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Correcting pandemic data analysis through environmental fluid dynamics
Author(s) -
Talib Dbouk,
Dimitris Drikakis
Publication year - 2021
Publication title -
physics of fluids
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 180
eISSN - 1089-7666
pISSN - 1070-6631
DOI - 10.1063/5.0055299
Subject(s) - pandemic , physics , covid-19 , tracing , statistical physics , econometrics , data science , meteorology , computer science , medicine , economics , disease , pathology , infectious disease (medical specialty) , operating system
It is well established that the data reported for the daily number of infected cases during the first wave of the COVID-19 pandemic were inaccurate, primarily due to insufficient tracing across the populations. Due to the uncertainty of the first wave data mixed with the second wave data, the general conclusions drawn could be misleading. We present an uncertainty quantification model for the infected cases of the pandemic's first wave based on fluid dynamics simulations of the weather effects. The model is physics-based and can rectify a first wave data's inadequacy from a second wave data's adequacy in a pandemic curve. The proposed approach combines environmental seasonality-driven virus transmission rate with pandemic multiwave phenomena to improve statistical predictions' data accuracy. For illustration purposes, we apply the new physics-based model to New York City data.

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