Correcting pandemic data analysis through environmental fluid dynamics

Talib Dbouk, Dimitris Drikakis

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish
    Article number067116
    JournalPhysics of Fluids
    Issue number6
    Publication statusPublished - 1 Jun 2021


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