PLOS Computational Biology

Wastewater is increasingly being recognized as an important data stream that can contribute to infectious disease surveillance and forecasting. With this recognition, a growing number of statistical inference approaches are being developed to use wastewater data to provide quantitative insights into epidemiological dynamics. However, few existing approaches have allowed for systematic integration of data streams for inference, for example by combining case incidence data and/or serological data ...

Biological fitness quantifies the efficiency and selective advantage of pathogens and hosts in their bilateral interaction. Key questions--such as how much more infectious an emerging variant is compared with its predecessor, or how much protection vaccination offers relative to no vaccination--require fitness to be measured systematically, in real time, and ideally beyond controlled laboratory settings. We propose an approach that infers biological fitness from mostly non-biological data on inf...

Background: Human-to-human transmission of pathogens fundamentally depends on interactions among infectious and susceptible individuals, yet traditional population-scale models often overlook the stochastic, behaviour-driven, and highly heterogeneous nature of these interactions. Methods: Here, we develop a large-scale actor-based model capturing early epidemic dynamics of a novel respiratory pathogen on dynamic contact networks. We build these networks upon explicitly integrating detailed demog...

We introduce PerTexP (Pertussis Time Exploration), an interactive modelling tool designed to investigate pertussis transmission dynamics and to support the evaluation of vaccination strategies and short-term projections. PerTexP allows users to explore and compare maternal, infant, and non-infant booster vaccination scenarios and to assess their potential impact on disease transmission, with a particular focus on the Italian epidemiological context. The tool is based on a discrete-time, stage-st...

We present results from the second season of Influcast, a multi-model collaborative forecasting hub focused on influenza in Italy. During the 2024/25 winter season, Influcast collected one-to four-week-ahead probabilistic forecasts of influenza-like illness (ILI) incidence alongside influenza A and B ILI+ incidence signals. New ILI+ targets were constructed integrating syndromic surveillance data with virological detections collected weekly by the Italian National Institute of Health. Forecasts ...

Visceral leishmaniasis (VL) is considerably more severe among individuals infected with human immunodeficiency virus (HIV), leading to higher parasite loads, frequent relapse, and increased mortality. To examine the epidemiological interaction between the two diseases, we develop a comprehensive VL-HIV co-infection model that incorporates transmission pathways, treatment effects, and relapse dynamics. The model is parameterized using real-time data from Bihar, India, including monthly VL-only an...

Nipah virus (NiV) is a sporadic yet extremely deadly zoonotic pathogen, with reported case fatality rates of 40%-75% in impacted areas. Prolonged incubation, documented relapse, and delayed-onset encephalitis following apparent recovery indicate that NiV dynamics are influenced by intricate temporal processes. However, mechanistic contributions of these processes to epidemic persistence remain poorly understood. In this study, we develop and analyze a delay differential equation model for NiV tr...

With significant population fractions in many societies who refuse vaccines, it is important to reconsider how vaccination is incorporated into compartmental epidemiology models. It is still most common to apply the vaccination rate to the entire class of susceptibles, rather than to use the more realistic assumption that the vaccination rate function should depend only on the population of susceptibles who are willing and able to receive a vaccination. This study uses a simple generic disease m...

Hybrid controlled trials (HCTs) incorporate real-world data into randomized controlled trials (RCTs) by augmenting the internal control arm with patients receiving the same treatment in routine care. Beyond increasing power, HCTs may improve recruitment by supporting unequal randomization ratios that increase patient access to experimental treatments. However, HCT validity is threatened by bias from unmeasured confounding due to lack of randomization of external controls, leading to outcome non-...

Molecular subtyping of cancer is traditionally defined in transcriptomic space, yet routine clinical deployment is limited by the availability and cost of sequencing. Meanwhile, histopathology captures rich morphological information that is known to correlate with molecular state but lacks a principled, mechanistic bridge to gene-level representations. We propose a graph-constrained learning framework that aligns morphology-derived signals with a fixed, data-driven gene network discovered via hi...

The spread of Anopheles stephensi into the Horn of Africa represents one of the main challenges for malaria control, given the species ecological plasticity and resistance to multiple insecticides. In response to the World Health Organizations 2022 vector alert, an adaptive, model-based spatial surveillance framework was developed and evaluated to improve detection, mapping accuracy, and operational responsiveness during invasion. Adaptive surveillance utilises initial observations to guide subs...

BackgroundArtemisinin-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 distribut...

A critical challenge in endocrine neurosurgery is intraoperative discrimination between normal pituitary tissue and pituitary neuroendocrine tumors (PitNETs). Suggesting the universal persistence of near-infrared autofluorescence (NIRAF) in endocrine organs and inspired by routine clinical use of NIRAF for parathyroid gland identification, we discovered that pituitary NIRAF can be employed for label-free transsphenoidal surgery guidance. Ex vivo confocal spectral imaging of 33 specimens identifi...

Study question: How is glycodelin, a glycoprotein secreted by reproductive tissues, causally related to reproductive diseases and traits? Summary answer: We present evidence for a causal role of sex hormones in determining glycodelin levels, but limited evidence that glycodelin subsequently causally impacts reproductive traits. What is known already: Glycodelin is expressed in female and male reproductive tissues and has four glycoforms (-A, -C, -F and -S), with the glycosylation pattern determi...

BackgroundThe widespread insecticide resistance increasingly threatens malaria elimination, prompting a reassessment of vector control strategies. As Tanzania transitions from standard pyrethroid-only insecticide-treated nets (ITNs) to new-generation nets, evaluating the impact of this shift on malaria transmission and resistance is critical. MethodsUsing the agent-based malaria model, EMOD, we assessed the impact of three ITN types, standard pyrethroid-only nets, pyrethroid-PBO nets (Olyset(R)...

BackgroundThe 2024 blood culture bottle shortage brought diagnostic resource allocation to the forefront, reflecting persistent, foundational challenges with low-value testing and empiric treatment approaches under clinical uncertainty. ObjectiveTo determine whether a machine learning approach using electronic medical record data can predict bacteremia more effectively than existing systems and practices to guide diagnostic testing and empiric treatment strategies. MethodsIn a retrospective co...

Introduction: Manual data extraction from unstructured clinical notes is labor-intensive and impractical for large-scale clinical and research operations. Existing automated approaches typically require large language models, dedicated computational infrastructure, and/or task-specific fine-tuning that depends on curated data. The objective of this study is to enable accurate extraction with smaller locally deployed models using a disease-site specific pipeline and prompt configuration that are ...

Ambient air pollution has been associated with increased incidence of chronic disease and is estimated to contribute towards 4.2 million early deaths annually. Whilst the health impacts are well described, less is understood about the underlying biological mechanisms, particularly when considering the co-occurrence of multiple pollutants. Using an atmospheric chemistry transportation model (EMEP4UK), we generate pre-baseline sampling pollution exposure estimates for eight pollutants in Generatio...

Biological ageing begins before birth, with early-life exposures shaping late-life health. These exposures drive health inequities early, yet specific exposures and the composition of the ageing exposome remain largely undefined. This gap may persist as the field lacks agnostic investigations accounting for non-linearity, interactions and subtle signals. We aimed to identify exposures predictive of epigenetic ageing accumulated during childhood and adolescence and explore the composition of the...

BackgroundPropofol dosing guidelines recommend age-based reductions because hypnotic sensitivity increases in older adults. Most real-world evaluations of induction practice, however, have relied on total weight-normalized dose (mg/kg) rather than estimating cerebral exposure using pharmacokinetic models. Because age-related pharmacokinetic changes alter the relationship between administered dose and peak effect-site concentration (Ce,max), mg/kg surrogates may misrepresent true age-dependent ex...