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Results for “superspreading”

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PubMed2026

Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.

Understanding the geographic spread of emerging respiratory viruses is critical for pandemic preparedness, yet the early spatiotemporal dynamics of the 2009 H1N1 pandemic influenza and severe acute respiratory syndrome coronavirus 2 in the United States remain unclear. While mobility and genomic data have revealed important aspects of pandemic spatial spread, several key questions remain: Did the two pandemics follow similar spatial transmission routes? How rapidly did they spread across the United States? What role did stochastic processes play in early spatial transmission? To address these questions, we integrated high-resolution disease data with a robust, data-efficient inference framework combining air travel, commuting flows, and pathogen superspreading potentials to reconstruct their spatial spread across US metropolitan areas. The two pandemics exhibited distinct transmission pathways across locations; however, both pandemics established local circulation in most metropolitan areas within weeks, driven by several shared transmission hubs. Early spatial spread was more strongly associated with air travel than with commuting, though stochastic dynamics introduced substantial uncertainty in transmission routes, creating challenges for timely detection and control. Simulations indicate that broad wastewater surveillance coverage beyond top transmission hubs coupled with effective infection control may slow initial spatial expansion. Our findings highlight the rapid, stochastic spread of pandemic respiratory pathogens and the difficulties of early outbreak containment.

Humans↗

PubMed2025

The potential impacts of human genetics on virus emergence.

Human monogenic traits can confer resistance to viral infection in exposed individuals or predisposition to severe disease in infected individuals. Enhanced susceptibility can be driven directly by mutations in genes essential for control of the virus or indirectly via the production of autoantibodies against components of host defense. While the impact of viruses on individuals carrying these genotypes permitted their identification and has been amply studied, little is known about the impact of these human genotypes on the natural history of viruses, including not only persisting but also emerging viruses. We envisage several scenarios, including the possibility that genetically susceptible individuals serve as patient zeros, superspreaders, or mutation incubators, or that genetically resistant individuals even permit the selection of new viral mutants. Viruses are continually shared between individuals and even host species, where they can benefit from adaption to new environments. Current human viruses, as well as novel viruses from animal reservoirs, will continue to threaten the human population. Improvements in the scale of human genomic sequencing and analysis will permit testing hypotheses about the impact of human genetics on the origin and trajectory of viral infections, including future pandemics, which may ultimately help to prevent or curtail impending outbreaks.

Humans↗

PubMed2025

Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2.

The wealth of genomic data that was generated during the COVID-19 pandemic provides an exceptional opportunity to obtain information on the transmission of SARS-CoV-2. Specifically, there is great interest to better understand how the effective reproduction number [Formula: see text] and the overdispersion of secondary cases, which can be quantified by the negative binomial dispersion parameter k, changed over time and across regions and viral variants. The aim of our study was to develop a Bayesian framework to infer [Formula: see text] and k from viral sequence data. First, we developed a mathematical model for the distribution of the size of identical sequence clusters, in which we integrated viral transmission, the mutation rate of the virus, and incomplete case-detection. Second, we implemented this model within a Bayesian inference framework, allowing the estimation of [Formula: see text] and k from genomic data only. We validated this model in a simulation study. Third, we identified clusters of identical sequences in all SARS-CoV-2 sequences in 2021 from Switzerland, Denmark, and Germany that were available on GISAID. We obtained monthly estimates of the posterior distribution of [Formula: see text] and k, with the resulting [Formula: see text] estimates slightly lower than estimates obtained by other methods, and k comparable with previous results. We found comparatively higher estimates of k in Denmark which suggests less opportunities for superspreading and more controlled transmission compared to the other countries in 2021. Our model included an estimation of the case detection and sampling probability, but the estimates obtained had large uncertainty, reflecting the difficulty of estimating these parameters simultaneously. Our study presents a novel method to infer information on the transmission of infectious diseases and its heterogeneity using genomic data. With increasing availability of sequences of pathogens in the future, we expect that our method has the potential to provide new insights into the transmission and the overdispersion in secondary cases of other pathogens.

COVID-19↗