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Incorporating Mobility Information into an Epidemiological Model to Model Infected Cases at the Onset of COVID-19 Pandemic
Abstract
In the wake of the rampage of the COVID-19 pandemic, its ripple effects have changed every aspect of our lives. Restricting mobility becomes one of the primary countermeasures adopted by governments to mitigate the spreading of this airborne COVID-19 virus. Considering the undeniable benefits of restricting mobility, restricting mobility is still not a viable option for many countries in the world because of its heavy toll on economy and people’s mental health. This study proposes two models – Mobility SEIR (Susceptible – Exposed – Infected – Removed) and DLSTM-MSEIR (Deep Long Short-Term Mobility SEIR) based on the classical epidemiological models – SEIR to model the COVID-19 cases at the onset of the pandemic and reveal the importance of considering mobility information in the traditional and classical epidemiological model-SEIR. The modeling results show the two proposed models could outperform the classic SEIR model in modeling and predicting the COVID-19 cases at the onset of the pandemic and bring meaningful insights into reducing the spread of the pandemic. Moreover, the hybrid model DLSTM-MSEIR has a better performance on modeling COVID-19 cases than the Mobility SEIR model when the change point indications are less clear at the period of study.
Citation
Kong, Xiaoqiang (2022). Incorporating Mobility Information into an Epidemiological Model to Model Infected Cases at the Onset of COVID-19 Pandemic. Doctoral dissertation, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /197965.