Probabilistic forecasts of international bilateral migration flows.

We propose a method for forecasting global human migration flows. A Bayesian hierarchical model is used to make probabilistic projections of the 39,800 bilateral migration flows among the 200 most populous countries. We generate out-of-sample forecasts for all bilateral flows for the 2015 to 2020 period, using models fitted to bilateral migration flows for five 5-y periods from 1990 to 1995 through 2010 to 2015. We find that the model produces well-calibrated out-of-sample forecasts of bilateral flows, as well as total country-level inflows, outflows, and net flows. The mean absolute error decreased by 61% using our method, compared to a leading model of international migration. Out-of-sample analysis indicated that simple methods for forecasting migration flows offered accurate projections of bilateral migration flows in the near term. Our method matched or improved on the out-of-sample performance using these simple deterministic alternatives, while also accurately assessing uncertainty. We integrate the migration flow forecasting model into a fully probabilistic population projection model to generate bilateral migration flow forecasts by age and sex for all flows from 2020 to 2025 through 2040 to 2045.

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data.

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.

Daily resting-state intracranial EEG connectivity for seizure risk forecasts.

Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure-during a vigilance-controlled period-to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.

Connection between Variations of the Probability Distribution of the Recurrence Time and Phases of the Seismic Activity.

The probability distribution of the interevent time between two successive earthquakes has been the subject of numerous studies for its key role in seismic hazard assessment. In recent decades, many distributions have been considered, and there has been a long debate about the possible universality of the shape of this distribution when the interevent times are properly rescaled. In this work, we aim to discover if there is a link between the different phases of a seismic cycle and the variations in the distribution that best fits the interevent times. To do this, we consider the seismic activity related to the Mw 6.1 L'Aquila earthquake that occurred on 6 April 2009 in central Italy by analyzing the sequence of events recorded from April 2005 to July 2009, and then the seismic activity linked to the sequence of the Amatrice-Norcia earthquakes of Mw 6 and 6.5, respectively, and recorded in the period from January 2009 to June 2018. We take into account some of the most studied distributions in the literature: q-exponential, q-generalized gamma, gamma and exponential distributions and, according to the Bayesian paradigm, we compare the value of their posterior marginal likelihood in shifting time windows with a fixed number of data. The results suggest that the distribution providing the best performance changes over time and its variations may be associated with different phases of the seismic crisis.

Forecasting COVID-19 daily cases using phone call data.

The need to forecast COVID-19 related variables continues to be pressing as the epidemic unfolds. Different efforts have been made, with compartmental models in epidemiology and statistical models such as AutoRegressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS) or computing intelligence models. These efforts have proved useful in some instances by allowing decision makers to distinguish different scenarios during the emergency, but their accuracy has been disappointing, forecasts ignore uncertainties and less attention is given to local areas. In this study, we propose a simple Multiple Linear Regression model, optimised to use phone call data to forecast the number of daily confirmed cases. Moreover, we produce a probabilistic forecast that allows decision makers to better deal with risk. Our proposed approach outperforms ARIMA, ETS, Seasonal Naive, Prophet and a regression model without call data, evaluated by three point forecast error metrics, one prediction interval and two probabilistic forecast accuracy measures. The simplicity, interpretability and reliability of the model, obtained in a careful forecasting exercise, is a meaningful contribution to decision makers at local level who acutely need to organise resources in already strained health services. We hope that this model would serve as a building block of other forecasting efforts that on the one hand would help front-line personal and decision makers at local level, and on the other would facilitate the communication with other modelling efforts being made at the national level to improve the way we tackle this pandemic and other similar future challenges.

[Characteristic of the behavioral response and cognitive evoked potentials at 60-74 aged women.]

The article presents the results of the study of the characteristics of behavioral response and cognitive visual event related potentials in 90 elderly women. To analyze a behavioral reaction the computer complex KPFC-99 «PSYCHOMAT¼, which includes the computer test system Binatest (Moscow) was used. The event related potentials were registered for all participants with using of 128-channel system GES-300. Latency of P300-wave and reaction time were calculated. It is shown that time reaction in stochastic, probabilistic and deterministic environmental conditions increase with age. The search activity is random, that leads to restriction of adaptive capacity of the organism. The increase in the number of errors in older women indicates a lack of mobility of mental processes. According to the temporal characteristics of the wave P300 it has been revealed that latent time increases with age mainly in the posterior-temporal, parieto-occipital and occipital areas of the right hemisphere. The inter-hemispheric asymmetry with a predominance of the left hemisphere was noted. The obtained age-related changes are most likely due to sensory deficiency, reduced neurotransmission and displacement of the locus of cognitive activity in the left hemisphere.

Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture.

Routine surveillance of notifiable infectious diseases gives rise to daily or weekly counts of reported cases stratified by region and age group. From a public health perspective, forecasts of infectious disease spread are of central importance. We argue that such forecasts need to properly incorporate the attached uncertainty, so they should be probabilistic in nature. However, forecasts also need to take into account temporal dependencies inherent to communicable diseases, spatial dynamics through human travel and social contact patterns between age groups. We describe a multivariate time series model for weekly surveillance counts on norovirus gastroenteritis from the 12 city districts of Berlin, in six age groups, from week 2011/27 to week 2015/26. The following year (2015/27 to 2016/26) is used to assess the quality of the predictions. Probabilistic forecasts of the total number of cases can be derived through Monte Carlo simulation, but first and second moments are also available analytically. Final size forecasts as well as multivariate forecasts of the total number of cases by age group, by district and by week are compared across different models of varying complexity. This leads to a more general discussion of issues regarding modelling, prediction and evaluation of public health surveillance data. Copyright © 2017 John Wiley & Sons, Ltd.

[Characteristics of behavioral response of elderly women with high level of personal anxiety].

The results of studies of the behavioral response in 69 elderly women with normal and high levels of anxiety are presented in the article. Personal anxiety level was determined by «Integrative anxiety test¼. The indicators of strategies alteration for decision-making were assessed through a computer complex KPFK-99 «PSIHOMAT¼, which includes a test computer system «Binatest¼. It is found that more complex and structured programs of behavior are characteristic for women with normal levels of anxiety. The restriction tendency of adaptation opportunities of an organism, decrease in resistance to influence negative the stress - factors, and also tendency to doubts in correctness of a choice against any situation.

Probabilistic forecasting of hourly emergency department arrivals.

An accurate forecast of Emergency Department (ED) arrivals by an hour of the day is critical to meet patients' demand. It enables planners to match ED staff to the number of arrivals, redeploy staff, and reconfigure units. In this study, we develop a model based on Generalised Additive Models and an advanced dynamic model based on exponential smoothing to generate an hourly probabilistic forecast of ED arrivals for a prediction window of 48 hours. We compare the forecast accuracy of these models against appropriate benchmarks, including TBATS, Poisson Regression, Prophet, and simple empirical distribution. We use Root Mean Squared Error to examine the point forecast accuracy and assess the forecast distribution accuracy using Quantile Bias, PinBall Score and Pinball Skill Score. Our results indicate that the proposed models outperform their benchmarks. Our developed models can also be generalised to other services, such as hospitals, ambulances or clinical desk services.

A novel approach to predict acute radiation dermatitis in patients with head and neck cancer using a model based on Bayesian probability.

PURPOSE: In this study, we aimed to establish a method for predicting the probability of each acute radiation dermatitis (ARD) grade during the head and neck Volumetric Modulated Arc Therapy (VMAT) radiotherapy planning phase based on Bayesian probability. METHODS: The skin dose volume >50 Gy (V50), calculated using the treatment planning system, was used as a factor related to skin toxicity. The empirical distribution of each ARD grade relative to V50 was obtained from the ARD grades of 119 patients (55, 50, and 14 patients with G1, G2, and G3, respectively) determined by head and neck cancer specialists. Using Bayes' theorem, the Bayesian probabilities of G1, G2, and G3 for each value of V50 were calculated with an empirical distribution. Conversely, V50 was obtained based on the Bayesian probabilities of G1, G2, and G3. RESULTS: The empirical distribution for each graded patient group demonstrated a normal distribution. The method predicted ARD grades with 92.4 % accuracy and provided a V50 value for each grade. For example, using the graph, we could predict that V50 should be ≤24.5 cm3 to achieve G1 with 70 % probability. CONCLUSIONS: The Bayesian probability-based ARD prediction method could predict the ARD grade at the treatment planning stage using limited patient diagnostic data that demonstrated a normal distribution. If the probability of an ARD grade is high, skin care can be initiated in advance. Furthermore, the V50 value during treatment planning can provide radiation oncologists with data for strategies to reduce ARD.

Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.

Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. In this work, we leverage the statistical approach of bootstrap aggregating (bagging) to robustify the sparse identification of the nonlinear dynamics (SINDy) algorithm. First, an ensemble of SINDy models is identified from subsets of limited and noisy data. The aggregate model statistics are then used to produce inclusion probabilities of the candidate functions, which enables uncertainty quantification and probabilistic forecasts. We apply this ensemble-SINDy (E-SINDy) algorithm to several synthetic and real-world datasets and demonstrate substantial improvements to the accuracy and robustness of model discovery from extremely noisy and limited data. For example, E-SINDy uncovers partial differential equations models from data with more than twice as much measurement noise as has been previously reported. Similarly, E-SINDy learns the Lotka Volterra dynamics from remarkably limited data of yearly lynx and hare pelts collected from 1900 to 1920. E-SINDy is computationally efficient, with similar scaling as standard SINDy. Finally, we show that ensemble statistics from E-SINDy can be exploited for active learning and improved model predictive control.

Discriminative algorithm approach to forecast Cd threshold exceedance probability for rice grain based on soil characteristics.

The relationship between cadmium (Cd) concentration in rice grains and the soil that they are cultivated in is highly uncertain due to the influence of soil properties, rice varieties, and other undetermined factors. In this study, we introduce the probability of exceeding the threshold to characterize this uncertainty and then, build a probabilistic forewarning model. Additionally, a number of associated factors have been used as parameters to improve model performance. Considering that the physicochemical properties and Cd concentration in the soil (Cdsoil) do not follow a normal distribution, and are not independent of each other, a discriminative algorithm, represented by a logistic regression (LR), performed better than generative algorithms, such as the naive Bayes and quadratic discriminant analysis models. The performance of the LR based model was found to be 0.5% better in the case of the univariate model (Cdsoil) and 4.1% better with a multivariate model (soil properties used as additional factors) (p < 0.01). The output of the LR based model predicted probabilities that were positively correlated to the true exceedance rate (R2 = 0.949,p < 0.01), within an exceedance threshold range of 0.1-0.4 mg kg-1 and a mean deviation of 5.75%. A sensitivity analysis showed that the effect of soil properties on the exceedance probability weakens with an increase in Cd concentration in rice grains. When the threshold is below 0.15 mg kg-1, soil pH strongly influences the exceedance probability. As the threshold increases, the influence of pH on the exceedance probability is gradually superseded. By quantifying the uncertainty regarding the relationship between Cd concentration in rice grains and soil, the discriminative algorithm-based probabilistic forecasting model offers a new way to assess Cd pollution in rice grown in contaminated paddy fields.

Probabilistic forecasting for extreme NO2 pollution episodes.

In this study, we investigate the convenience of quantile regression to predict extreme concentrations of NO2. Contrarily to the usual point-forecasting, where a single value is forecast for each horizon, probabilistic forecasting through quantile regression allows for the prediction of the full probability distribution, which in turn allows to build models specifically fit for the tails of this distribution. Using data from the city of Madrid, including NO2 concentrations as well as meteorological measures, we build models that predict extreme NO2 concentrations, outperforming point-forecasting alternatives, and we prove that the predictions are accurate, reliable and sharp. Besides, we study the relative importance of the independent variables involved, and show how the important variables for the median quantile are different than those important for the upper quantiles. Furthermore, we present a method to compute the probability of exceedance of thresholds, which is a simple and comprehensible manner to present probabilistic forecasts maximizing their usefulness.

Quantification and probabilistic modeling of CRT obsolescence for the State of Delaware.

The cessation of production and replacement of cathode ray tube (CRT) displays with flat screen displays have resulted in the proliferation of CRTs in the electronic waste (e-waste) recycle stream. However, due to the nature of the technology and presence of hazardous components such as lead, CRTs are the most challenging of electronic components to recycle. In the State of Delaware it is due to this challenge and the resulting expense combined with the large quantities of CRTs in the recycle stream that electronic recyclers now charge to accept Delaware's e-waste. Therefore it is imperative that the Delaware Solid Waste Authority (DSWA) understand future quantities of CRTs entering the waste stream. This study presents the results of an assessment of CRT obsolescence in the State of Delaware. A prediction model was created utilizing publicized sales data, a variety of lifespan data as well as historic Delaware CRT collection rates. Both a deterministic and a probabilistic approach using Monte Carlo Simulation (MCS) were performed to forecast rates of CRT obsolescence to be anticipated in the State of Delaware. Results indicate that the peak of CRT obsolescence in Delaware has already passed, although CRTs are anticipated to enter the waste stream likely until 2033.