Statistical Inference of COVID‑۱۹ Outbreak: Delay Distribution Effect in EQIR Modeling of Epidemic
Publish place: Journal of medical signals and sensors، Vol: 12، Issue: 2
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JMSI-12-2_002
تاریخ نمایه سازی: 28 تیر 1402
Abstract:
Background: The world is experiencing another pandemic called COVID‑۱۹. Several mathematical
models have been proposed to examine the impact of health interventions in controlling pandemic
growth. Method: In this study, we propose a fractional order distributed delay dynamic system,
namely, EQIR model. In order to predict the outbreak, the proposed model incorporates changes in
transmission rate, isolation rate, and identification of infected people through time varying deterministic
and stochastic parameters. Furthermore, proposed stochastic model considers fluctuations in population
behavior and simulates different scenarios of outbreak at the same time. Main novelty of this model
is its ability to incorporate changes in transmission rate, latent periods, and rate of quarantine through
time varying deterministic and stochastic assumptions. This model can exactly follow the disease trend
from its beginning to current situation and predict outbreak future for various situations. Results:
Parameters of this model were identified during fitting process to real data of Iran, USA, and South
Korea. We calculated the reproduction number using a Laplace transform‑based method. Results of
numerical simulation verify the effectiveness and accuracy of proposed deterministic and stochastic
models in current outbreak. Conclusion: Justifying of parameters of the model emphasizes that,
although stricter deterrent interventions can prevent another peak and control the current outbreak, the
consecutive screening schemes of COVID‑۱۹ plays more important role. This means that the more
diagnostic tests performed on people, the faster the disease will be controlled.
Keywords:
Authors
Mahnoosh Tajmirriahi
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences
Zahra Amini
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences
Rahele Kafieh
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences
Hossein Rabbani
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences