PLOS Computational Biology

The time-varying reproduction number (Rt) is a critical quantity in monitoring an infectious disease outbreak. We propose a new method for estimating Rt from an infectivity profile, expressed as a generation time distribution, and a time series of probabilistic estimates of disease incidence, modelled as log-normally distributed random variables. This is a common output of disease incidence models that are based on Poisson or negative binomial regression of case counts with a logarithmic link fu...

Biological and behavioral differences between genders influence infectious disease dynamics. Yet, most epidemiological models overlook these aspects in favor of age stratification alone. Here, we systematically evaluate the impact of incorporating gender-specific features into an age-structured epidemic compartmental model, calibrated to COVID-19 mortality data from the second wave in Italy (Autumn 2020-Winter 2021). We develop eight model versions representing different combinations of three da...

Assessing epidemic risk following pathogen introduction is crucial in infectious disease epidemiology. Risk is commonly encoded through reproduction ratios, which underpin operational decision-making. In spatially structured populations, both local and cross-community transmission shape epidemic trends, a feature that standard reproduction ratios fail to capture simultaneously. Here, we use multitype branching processes to define the outbreak reproduction ratio Rob, a reformulation applicable ac...

1The timeliness of infectious disease surveillance systems largely determines the speed at which mitigation interventions may be implemented. However, it is unclear how surveillance timeliness evolves during a pandemic with changing government policies, testing tools, and population-level infection and immunity landscapes. Here, we adapt an agent-based model for COVID-19 transmission to explore the timeliness of the surveillance signals obtained from polymerase chain reaction (PCR) and rapid ant...

This paper presents a smoothing method to estimate age-specific human contact patterns and their variations over different periods. Specifically, it examines how age-specific contact patterns shift under varying conditions, such as holiday periods and levels of public health intervention. The method uses Bayesian P-splines to smooth age-specific contact rates and leverages Laplace approximations for fast Bayesian inference, significantly reducing computational complexity. The proposed methodolog...

Mathematical models of infectious disease dynamics are routinely fitted to surveillance data to estimate epidemiological parameters and inform public health decisions. Such data are typically discrete and noisy, but before attempting estimation, it is essential to ask whether the model structure itself permits unique parameter identification at least under perfect (continuous, noise-free) observations. This mathematical property of a model with respect to observation(s), known as structural iden...

BackgroundViral interference, in which infection with one pathogen reduces susceptibility to another, may influence respiratory virus dynamics. Inference from surveillance data is complicated by time-varying testing behavior that can induce correlated detection patterns independent of biological interaction. MethodsWe developed a multi-pathogen renewal model augmented with a ratio penalty that constrains interference estimates to be consistent with observed log-odds ratios of pathogen positivit...

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 ...

Early during emerging infectious disease outbreaks, case-based surveillance is constrained by limited test availability, diagnostic delays, and low clinical suspicion. Novel pathogens that mimic symptoms of established conditions may generate detectable outbreak signals in routine testing data, as infected individuals seek testing for known conditions and test negative. We developed analytic and simulation frameworks using Poisson and negative binomial models to evaluate whether total and negati...

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative approaches beyond traditional epidemiological models. This study introduces an advanced method for automated model discovery using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, leveraging a dataset from the COVID-19 outbreak in Thuringia, Germany, encompassing over 400,000 patient records and vaccination data. By analysing this dat...

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...

Wastewater-based epidemiology provides a low-cost, scalable view of community infection dynamics, but converting these signals into actionable epidemiological insights remains difficult. Mechanistic models offer interpretability, yet, assumptions such as a constant transmission rate limit realism over long simulation horizons and heterogeneous settings. We present a susceptible-exposed-infectious-recovered (SEIR) universal differential equation (UDE) that links wastewater viral loads to case cou...

BackgroundIn public health surveillance, silence--the absence of data--is often more significant than the signal. Traditional epidemiological mapping tools efficiently visualize data density but struggle to mathematically define data absence. Standard approaches conflate stochastic sparsity with systemic suppression and remain vulnerable to edge effects. MethodsWe introduce a topological framework that detects structural voids--regions of unexpected data absence within clusters. Using Distance-...

Nosocomial transmission of respiratory infections poses a major threat to patient safety, while also affecting healthcare workers (HCW) health, generating substantial costs for hospitals. These infections spread through both close-proximity interactions at short distances, and via aerosols that remain suspended in the air, enabling long-range transmission. The relative contribution of each transmission route is pathogen-dependent, and evidence to distinguish them remains scarce. Here, we propose...

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...

1Parameter estimation is often necessary to inform transmission models of infectious diseases. This estimation requires choosing an observation model that links the model outputs to the observed data. Although potentially consequential, this choice has received little attention in the literature. Here, we aimed to compare eight observation models, including common distributions such as the Poisson, binomial, negative binomial, and normal (equivalent to least-squares estimation). Using Bayesian i...

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...

BackgroundEpidemic forecasting research often assesses ensembles and their component models using probabilistic scoring rules. Quantifying how individual models affect ensemble performance is challenging, particularly across multiple targets and spatial scales. MethodsWe present Winter 2024-25 forecasts of Influenza and COVID-19 hospital admissions in England and conduct a retrospective simulation using the operational component models. Forecasts were scored using the per capita weighted interv...

Human contact network structure fundamentally shapes infectious disease transmission and control. Most COVID-19 epidemic models assumed approximately homogeneous contact patterns, yet real-world networks are highly heterogeneous. We analysed 59,585 daily non-household contact reports from Germanys COVIMOD study (2020-2021) using a novel heavy-tail regression framework. Throughout the pandemic, contact distributions remained strongly heavy-tailed despite substantial non-pharmaceutical interventio...

During an outbreak, infectious disease can spread among populations through host movement, potentially fueling local outbreaks with their own epidemiological dynamics. However, it is difficult to know how often infections between populations are transmitted by diseased travelers infecting healthy residents when abroad, rather than by diseased residents infecting healthy travelers, who later return home with the new pathogen. In this paper, we introduce a phylogeographic model where pathogens spr...