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Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data is updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution…
Influenza forecasting in the United States (US) is complex and challenging for reasons including substantial spatial and temporal variability, nested geographic scales of forecast interest, and heterogeneous surveillance participation. Here…
Accurate and representative data is vital for precisely reporting the impact of influenza in healthcare systems. Northern hemisphere winter 2022/23 experienced the most substantial influenza wave since the COVID-19 pandemic began in 2020.…
Individuals in low socioeconomic brackets are considered at-risk for developing influenza-related complications and often exhibit higher than average influenza-related hospitalization rates. This disparity has been attributed to various…
Forecasting infectious disease incidence can provide important information to guide public health planning, yet is difficult because epidemic dynamics are complex. Current mechanistic and statistical approaches often struggle to capture…
Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are…
Forecasting influenza like illnesses (ILI) has rapidly progressed in recent years from an art to a science with a plethora of data-driven methods. While these methods have achieved qualified success, their applicability is limited due to…
Influenza-like illness (ILI) estimation from web search data is an important web analytics task. The basic idea is to use the frequencies of queries in web search logs that are correlated with past ILI activity as features when estimating…
This study incorporates mathematical analysis, focusing on developing theories and conducting numerical simulations of Influenza virus transmission using real-world data. The terms in the equations introduce parameters which are determined…
This article describes a new method for estimating weekly incidence (new onset) of symptoms consistent with Influenza and COVID-19, using data from the Flutracking survey. The method mitigates some of the known self-selection and…
This is part of a series of weekly influenza forecasts made during the 2012-2013 influenza season. Here we present results of forecasts initiated following assimilation of observations for Week 1 (i.e. the forecast begins January 6, 2013)…
While machine learning is rapidly being developed and deployed in health settings such as influenza prediction, there are critical challenges in using data from one environment in another due to variability in features; even within disease…
Influenza epidemics result in a public health and economic burden around the globe. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1-2 weeks. A means of obtaining real-time data and…
Background: Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative…
Much traditional statistical modelling assumes that the outcome variables of interest are independent of each other when conditioned on the explanatory variables. This assumption is strongly violated in the case of infectious diseases,…
In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important…
In this manuscript, we use meteorological information in Galicia (Spain) to propose a novel approach to predict the incidence of influenza. Our approach extends the GLS methods in the multivariate framework to functional regression models…
Health-policy planning requires evidence on the burden that epidemics place on healthcare systems. Multiple, often dependent, datasets provide a noisy and fragmented signal from the unobserved epidemic process including transmission and…
Influenza, an infectious disease, causes many deaths worldwide. Predicting influenza victims during epidemics is an important task for clinical, hospital, and community outbreak preparation. On-line user-generated contents (UGC), primarily…
Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity. Timely knowledge of the outbreak state is valuable for managing an effective response. The current…