Recalibrating and Combining Ensemble Predictions

The “model output statistics” (MOS) approach has long been used in forecasting to correct systematic errors of numerical models and to predict quantities not included in the model (Glahn and Lowry 1972). The MOS procedure is based on capturing the statistical relation between model outputs and observations and, in its simplest form, consists of a linear regression between these quantities. In theory, this procedure optimally calibrates the model forecast and provides reliable forecasts. In practice, the regression parameters must be estimated from data. In seasonal forecasting, forecast histories are short, and skill is modest. Both factors lead to substantial sampling errors in the estimates. This work examines two problems where sampling error affects the reliability of regression-calibrated forecasts and provides solutions based on two “penalized” methods: ridge regression and lasso regression (Hoerl and Kennard 1988; Tibshirani 1996). The first problem comes from the observation that, even in a bivariate setting, ordinary least squares estimates lead to unreliable forecasts. The second problem arises in the context of multivariate MOS and is that common methods of predictor selection lead to negative skill and unreliable forecasts

Influenza vaccination of healthcare workers in acute-care hospitals: a case-control study of its effect on hospital-acquired influenza among patients

<p>Abstract</p> <p>Background</p> <p>In acute-care hospitals, no evidence of a protective effect of healthcare worker (HCW) vaccination on hospital-acquired influenza (HAI) in patients has been documented. Our study objective was to ascertain the effectiveness of influenza vaccination of HCW on HAI among patients.</p> <p>Methods</p> <p>A nested case-control investigation was implemented in a prospective surveillance study of influenza-like illness (ILI) in a tertiary acute-care university hospital. Cases were patients with virologically-confirmed influenza occurring ≥ 72 h after admission, and controls were patients with ILI presenting during hospitalisation with negative influenza results after nasal swab testing. Four controls per case, matched per influenza season (2004-05, 2005-06 and 2006-07), were randomly selected. Univariate and multivariate conditional logistic regression models were fitted to assess factors associated with HAI among patients.</p> <p>Results</p> <p>In total, among 55 patients analysed, 11 (20%) had laboratory-confirmed HAI. The median HCW vaccination rate in the units was 36%. The median proportion of vaccinated HCW in these units was 11.5% for cases vs. 36.1% for the controls (<it>P </it>= 0.11); 2 (20%) cases and 21 (48%) controls were vaccinated against influenza in the current season (<it>P </it>= 0.16). The proportion of ≥ 35% vaccinated HCW in short-stay units appeared to protect against HAI among patients (odds ratio = 0.07; 95% confidence interval 0.005-0.98), independently of patient age, influenza season and potential influenza source in the units.</p> <p>Conclusions</p> <p>Our observational study indicates a shielding effect of more than 35% of vaccinated HCW on HAI among patients in acute-care units. Investigations, such as controlled clinical trials, are needed to validate the benefits of HCW vaccination on HAI incidence in patients.</p

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO2 concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2 to 5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6 to 9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2 to 5 years and 6 to 9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6 to 9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions

Vaccine herd effect

Vaccination ideally protects susceptible populations at high risk for complications of the infection. However, vaccines for these subgroups do not always provide sufficient effectiveness. The herd effect or herd immunity is an attractive way to extend vaccine benefits beyond the directly targeted population. It refers to the indirect protection of unvaccinated persons, whereby an increase in the prevalence of immunity by the vaccine prevents circulation of infectious agents in susceptible populations. The herd effect has had a major impact in the eradication of smallpox, has reduced transmission of pertussis, and protects against influenza and pneumococcal disease. A high uptake of vaccines is generally needed for success. In this paper we aim to provide an update review on the herd effect, focusing on the clinical benefit, by reviewing data for specific vaccines

Epidemiology of influenza-associated hospitalization in adults, Toronto, 2007/8

The purpose of this investigation was to identify when diagnostic testing and empirical antiviral therapy should be considered for adult patients requiring hospitalization during influenza seasons. During the 2007/8 influenza season, six acute care hospitals in the Greater Toronto Area participated in active surveillance for laboratory-confirmed influenza requiring hospitalization. Nasopharyngeal (NP) swabs were obtained from patients presenting with acute respiratory or cardiac illness, or with febrile illness without clear non-respiratory etiology. Predictors of influenza were analyzed by multivariable logistic regression analysis and likelihoods of influenza infection in various patient groups were calculated. Two hundred and eighty of 3,917 patients were found to have influenza. Thirty-five percent of patients with influenza presented with a triage temperature ≥38.0°C, 80% had respiratory symptoms in the emergency department, and 76% were ≥65 years old. Multivariable analysis revealed a triage temperature ≥38.0°C (odds ratio [OR] 3.1; 95% confidence interval [CI] 2.3–4.1), the presence of respiratory symptoms (OR 1.7; 95% CI 1.2–2.4), admission diagnosis of respiratory infection (OR 1.8; 95% CI 1.3–2.4), admission diagnosis of exacerbation of chronic obstructive pulmonary disease (COPD)/asthma or respiratory failure (OR 2.3; 95% CI 1.6–3.4), and admission in peak influenza weeks (OR 4.2; 95% CI 3.1–5.7) as independent predictors of influenza. The likelihood of influenza exceeded 15% in patients with respiratory infection or exacerbation of COPD/asthma if the triage temperature was ≥38.0°C or if they were admitted in the peak weeks during the influenza season. During influenza season, diagnostic testing and empiric antiviral therapy should be considered in patients requiring hospitalization if respiratory infection or exacerbation of COPD/asthma are suspected and if either the triage temperature is ≥38.0°C or admission is during the weeks of peak influenza activity

Genetically-Based Olfactory Signatures Persist Despite Dietary Variation

Individual mice have a unique odor, or odortype, that facilitates individual recognition. Odortypes, like other phenotypes, can be influenced by genetic and environmental variation. The genetic influence derives in part from genes of the major histocompatibility complex (MHC). A major environmental influence is diet, which could obscure the genetic contribution to odortype. Because odortype stability is a prerequisite for individual recognition under normal behavioral conditions, we investigated whether MHC-determined urinary odortypes of inbred mice can be identified in the face of large diet-induced variation. Mice trained to discriminate urines from panels of mice that differed both in diet and MHC type found the diet odor more salient in generalization trials. Nevertheless, when mice were trained to discriminate mice with only MHC differences (but on the same diet), they recognized the MHC difference when tested with urines from mice on a different diet. This indicates that MHC odor profiles remain despite large dietary variation. Chemical analyses of urinary volatile organic compounds (VOCs) extracted by solid phase microextraction (SPME) and analyzed by gas chromatography/mass spectrometry (GC/MS) are consistent with this inference. Although diet influenced VOC variation more than MHC, with algorithmic training (supervised classification) MHC types could be accurately discriminated across different diets. Thus, although there are clear diet effects on urinary volatile profiles, they do not obscure MHC effects

An empirical model for probabilistic decadal prediction: global attribution and regional hindcasts

Empirical models, designed to predict surface variables over seasons to decades ahead, provide useful benchmarks for comparison against the performance of dynamical forecast systems; they may also be employable as predictive tools for use by climate services in their own right. A new global empirical decadal prediction system is presented, based on a multiple linear regression approach designed to produce probabilistic output for comparison against dynamical models. A global attribution is performed initially to identify the important forcing and predictor components of the model . Ensemble hindcasts of surface air temperature anomaly fields are then generated, based on the forcings and predictors identified as important, under a series of different prediction ‘modes’ and their performance is evaluated. The modes include a real-time setting, a scenario in which future volcanic forcings are prescribed during the hindcasts, and an approach which exploits knowledge of the forced trend. A two-tier prediction system, which uses knowledge of future sea surface temperatures in the Pacific and Atlantic Oceans, is also tested, but within a perfect knowledge framework. Each mode is designed to identify sources of predictability and uncertainty, as well as investigate different approaches to the design of decadal prediction systems for operational use. It is found that the empirical model shows skill above that of persistence hindcasts for annual means at lead times of up to 10 years ahead in all of the prediction modes investigated. It is suggested that hindcasts which exploit full knowledge of the forced trend due to increasing greenhouse gases throughout the hindcast period can provide more robust estimates of model bias for the calibration of the empirical model in an operational setting. The two-tier system shows potential for improved real-time prediction, given the assumption that skilful predictions of large-scale modes of variability are available. The empirical model framework has been designed with enough flexibility to facilitate further developments, including the prediction of other surface variables and the ability to incorporate additional predictors within the model that are shown to contribute significantly to variability at the local scale. It is also semi-operational in the sense that forecasts have been produced for the coming decade and can be updated when additional data becomes available