Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

The measurement of light received at the cornea of the eye is a paramount consideration for the understanding of the relation between environmental illumination and the non-image-forming effects of light. The field of view (FOV) at the cornea is less than a full hemisphere, because it is partially occluded by human facial morphology. The International Commission on Illumination (CIE) has defined a standard model of human FOV. A suitably designed physical occluder attached to the sensor (of a light meter) has been proposed as a means of incorporating the effect of human FOV when taking measurements. Similarly, when using simulation to predict light received at the cornea, a geometrical description of the occluder at the eye point(s) can be added to the 3D model of the scene. The first occluder model proposed to represent CIE human FOV was enumerated in terms of: the CIE definition; the radius of the occluder; and, the radius of the light sensor disc. We present a simpler model based only on the CIE definition and the occluder radius. Both models were tested using a virtual goniophotometer. Various sensor response functions describing the spatial sensitivity across the sensor disc, including several we characterized through laboratory measurements, were included in the test. For all functions considered, the performance of the simpler occluder model was equivalent to or better than the model first proposed.

Cardiac blood flow is a regulator of several important developmental and remodelling processes in the heart, including through fluid shear forces sensed by the endothelial cells lining the heart. However, optically mapping these flow fields in the complex 3D geometry of the heart is challenging even in transparent animal models such as the zebrafish. One of the main challenges is the difficulty in measuring the out-of-plane (axial) velocity component, preventing accurate mapping of the complete 3-component-3-dimension (3C-3D) blood flow velocity field; image-based techniques such as microscopic particle image velocimetry ({micro}PIV) traditionally only provide the in-plane flow components. Here we present a computational approach to achieve full time-varying 3C-3D blood flow vector mapping using a standard selective plane illumination microscope (SPIM), based on robust cardiac phase assignment, precise measurement-driven registration of sequentially acquired z-stacks, and PIV data fusion from multiple sample orientations. Our approach holds the key to understanding the complex dynamic flow fields within the developing heart, and their role in shaping cardiac development.

The lifetime of fluorescent molecules provides an orthogonal readout to fluorescence intensity, opening experimental possibilities of measuring changes in local molecular environments, mechanical tension, and metabolism, among other factors. These changes are best studied live and in vivo; however, limitations of slow imaging speeds, high phototoxicity, and increased data size and complexity have significantly impeded progress on this front. Here, we present a complete and transferable pipeline consisting of a light sheet FLIM microscope and an accompanying machine learning model for data processing that renders long-term and/or high-speed volumetric FLIM (vFLIM) tractable in living systems. We benchmark this pipeline across several biological use cases, model systems, lifetime ranges, and spatiotemporal scales, showcasing a suite of possibilities that our workflow enables. This comprehensive pipeline from imaging to analysis is a crucial step forward towards disseminating the power of live vFLIM to the broader bioimaging community.

Diffusion within the cytoplasm is fundamental to numerous biological processes. Fluorescence recovery after photobleaching (FRAP) is one of the most common method for quantifying molecular diffusivity in living cells using standard laser scanning confocal microscopy (LSCM). However, accurately measuring fast cytoplasmic diffusion (typically >10 m^2/s) is challenging due to rapid recovery kinetics, weak signal-to-noise ratios, post-bleach signal artifacts, and spatial restrictions affecting normalization. While individual challenges have been addressed in specific contexts, a simple and robust framework to quantify cytoplasmic diffusivity remains elusive. Here, we present a FRAP methodology specifically designed to overcome these obstacles. By utilizing the Gaussian function -- the impulse response (ImpRes) of the diffusion equation in an infinite medium -- our approach leverages the full spatiotemporal dataset through a single-equation three-parameter fitting procedure, thus releasing restrictions to small regions of interest and arbitrary initial time-points. The methodology was validated on three datasets of increasing complexity: in silico simulated recovery profiles, in vitro data from FITC-dextran in glycerol solution, and live-cell imaging of free cytoplasmic GFP. Systematic comparison with existing models demonstrates that the ImpRes approach significantly reduces sensitivity to noise and imperfect fluorescence normalization, while remaining robust against short-term biases, such as transient probe photo-activation. Given its robustness under realistic experimental conditions and its ease of implementation, the proposed FRAP methodology provides a reliable tool for quantitative cytoplasmic analysis.

Timed phylogenetic trees express the evolutionary history of a pathogen outbreak in units of time, providing an estimate of the elapsed time across the shared ancestry of a set of taxa. By combining this elapsed time with known information about the epidemiology of a disease, we can relate the total branch length to the total number of cases related to the phylogeny. This gives information about the number of unsequenced cases that are related to the phylogeny. We call these ``cryptic'' cases. We present ECHO (Estimation of Cryptic Hosts from Outbreak trees), a collection of three lightweight estimators of the number of cryptic cases in a phylogeny. ECHO is agnostic to the form of the sampling process, making it robust to a variety of forms of sampling heterogeneity. We demonstrate ECHO's baseline accuracy and its robustness to heterogenous sampling frameworks through simulation. Additionally, we apply ECHO to measles virus sequences that were collected during an outbreak in the USA in 2021. ECHO is able to recover the number of cryptic cases with a reasonable degree of accuracy both in simulation and in practice. We discuss the contexts in which ECHO is most applicable, and the interpretation of its estimates.

Quantitative fluorescence imaging techniques such as fluorescence lifetime imaging microscopy and hyperspectral imaging infer molecular contrast from photons distributed across spatial pixels and temporal or spectral channels. In the few-photon regime, however, conventional pixel-wise analysis discards the spatial relationships imposed across neighboring pixels by the microscope point-spread function (PSF). Here we show that this spatially distributed information can be recovered without prior knowledge of emitter positions, spatial support or component assignments. We introduce SPOOL (Spatially Pooled Optical Observation Likelihood), a training-free Poisson inverse framework that jointly recovers source-space amplitudes and quantitative contrast by combining the PSF with temporal-decay or spectral-response dictionaries. For an isolated source, the attainable precision gain is governed by a dimensionless optical quantity: the PSF width expressed in detector pixels. The predicted gain therefore scales with optical sampling rather than with the physical origin of the contrast. The model predicts that lifetime-precision gain scales approximately linearly with the number of pixels spanning the PSF full width at half maximum, a scaling reproduced by Monte Carlo simulations. At one detected photon per foreground pixel, the reconstruction reduces lifetime dispersion sixfold in fluorescent-bead experiments and decreases the lifetime root-mean-square error relative to a high-photon reference from 1.19 to 0.45 ns in dual-labeled cells. The same framework transfers unchanged to hyperspectral imaging, recovering spectral contrast from generic emission bands without prior fluorophore spectra.

The sea anemone Aiptasia is a model system for understanding cnidarian loss of symbiotic algae under heat stress (bleaching). While Aiptasia polyps have been widely used to study this process, accurate symbiosis phenotyping grapples with discordant length scales: fine spatial resolution (~100 um) is needed across a whole organism (~5 mm). To address this, we consider small (~100 um), optically transparent Aiptasia larvae as a bleaching model suitable for whole-organism phenotyping by fluorescence microscopy with larvae classified as symbiotic when algae are localized within gastrodermal cells. To expedite phenotyping, we introduce a machine-learning (ML) image-analysis pipeline (SYMPHONY) designed for single-larva resolution analysis of intact larvae. SYMPHONY efficiently identifies the cellular location of internalized algae (accuracy: 79%, precision: 82%, recall: 79%, F1 score: 79%; training dataset composed of 1611 total objects). Additionally, SYMPHONY reports statistically significant larval bleaching under heat stress and corroborates manual phenotyping results, while significantly reducing operator labor from hours to minutes. The combination of the Aiptasia larvae model and the SYMPHONY pipeline aims to accelerate our understanding of symbiosis breakdown.

Influenza surveillance has typically been carried out using influenza-like illness (ILI) rates and proportions of laboratory tests positive for influenza as metrics to monitor, with sample sizes for the number of tests to carry out based on the precision of the resulting estimate of proportions positive. The transition out of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic period has encouraged the establishment of integrated surveillance of respiratory pathogens, in the context of multiple surveillance objectives, as set out by WHO in its revised integrated surveillance guidance and Mosaic Respiratory Surveillance Framework. These objectives include outbreak detection, situational awareness and intensity evaluation, among others. We illustrate how to design respiratory surveillance in primary care, by considering multiple surveillance objectives for different metrics of different types of respiratory pathogen circulation seasons in England, the USA and Hong Kong. We focus on a proxy of influenza activity as a metric to compare between these countries/regions. Taking advantage of England's integrated sentinel primary care surveillance system, we propose further metrics to monitor: a proxy of respiratory activity, novelly defined as the product of an acute respiratory infection (ARI) consultation rate and the proportion of tests positive for \emph{at least one pathogen}; pathogen-specific ARI-based activity proxies for more detailed monitoring of influenza and SARS-CoV-2; and integrated monitoring of proportions positive for all pathogens tested. We use a simulation approach to determine sample sizes by optimising either the probability of, or time to, detection of different events in monitored metrics, according to the different surveillance objectives. We find that sample sizes to maximise detection probabilities or minimise detection times vary by metric, objective, event and country/region. At a national level, the current sample sizes used are sufficient to detect most events in most weeks for both the USA and Hong Kong, but for England the numbers of swabs taken for ILI consultations may not be sufficient in all weeks, particularly at the start of the season when outbreak detection is important. However, broadening the criteria for swabbing to acute respiratory symptoms does allow for sufficient sample sizes.

Beta-band (13-30 Hz) oscillations are increasingly understood to occur as transient "bursts" rather than sustained rhythms, with altered burst dynamics, specifically increased duration and power alongside reduced burst rates, in patients with Parkinsons disease (PD). In this study, we utilise resting state magnetoencephalography (MEG) data from healthy adults to quantify the temporal fluctuations in the beta-band, and examine the distributions of burst statistics. We then fit a stochastic next-generation neural mass model to these empirical statistics using a Genetic Algorithm. Systematic parameter sweeps reveal that reducing background drive to excitatory and inhibitory neuronal populations reproduces the altered burst statistics observed in PD. Crucially, we show that strengthening synaptic coupling can counteract these deficits and restore healthy bursting dynamics. Together, this work establishes a computational framework linking cellular-level mechanisms to macroscale burst statistics, and highlights potential targets for therapeutic neuromodulation in movement disorders. Author summaryBrain activity is comprised of rhythmic electrical patterns called "brain waves." Traditionally, these waves were viewed as smooth and continuous, but recent evidence reveals that they actually occur in brief, intense bursts. In conditions such as Parkinsons disease, these bursts become altered--lasting longer, growing stronger, and occurring less frequently. In this study, we developed a mathematical model of brain tissue to understand what drives these burst patterns. Using real brain scans from healthy human volunteers, we tuned our model with an optimisation algorithm until its simulated bursts closely matched real human brain activity. We then systematically varied the models settings to investigate how abnormal bursting arises in disease. We discovered that reducing the background signals to the brain cells reproduces the burst alterations seen in Parkinsons disease. Importantly, our simulations showed that strengthening the connections between brain cells can counteract this deficit, restoring healthy burst patterns. By connecting microscopic cell properties to whole-brain rhythms, our work offers new insights into how movement disorders disrupt brain networks and highlights potential cellular targets to guide future brain stimulation therapies or medications.

Spectroscopic single-molecule localization microscopy (sSMLM) enables multiplexed super-resolution imaging by simultaneously acquiring the spatial position and spectral information of individual fluorophores. Dual-wedge prism (DWP)-based implementations provide a compact, alignment-stable approach to spectral dispersion, but trade-offs between localization precision, spectral precision, and experimental complexity remain. We systematically compare five DWP-based sSMLM configurations, including two-dimensional (2D) and three-dimensional (3D) implementations using single DWP (DWP-sSMLM) and symmetrically-dispersed DWP (SDDWP-sSMLM). We evaluate lateral precision, spectral precision, and ease of use. SDDWP configurations acquire spectral images in both channels and utilize both for spatial localization, yielding the highest lateral and spectral precision. However, for applications that do not require axial information, 2D-DWP provides a simple, plug-and-play solution with robust performance. This work offers a guideline for selecting DWP configurations based on experimental needs.

Selecting stochastic gene-expression models from single-cell counts requires accurate parameter inference and efficient model selection. Likelihood methods in count space can be costly when full stationary count distributions are unavailable, whereas approximate methods may lose accuracy. Probability generating functions (PGFs) offer a compact analytical alternative, but existing PGF workflows are generally not likelihood based and therefore rely on computationally intensive cross-validation. We develop a likelihood-based PGF framework for both tasks. Correlated empirical PGF values are used to construct a Gaussian quasi-likelihood for parameter inference and PGF-based Bayesian information criterion (BIC) for model selection. We show that the empirical PGF is exactly unbiased and that the parameter estimator is consistent, converges at the inverse-square-root sample-size rate, and is first-order asymptotically unbiased. For large samples and a uniquely preferred model, PGF-BIC selects the same model as leave-one-out cross-validation in PGF space.

Living cells are highly dynamic and densely crowded environments in which organelles such as vesicles undergo continuous motion that is essential for cellular processes. Therefore, accurate tracking of individual organelles is crucial for understanding intercellular dynamics and functions. However, precise tracking of individual organelles in living cells remains challenging due to high organelle densities, frequent particle overlap, and the coexistence of stationary and motile organelles. In particular, stationary organelles can obscure the trajectories of moving organelles, leading to tracking errors and fragmented tracks. To overcome these challenges, we developed Multiple Particle Tracking via Velocity Filtering (MPT-vVF), an unbiased, semi-automated tracking framework that incorporates a mathematically derived velocity-filtering algorithm to selectively identify and track moving organelles with high accuracy in crowded intracellular environments. MPT-vVF integrates denoising, background subtraction, and a velocity-matching detection step that discriminates true particle motion from noise based on spatiotemporal continuity, followed by robust trajectory linking. We demonstrate that MPT-vVF can accurately resolve nanometer-scale displacements of immobilized beads, highlighting its high tracking precision. We also validate the robustness of MPT-vVF by quantifying the transport of brain-derived neurotrophic factor (BDNF)-mRFP-containing vesicles in living hippocampal neurons. Furthermore, MPT-vVF reveals that exposure to 50-nm nanoplastics impairs vesicular transport, reducing both travel length and speed of BDNF-containing vesicles in living neurons. These findings establish MPT-vVF as a powerful method for quantitative analysis of intracellular organelles in crowded living cells and suggest its broad application to biophysics, cell biology, and soft matter research.

Determining the amount of information a neuron receives per unit of time is key to understanding brain connectivity and how neural networks encode and transmit information. In particular, estimating this information flow by distinguishing between excitatory and inhibitory synaptic contributions is critical to understanding neural network function, as maintaining the excitation-inhibition (E/I) balance regulates neuronal excitability and circuit stability, whereas its disruption can lead to a plethora of brain disorders, including neurodegenerative and psychiatric conditions. However, because synaptic conductances cannot be measured directly, inverse methods are required to infer them from the membrane potential --a readily measurable quantity. Although partial solutions have been proposed, accurately estimating these conductances remains a significant challenge due to the complexity and diversity of the inputs. This is particularly true in the spiking regime, where neurons actively fire. In this work, we introduce a novel computational strategy that combines two critical metrics extracted from the time course of the membrane potential recording: the amplitude of the spike and the interspike interval. By using these quantities, the proposed method enables the accurate separation of excitatory and inhibitory contributions, yielding highly favorable results in the spiking regime. Author summaryQuantifying the continuous stream of inputs a neuron receives is key to understanding brain connectivity. Inside the brain, individual cells must maintain a tight balance between excitation and inhibition (E/I) to process information correctly, as any disruption in this equilibrium can impair its functionality. However, directly measuring the underlying excitatory and inhibitory synaptic conductances is technically challenging, and existing mathematical tools often fail when neurons enter their active firing regime. In this work, we introduce a novel computational strategy designed to extract and separate these time-varying conductances directly from the neurons spiking activity. By dynamically tracking just two accessible metrics - the amplitude of the spikes and the time intervals between them - our algorithm estimates both conductance profiles with high precision. Furthermore, we demonstrate that this procedure is highly robust against realistic experimental noise and data variability, providing an accessible framework that does not require complex hardware or an unfeasible number of repetitive experimental trials. By tracking changes in the E/I ratio of the synaptic input, this method provides an efficient approach to detecting pathological imbalances and understanding how local connectivity shapes cellular functionality.

Bacterial sexually transmitted infections (STIs) pose a major global public health challenge, with Neisseria gonorrhoeae being of particular concern due to its persistently high prevalence and increasing antimicrobial resistance. The emergence of multidrug-resistant strains has narrowed treatment options, highlighting the importance of prevention. In this context, knowing whether there is superspreading (transmission heterogeneity) within a population becomes crucial for accurate public health measures. However, classic methods to quantify superspreading rely on dense contact tracing, and this is not always feasible. As an alternative, we can use Bayesian phylodynamic modelling to infer transmission dynamics, including superspreading. Yet modelling transmission dynamics using bacterial data remains problematic, although it is widely used for viral data. Here, we apply a multi-type birth-death model parametrised to quantify superspreading in N. gonorrhoeae outbreaks, estimating the fraction and relative impact of superspreaders and reproductive numbers for superspreaders and non-superspreaders. We also use a hierarchical modelling strategy with partial pooling to increase the power for detecting superspreading in each cluster. Model performance was successfully evaluated across a range of superspreading scenarios using both transmission-informed phylogenies and sequence data with phylogenetic uncertainty. Application to empirical genomic data revealed a substantial role of superspreading in N. gonorrhoeae transmission during the COVID-19 pandemic in Australia. These results highlight the impact of superspreading in N. gonorrhoeae transmission and the importance of detecting it to efficiently stop the dissemination of the disease

Anticipating which health zone will report the next confirmed case is operationally distinct from forecasting national case counts and matters for prepositioning response capacity; most spatial spread models rely on mobile-phone mobility data unavailable in the Democratic Republic of the Congo (DRC). We modelled the discrete-time hazard of a first reported confirmed case across 106 health zones in four provinces affected by the 2026 Bundibugyo virus disease outbreak (47 affected, 59 at risk, 26 July 2026), comparing four connectivity specifications,none, road-distance, a gravity score, and an incidence-weighted force-of-infection (FOI) term, fitted within an identical Bayesian hierarchical hazard architecture. Evaluation used a rolling-origin design, cluster bootstrap resampling, leave-one-origin-out and non-overlapping-origin checks, and a kernel-parameter sensitivity grid, with top-10 hit rate the pre-specified primary metric, matched to the operational question of which few zones warrant attention; AUC-PR, top-5 hit rate, and median rank percentile were secondary. FOI had the highest top-10 hit rate (42.6%), approaching conventional significance against road-distance and no-connectivity comparators. On AUC-PR, a model with no connectivity term performed as well as or better than any connectivity specification (0.437 vs. 0.409 for FOI), a discrepancy we report rather than omit. Rankings were stable across the sensitivity grid (Spearman; 0.90-0.99) and across robustness checks. An incidence-weighted connectivity term modestly and specifically improves identification of the highest-risk zones, concentrated in top-k ranking rather than uniform across metrics. The evaluation is pseudo-prospective, since historical data-vintage snapshots could not rule out retrospective revision, pending verification via a pre-registered top-20 ranking. Keywords: Bundibugyo virus disease; Ebola; spatial epidemiology; hazard model; Bayesian statistics; Democratic Republic of the Congo; disease surveillance

While light-sheet microscopy is emerging as a robust method for volumetric imaging with improved axial resolution, its capability regarding two-dimensional, surface-level mapping is often hindered by limitations in data redundancy and reconstruction efficiency stemming from volumetric registration methods. We demonstrate that a multiview imaging approach in an axially-swept, dithered light-sheet microscope paired with computational image reconstruction of view projections is able to address these trade-offs to enable large-scale mapping of surface structural features, leveraging the advantages of multiview light-sheet in scalable field of view, working distance, and near isotropic resolution across the entire imaging depth. To aid in the acquisition and analysis of two-dimensional surface structures, we present a tailored surface mapping workflow and a Fiji plugin for computational reconstruction, promoting robust and comprehensive visualization of surface features of uncleared volumetric samples. Our strategy, termed projection reconstruction for imaging surface morphology (PRISM), integrates axially swept dithered light-sheet microscopy and post-processing software for multiview imaging. The imaging hardware enables near-isotropic resolution across its entire field of view, while the software implementation leverages rigid and affine transformations to align two-dimensional projections of multiview samples. It is designed to work with the BigStitcher pipeline, leveraging its robust algorithm to provide support for two-dimensional image alignment and stitching. We demonstrate the capability of PRISM in studies of lymphatic network mapping in the epicardial layer of intact mouse hearts, as well as surface profiles of FaDu spheroids labeled with antibody-nanodiamond conjugates. This method allows us to quantify cardiac lymphatic branch numbers, diameters, and lengths of a Prox1-tdTomato mouse cardiac model, as well as cluster number and diameters of epidermal growth factor receptor within a FaDu spheroid labeled with a nanodiamond-antibody conjugate, with a significant reduction of post-processing data size. PRISM leverages multiview image projections to promote studies of cardiac lymphatics in mouse models and surface receptor distributions within spheroid models, enabling efficient surface mapping of large, intact, and uncleared biological samples across a variety of scales.

Quantification of microbial growth inhibition is central to assays ranging from antibiotic susceptibility of bacteria to sensitivity of yeasts to antifungal therapeutics. Classical analysis approaches derive from zone-of-inhibition (termed halo) formats using filter paper discs, spanning methods from laser detection to machine learning. However, these tools struggle with non-uniform halos, fail to account for lawn density variability despite its experimental influence, and lack accessible, reproducible code. Here, we present Halo Unbiased Measurement of growth Inhibition (HaloUMI); an open-source Python graphical user interface for automated, high-throughput analysis of lawn-based microbial assays. HaloUMI integrates robust image processing with physics-informed models to quantify inhibition zones irrespective of shape, enabling accurate segmentation of uniform and irregular halo phenotypes. This analysis pipeline incorporates the critical correction for spatial heterogeneity in lawn density, improving reproducibility across experimental conditions. The software enhances usability without sacrificing precision, allowing rapid batch processing and intuitive parameter control. HaloUMI can be applied to multiple assay types, including yeast toxin halo, microbial mating, and conventional filter paper disc assays. It yields high-precision measurement of halo size and morphology, with improved consistency compared to standard thresholding and circular fitting. By combining accessibility, flexibility, and biophysical modelling, HaloUMI provides a quantitative framework for irregularly shaped halos of lawns of varying growth potential, enabling generalisable analysis of broad microbial interactions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/743694v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1bd7c88org.highwire.dtl.DTLVardef@13ac9b6org.highwire.dtl.DTLVardef@9108e1org.highwire.dtl.DTLVardef@1de06bd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Based on our experimental observation of activation state oscillations of different frequencies used to communicate information by the master human stress response regulator protein p38 MAPK 1, we develop a simple graphical approach for understanding and predicting the behavior of complex biological networks acting upon signals carrying information as different frequency waves of chemicals. This approach uses the same techniques used for analyzing and understanding information transmission using waves of electrical currents and fields used in electrical alternating current (AC) circuits. We show how biological components can be organized to behave as standard components found in electronic telecommunications circuits. Finally, we demonstrate how such components can be organized into complex biological signaling cascades whose behavior can be qualitatively and quantitatively understood, and whose output resembles that experimentally observed in p38.

Surface phenotyping underpins plant science, preclinical animal research and entomology, yet across all three the measurement is almost always a photograph, which records a projection and not the surface itself. Here we present the Gentschinator3000, an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components. It resolves a planar reference to 45 m local flatness, registers full rotations to a loop closure of 156 m, and performs stably across acquisition ranges that we define. Applying one workflow to a leaf before and after desiccation, to murine anatomy and to a spread lepidopteran, we find that projection underestimates surface area by 11 to 41 %. That error grows with the condition under study, with the evaluation scale and with the direction of view, so it can confound phenotype comparisons dramatically. In murine limbs a 15-degree change of viewing direction shifts a projected inter-segment angle by up to 23.2 degrees, while the three-dimensional angle does not move. Projection geometry can therefore contribute as much to a measured phenotype as the biology it is meant to quantify.

Longitudinal household studies, combined with mathematical modeling, are widely used to characterize the drivers of respiratory pathogen transmission, including the effects of age and symptoms. In practice, household recruitment protocols vary across studies, potentially introducing biases into observed data. However, these biases are typically overlooked in statistical inference, and their impact on parameter estimates remains unknown. Here, we use synthetic household outbreak data simulated under different recruitment protocols to evaluate how recruiting through infected children affects estimates of age-specific infectiousness and susceptibility. We show that, under child-based recruitment, the standard likelihood, which accounts only for transmission dynamics, leads to underestimating child infectiousness and overestimating child susceptibility by more than 30%. We then propose a novel estimation framework that explicitly incorporates the household recruitment process into the likelihood and show that it substantially reduces these biases. Applying this new approach to a French household study conducted during the COVID-19 pandemic, we estimated that children under 6 had 49% lower infectiousness than teenagers and adults during the Alpha wave, whereas no difference was observed during the Omicron wave. This study demonstrates that ignoring recruitment protocols can bias key epidemiological parameter estimates and highlights the importance of accounting for study design.