Epidemics

Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestive...

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...

Understanding the spatiotemporal dynamics of infectious disease spread is critical for anticipating epidemic trajectories and guiding public health responses. Accurate forecasts of where and when outbreaks are likely to emerge can support efficient resource allocation, particularly during the early stages of epidemics when surveillance data are limited. In this study, we used empirical human mobility data derived from county-level commuting and air traffic flows, and a theoretical mobility model...

Scoring rules are critical for evaluating the predictive performance of epidemic models by quantifying how well their projections and forecasts align with observed data. In this study, we introduce the energy score as a performance metric for stochastic trajectory-based epidemic models. As a multivariate extension of the continuous ranked probability score (CRPS), the energy score provides a single, unified measure for time-series predictions. It evaluates both calibration and sharpness by consi...

In 2023 the European Centre for Disease Prevention and Control (ECDC) launched RespiCast, the first European Respiratory Diseases Forecasting Hub, to provide probabilistic forecasts for influenza-like illness (ILI) and acute respiratory infection (ARI) incidence across 26 European countries. During the 2023/24 and 2024/25 winter seasons, RespiCast collected one- to four-week-ahead forecasts from multiple models contributed by different international teams and combined them into an ensemble. Our ...

The coronavirus disease 2019 (COVID-19) pandemic disrupted daily life and changes to routines were made in accordance with public health regulations. In 2020, nonpharmaceutical interventions were put in place to reduce exposure to and spread of the disease. The goal of this work was to quantify the effect of school closure during the first year of COVID-19 pandemic in Switzerland. This allowed us to determine the usefulness of school closures as a pandemic countermeasure for emerging coronavirus...

BackgroundDespite only comprising about a quarter of the total population of Canada, foreign-born individuals bear about three-quarters of the burden of active tuberculosis (TB) cases. New immigrants arriving in Canada are screened for active TB, but generally not for latent TB infection (LTBI); thus the burden of LTBI among foreign-born Canadians is not well understood. MethodsTo investigate the impact of immigration on the burden of TB among foreign-born Canadians, we develop an SEIR-compartm...

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...

Understanding the spatiotemporal dynamics of seasonal influenza spread across the United States (US) is crucial for informed public health planning. We explored patterns of influenza transmission during the 2022/23 season in the US and used a mathematical model to infer potential drivers and underlying mechanisms. Leveraging emergency department visit data, we first estimated the timing of influenza onset for the 2022/23 season at the Health Service Area (HSA) level. We then combined the estimat...

BackgroundSince the emergence of the COVID-19 pandemic, substantial concern has surrounded its impact among the Rohingya refugees living in the Kutupalong-Balukhali refugee camps in Bangladesh. Early modeling work projected a massive outbreak was likely after an introduction of the SARS-CoV-2 virus into the camps. Despite this, only 317 laboratory-confirmed cases and 10 deaths were reported through October 2020. While these official numbers portray a situation where the virus has been largely co...

Models of infectious disease dynamics should align the spatial scale of mobility data to the scale of travel relevant to infer disease introduction events and subsequent local transmission. Despite this, the biases of spatially aggregating mobility data, which are more commonly available, on model inferences are rarely explored. Here, we examine the sensitivity of infectious disease modeling results to different spatial scales of human mobility by integrating multiscale mobility data from Sri L...

Accurate models are fundamental to understand the dynamics of the COVID-19 pandemic and to evaluate different mitigation strategies. Here, we present a multi-compartmental model that fits the epidemiological data for eleven countries, despite the reduced number of fitting parameters. This model consistently explains the data for the daily infected, recovered, and dead over the first six months of the pandemic. The good quality of the fits makes it possible to explore different scenarios and eval...

BackgroundHighly Pathogenic Avian Influenza (HPAI) is a prominent candidate for a future human pandemic arising from a zoonotic spillover event. Its best-known subtype is H5N1, with South-or South-East Asia a likely location for an initial outbreak. Such an outbreak would be initiated through a primary event of bird-to-human infection, followed by sustained human-to-human transmission. Early interventions require the extraction, integration and interpretation of epidemiological information from ...

The SARS-CoV-2 (COVID-19) pandemic has had catastrophic effects on public health and economies. Around the world, many countries employed modelling efforts to help guide pharmaceutical and non-pharmaceutical measures designed to reduce the spread of the virus. Modelling efforts for future pandemics could use the theory of early warning signals (EWS), which aims to predict critical transitions in complex dynamical systems. In infectious disease systems, such transitions correspond to (re-)emerge...

Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights. In this work, we examined the applicat...

The COVID-19 pandemic highlighted shortcomings in forecasting models, such as unreliable inputs/outputs and poor performance at critical points. As COVID-19 remains a threat, it is imperative to improve current forecasting approaches by incorporating reliable data and alternative forecasting targets to better inform decision-makers. Wastewater-based epidemiology (WBE) has emerged as a viable method to track COVID-19 transmission, offering a more reliable metric than reported cases for forecasti...

During epidemic outbreaks, populations adapt their behavior in response to disease burden, fundamentally altering transmission dynamics. Despite this, most compartmental models assume constant contact rates throughout outbreaks. To quantify biases from this assumption, we fitted a baseline SEIRD model with constant transmission and three behavioral variants--incorporating mortality-driven transmission reduction via exponential, rational, and mixed functional forms--to COVID-19 mortality data fro...

BackgroundDecision-making in public health is limited by data availability where the most recent reports do not reflect the actual trajectory of an epidemic. Nowcasting is a modeling tool that can estimate eventual case counts by accounting for reporting delays. While these tools have generated reliable predictions when designed for specific use cases, several limitations exist when scaling the models to systems composed of multiple distinct surveillance systems. We seek to identify flexible app...

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 ...