Epidemics

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 ...

Infectious disease forecasts can inform public health decision-making. Wastewater monitoring is a relatively new epidemiological data source with multiple potential applications, including forecasting. Incorporating wastewater data into epidemiological forecasting models is challenging, and relatively few studies have assessed whether this improves forecast performance. We present and evaluate a semi-mechanistic wastewater-informed forecasting model. The model forecasts COVID-19 hospital admissi...

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 ...

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...

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...

Increasing human mobility and population connectivity have intensified the risks of global pathogen spread, while concurrent shifts in human demographic patterns, ecological factors, and climatic conditions have altered the global landscape of this risk. Genomic surveillance can serve as a critical tool for early detection of emerging pathogen threats; however, challenges remain in deciding where to monitor, in understanding trade-offs among surveillance modalities, and in translating detections...

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...

A concern in infectious disease modelling is how accurately population mixing is incorporated, as it shapes the type and frequency of contacts through which infection spreads, and consequently, estimated intervention effectiveness. Although synthesizing mixing patterns from diary-based surveys is an established framework, geographical information is poorly or sparsely captured. Here we propose a generalizable workflow to quantify geographical connectivity from job registry data covering over 8 m...

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...

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...

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...

Hepatitis E virus (HEV) is considered a predominantly foodborne pathogen in developed settings. During COVID-19 lockdown periods, however, HEV concentrations in wastewater at a treatment plant in Munich, Germany decreased, suggesting that pandemic-related behaviour changes inadvertently influenced transmission. In contrast, reported cases and wastewater data from a smaller catchment showed no comparable decline. To assess whether the observed reduction is compatible with a near-exclusively foodb...

The two largest US measles outbreaks in over two decades (2025 Gaines County, Texas: 414 cases, contained; 2025-2026 Spartanburg County, South Carolina: 923+ cases, ongoing) occurred in counties with similar sub-threshold K-12 MMR coverage (85.1% vs 88.8%), yet their trajectories diverged dramatically. Using kernel density estimation with a common bandwidth and bootstrap uncertainty quantification, we compared sub-county vaccination data at the district level for Texas (3 districts, 3,560 studen...

BackgroundRoutine immunization (RI) is widely used to increase population immunity against measles. In low-resource settings, achieving immunity goals using RI alone has proved challenging and supplemental immunization activities (SIAs), large community-based vaccination campaigns conducted every few years, have been used to close immunity gaps. Although effective at covering the population unreached by RI and boosting the population immunity, SIAs are labor-intensive and expensive, allowing for...

BackgroundStrikingly low allocation of SARS-CoV-2 vaccine to the African Continent limits its capacity to control transmission. Characterizing the trajectory of vaccination efforts and their impact on the expected burden of SARS-CoV-2 will help planning vaccine delivery strategies, and public health interventions more broadly. As the burden is strongly age-dependent, this requires an understanding of the age-structured dynamics of susceptible individuals, accounting for the combined effects of v...

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...

The COVID-19 pandemic exposed major vulnerabilities of hospital capacity and management worldwide, particularly in intensive care units (ICUs) and emergency rooms (ER), imposing prompt adaptation and resource reallocation. Although SARS-CoV-2 is no longer endangering healthcare systems, winter seasons continue to bring recurrent overload of critical care services, primarily due to respiratory infections. In France e.g., this pattern led to the reactivation of the national emergency response plan...

We describe a fast, noninvasive, low-cost survey method designed to understand the mode of transmission of an emerging pathogen. It is inspired from the standard household prevalence survey consisting in sampling households and counting the total number of people infected in each household, but refines it with the aim of improving diagnosis and estimating more parameters of the model of intra-household transmission. The survey was carried out in May-June 2020, during part of the first national ...

Dengue is one of the worlds highest-burden arboviral diseases. Although classically considered an urban disease, many regions experience a substantial dengue burden in rural areas. The combined influence of long-term climate, short-term weather variation, local built environments, and land-use gradients on dengue dynamics in rural settings remains poorly understood, limiting our ability to predict shifting risk under global change. Here, we investigate these dynamics in Costa Rica to disentangle...

Programmatic decisions regarding surveillance and intervention for trachoma are made at the district level, reflecting an implicit assumption that transmission within districts is sufficiently homogeneous. However, as trachoma transmission declines, residual pockets of transmission may become spatially heterogeneous at sub-district scales. Using cluster-level data from 12 districts in Amhara, Ethiopia (2019-2023), we assess the spatial structure of Pgp3 antibody responses, a sensitive measure of...