Timely and accurate forecasts of seasonal influenza would assist public health decision-makers in planning intervention strategies, efficiently allocating resources, and possibly saving lives. For these reasons, influenza forecasts are consequential. Producing timely and accurate influenza forecasts, however, have proven challenging due to noisy and limited data, an incomplete understanding of the disease transmission process, and the mismatch between the disease transmission process and the data-generating process. In this paper, we introduce a dynamic Bayesian (DB) flu forecasting model that exploits model discrepancy through a hierarchical model. The DB model allows forecasts of partially observed flu seasons to borrow discrepancy information from previously observed flu seasons. We compare the DB model to all models that competed in the CDC's 2015--2016 flu forecasting challenge. The DB model outperformed all models, indicating the DB model is a leading influenza forecasting model.
The characteristics of influenza seasons varies substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the societal impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. A subset of participating teams has worked together to produce a collaborative multi-model ensemble, the FluSight N…
In temperate climates, mortality is seasonal with a winter-dominant pattern, due in part to pneumonia and influenza. Cardiac causes, which are the leading cause of death in the United States, are also winter-seasonal although it is not clear why. Interactions between circulating respiratory viruses (f.e., influenza) and cardiac conditions have been suggested as a cause of winter-dominant mortality patterns. We propose and implement a way to estimate an upper bound on mortality attributable to winter-dominant viruses like influenza. We calculate 'pseudo-seasonal' life expectancy, dividing the y…
As COVID-19 pandemic progresses, severe flu seasons may happen alongside an increase in cases in cases and death of COVID-19, causing severe burdens on health care resources and public safety. A consequence of a twindemic may be a mixture of two different infections in the same person at the same time, "flurona". Admist the raising trend of "flurona", forecasting both influenza outbreaks and COVID-19 waves in a timely manner is more urgent than ever, as accurate joint real-time tracking of the twindemic aids health organizations and policymakers in adequate preparation and decision making. Und…
Public health surveillance systems often fail to detect emerging infectious diseases, particularly in resource limited settings. By integrating relevant clinical and internet-source data, we can close critical gaps in coverage and accelerate outbreak detection. Here, we present a multivariate algorithm that uses freely available online data to provide early warning of emerging influenza epidemics in the US. We evaluated 240 candidate predictors and found that the most predictive combination does \textit{not} include surveillance or electronic health records data, but instead consists of eight …
For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the current epidemic trends. Here we present a methodology, ARGOX (Augmented Regression with GOogle data CROSS space), for accurate real-time tracking of state-level influenza epidemics in the United States. ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for …
We present results of a forecast initiated Week 49 (beginning December 9, 2012) of the 2012-2013 influenza season for municipalities in the United States. The forecast was made on December 14, 2012. Results from forecasts initiated the two previous weeks (Weeks 47 and 48) are also presented. Also results from the forecast generated with the SIRS model without AH forcing (no AH) are shown
Accurate real-time monitoring systems of influenza outbreaks help public health officials make informed decisions that may help save lives. We show that information extracted from cloud-based electronic health records databases, in combination with machine learning techniques and historical epidemiological information, have the potential to accurately and reliably provide near real-time regional predictions of flu outbreaks in the United States.
We present results of a forecast initiated following assimilation of observations for week Week 50 (i.e. the forecast begins December 16, 2012) of the 2012-2013 influenza season for municipalities in the United States. The forecast was made on December 21, 2012. Results from forecasts initiated the three previous weeks (Weeks 47-49) are also presented. Also results from forecasts generated with an SIRS model without absolute humidity forcing (no AH) are shown.
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) for municipalities in the United States. These forecasts were performed on January 11, 2013. Results from forecasts initiated the six previous weeks (Weeks 47-52) are also presented. The accuracy of these predictions will not be known for certain until the conclusion of the current influenza season; however, at the moment a number of the forecasted peaks appe…
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 we present a flexible influenza forecasting model called Dante, a multiscale flu forecasting model that learns rather than prescribes spatial, temporal, and surveillance data structure. Forecasts at the Health and Human Services (HHS) regional and national scales are generated as linear combinations of state forecasts with weights proportional to US Census …
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 factors, including restricted access to preventative and therapeutic health care, limited sick leave, and household structure. Adequate influenza surveillance in these at-risk populations is a critical precursor to accurate risk assessments and effective intervention. However, the United States of America's primary national influenza surveillance system (ILINet…
We propose a method for obtaining joint probabilistic projections of migration rates for all countries, broken down by age and sex. Joint trajectories for all countries are constrained to satisfy the requirement of zero global net migration. We evaluate our model using out-of-sample validation and compare point projections to the projected migration rates from a persistence model similar to the method used in the United Nations' World Population Prospects, and also to a state of the art gravity model. We also resolve an apparently paradoxical discrepancy between growth trends in the proportion…
Predicting the intensity and amount of sunlight as a function of location and time is an essential component in identifying promising locations for economical solar farming. Although weather models and irradiance data are relatively abundant, these have yet, to our knowledge, been hybridized on a continental scale. Rather, much of the emphasis in the literature has been on short-term localized forecasting. This is probably because the amount of data involved in a more global analysis is prohibitive with the canonical toolkit, via the Gaussian process (GP). Here we show how GP surrogate and dis…
Seasonal influenza infects between 10 and 50 million people in the United States every year, overburdening hospitals during weeks of peak incidence. Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza-like illness (ILI) forewarn public health officials about when, and where, seasonal influenza outbreaks will hit hardest. Multi-model ensemble forecasts---weighted combinations of component models---have shown positive results in forecasting. Ensemble forecasts of influenza outbreaks have been static, training on al…
As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread is crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes has been previously shown to contain valuable information for tracking infectious disease dynamics. In this study, we evaluate the feasibility of using Internet search volume of relevant queries to track and predict COVID-19 …
Calibration-guided data fusion: A framework for understanding data contribution in multi-source inference through efficient Bayesian optimization with application to virus dynamics parameter estimation
Independent Researcher（独立研究者）；Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland（赫尔辛基大学数学与统计系）；Finnish Center for Artificial Intelligence (FCAI), Espoo, Finland（芬兰人工智能中心）；RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), Wako, Japan（理化学研究所跨学科理论与数学科学中心）
Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden. In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the …
