Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

Reducing the health risks of severe winter weather among older people in the United Kingdom: an evidence-based intervention

Excess winter morbidity and mortality among older people remain significant public health issues in those European countries which experience relatively mild winter temperatures, particularly the United Kingdom (UK), Ireland, Portugal and Spain. In the UK, episodes of severe winter weather, when ambient temperatures fall below 5x C, are associated with peaks in general practitioner consultations,hospital admissions, and cardiovascular deaths among those aged over 65. While research indicates that such health risks could be substantially reduced by the adoption of appropriate behavioural strategies, accessible and credible advice on how older people can reduce risk during ‘cold snaps’ is lacking. This paper describes a programme of research that aimed: (a) to translate the relevant scientific literature into practical advice for older people in order to reduce health risk during episodes of severe winter weather ; and (b) to integrate this advice with a severe winter weather ‘Early Warning System’ developed by the UK Met Office. An advice booklet was generated through a sequential process of systematic review, consensus development, and focus group discussions with older people. In a subsequent field trial, a combination of the Met Office ‘Early Warning System’ and the advice booklet produced behavioural change among older people consistent with risk reduction. The results also show that long-held convictions about ‘healthy environments ’ and anxieties about fuel costs are barriers to risk reduction

Forecasting summer-time overheating in UK homes using time series models

Heatwaves are projected to become more frequent, intense and long-lasting in the UK and the prevalence of overheating in dwellings is set to increase. As a result, occupants will experience increased levels of thermal discomfort, heat stress and heat-related morbidity and mortality. Since the use of mechanical air conditioning in dwellings is unsustainable, and not widely affordable, it is of utmost importance to understand when heat related health risks are anticipated in free-running dwellings. This is crucial for vulnerable occupants, such as the elderly, for whom the accurate detection of future heat risks could prepare them (or their carers) for timely mitigation, for example, through additional window ventilation or the use of shading. Many countries deploy Heat-Health Warning Systems (HHWS) to alert their populations, however, these generally apply to a wide area and are based exclusively on regional weather forecasts. Consequently, HHWSs are unable to identify where, when, or to what extent individual buildings (and their occupants) will be affected. Previous studies have investigated the use of time series forecasting models, with the majority considering the use of Model Predictive Control. There is, however, no rigorous scientific evidence to support the belief that such models can provide accurate predictions in free-running dwellings during heatwaves and over multi-day forecasting horizons. This thesis therefore examines the use of black-box forecasting models to provide reliable predictions of the impending indoor temperatures in UK homes. Having established the viability of this approach, the application of such models in the context of an indoor Heat-Health Warning System (iHHWS) has been explored. This research led to five main findings: (i) linear AutoRegressive forecasting models with eXogenous inputs (ARX), i.e. weather forecasts, can provide satisfactory accuracies during heatwaves for time horizons up to 72 h ahead; (ii) more complex semi-parametric Generalized Additive Models (GAMs) were not capable of significantly improving the forecasting accuracy at forecasting horizons over 6 h (iii) logistic GAMs can predict the window opening state with adequate discrimination, however, integration of the window state into forecasting models did not improve their accuracy; (iv) forecasting models could be usefully incorporated within an iHHWS, however, the warning lead-time should be constrained to less than 24 h in order to guarantee high confidence in such a system; (v) a weighted metric such as the Cumulative Heat Index (CHI) could further reduce the risks of false or missed warnings, increasing the dependability of the iHHWS.</div

Anomaly Detection in Time Series: Theoretical and Practical Improvements for Disease Outbreak Detection

The automatic collection and increasing availability of health data provides a new opportunity for techniques to monitor this information. By monitoring pre-diagnostic data sources, such as over-the-counter cough medicine sales or emergency room chief complaints of cough, there exists the potential to detect disease outbreaks earlier than traditional laboratory disease confirmation results. This research is particularly important for a modern, highly-connected society, where the onset of disease outbreak can be swift and deadly, whether caused by a naturally occurring global pandemic such as swine flu or a targeted act of bioterrorism. In this dissertation, we first describe the problem and current state of research in disease outbreak detection, then provide four main additions to the field. First, we formalize a framework for analyzing health series data and detecting anomalies: using forecasting methods to predict the next day's value, subtracting the forecast to create residuals, and finally using detection algorithms on the residuals. The formalized framework indicates the link between the forecast accuracy of the forecast method and the performance of the detector, and can be used to quantify and analyze the performance of a variety of heuristic methods. Second, we describe improvements for the forecasting of health data series. The application of weather as a predictor, cross-series covariates, and ensemble forecasting each provide improvements to forecasting health data. Third, we describe improvements for detection. This includes the use of multivariate statistics for anomaly detection and additional day-of-week preprocessing to aid detection. Most significantly, we also provide a new method, based on the CuScore, for optimizing detection when the impact of the disease outbreak is known. This method can provide an optimal detector for rapid detection, or for probability of detection within a certain timeframe. Finally, we describe a method for improved comparison of detection methods. We provide tools to evaluate how well a simulated data set captures the characteristics of the authentic series and time-lag heatmaps, a new way of visualizing daily detection rates or displaying the comparison between two methods in a more informative way

Forecasting indoor temperatures during heatwaves using time series models

Early prediction of impending high temperatures in buildings could play a vital role in reducing heat-related morbidity and mortality. A recursive, AutoRegressive time series model using eXogenous inputs (ARX) and a rolling forecasting origin has been developed to provide reliable short-term forecasts of the internal temperatures in dwellings during hot summer conditions, especially heatwaves. The model was tested using monitored data from three case study dwellings recorded during the 2015 heatwave. The predictor variables were selected by minimising the Akaike Information Criterion (AIC), in order to automatically identify a near-optimal model. The model proved capable of performing multi-step-ahead predictions during extreme heat events with an acceptable accuracy for periods up to 72 h, with hourly results achieving a Mean Absolute Error (MAE) below 0.7 °C for every forecast. Comparison between ARX and AutoRegressive Moving Average models with eXogenous inputs (ARMAX) models showed that the ARX models provided consistently more reliable multi-step-ahead predictions. Prediction intervals, at the 95% probability level, were adopted to define a credible interval for the forecast temperatures at different prediction horizons. The results point to the potential for using time series forecasting as part of an overheating early-warning system in buildings housing vulnerable occupants or contents

Projecting health-care expenditure for Switzerland: further evidence against the 'red-herring' hypothesis

This paper contributes to the debate about the impact of population ageing on health care expenditure. Some health economists claim that the commonly presumed impact of population ageing is a "red herring". Based on empirical studies these authors conclude that proximity to death and not age per se matters. In projecting health care expenditure for Switzerland the present study provides evidence that proximity to death is of marginal importance. These projections suggest that population ageing is still the most important age-related cost-driver. Moreover, morbidity outweighs mortality as a factor of health-care expenditure. But most vital are non-demographic drivers such as medical progress. Thus, from the point of view of cost-benefit analysis one should even ignore costs of dying when projecting health care expenditure. Moreover, regressions might overestimate proximity to death due to systematic biases. Finally, ever-increasing health-care expenditure can be slowed down by appropriate policy measures.health-care expenditure; population ageing; public health-care budget; proximity to death, morbidity