arXiv subjects

Freya M. Shearer

Publications and source records attributed to Freya M. Shearer.

5 recordsLinked to original sources

arXiv2026

On-farm management strategies for reducing H5N1 transmission in dairy cattle

Introductions of H5N1 clade 2.3.4.4b into dairy cattle have resulted in outbreaks on dairy farms across the United States since early-2024. Outbreaks have significant consequences for animal health, result in economic losses for the dairy industry, and pose a threat to human health. Though the relative contributions of different on-farm transmission pathways remain a key uncertainty, a major route is considered to be through repeated contamination of milking stalls (i.e. the equipment and area where an individual cow is milked) due to the milking of infected animals. Here we develop mathematical models of H5N1 transmission dynamics on dairy farms, considering multiple possible transmission pathways, and identify factors that contribute to outbreak risk and on-farm interventions for mitigating risk. In particular, we demonstrate that dividing cattle into 'milking cohorts', with cohorts kept in separate pens or paddocks and milked in the same order every day, would be highly effective at mitigating outbreaks irrespective of the dominant transmission pathway. Cohorting cattle is most effective when implemented pre-emptively (i.e. before an outbreak) and when newly introduced cattle are kept in the final milking cohort. Additionally, we demonstrate that frequent bulk milk sample testing (e.g. weekly) would enable the rapid detection of outbreaks and implementation of reactive interventions (or scaling up of existing interventions). Our findings can support the development of management guidelines for effectively responding to H5N1 outbreaks in dairy cattle.

q-bio.PE↗

arXiv2026

Incorporating vaccine effects into epidemiological models: common pitfalls and solutions

Incorporating vaccination into mathematical models appears deceptively simple: models integrate vaccine-derived protections, such as reduced susceptibility to infection, using parameters informed by empirical estimates of vaccine efficacy or effectiveness (VE). In practice, however, empirical VE estimates often do not correspond directly to the parameters of epidemiological models. Here, we extend previous work to demonstrate that in order to accurately parameterize a model, one must consider both a vaccine's mechanism of action and the statistic used to infer VE from empirical data. When a vaccine confers leaky protection -- that is, vaccination partially rather than completely reduces individual infection risk -- we show that common empirical VE estimation methods do not provide directly applicable values for model parameters. Naive (i.e. direct) incorporation of these VE estimates into models results in an underestimate of population-level vaccine impact. To make progress when these estimates are the only available sources for VE, we introduce a parameterization approach which more accurately aligns the modeled effect of vaccination with empirical estimates. Under this adjusted parameterization approach, models predict fewer total infections and lower herd immunity thresholds for leaky vaccines than would be predicted under current parameterization practices. Our parameterization guidelines and adjustment approach can be used to improve accuracy in models that are used in vaccine decision making and public health planning.

q-bio.PE↗

arXiv2023

Key challenges for the surveillance of respiratory viruses: transitioning out of the acute phase of the SARS-CoV-2 pandemic

To support the ongoing management of viral respiratory diseases, many countries are moving towards an integrated model of surveillance for SARS-CoV-2, influenza, and other respiratory pathogens. While many surveillance approaches catalysed by the COVID-19 pandemic provide novel epidemiological insight, continuing them as implemented during the pandemic is unlikely to be feasible for non-emergency surveillance, and many have already been scaled back. Furthermore, given anticipated co-circulation of SARS-CoV-2 and influenza, surveillance activities in place prior to the pandemic require review and adjustment to ensure their ongoing value for public health. In this perspective, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies, their contribution to epidemiological assessment, forecasting, and public health decision making.

physics.soc-ph↗

arXiv2021

From climate change to pandemics: decision science can help scientists have impact

Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious diseases, climate change and the biodiversity crisis. For any scientist, whether they work primarily in fundamental knowledge generation or in the applied sciences, it is important to understand how science fits into a decision-making framework. Decision science is a field that aims to pinpoint evidence-based management strategies. It provides a framework for scientists to directly impact decisions or to understand how their work will fit into a decision process. Decision science is more than undertaking targeted and relevant scientific research or providing tools to assist policy makers; it is an approach to problem formulation, bringing together mathematical modelling, stakeholder values and logistical constraints to support decision making. In this paper we describe decision science, its use in different contexts, and highlight current gaps in methodology and application. The COVID-19 pandemic has thrust mathematical models into the public spotlight, but it is one of innumerable examples in which modelling informs decision making. Other examples include models of storm systems (eg. cyclones, hurricanes) and climate change. Although the decision timescale in these examples differs enormously (from hours to decades), the underlying decision science approach is common across all problems. Bridging communication gaps between different groups is one of the greatest challenges for scientists. However, by better understanding and engaging with the decision-making processes, scientists will have greater impact and make stronger contributions to important societal problems.

cs.CY↗

arXiv2021

COVID-19 in low-tolerance border quarantine systems: impact of the Delta variant of SARS-CoV-2

In controlling transmission of COVID-19, the effectiveness of border quarantine strategies is a key concern for jurisdictions in which the local prevalence of disease and immunity is low. In settings like this such as China, Australia, and New Zealand, rare outbreak events can lead to escalating epidemics and trigger the imposition of large scale lockdown policies. Here, we examine to what degree vaccination status of incoming arrivals and the quarantine workforce can allow relaxation of quarantine requirements. To do so, we develop and apply a detailed model of COVID-19 disease progression and transmission taking into account nuanced timing factors. Key among these are disease incubation periods and the progression of infection detectability during incubation. Using the disease characteristics associated with the ancestral lineage of SARS-CoV-2 to benchmark the level of acceptable risk, we examine the performance of the border quarantine system for vaccinated arrivals. We examine disease transmission and vaccine efficacy parameters over a wide range, covering plausible values for the Delta variant currently circulating globally. Our results indicate a threshold in outbreak potential as a function of vaccine efficacy, with the time until an outbreak increasing by up to two orders of magnitude as vaccine efficacy against transmission increases from 70% to 90%. For parameters corresponding to the Delta variant, vaccination is able to maintain the capacity of quarantine systems to reduce case importation and outbreak risk, by counteracting the pathogen's increased infectiousness. To prevent outbreaks, heightened vaccination in border quarantine systems must be combined with mass vaccination. The ultimate success of these programs will depend sensitively on the efficacy of vaccines against viral transmission.

q-bio.PE↗