optimization problem with three observed inputs: the number of infected, deceased, and reported cases. The model's objective function is weighted over the training days so as to guide the fitting algorithm towards the latest pandemic period and lead to more accurate trend predictions for a stronger forecast. We solve the fitting problem with the Levenberg-Marquardt algorithm; we compare the performance of the model generated from this algorithm to the one of another state-of-the-art fitting algorithm as well as to the one of another compartmental model widely used in literature. We test the model on the COVID-19 data from four highly afflicted countries. The fitting algorithm has been validated graphically and through numerical metrics, and results show significantly accurate results for most of the countries. Once the model's parameters are estimated, forecasting results are derived and uncertainty regions of the expected scenarios are provided.
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