Jennifer A. Flegg

Japanese encephalitis virus (JEV) is a leading cause of viral encephalitis in humans in Asia. Prior to 2021, only sporadic outbreaks had been detected in Australia’s tropical northern extremes. However, during 2021–23, JEV infection was identified in 45 people and in more than 85 piggeries, together with many detections in feral pigs, mosquitoes, and other livestock across Australia, including in the temperate far south. Here we estimated the spatial extent of virus transmission during the outbreak (the realised niche) and the area environmentally suitable for transmission (the potential niche). We collated a national dataset of JEV surveillance and detection in humans, mosquitoes, domestic pigs, feral pigs, and sentinel chickens from 2021–23. We combined these data with (1) previously published predictions of habitat suitability for vertebrate host and vector species, and (2) estimated densities of humans, domestic pigs, and feral pigs, in a multipart assessment of spatial risk. Our model projected broad potential environmental suitability over much of northern and eastern Australia during the outbreak period, but floodplain wetland areas in the north and the inland east of Australia had both the highest estimated environmental suitability for transmission, and the strongest evidence for virus circulation.

Musculoskeletal injuries (MSI) in Thoroughbred racehorses are a leading cause of death and premature retirement, and are heavily influenced by training practices. Greater distances of high-speed galloping accumulated during racing campaigns are associated with MSI. Bone injury is the most common MSI, and understanding how training practices influence bone damage accumulation is critical for improving both horse welfare and racing outcomes. This study builds on an existing mathematical model of bone adaptation and damage to investigate the impact of different training programmes on bone injury risk. Several training programmes (three progressive, four race-fit, six rest programmes and two with rest replaced by low-intensity training) were constructed to reflect representative practices undertaken by professional trainers in Victoria, Australia. Training programmes varied in training volume, rest frequency and programme duration. Lower-volume training programmes that included high-speed training achieved sufficient bone adaptation with less accumulation of bone damage, and subsequently lower risk of bone failure. In addition, incorporating more frequent rests (minimum of two per year) and/or longer rest periods (minimum of six weeks) reduced bone damage owing to the extended opportunity to remove and repair bone damage. These results provide an in silico mathematical model of the bone's response to training, demonstrating the effects of training programmes on bone adaptation, damage formation and repair. The findings can guide the design of training programmes that balance both bone adaptation and bone health throughout a horse's racing career.

Multi-scale systems often exhibit a combination of stochastic and deterministic dynamics. In compartmental models, low occupancy compartments tend to exhibit stochastic dynamics while high occupancy compartments tend to follow deterministic dynamics. Representing both dynamics with existing methods is challenging. Failing to account for stochasticity in small populations can produce “atto-foxes”, for example in the Lotka-Volterra ordinary differential equation (ODE) model. This limitation becomes problematic when studying the extinction of species or the clearance of infection, but it can be overcome by using discrete stochastic models, such as continuous-time Markov chains (CTMCs). Unfortunately, simulating CTMCs is impractical for many realistic models, where discrete events have very high frequencies. In this work, we develop a mathematical framework to couple continuous ODEs and discrete CTMCs: “Jump-Switch-Flow” (JSF). In this framework, compartments can reach extinct states (“absorbing states”), thereby resolving atto-fox-type problems. JSF has the desired behaviours of exact CTMC simulation, but is substantially computationally faster than existing alternatives, by at least one order of magnitude, and can even obtain constant scaling, irrespective of compartment occupancy. We demonstrate JSF’s utility for simulation-based inference, particularly multi-scale problems, with several case-studies. In a simulation study, we demonstrate how JSF can enable a more nuanced analysis of the efficacy of public health interventions. We also carry out a novel analysis of longitudinal within-host data from SARS-CoV-2 infections to quantify the timing of viral clearance. In this work, we show how JSF offers a novel approach to compartmental model simulation.

A bstract: The stochastic simulation algorithm (SSA) is widely used to perform exact forward simulation of discrete stochastic processes in biology. However, the computational cost, driven by sequential event-by-event sampling across large ensembles, remains a computational barrier. We investigate whether reduced-precision floating-point arithmetic can accelerate SSA without degrading statistical fidelity, drawing on the success of reduced-precision methods in weather and climate modelling. We evaluate two strategies across five canonical models (birth–death, Schlögl, Telegraph, dimerisation, repressilator): (i) mixed precision, computing propensities in 16-bit while maintaining accumulators in 32-bit; and (ii) uniform precision, performing all arithmetic in 16-bit. Mixed-precision SSA produces ensemble statistics that closely match the 64-bit reference for all models, as measured by Kolmogorov–Smirnov tests and Wasserstein distances. Under uniform precision, deterministic rounding introduces systematic biases across several models, with catastrophic failures in some cases. Stochastic rounding (SR) and propensity normalisation eliminate these biases, restoring distributional fidelity across all models tested (KS p > 0.05). Our results establish mixed-precision SSA with SR as a viable acceleration strategy for mathematical biology: 16-bit formats shrink per-variable data size by 2–4× relative to fp32 / fp64 , yielding comparable reductions in memory footprint and up to ~ 1.5× wall-clock speedup on CPU hardware that lacks native 16-bit arithmetic. As a hardware-level acceleration, mixed-precision SSA complements algorithmic methods such as tau-leaping and maps naturally onto modern GPU and TPU architectures with native 16-bit arithmetic.

Background: Artemisinin-based combination therapies (ACTs) are the most widely used treatment for Plasmodium falciparum malaria. Kelch 13 mutations associated with artemisinin partial resistance (ART-R) have emerged in Sub-Saharan Africa (SSA) and are now reported in an increasing number of countries. ACT treatment failure rates are at risk of unprecedented increase. To summarise existing surveillance data and guide future surveillance, we produce modelled estimates of the spatiotemporal distributions of Kelch 13 and partner drug marker prevalence in SSA. Methods: We develop and validate spatiotemporal statistical models, fitted within a Bayesian framework, given molecular surveillance data. We estimate the prevalence of Kelch 13 mutations that are validated or candidate markers of ART-R and the prevalence of the mutations Pfcrt -K76T, Pfmdr1 -N84Y, Pfmdr1 -Y186F, and Pfmdr1 -D1246Y, associated with selection by pressure from the ACT partner drugs amodiaquine and lumefantrine. Findings: Our models reflect all existing clusters of ART-R-associated Kelch 13 mutations. We estimate the prevalence of these Kelch 13 mutations to be greater than 10% in 23% of the area of endemic malaria transmission in SSA in 2026. We also estimate that 5.8% of malaria cases in 2026 will be affected by a validated or a candidate ART-R marker. Our estimates of the prevalence of Pfcrt -K76T and other partner drug markers reflect sustained pressure from artemether-lumefantrine: we estimate the median prevalence of Pfcrt -76T across SSA to be 19% in 2026. Interpretation: Our models allow readers to visualise variation in observed mutation prevalences and to extrapolate prevalence to regions in space and time that are not represented in surveillance data. To monitor the changing distribution of antimalarial resistance markers within the constraints of the current global health funding climate it is critical that validated, statistical frameworks are incorporated into decision-making workflows to make the best use of molecular surveillance data. Funding: This research was funded by the European Union under the Global Health EDCTP3 Joint Undertaking (grant agreement 101103076) and the Australian National Health and Medical Research Council (APP2019093). Research in context: Evidence before this study: Ongoing systematic reviews of molecular surveillance of antimalarial resistance markers collate evidence of changing prevalences of Kelch 13 mutations associated with artemisinin partial resistance. However, there is a high degree of sampling bias in this data, and there are regions where limited surveillance has been carried out. We searched PubMed with the search terms: (((spatial OR spatiotemporal) AND (artemisinin OR Kelch)) AND (Africa)) AND (model* OR map OR mapping) which returned 30 results. We identified one recent pre-print describing spatiotemporal models of molecular markers of ART-R and partner drug resistance, however these models were not formally validated and model uncertainty may have been under-estimated. Added value of this study: We use spatiotemporal statistical models to estimate resistance marker prevalence in regions where there has been no molecular surveillance. Our models’ predictions are contextualised by estimates of model uncertainty, and we validate our modelling framework through posterior predictive checks and by evaluating its predictive performance on held-out data. Implications of all available evidence: Kelch 13 mutation prevalences are rising in all existing clusters where mutations have been identified, including in southern Africa. We estimate elevated prevalences in regions that neighbour existing clusters that are not well-represented in our surveillance dataset.