Scholarly Work - Mathematical Sciences
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/8719
Browse
2 results
Search Results
Item Pathogen spillover driven by rapid changes in bat ecology(Springer Science and Business Media LLC, 2023-01) Eby, Peggy; Peel, Alison J.; Hoegh, Andrew; Madden, Wyatt; Giles, John R.; Hudson, Peter J.; Plowright, Raina K.During recent decades, pathogens that originated in bats have become an increasing public health concern. A major challenge is to identify how those pathogens spill over into human populations to generate a pandemic threat1. Many correlational studies associate spillover with changes in land use or other anthropogenic stressors2,3, although the mechanisms underlying the observed correlations have not been identified4. One limitation is the lack of spatially and temporally explicit data on multiple spillovers, and on the connections among spillovers, reservoir host ecology and behaviour and viral dynamics. We present 25 years of data on land-use change, bat behaviour and spillover of Hendra virus from Pteropodid bats to horses in subtropical Australia. These data show that bats are responding to environmental change by persistently adopting behaviours that were previously transient responses to nutritional stress. Interactions between land-use change and climate now lead to persistent bat residency in agricultural areas, where periodic food shortages drive clusters of spillovers. Pulses of winter flowering of trees in remnant forests appeared to prevent spillover. We developed integrative Bayesian network models based on these phenomena that accurately predicted the presence or absence of clusters of spillovers in each of the 25 years. Our long-term study identifies the mechanistic connections between habitat loss, climate and increased spillover risk. It provides a framework for examining causes of bat virus spillover and for developing ecological countermeasures to prevent pandemics.Item Estimating viral prevalence with data fusion for adaptive two‐phase pooled sampling(Wiley, 2021-10) Hoegh, Andrew; Peel, Alison J.; Madden, Wyatt; Ruiz-Aravena, Manuel; Morris, Aaron; Washburne, Alex D.; Plowright, Raina K.The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second- phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second- phase samples.2. To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two- phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.3. Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.4. The manuscript presents guidance on implementing the first- phase and second- phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS-CoV-2 in populations.