Yuxuan Wu, Sirish Namilae, Ashok Srinivasan, Anuj Mubayi, Mathew Scotch.
Abstract
Transportation systems involve high-density crowds of geographically diverse people with variations in susceptibility; therefore, they play a large role in the spread of infectious diseases like SARS-CoV-2. Dose-response models are widely used to model the relationship between the trigger of a disease and the level of exposure in transmission scenarios. In this study, we quantified and bounded viral exposure-related parameters using empirical data from five transportation-related events of SARS-CoV-2 transmission.
Introduction
There has been significant concern regarding the spread of SARS-CoV-2 on transportation systems. The earliest superspreading events were associated with transportation on cruise ships. Multiple incidents of secondary infection spread have been recorded in various transportation systems including airplanes, cruise ships, trains, and buses. Transportation systems by their very nature involve high-density crowds in relatively small spaces, with limited ventilation.
Methods:
Susceptible individuals with similar dosage exposure levels respond differently because of the biological stochasticity and variations in the vulnerability and receptivity of individuals. The probability of infection for a given exposure to dose (d) was modeled using dose-response models. There have been many applications of dose-response models for the quantitative evaluation of disease transmission from sources, such as sewage, polluted animals, and pathogen-containing aerosols.
Discussion
Despite much literature focusing on SARS-COV-2 transmission, precise quantification of the amount of virions needed to trigger a successful infection is not known. Inherent biological stochasticity combined with differences in intervention usage leads to a wide variation in susceptibility of individuals. Variations in dose levels have resulted in varying outcomes of infection spreading with similar numbers of infective individuals in comparable situations. Understanding and modeling this variability is crucial to effectively model infectious disease spread and for the design of mitigation measures.
Citation: Wu Y, Namilae S, Srinivasan A, Mubayi A, Scotch M (2024) Parametric analysis of SARS-CoV-2 dose-response models in transportation scenarios. PLoS ONE 19(6): e0301996. https://doi.org/10.1371/journal.pone.0301996
Editor: Quan Yuan, Tsinghua University, CHINA
Received: September 29, 2023; Accepted: March 26, 2024; Published: June 12, 2024.
Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: We acknowledge the support of NIH grant R15LM013382. SN acknowledges the support of DOT through UTC-Center for Advanced Transportation Mobility (CATM).
Competing interests: The authors have declared that no competing interests exist.
