Journal of The Royal Society Interface

Buzz pollination is an important behavior in which bees use vibrations to extract pollen from poricidal anthers. However, the extent to which vibration frequency influences pollen release remains unclear. Here, we quantified pollen expulsion from Solanum sisymbriifolium anthers subjected to harmonic excitation over a broad frequency range encompassing the anthers first natural frequency. We excited anthers to expel pollen and measured anther kinematics and pollen release using high-speed videography. Particle tracking enabled continuous estimation of pollen release throughout each buzzing event, allowing both initial pollen flux and total pollen released to be quantified. Pollen release depended strongly on excitation frequency. Initial pollen flux, total pollen release, and anther kinematics peaked when excitation frequency approached the anthers natural frequency. Anther tip velocity amplitude exhibited the strongest correlation with total pollen release (r = 0.755) and initial pollen flux (r = 0.898). Experimental observations were compared with nonlinear and linear statistical models of pollen release. While both models captured trends in normalized pollen flux, they overpredicted total pollen release, suggesting that adhesive interactions play important roles during extended buzzing events. These findings demonstrate that anther structural dynamics influence pollen release and suggest that vibration amplification may improve the efficiency of buzz pollination.

Epithelial tissues maintain mechanical integrity through a balance between cell-cell adhesion and cortical contractility. Disruption of E-cadherin-mediated adhesion is a hallmark of epithelial-mesenchymal transition and cancer progression; yet how local adhesion defects propagate to tissue-scale mechanical changes remains poorly understood. Here, we use a two-dimensional vertex model (varying mutant cell fraction, spatial arrangement, and initial tissue disorder) to investigate how adhesion-deficient cells regulate epithelial mechanics. We show that increasing the fraction of mutant cells drives the tissue towards geometric signatures associated with reduced mechanical rigidity, characterised by elevated cellular shape index and increased prevalence of non-hexagonal cells. Crucially, spatial organisation acts as an independent structural variable that modulates tissue mechanics beyond mutant fraction alone. For identical mutant fractions, randomly distributed mutants undergo rapid, spatially isolated T2-mediated removal events producing only transient shape-index perturbations. Clustered mutants, by contrast, undergo sequential boundary removal, delaying elimination and sustaining elevated shape index in the surrounding tissue. This persistent elevation induces topological disorder within the local neighbourhood that outlasts mutant clearance itself. Our results establish spatial organisation as a key determinant of epithelial rigidity transitions, with implications for understanding early-stage cancer progression.

Within plant cells, the self-organization of cortical microtubules (MTs) into ordered arrays is an important process for directional growth. There is growing evidence that cortical MTs respond to cell shape and/or mechanical stresses in the cell wall, requiring in silico models on complicated surfaces to provide a complete understanding. Most models assume that MTs are directionally persistent, following geodesics of the surface. This ignores the expected tendency of these elastic filaments to minimize curvature. Our recent model incorporated minimization of MT curvature in cylindrical cells and found curvature to be significant in biasing the array organization. Here, we generalize to a larger class of surfaces, studying individual microtubule shapes to provide insights into the role of geometric cues and highlight differences with previous models that use the geodesic assumption. We first show that geodesic models, including current models with finite persistence lengths, exhibit an invariance across certain geometries, leading to biophysically counterintuitive results. Incorporating curvature minimization, we show the difficulties imposed by high-curvature cell edges, elucidating potential new roles of proteins in helping microtubules traverse edges. Lastly, we show that geometries with competing curvature cues result in diverse curves previously not considered. These results provide geometric intuition for how various cell geometries affect individual cortical microtubules, helping us to better understand the processes required for the establishment of microtubule arrays in broad contexts such as: bundles spanning adjacent cell faces in prism-like root and leaf epidermis cells, protruding geometries of trichome cells, and rounded surfaces such as confined protoplasts.

Cancer phenotypic plasticity drives invasion, treatment resistance, and relapse. Quantifying how cells dynamically couple morphology and migration in real time, without molecular labels, remains unsolved. Static molecular markers report on protein expression state rather than functional migratory behavior. Existing image-based metrics treat shape and migration as independent features, missing the coordinated coupling that defines plastic migratory states. We introduce Directional Shape Coupling (DSC), a quantitative metric purpose-built for live label-free imaging. DSC integrates movement direction consistency, shape deformation, and directional-shape alignment into a single interpretable score. Component weights are derived from PCA, adapting automatically to any dataset without manual tuning. Applied to differential interference contrast imaging of pancreatic cancer cells on a tissue-mimicking substrate recapitulating desmoplastic tumor stroma, DSC exhibited a large phenotype-associated effect size,{varepsilon} 2 = 0.65, across five distinct migratory phenotypes within a genetically homogeneous population, demonstrating that behavioral heterogeneity is structured and non-genetic. DSC encodes information orthogonal to classical shape and motion descriptors. Critically, DSC reveals that dynamic shape adaptation to mechanical cues rather than directional commitment drives phenotypic identity in this system. DSC provides the label-free imaging community a transparent, generalizable framework for quantifying dynamic non-genetic plasticity directly from live imaging data.

Pitch control models quantify spatial dominance in football by estimating which player can arrive first at each pitch location, but they treat all players as equivalently capable regardless of preceding effort. We introduce physiology-aware pitch control (Phys-PC), a model-agnostic time-to-arrive correction that imposes two physiological capacity channels calibrated from tracking data: a transient recoverable burden capturing incomplete recovery from recent high-intensity efforts, and a cumulative non-recoverable drain accumulating across match play. Both channels reduce a bounded access scale that modulates kinematic TTA before any downstream pitch-control computation. All parameters are anchored to exercise-physiology benchmarks; no laboratory measurements are assumed. Applied to a 64-match international tournament, Phys-PC reveals structure that kinematic models cannot detect. In head-to-head races, the dominant burden channel shifts from transient to cumulative over the course of a match, with a transient resurgence in the final 15 minutes. These physiological asymmetries predict match outcomes: relative reserve advantage is associated with higher odds of winning ground challenges (OR = 1.20, p = 0.006; +4.2 pp), completing over-the-top passes past recovering defenders (OR = 1.45, p = 0.034; +8.3 pp), and progressing possession sequences into the final third (OR = 1.31, p = 0.013; +5.1 pp). At the player-profile level, an acute-cumulative decomposition of contested space access separates roles and individuals whose territorial reach is maintained through sustained positioning from those whose access is rebuilt through repeated high-intensity actions, providing a physiological lens on team tactical structure.

Background. Self-exciting (Hawkes) point processes are a natural model for the temporal clustering of human physical activity (PA) recorded by accelerometers, yet they have seldom been used in this setting---in part because the usual maximum-likelihood fitting is challenging due to potential estimation bias and convergence failures on these data. A moment-based alternative---estimating the Hawkes branching ratio from the dispersion index, the variance-to-mean ratio of event counts---is kernel-independent and computationally trivial, but it has not been evaluated for accelerometry or adapted to the intensity-marked recordings accelerometers provide. Methods. Treating each minute above a sedentary threshold as an event, we estimated the Hawkes branching ratio $n$ by maximum likelihood and, as a kernel-independent and far cheaper alternative, from the dispersion index. We compared four dispersion-based estimators---event-count-based, intensity-mark-weighted using the mark-moment ratio, and time-of-day (TOD) adjusted variants of each---against the marked and unmarked maximum-likelihood estimates. Estimators were evaluated for mutual agreement, goodness of fit, and finite-window results in two National Health and Nutrition Examination Survey (NHANES) accelerometry cohorts (hip-worn, $n=2{,}560$; wrist-worn, $n=3{,}132$). We related the resulting temporal clustering measures to all-cause mortality using survey-weighted Cox models, adjusting for PA frequency, Peak30 (the average of the 30 highest PA values), and demographic covariates. Results. Event-count-based dispersion estimates agreed strongly with maximum-likelihood branching ratios ($r\approx0.74$ in both cohorts); the intensity-marked variant incorporating PA intensity variability agreed less well. Marked and unmarked Hawkes models yielded similar excitation and decay parameters, suggesting PA intensity added little clustering information beyond event timing. In the survival analysis, temporal clustering was associated with all-cause mortality independently of PA frequency and Peak30; the direction of association differed between the hip- and wrist-worn cohorts. Conclusions. A scalable dispersion-index estimator recovers the Hawkes branching ratio and matches maximum-likelihood estimates without requiring kernel specification or iterative optimization. It offers a practical tool for quantifying temporal clustering in accelerometry, enabling decomposition of temporal PA patterns into its exogenous initiation and endogenous persistence. Such temporal patterns carry health-relevant information beyond PA intensity and volume. Keywords: dispersion index; Hawkes process; branching ratio; temporal clustering; point process estimation; accelerometry; mortality

The classical model of bacterial flagellar motility posits a rotary engine driven by proton motive force (pmf), with torque generated by stator-rotor interactions and transmitted through a flexible hook to a helical filament. Despite decades of acceptance, this model faces fundamental challenges in thermodynamics, structural mechanics, evolutionary parsimony, and direct observational evidence. We develop and quantitatively test the murburn model, a new paradigm for bacterial motility in which water, produced as an inevitable byproduct of metabolic redox activity, is ejected via the basal secretory module and channelled along the spiral grooves of the flagellar filament. The ejected flow creates a local shear field that induces a transverse bending wave; the precession of this wave is observed as apparent rotation and generates thrust through anisotropic viscous drag, without any rotary motor, ion gradient, or axial rotation. The principal contribution of this work is a self-contained, first-principles treatment of this mechanism: for a unipolar flagellated cell we derive the governing low-Reynolds-number elastohydrodynamic relations from slender-body theory and show that physiologically realistic rates of metabolic water production reproduce the observed swimming speeds and apparent-rotation frequencies at a small fraction of the cellular energy budget, while direct jet propulsion is quantitatively excluded. Building on this derivation, we provide a force-balance comparison of the competing propulsion mechanisms, obtain a set of falsifiable predictions that distinguish the murburn model from the rotary motor, and report a structural analysis of cryo-EM flagellar-hook architectures that reveals solvent-accessible radial canals consistent with lateral water transport. The same single principle accounts for swimming, tumbling, gliding, spirochete undulation, and archaeal motility, without requiring rotating shafts, ion-gradient coupling, or complex switching mechanisms.

Contagion processes across biological, behavioral, and informational systems are shaped not only by transmission dynamics, but also by adaptive social responses emerging through human interactions. Understanding how social context regulates contagion spread is therefore critical for characterizing real-world spreading processes. Yet standard epidemic models often focus primarily on contagion states, treating social context as static or only weakly coupled to transmission. Here, we develop a multiplex-network framework that couples contagion dynamics with co-evolving social context. Unlike classical threshold contagion models, which apply thresholds directly to contagion prevalence or adoption states, our framework applies heterogeneous local and global thresholds to evolving context dynamics. The model further captures context-mediated transmission through targeted spread, in which contagion selectively propagates toward the locally most context-vulnerable susceptible individual. This contrasts with broadcast transmission, where spreading effort is distributed uniformly across susceptible neighbors. We show that coupling contagion with evolving context fundamentally reshapes spreading dynamics, producing delayed convergence and non-monotonic final prevalence. Targeted and broadcast transmission mechanisms exhibit distinct sensitivities to local and global social responses, highlighting tradeoffs in intervention strategies. We further show that co-evolving context can generate resilience by slowing propagation and delaying equilibrium, while pre-existing social resilience can substantially suppress contagion even under high transmission rates. These results suggest that contagion outcomes can vary substantially as a function of evolving social response and pre-existing social resilience. SignificanceContagion outcomes are often shaped before transmission begins. Existing social environments can make populations more vulnerable or more resistant to future spread, yet this latent resilience is difficult to capture when interventions are represented only as changes to contact or transmission rates. Our results show that social context can act as a regulatory mechanism that suppresses and delays contagion spread. In particular, prior pro-social alignment can create resilience before exposure occurs, helping explain why community prevention, peer support, and reintegration programs may alter contagion outcomes even when they do not directly target the transmission process.

Understanding how animals respond to their physical environment requires environmental observations at the scale at which behavioural decisions are made. For soaring birds, the coarse resolution of weather products has long hindered the analysis of their behavioural response to fine-scale atmospheric dynamics, forcing uplift sources to be inferred largely from behaviour itself. Here, we combined high-resolution movement data from 24 golden eagles with the kilometre-scale COSMO weather model. We first classified thermal, orographic, and gravity-wave uplifts using independent atmospheric predictors and then quantified the birds' use of each uplift type and their fine-scale behavioural responses. Eagles relied predominantly on thermals, but opportunistically adjusted their use of uplift sources seasonally. The birds' flight behaviour could not reliably indicate which uplift type was primarily used, and thus suggests that both atmospheric processes and behavioural responses are better described as continua than discrete categories. Finally, we compared vertical wind velocities derived from eagles soaring behaviour with those modelled by the COSMO weather model, showing that most of the thermals exploited by eagles remain unresolved at kilometre-scale model resolution. Our results demonstrate how high-resolution weather models provide new insights into bird movement decisions, while also highlighting the potential of soaring birds as biologically embedded atmospheric sensors that could help closing the resolution gap in atmospheric models.

Tissue shape and function are defined by the mechanical interactions of cellular and extracellular components. Lymph nodes cyclically remodel in response to immune challenges whilst preserving essential stromal structures. However, the relative contributions of the fibroblastic reticular stromal cell network and the ensheathed extracellular matrix, remain undefined. We quantified the contribution of ECM to the viscoelastic properties of lymph nodes to parameterise an in silico model exploring the FRC network's adaptation to pressure-driven tissue expansion. The balance between tissue pressure, FRC contractility and ECM stiffness permit robust remodelling and growth, while maintaining physiological geometries and balancing force distribution. Local perturbation of ECM stiffness or FRC contractility disrupts force distribution globally and impacts FRC proliferation and tissue expansion. Spatially dispersed perturbations exert higher impact on tissue architecture than equivalent localised perturbations, with effects propagating across the network. The integration of cellular and extracellular mechanics thereby enables robust lymph node remodelling.

Catalysis and allostery are complementary principles of biological function: catalysis accelerates bio-chemical reactions, whereas allosteric regulation dynamically controls molecular interactions. The emergence and persistence of early autocatalytic ribozyme systems likely depended not only on template-directed self-replication but also on ATP production, utilization, and recycling through prebiotically plausible energy-conversion processes. We previously proposed that resistance to molecular parasitism in autocatalytic RNA networks can emerge through hyperparasitic regulation, whereby hyperparasitic ribozymes compete with parasitic ribozymes for binding to the host replicase while additionally binding parasitic ribozymes, thereby redirecting competitive interactions away from host exploitation. Here, we develop a minimal mathematical model of an autocatalytic RNA-world system in which replication, ATP/ADP-based energy conversion, and ATP-dependent allosteric regulation become dynamically coupled. Building on a general reaction framework describing ribozyme replication, catalytic interactions, and ATP/ADP cycling, we identify the minimal regulatory architecture required for stable RNA replication in the presence of parasitic mutants. Our simulations reveal a sequential evolutionary transition in which control precedes optimization. ATP-dependent allosteric regulation first evolves to tame molecular parasitism through hyperparasitic binding, whereby ATP-loaded parasitic ribozymes compete with ATP-free parasites for binding to the host replicase while also binding directly to parasitic ribozymes. This stabilizes RNA replication but simultaneously imposes an energetic cost by sequestering ATP and thereby reducing ATP-ADP turnover. The resulting regulatory burden creates selective pressure for the evolution of ATP synthase/ATPase ribozymes that accelerate ATP-ADP cycling and restore the metabolic flux required for sustained RNA replication. We further identify two plausible evolutionary routes to parasite control: parasitic ribozymes either become intrinsically allostery-prone or are converted into allostery-prone forms by an evolved allosterase ribozyme. Once ATP turnover is sufficiently rapid, both mechanisms confer long-term resistance to recurrent parasitic invasion. These results suggest that stable RNA-based evolution required the progressive integration of information replication, molecular regulation, and increasingly efficient metabolic energy conversion. More generally, the model identifies ATP-dependent allosteric regulation as a plausible evolutionary bridge linking autocatalytic RNA replication to the emergence of regulated proto-metabolism, transforming a parasite-limited replicating system into a self-regulating proto-biological organization capable of sustained evolutionary dynamics. HighlightsO_LIA minimal autocatalytic RNA network achieves stable self-replication despite recurrent parasitic invasion C_LIO_LIATP-dependent allostery tames molecular parasitism through hyperparasitic binding C_LIO_LIControl of molecular parasitism precedes optimization of metabolic energy conversion C_LIO_LIATP sequestration creates an energetic burden that drives increased ATP-ADP turnover C_LIO_LIStable parasite control evolves through either intrinsic allostery or allosterase-mediated regulation C_LIO_LIStable RNA autocatalysis emerges through the sequential integration of replication, molecular regulation, and energy conversion C_LI

Structural network representations of metastatic dissemination typically focus on static topology without resolving transport dynamics, relaxation timescales, or steady-state behaviour. Here, we formulate a discrete Markovian transport model on a directed higher-order network with transition rates derived from qualitative clinical affinity classes. By constructing a non-Hermitian row-stochastic transfer operator, we characterise the relaxation dynamics through its spectral decomposition. The system exhibits a fast-mixing regime characterised by a spectral gap of {gamma} {approx} 0.67, corresponding to a characteristic relaxation timescale of {tau} {approx} 1.49 discrete steps, with the influence of the primary tumour origin progressively attenuated during dissemination. Convergence towards a non-equilibrium steady state (NESS) is accompanied by a reduction in Shannon entropy, concentrating probability mass within specific topological sinks. This spectral relaxation delineates two distinct dynamical regimes: early transient dissemination (n < {tau}), dominated by local organ-specific transition probabilities (organotropism), and the asymptotic regime (n > {tau}), determined increasingly by the global transport architecture of the network. Comparison with independent clinical and autopsy observations across 21 primary tumours and 23 target organs indicates that the predicted stationary distribution is consistent with the observed hierarchy of metastatic organ involvement.

Background: Immigrant patients with limited dominant-language proficiency may face intersecting challenges when navigating cancer care and long-term oral anticancer treatment. Although studies have reported lower medication adherence among migrant and ethnic minority populations, less is known about how migration-related, linguistic, experiential and contextual factors shape treatment engagement from patients own perspectives. This study explored how immigrant patients experience illness, navigate treatment and engage with oral anticancer medication within the broader context of cancer care. Methods: Thirteen immigrant patients with limited dominant-language proficiency receiving oral anticancer medication for haematological malignancies were recruited from the haematology outpatient clinic of a Belgian university hospital. Semi-structured interviews were conducted in participants native languages using an adapted version of the McGill Illness Narrative Interview, with professional interpreters or intercultural mediators. Interviews were analysed using inductive reflexive thematic analysis within an interpretivist framework. Results: Analysis of patients illness narratives generated five experiential dimensions: 1) bodily, biographical and identity rupture; 2) temporal disruption and uncertainty; 3) linguistic vulnerability shaping the illness experience; 4) meaning-making and explanatory frameworks; and 5) resources sustaining treatment engagement. Linguistic vulnerability shaped access to biomedical knowledge, participation in healthcare encounters and patient autonomy, while patients mobilised personal, relational, existential, linguistic and institutional resources to sustain treatment continuity. Treatment engagement emerged as a dynamic and relational process embedded within broader migration-related, linguistic and healthcare contexts. Rather than representing fixed determinants or sequential stages, the five dimensions formed an evolving configuration whose relative salience varied throughout the illness trajectory. Conclusion: This study proposes a multidimensional interpretive model of engagement with oral anticancer medication among immigrant patients with limited dominant-language proficiency. Rather than conceptualising adherence as an isolated individual behaviour, the findings show how migration-related contexts shape the conditions under which treatment engagement becomes possible, difficult or fragile. By foregrounding immigrant patients lived experiences, the study identifies experiential, linguistic, relational and structural dimensions of cancer care that are difficult to capture through behavioural adherence measures alone and offers insights for more equitable, context-sensitive and patient-centred oncology care.

Motile microorganisms explore complex environments in search of nutrients, hosts and favourable ecological niches. Plant-pathogenic oomycetes, for instance, undergo such an exploratory phase through biflagellate zoospores that actively swim through water-filled soil pores before infecting host tissues. Linking individual zoospore swimming behaviour to emergent dispersal remains challenging. Here, we present an end-to-end, data-driven framework that transforms time-lapse microscopy image sequences into generative agent-based simulations of zoospore dispersal by inferring local behavioural rules directly from experimental trajectories. Using Phytophthora nicotianae as a model system, we isolated nearly 60,000 zoospore trajectories and quantified both local behavioural descriptors and emergent trajectory properties. Local behavioural measurements were first used to infer an empirical two-state model distinguishing SLOW and FAST swimming regimes while capturing temporal memory and the coupling between speed and turning. Implemented within an agent-based cellular automaton, this model reproduced the principal emergent properties of experimental dispersal. We then independently inferred the behavioural organisation of zoospore swimming using hidden Markov models. The most parsimonious two-state HMM recovered a closely related behavioural organisation, while revealing that the inferred states jointly reflected swimming speed, turning dynamics and directional persistence rather than speed alone. Finally, we challenged the inferred behavioural rules in an independent obstacle-filled environment. Combined with simple collision hypotheses, the model reproduced emergent dispersal without recalibrating the swimming rules and identified transient post-collision slowdown as a key response required to account for the experimental trajectories. Together, these results demonstrate that experimentally inferred local behavioural rules possess predictive power beyond the conditions used for their calibration. More broadly, this work establishes a predictive framework linking quantitative microscopy, behavioural-rule inference and generative modelling of microbial dispersal.

Public-health surveillance systems rely on downstream indicators to infer latent infection incidence, but delays and observation noise provide only an indirect and temporally distorted view of the underlying epidemic process. Reconstructing upstream epidemic trajectories from these observations is therefore an ill-posed inverse problem, in which different reconstruction assumptions may produce different trajectories that remain consistent with the observed data. Here, we develop a general spectral framework that quantifies the statistical distinguishability of candidate upstream trajectories under delayed and noisy observations. We show that epidemiological delay distributions impose a frequency-dependent temporal resolution limit on epidemic surveillance, fundamentally constraining the distinguishability of rapid upstream variation. This limitation propagates to epidemiological inference, making some quantities substantially more sensitive to reconstruction assumptions than others and rendering distinct event-impact profiles difficult to distinguish from downstream observations.

Malaria and diabetes represent two globally significant metabolic disorders whose co-occurrence leads to complex, poorly understood pathophysiological interactions. Plasmodium infection disrupts glucose homeostasis through parasite-driven glucose consumption, inflammatory cytokine production, and pancreatic /{beta}-cell dysfunction, while diabetes impairs host immunity and increases malaria susceptibility. To date, no mathematical framework has captured the bidirectional coupling between these systems. Here we extend the insulin-glucose-glucagon (IGG) model of Dalton et al.\ (2026) by introducing a fourth state variable representing parasite load, incorporating malaria-induced insulin suppression, parasite-driven glucose consumption, inflammatory gluconeogenesis, bidirectional glucagon dysregulation, and insulin-dependent immune enhancement of parasite clearance. We establish positivity, boundedness, existence and uniqueness of steady states, local stability via Routh-Hurwitz criteria, global stability via Lyapunov functions, and sensitivity analysis of parameters driving hypoglycemia risk. Numerical simulations characterise the model across healthy, diabetic, and co-infected states. They show that parasite-driven glucose consumption and inflammatory gluconeogenesis act antagonistically on circulating glucose, that insulin-enhanced immunity lowers peak parasitemia through a saturating clearance term, and that increasing the half-life of exogenous insulin raises hypoglycemia risk in all host states. These mechanisms provide testable hypotheses for the clinical management of malaria-diabetes patients and identify potential therapeutic targets (TNF- blockade, glucagon analogues) for mitigating co-infection morbidity.

A variety of connected systems, ranging from the cytoskeleton to human organizations, dynamically rearrange themselves in order to move through physical or abstract space. However, our understanding of how systems-level behaviors arise from local restructuring actions remains limited, necessitating comparison of real-world data to models that predict network structure and dynamics. To understand these systems, we study an accessible example, the branching slime mold Physarum polycephalum, by imaging the organism as it travels and extracting key fundamental quantities from its continuously remodeling tubular network. By using these quantities as input parameters to a traveling network model, we find that with no further fitting, the model quantitatively matches key emergent properties from P. polycephalum dynamics including path length, relocation time, and search efficiency at different spatial resolutions. These findings demonstrate how a traveling network model can capture P. polycephalum behaviors, highlighting the potential to use traveling networks more broadly for understanding and predicting connected dynamic systems by linking local measurements to emergent, system-wide behaviors.

Climate-driven disease forecasts typically assess whether environmental conditions favor pathogen growth, yet epidemic spread depends critically on how physiological processes within infected hosts shape transmission over time. This distinction is particularly consequential for vector-borne plant diseases, where vectors acquire infection from hosts whose pathogen load, symptom severity, and recovery are themselves temperature-dependent. Here, we develop a mechanistic epidemic framework that couples temperature-driven within-host pathogen dynamics to vector-mediated transmission. Infected hosts progress through ordered infection stages with stage-specific infectiousness, while transitions among stages-both progression and regression-are governed by thermal effects on pathogen accumulation and decay. We parameterize the model using experimental data for Pierce's disease of grapevine, caused by Xylella fastidiosa, and analyze epidemic invasion under constant, seasonal, stochastic, and empirical temperature regimes. We show that temperature affects invasion not only by altering pathogen growth rates but also by reshaping the time hosts spend in transmissible infection stages. This generates a slow-growth paradox: temperatures that maximize within-host pathogen growth need not maximize epidemic spread, because rapid progression shortens the effective transmission window, whereas mildly suboptimal temperatures can prolong infectiousness and sustain larger epidemics. Conversely, cold conditions can suppress invasion by either halting progression or inducing regression and recovery. Analytical expressions for the basic reproduction number under constant and seasonal forcing capture these mechanisms and predict final epidemic size across diverse climatic regimes. Short-term temperature variability has its strongest effects near thermal thresholds, and empirical temperature series from invaded regions generate markedly different epidemic trajectories despite similar invasion suitability. These results show that ignoring the coupling between within-host physiology and transmission can qualitatively mislead predictions of plant disease dynamics under climate change, misidentifying the thermal regimes that pose the greatest epidemic risk.

The basic reproduction number R0 confounds pathogen biology with adaptive human contact behavior. Earlier epidemiological--economic theory predicted a forward-looking behavioral contact response but could not test it in the absence of appropriate behavioral data. Using directly measured mobility as an observable proxy for contact, we (i) estimate the behavioral response function directly from data; (ii) show that the biology/behavior decomposition and hence the behavioral correction to R0 is not identified from an epidemic trajectory, the apparent constant-contact R0 being one endpoint of an observational-equivalence class that fits the factual curve identically yet diverges under counterfactual; and (iii) characterize that divergence ("what R0 deletes") as state-dependent, unimodal in counterfactual severity and vanishing when behavior saturates. We then show that, across US jurisdictions, the correction is empirically bounded because risk-responsiveness and behavioral non-saturation are confounded (r=-0.57, n=51): where behavior could compensate, it was already maximal, and where it was not maximal it did not respond. What R0 deletes is thus real and structurally characterizable yet empirically modest here, for reasons the framework itself supplies.

Stochastic epidemic models are a cornerstone of infectious disease epidemiology and are often used to study intervention scenarios. However, large run-to-run variability can make intervention effects difficult to estimate precisely. We revisit the epidemic Sellke construction, which assigns each individual an infection threshold for the cumulative infection hazard such that, conditional on the thresholds, the epidemic trajectory becomes deterministic. This enables coupling of simulations with and without an intervention, yielding low-variance effect estimates even when outcomes such as final size or peak incidence vary widely between runs. We develop an exact, event-driven implementation that maintains infection and recovery events in priority queues. Cumulative infection-hazard updates require O(log N) time per event, yielding overall complexity O(Elog N) for E events in a population of size N. The implementation achieves computational performance comparable to the classical Gillespie algorithm while naturally accommodating non-Markovian infectious periods and complex infectiousness profiles. We illustrate the approach using distance-dependent spread of avian influenza between poultry farms in the Netherlands and a multilayer population with households, schools, and workplaces. In both examples, coupling enables efficient within-run comparisons of intervention scenarios across stochastic realisations.