Correcting pandemic data analysis through environmental fluid dynamics

Abstract

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.

FIG. 1.

Weather-dependent transmission rate (β) in NYC between March 2020 and March 2021 (one-year period). A maximum transmission rate of 0.5 per $\text{day}$ means that the probability is P = 1 (100%) for a susceptible individual to be infected in two days ( $1/0.5$) due to the weather conditions (wind speed, temperature, and relative humidity) in a region. (a) NYC weather history data with the hat symbol denoting daily weather data averaged per month. (b) Weather dependent transmission rate (airborne infection rate index ( $\mathit{AIR}=\beta$)) showing three trends denoted high, medium, and low separated by the respective threshold values 0.4 and 0.3.

FIG. 2.

The first (n = 1) and second (n = 2) waves of the COVID-19 pandemic in NYC between March 2020 and March 2021. (a) The cumulative number of infections (I_c); (b) the cumulative number of deaths (D_c); (c) the weather-dependent transmission rate (β) as obtained by Dbouk and Drikakis. $\beta (1)$ and $\beta (2)$ are the transmission rate during the first and the second wave of the pandemic, respectively. The pandemic data can be downloaded free of charge from.

FIG. 3.

The mortality rate $\Psi$, per unit time, in the infected population of NYC between March 2020 and March 2021 showing the two waves of the pandemic. An inaccurate $\Psi \prime (1)$ is observed different from an accurate $\Psi (2)$. This asymmetry between $\Psi \prime (1)$ and $\Psi (2)$ is a measure of uncertainty quantification of the first wave data's inadequacy concerning the second wave data's adequacy. The daily number of infected cases $I\prime (1)$ reported during the first pandemic wave also needs to be corrected according to $\Psi (1)\approx \Psi (2)$.

FIG. 4.

The daily number of COVID-19-infected persons reported for NYC between 03 March 2020 and 03 March 2021 showing the two waves of the pandemic. Squares-line curve: inaccurate reported first wave data $I\prime (1)$; circles-line curve: corrected first wave data using Eq. (6) for accurate daily number of infected cases I(1). The reported pandemic data can be downloaded free of charge from.