Prediction of infectious disease epidemics via weighted density ensembles

Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Experience has shown that the performance of these models varies; some tend to do better or worse in different seasons or at different points within a season. Ensemble methods combine multiple models to obtain a single prediction that leverages the strengths of each model. We considered a range of ensemble methods that each form a predictive density for a target of interest as a weighted sum of the predictive densities from component models. In the simplest case, equal weight is assigned to each component model; in the most complex case, the weights vary with the region, prediction target, week of the season when the predictions are made, a measure of component model uncertainty, and recent observations of disease incidence. We applied these methods to predict measures of influenza season timing and severity in the United States, both at the national and regional levels, using three component models. We trained the models on retrospective predictions from 14 seasons (1997/1998 - 2010/2011) and evaluated each model's prospective, out-of-sample performance in the five subsequent influenza seasons. In this test phase, the ensemble methods showed overall performance that was similar to the best of the component models, but offered more consistent performance across seasons than the component models. Ensemble methods offer the potential to deliver more reliable predictions to public health decision makers.Comment: 20 pages, 6 figure

Complex networks in brain electrical activity

We analyze the complex networks associated with brain electrical activity. Multichannel EEG measurements are first processed to obtain 3D voxel activations using the tomographic algorithm LORETA. Then, the correlation of the current intensity activation between voxel pairs is computed to produce a voxel cross-correlation coefficient matrix. Using several correlation thresholds, the cross-correlation matrix is then transformed into a network connectivity matrix and analyzed. To study a specific example, we selected data from an earlier experiment focusing on the MMN brain wave. The resulting analysis highlights significant differences between the spatial activations associated with Standard and Deviant tones, with interesting physiological implications. When compared to random data networks, physiological networks are more connected, with longer links and shorter path lengths. Furthermore, as compared to the Deviant case, Standard data networks are more connected, with longer links and shorter path lengths--i.e., with a stronger ``small worlds'' character. The comparison between both networks shows that areas known to be activated in the MMN wave are connected. In particular, the analysis supports the idea that supra-temporal and inferior frontal data work together in the processing of the differences between sounds by highlighting an increased connectivity in the response to a novel sound.Comment: 22 pages, 5 figures. Starlab preprint. This version is an attempt to include better figures (no content change

Phase Space Reconstruction and Nonlinear Equilibrium Dynamics in the United States Beef Market

This paper investigates dynamic interactions in the US beef market using phase space reconstruction, which has been developed to analyze nonlinear dynamical systems. This approach provides important and unique empirical insights into consumers behavior in the beef market. Our results from a phase space reconstruction analysis demonstrate distinct differences between intertemporal short run impacts from food safety outbreaks (e.g., E. Coli) and longer run health effects (e.g., cholesterol). Adjustments due to factors such as cholesterol are permanent changes and do not affect the manner by which people consume, while consumers react to food safety scares by adjusting consumption for a short period of time and then returning to their normal steady state cycle of consumption.nonlinear time series, phase space reconstruction, food safety, health effects, Livestock Production/Industries, Marketing,

Investigation of the chemical kinetics of an advanced high energy propellant system Quarterly progress report, 1 Jun. - 1 Sep. 1968

Reaction kinetics of high energy oxygen difluoride diborane propellant syste

Can only flavor-nonsinglet H dibaryons be stable against strong decays?

Using the QCD sum rule approach, we show that the flavor-nonsinglet $H$ dibaryon states with J$^{\pi} = 1^+$, J$^{\pi} = 0^+$, I=1 (27plet) are nearly degenerate with the J$^{\pi} = 0^+$, I=0 singlet $H_0$ dibaryon, which has been predicted to be stable against strong decay, but has not been observed. Our calculation, which does not require an instanton correction, suggests that the $H_0$ is slightly heavier than these flavor-nonsinglet $H$s over a wide range of the parameter space. If the singlet $H_0$ mass lies above the $\Lambda \Lambda$ threshold (2231~MeV), then the strong interaction breakup to $\Lambda \Lambda$ would produce a very broad resonance in the $\Lambda \Lambda$ invariant mass spectrum which would be very difficult to observe. On the other hand, if these flavor-nonsinglet J=0 and 1 $H$ dibaryons are also above the $\Lambda \Lambda$ threshold, but below the $\Xi^0n$ breakup threshold (2254 MeV), then because the direct, strong interaction decay to the $\Lambda \Lambda$ channel is forbidden, these flavor-nonsinglet states might be more amenable to experimental observation. The present results allow a possible reconciliation between the reported observation of $\Lambda \Lambda$ hypernuclei, which argue against a stable $H_0$, and the possible existence of $H$ dibaryons in general.Comment: 10 pages, 2 figure

Maneuver and buffet characteristics of fighter aircraft

Recent research efforts in the improvement of the maneuverability of fighter aircraft in the high-subsonic and transonic speed range are reviewed with emphasis on the factors affecting aerodynamic boundaries, such as maximum obtainable lift, buffet onset, pitchup, wing rock, and nose slice. The investigations were made using a general research configuration which encompassed a systematic matrix of wing-design parameters. These results illustrated the sensitivity of section and planform geometry to a selected design point. The incorporation of variable-geometry wing devices in the form of flaps or leading-edge slats was shown to provide controlled flow over a wide range of flight conditions and substantial improvements in maneuver capabilities. Additional studies indicated that the blending of a highly swept maneuver strake with an efficient, moderately swept wing offers a promising approach for improving maneuver characteristics at high angles of attack without excessive penalties in structural weight

Evaluating epidemic forecasts in an interval format

For practical reasons, many forecasts of case, hospitalization and death counts in the context of the current COVID-19 pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction

AN INTRASEASONAL BIOECONOMIC MODEL OF PLRV NET NECROSIS

A bioeconomic model is developed as an IPM planning tool to combat PLRV net necrosis in the PNW potato industry. Environmental/biological and production processes are linked to marketing activities using discrete time control. We find that pesticides can be optimally timed to reduce applications and still protect against net necrosis.Crop Production/Industries, Environmental Economics and Policy,