Document Type
Article
Abstract
The large spatial scale, geographical overlap, and similarities in transmission mode between the 1918 H1N1 influenza and 2020 SARS-CoV-2 pandemics together provide a novel opportunity to investigate relationships between transmission of two different diseases in the same location. To this end, we use initial exponential growth rates in a Bayesian hierarchical framework to estimate the basic reproductive number, R0, of both disease outbreaks in a common set of 43 cities in the United States. By leveraging multiple epidemic time series across a large spatial area, we are able to better characterize the variation in R0 across the United States. Additionally, we provide one of the first city-level comparisons of R0 between these two pandemics and explore how demography and outbreak timing are related to R0. Despite similarities in transmission modes and a common set of locations, R0 estimates for COVID-19 were uncorrelated with estimates of pandemic influenza R0 in the same cities. Also, the relationships between R0 and key population or epidemic traits differed between diseases. For example, epidemics that started later tended to be less severe for COVID-19, while influenza epidemics exhibited an opposite pattern. Our results suggest that despite similarities between diseases, epidemics starting in the same location may differ markedly in their initial progression.
Digital Object Identifier (DOI)
Publication Info
Published in PNAS Nexus, Volume 1, Issue 4, 2022, pages pgac194-.
APA Citation
Foster, G., Elderd, B. D., Richards, R. L., & Dallas, T. (2022). Estimating r0 from early exponential growth: Parallels between 1918 influenza and 2020 SARS-COV-2 pandemics. PNAS Nexus, 1(4). https://doi.org/10.1093/pnasnexus/pgac194
Rights
©The Author(s) 2022. Published by Oxford University Press on behalf of National Academy of Sciences. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.