arXiv subjects

Michael J. Lydeamore

Publications and source records attributed to Michael J. Lydeamore.

3 recordsLinked to original sources

arXiv2025

Sunlight-heated refugia protect frogs from chytridiomycosis: a mathematical modelling study

The fungal disease Chytridiomycosis poses a threat to frog populations worldwide. It has driven over 90 amphibian species to extinction and severely affected hundreds more. Difficulties in disease management have shown a need for novel conservation approaches. We present a novel mathematical model for chytridiomycosis transmission in frogs that includes the natural history of infection, to test the hypothesis that sunlight-heated refugia reduce transmission. The model is fit using approximate Bayesian computation to experimental data where a cohort of frogs, a fixed subset of which had cleared a prior infection, were provided access to either sunlight-heated or shaded refugia. Using our model, we can estimate the extent to which prior chytridiomycosis infection protects against subsequent infection, and quantify the effect of sunlight-heating of refugia. Results estimate a 40% reduction in chytridiomycosis transmission when frogs have access to sunlight-heated refugia, compared to shaded refugia. This strongly supports the hypothesis that the sunlight-heated refugia reduce disease transmission. Frogs that were infected and recovered were estimated to have a reduction in susceptibility of approximately 97% compared to frogs with no prior infection. This research provides quantitative evidence supporting sunlight-heated refugia as an effective disease management tool for chytridiomycosis in frog populations. By estimating both the impact of refugia and the protective effects of prior infection, the model provides an evidence base for implementing sunlight-heated refugia as part of amphibian conservation strategies. This work represents an important first step in using mathematical modelling to inform policy on the design and implementation of habitat-based interventions to support amphibian population recovery and long-term sustainability.

q-bio.PE↗

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↗

arXiv2020

Risk mapping for COVID-19 outbreaks in Australia using mobility data

COVID-19 is highly transmissible and containing outbreaks requires a rapid and effective response. Because infection may be spread by people who are pre-symptomatic or asymptomatic, substantial undetected transmission is likely to occur before clinical cases are diagnosed. Thus, when outbreaks occur there is a need to anticipate which populations and locations are at heightened risk of exposure. In this work, we evaluate the utility of aggregate human mobility data for estimating the geographic distribution of transmission risk. We present a simple procedure for producing spatial transmission risk assessments from near-real-time population mobility data. We validate our estimates against three well-documented COVID-19 outbreak scenarios in Australia. Two of these were well-defined transmission clusters and one was a community transmission scenario. Our results indicate that mobility data can be a good predictor of geographic patterns of exposure risk from transmission centres, particularly in scenarios involving workplaces or other environments associated with habitual travel patterns. For community transmission scenarios, our results demonstrate that mobility data adds the most value to risk predictions when case counts are low and spatially clustered. Our method could assist health systems in the allocation of testing resources, and potentially guide the implementation of geographically-targeted restrictions on movement and social interaction.

physics.soc-ph↗