Entrainment echoes in the cerebellum.

Evidence accumulates that the cerebellum's role in the brain is not restricted to motor functions. Rather, cerebellar activity seems to be crucial for a variety of tasks that rely on precise event timing and prediction. Due to its complex structure and importance in communication, human speech requires a particularly precise and predictive coordination of neural processes to be successfully comprehended. Recent studies proposed that the cerebellum is indeed a major contributor to speech processing, but how this contribution is achieved mechanistically remains poorly understood. The current study aimed to reveal a mechanism underlying cortico-cerebellar coordination and demonstrate its speech-specificity. In a reanalysis of magnetoencephalography data, we found that activity in the cerebellum aligned to rhythmic sequences of noise-vocoded speech, irrespective of its intelligibility. We then tested whether these "entrained" responses persist, and how they interact with other brain regions, when a rhythmic stimulus stopped and temporal predictions had to be updated. We found that only intelligible speech produced sustained rhythmic responses in the cerebellum. During this "entrainment echo," but not during rhythmic speech itself, cerebellar activity was coupled with that in the left inferior frontal gyrus, and specifically at rates corresponding to the preceding stimulus rhythm. This finding represents evidence for specific cerebellum-driven temporal predictions in speech processing and their relay to cortical regions.

Sensory and motor representations of internalized rhythms in the cerebellum and basal ganglia.

Both the cerebellum and basal ganglia are involved in rhythm processing, but their specific roles remain unclear. During rhythm perception, these areas may be processing purely sensory information, or they may be involved in motor preparation, as periodic stimuli often induce synchronized movements. Previous studies have shown that neurons in the cerebellar dentate nucleus and the caudate nucleus exhibit periodic activity when the animals prepare to respond to the random omission of regularly repeated visual stimuli. To detect stimulus omission, the animals need to learn the stimulus tempo and predict the timing of the next stimulus. The present study demonstrates that neuronal activity in the cerebellum is modulated by the location of the repeated stimulus and that in the striatum (STR) by the direction of planned movement. However, in both brain regions, neuronal activity during movement and the effect of electrical stimulation immediately before stimulus omission were largely dependent on the direction of movement. These results suggest that, during rhythm processing, the cerebellum is involved in multiple stages from sensory prediction to motor control, while the STR consistently plays a role in motor preparation. Thus, internalized rhythms without movement are maintained as periodic neuronal activity, with the cerebellum and STR preferring sensory and motor representations, respectively.

Segmenting and Predicting Musical Phrase Structure Exploits Neural Gain Modulation and Phase Precession.

Music, like spoken language, is often characterized by hierarchically organized structure. Previous experiments have shown neural tracking of notes and beats, but little work touches on the more abstract question: how does the brain establish high-level musical structures in real time? We presented Bach chorales to participants (20 females and 9 males) undergoing electroencephalogram (EEG) recording to investigate how the brain tracks musical phrases. We removed the main temporal cues to phrasal structures, so that listeners could only rely on harmonic information to parse a continuous musical stream. Phrasal structures were disrupted by locally or globally reversing the harmonic progression, so that our observations on the original music could be controlled and compared. We first replicated the findings on neural tracking of musical notes and beats, substantiating the positive correlation between musical training and neural tracking. Critically, we discovered a neural signature in the frequency range ∼0.1 Hz (modulations of EEG power) that reliably tracks musical phrasal structure. Next, we developed an approach to quantify the phrasal phase precession of the EEG power, revealing that phrase tracking is indeed an operation of active segmentation involving predictive processes. We demonstrate that the brain establishes complex musical structures online over long timescales (>5 s) and actively segments continuous music streams in a manner comparable to language processing. These two neural signatures, phrase tracking and phrasal phase precession, provide new conceptual and technical tools to study the processes underpinning high-level structure building using noninvasive recording techniques.

Rhythmic Entrainment Echoes in Auditory Perception.

Rhythmic entrainment echoes-rhythmic brain responses that outlast rhythmic stimulation-can demonstrate endogenous neural oscillations entrained by the stimulus rhythm. Here, we tested for such echoes in auditory perception. Participants detected a pure tone target, presented at a variable delay after another pure tone that was rhythmically modulated in amplitude. In four experiments involving 154 human (female and male) participants, we tested (1) which stimulus rate produces the strongest entrainment echo and, inspired by the tonotopical organization of the auditory system and findings in nonhuman primates, (2) whether these are organized according to sound frequency. We found the strongest entrainment echoes after 6 and 8 Hz stimulation, respectively. The best moments for target detection (in phase or antiphase with the preceding rhythm) depended on whether sound frequencies of entraining and target stimuli matched, which is in line with a tonotopical organization. However, for the same experimental condition, best moments were not always consistent across experiments. We provide a speculative explanation for these differences that relies on the notion that neural entrainment and repetition-related adaptation might exercise competing opposite influences on perception. Together, we find rhythmic echoes in auditory perception that seem more complex than those predicted from initial theories of neural entrainment.SIGNIFICANCE STATEMENT Rhythmic entrainment echoes are rhythmic brain responses that are produced by a rhythmic stimulus and persist after its offset. These echoes play an important role for the identification of endogenous brain oscillations, entrained by rhythmic stimulation, and give us insights into whether and how participants predict the timing of events. In four independent experiments involving >150 participants, we examined entrainment echoes in auditory perception. We found that entrainment echoes have a preferred rate (between 6 and 8 Hz) and seem to follow the tonotopic organization of the auditory system. Although speculative, we also found evidence that several, potentially competing processes might interact to produce such echoes, a notion that might need to be considered for future experimental design.

Cerebellar Direct Current Stimulation Reveals the Causal Role of the Cerebellum in Temporal Prediction.

Temporal prediction (TP) influences our perception and cognition. The cerebellum could mediate this multi-level ability in a context-dependent manner. We tested whether a modulation of the cerebellar neural activity, induced by transcranial Direct Current Stimulation (tDCS), changed the TP ability according to the temporal features of the context and the duration of target interval. Fifteen healthy participants received anodal, cathodal, and sham tDCS (15 min × 2 mA intensity) over the right cerebellar hemisphere during a TP task. We recorded reaction times (RTs) to a target during the task in two contextual conditions of temporal anticipation: rhythmic (i.e., interstimulus intervals (ISIs) were constant) and single-interval condition (i.e., the estimation of the timing of the target was based on the prior exposure of the train of stimuli). Two ISIs durations were explored: 600 ms (short trials) and 900 ms (long trials). Cathodal tDCS improved the performance during the TP task (shorter RTs) specifically in the rhythmic condition only for the short trials and in the single-interval condition only for the long trials. Our results suggest that the inhibition of cerebellar activity induced a different improvement in the TP ability according to the temporal features of the context. In the rhythmic context, the cerebellum could integrate the temporal estimation with the anticipatory motor responses critically for the short target interval. In the single-interval context, for the long trials, the cerebellum could play a main role in integrating representation of time interval in memory with the elapsed time providing an accurate temporal prediction.

Attentional modulation of beta-power aligns with the timing of behaviorally relevant rhythmic sounds.

It is largely unknown how attention adapts to the timing of acoustic stimuli. To address this, we investigated how hemispheric lateralization of alpha (7-13 Hz) and beta (14-24 Hz) oscillations, reflecting voluntary allocation of auditory spatial attention, is influenced by tempo and predictability of sounds. We recorded electroencephalography while healthy adults listened to rhythmic sound streams with different tempos that were presented dichotically to separate ears, thus permitting manipulation of spatial-temporal attention. Participants responded to stimulus-onset-asynchrony (SOA) deviants (-90 ms) for given tones in the attended rhythm. Rhythm predictability was controlled via the probability of SOA deviants per block. First, the results revealed hemispheric lateralization of beta-power according to attention direction, reflected as ipsilateral enhancement and contralateral suppression, which was amplified in high- relative to low-predictability conditions. Second, fluctuations in the time-resolved beta-lateralization aligned more strongly with the attended than the unattended tempo. Finally, a trend-level association was found between the degree of beta-lateralization and improved ability to distinguish between SOA-deviants in the attended versus unattended ear. Differently from previous studies, we presented continuous rhythms in which task-relevant and irrelevant stimuli had different tempo, thereby demonstrating that temporal alignment of beta-lateralization with attended sounds reflects top-down attention to sound timing.

Do Machine Learning Methods Improve Prediction of Ambient Air Pollutants with High Spatial Contrast? A Systematic Review.

BACKGROUND & OBJECTIVE: The use of machine learning for air pollution modelling is rapidly increasing. We conducted a systematic review of studies comparing statistical and machine learning models predicting the spatiotemporal variation of ambient nitrogen dioxide (NO2), ultrafine particles (UFPs) and black carbon (BC) to determine whether and in which scenarios machine learning generates more accurate predictions. METHODS: Web of Science and Scopus were searched up to June 13, 2024. All records were screened by two independent reviewers. Differences in the coefficient of determination (R2) and Root Mean Square Error (RMSE) between best statistical and machine learning methods were compared across categories of methodological elements. RESULTS: A total of 38 studies with 46 model comparisons (30 for NO2, 8 for UFPs and 8 for BC) were included. Linear non-regularized methods and Random Forest were most frequently used. Machine learning outperformed statistical models in 34 comparisons. Mean differences (95% confidence intervals) in R2 and RMSE between best machine learning and statistical models were 0.12 (0.08, 0.17) and 20% (11%, 29%) respectively. Tree-based methods performed best in 12 of 17 multi-model comparisons. Nonlinear or regularization regression methods were used in only 12 comparisons and provided similar performance to machine learning methods. CONCLUSION: This systematic review suggests that machine learning methods, especially tree-based methods, may be superior to linear non-regularized methods for predicting ambient concentrations of NO2, UFPs and BC. Additional comparison studies using nonlinear, regularized and a wider array of machine learning methods are needed to confirm their relative performance. Future air pollution studies would also benefit from more explicit and standardized reporting of methodologies and results.

Timing Patterns in the Extended Basal Ganglia System.

The human brain is a constructive organ. It generates predictions to modulate its functioning and continuously adapts to a dynamic environment. Increasingly, the temporal dimension of motor and non-motor behaviour is recognised as a key component of this predictive bias. Nevertheless, the intricate interplay of the neural mechanisms that encode, decode and evaluate temporal information to give rise to a sense of time and control over sensorimotor timing remains largely elusive. Among several brain systems, the basal ganglia have been consistently linked to interval- and beat-based timing operations. Considering the tight embedding of the basal ganglia into multiple complex neurofunctional networks, it is clear that they have to interact with other proximate and distal brain systems. While the primary target of basal ganglia output is the thalamus, many regions connect to the striatum of the basal ganglia, their main input relay. This establishes widespread connectivity, forming the basis for first- and second-order interactions with other systems implicated in timing such as the cerebellum and supplementary motor areas. However, next to this structural interconnectivity, additional functions need to be considered to better understand their contribution to temporally predictive adaptation. To this end, we develop the concept of interval-based patterning, conceived as a temporally explicit hierarchical sequencing operation that underlies motor and non-motor behaviour as a common interpretation of basal ganglia function.

Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for Compression.

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.

Temporal Prediction Signals for Periodic Sensory Events in the Primate Central Thalamus.

Prediction of periodic event timing is an important function for everyday activities, while the exact neural mechanism remains unclear. Previous studies in nonhuman primates have demonstrated that neurons in the cerebellar dentate nucleus and those in the caudate nucleus exhibit periodic firing modulation when the animals attempt to detect a single omission of isochronous repetitive audiovisual stimuli. To understand how these subcortical signals are sent and processed through the thalamocortical pathways, we examined single-neuron activities in the central thalamus of two macaque monkeys (one female and one male). We found that three types of neurons responded to each stimulus in the sequence in the absence of movements. Reactive-type neurons showed sensory adaptation and gradually waned the transient response to each stimulus. Predictive-type neurons steadily increased the magnitude of the suppressive response, similar to neurons previously reported in the cerebellum. Switch-type neurons initially showed a transient response, but after several cycles, the direction of firing modulation reversed and the activity decreased for each repetitive stimulus. The time course of Switch-type activity was well explained by the weighted sum of activities of the other types of neurons. Furthermore, for only Switch-type neurons the activity just before stimulus omission significantly correlated with behavioral latency, indicating that this type of neuron may carry a more advanced signal in the system detecting stimulus omission. These results suggest that the central thalamus may transmit integrated signals to the cerebral cortex for temporal information processing, which are necessary to accurately predict rhythmic event timing.SIGNIFICANCE STATEMENT Several cortical and subcortical regions are involved in temporal information processing, and the thalamus will play a role in functionally linking them. The present study aimed to clarify how the paralaminar part of the thalamus transmits and modifies signals for temporal prediction of rhythmic events. Three types of thalamic neurons exhibited periodic activity when monkeys attempted to detect a single omission of isochronous repetitive stimuli. The activity of one type of neuron correlated with the behavioral latency and appeared to be generated by integrating the signals carried by the other types of neurons. Our results revealed the neuronal signals in the thalamus for temporal prediction of sensory events, providing a clue to elucidate information processing in the thalamocortical pathways.

Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing.

The human brain continuously generates predictions of incoming sensory input and calculates corresponding prediction errors from the perceived inputs to update internal predictions. In human primary somatosensory cortex (area 3b), different cortical layers are involved in receiving the sensory input and generation of error signals. It remains unknown, however, how the layers in the human area 3b contribute to the temporal prediction error processing. To investigate prediction error representation in the area 3b across layers, we acquired layer-specific functional magnetic resonance imaging (fMRI) data at 7T from human area 3b during a task of index finger poking with no-delay, short-delay and long-delay touching sequences. We demonstrate that all three tasks increased activity in both superficial and deep layers of area 3b compared to the random sensory input. The fMRI signal was differentially modulated solely in the deep layers rather than the superficial layers of area 3b by the delay time. Compared with the no-delay stimuli, activity was greater in the deep layers of area 3b during the short-delay stimuli but lower during the long-delay stimuli. This difference activity features in the superficial and deep layers suggest distinct functional contributions of area 3b layers to tactile temporal prediction error processing. The functional segregation in area 3b across layers may reflect that the excitatory and inhibitory interplay in the sensory cortex contributions to flexible communication between cortical layers or between cortical areas.

Temporal Prediction Model-Based Fast Inter CU Partition for Versatile Video Coding.

Versatile video coding (VVC) adopts an advanced quad-tree plus multi-type tree (QTMT) coding structure to obtain higher compression efficiency, but it comes at the cost of a considerable increase in coding complexity. To effectively reduce the coding complexity of the QTMT-based coding unit (CU) partition, we propose a fast inter CU partition method based on a temporal prediction model, which includes early termination QTMT partition and early skipping multi-type tree (MT) partition. Firstly, according to the position of the current CU, we extract the optimal CU partition information of the position corresponding to the previously coded frames. We then establish a temporal prediction model based on temporal CU partition information to predict the current CU partition. Finally, to reduce the cumulative of errors of the temporal prediction model, we further extract the motion vector difference (MVD) of the CU to determine whether the QTMT partition can be terminated early. The experimental results show that the proposed method can reduce the inter coding complexity of VVC by 23.19% on average, while the Bjontegaard delta bit rate (BDBR) is only increased by 0.97% on average under the Random Access (RA) configuration.

Non-rhythmic temporal prediction involves phase resets of low-frequency delta oscillations.

The phase of neural oscillatory signals aligns to the predicted onset of upcoming stimulation. Whether such phase alignments represent phase resets of underlying neural oscillations or just rhythmically evoked activity, and whether they can be observed in a rhythm-free visual context, however, remains unclear. Here, we recorded the magnetoencephalogram while participants were engaged in a temporal prediction task, judging the visual or tactile reappearance of a uniformly moving stimulus. The prediction conditions were contrasted with a control condition to dissociate phase adjustments of neural oscillations from stimulus-driven activity. We observed stronger delta band inter-trial phase consistency (ITPC) in a network of sensory, parietal and frontal brain areas, but no power increase reflecting stimulus-driven or prediction-related evoked activity. Delta ITPC further correlated with prediction performance in the cerebellum and visual cortex. Our results provide evidence that phase alignments of low-frequency neural oscillations underlie temporal predictions in a non-rhythmic visual and crossmodal context.

Should I stay or should I go? How local-global implicit temporal expectancy shapes proactive motor control: An hdEEG study.

In this study, we investigated the effect of global temporal prediction on the brain capability to implicitly adjust proactive motor control. We used the Dynamic Temporal Prediction (DTP), in which local and global predictions of an imperative stimulus were manipulated by using different stimulus-onset asynchronies (SOAs), presented with several distribution probabilities. At a behavioural level, the results show a performance adjustment (reaction time decrease) depending on the implicit use of global prediction. At a neurophysiological level, three separate computational steps underlying motor control were investigated. First, the expectancy implementation was associated with global probability-dependent contingent negative variation (CNV) modulation supported by the recruitment of a frontoparietal network involving the anterior cingulate, the left intraparietal sulcus, the occipital, and the premotor areas. Second, the response implementation was modulated by the global prediction fostering stimulus processing (P3 increase) at the motor response level, as suggested by both oscillatory (beta desynchronization), as well as source analysis (frontal cortical network). Third, the expectancy violation lead to a negativity increase (omission-detection potential) time locked to the global rule violation and additionally, to delta and theta power increase interpreted as inhibitory control and rule violation detection, respectively. The expectancy violation further engaged a left lateralized network including the temporal parietal junction (TPJ) and the motor cortex, suggesting involvement of attentional reorienting and a motor adjustment. Finally, these findings provide new insights on the neurocognitive mechanisms underlying proactive motor control, suggesting an overlapping between implicit and explicit processes.

The developing predictive brain: How implicit temporal expectancy induced by local and global prediction shapes action preparation across development.

Human behavior is continuously shaped not just as a function of explicitly responding to external world events but also by internal biases implicitly driven by the capacity to extract statistics from complex sensory patterns. Two possible sources of predictability engaged to generate and update temporal expectancy are the implicit extraction of either local or global statistical contingencies in the events' temporal structure. In the context of action preparation the local prediction has been reported to be stable from the age of 6. However, there is no evidence about how the ability to extract and use global statistical patterns to establish temporal expectancy changes across development. Here we used a new, child-friendly reaction time task purposely designed to investigate how local (within-trial expectancy bias) and global (between-block expectancy bias) prediction interplay to generate temporal expectancy and consequently shape action preparation in young (5- to 6-year-old), middle-aged (7- to 8-year-old) and old (9- to 10-year-old) typically developing children. We found that while local temporal prediction showed stable developmental trajectories, the ability to use a global rule to action preparation in terms of both accuracy and speed becomes stable after the age of seven. These findings are discussed by adopting a neuroconstructivist-inspired theoretical account, according to which the developmental constraints on learning from hierarchically nested levels of sensory complexity may constitute a necessary prerequisite for mastering complex domains.

Bayesian modeling of temporal expectations in the human brain.

The brain predicts the timing of forthcoming events to optimize processes in response to them. Temporal predictions are driven by both our prior expectations on the likely timing of stimulus occurrence and the information conveyed by the passage of time. Specifically, such predictions can be described in terms of the hazard function, that is, the conditional probability that an event will occur, given it has not yet occurred. Events violating expectations cause surprise and often induce updating of prior expectations. While it is well-known that the brain is able to track the temporal hazard of event occurrence, the question of how prior temporal expectations are updated is still unsettled. Here we combined a Bayesian computational approach with brain imaging to map updating of temporal expectations in the human brain. Moreover, since updating is usually highly correlated with surprise, participants performed a task that allowed partially differentiating between the two processes. Results showed that updating and surprise differently modulated activity in areas belonging to two critical networks for cognitive control, the fronto-parietal (FPN) and the cingulo-opercular network (CON). Overall, these data provide a first computational characterization of the neural correlates associated with updating and surprise related to temporal expectation.

Tutorial and simulations with ADAM: an adaptation and anticipation model of sensorimotor synchronization.

Interpersonal coordination of movements often involves precise synchronization of action timing, particularly in expert domains such as ensemble music performance. According to the adaptation and anticipation model (ADAM) of sensorimotor synchronization, precise yet flexible interpersonal coordination is supported by reactive error correction mechanisms and anticipatory mechanisms that exploit systematic patterns in stimulus timing to plan future actions. Here, we provide a tutorial introduction to the computational architecture of ADAM and present a series of single- and dual-virtual agent simulations that examine the model parameters that produce ideal synchronization performance in different tempo conditions. In the single-agent simulations, a virtual agent synchronized responses to steady tempo sequence or a sequence containing gradual tempo changes. Parameters controlling basic reactive error (phase) correction were sufficient for producing ideal synchronization performance at the steady tempo, whereas parameters controlling anticipatory mechanisms were necessary for ideal performance with a tempo-changing sequence. In the dual-agent simulations, two interacting virtual agents produced temporal sequences from either congruent or incongruent internal performance templates specifying a steady tempo or tempo changes. Ideal performance was achieved with reactive error correction alone when both agents implemented the same performance template (either steady tempo or tempo change). In contrast, anticipatory mechanisms played a key role when one agent implemented a steady tempo template and the other agent implemented a tempo change template. These findings have implications for understanding the interplay between reactive and anticipatory mechanisms when agents possess compatible versus incompatible representations of task goals during human-human and human-machine interaction.

Predicting future contents of soil heavy metals and related health risks by combining the models of source apportionment, soil metal accumulation and industrial economic theory.

Enriched and bio-refractory soil heavy metals (SHMs) originate from the underground mineral, which supplies energy and materials for the development of economy and industry. Investigating soil metal contents and their adverse health impacts is the principal concern associated metal contaminated industrial areas, including both current assessments and future projections. In this research, we create a novel spatiotemporal model of SHMs prediction and risk characterization for future by citing a rigorous theory of industrial economics, and time series of activity intensity changes of various pollution sources are forecasted. The dynamic change of source contributions is quantitatively resolved and the mean SHMs concentrations are estimated by classical formulas for heavy metal accumulation. Human health risk in the future is described in a manner of time series. The results of the case study show that contribution rates of the five sources of the six metals change continuously over time. Pb, Cd and As assume the highest growth rates (400%, 500% and 165%), while Zn, Ni, Cr possesses relatively lower growth (< 130%), compared to their corresponding background values. Health risk of local sensitive population (children) is estimated at exceeding threshold in 2022 (non-carcinogenic) and 2012 (carcinogenic), and the upward trend will continue. Traffic emission, agriculture and household garbage are identified as major risky sources in the coming decades at the studied area, and improvement measures are recommended. Although a degree of uncertainties exists, the overall tendency is a conservative bias for chemical risk. Additionally, this paper is the first to explore a methodology of predicting future SHMs and associated human health risk, based on industrial economics and temporal source apportionment.

Implementation of long-term assessment of human health risk for metal contaminated groundwater: A coupled chemical mass balance and hydrodynamics model.

Assessing human health risk using spatiotemporal migration and geochemical evolution concurrently in an area where the groundwater is contaminated with heavy metals can provide more instructive information to protect specific potential negative impacts on human health. In this research, we established a model of long-term assessment of human health risk for metal contaminated groundwater by coupling two models: the geochemical (based on the law of chemical mass balance) model and the hydrodynamics module. The hydrodynamics module is used to initially identify the total temporal concentration of various elements, and the chemical mass balance module is used to gain the concentration and ionic activity of various toxic elements according to the range of environmental pH. Effective concentrations calculated using activity weight (based on speciation and ionic activity) were introduced into the formula for risk analysis. The results of the study show that, with the exploitation and recharge of groundwater, the non-carcinogenic and carcinogenic health risks cannot be reduced to acceptable levels until 18 and 22 years, respectively. The calculated risk values of using the coupling model are lower than that of statistics or single hydrokinetics. The sensitivity analysis results show that this model is reliable. The recharge, pH and the permeability coefficient are defined as the most sensitive factors.

Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore.

BACKGROUND: Dengue, a vector-borne infectious disease caused by the dengue virus, has spread through tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in the equatorial city state of Singapore, and frequent localised outbreaks occur, sometimes leading to national epidemics. Vector control remains the primary and most effective measure for dengue control and prevention. The objective of this study is to develop a novel framework for producing a spatio-temporal dengue forecast at a neighbourhood level spatial resolution that can be routinely used by Singapore's government agencies for planning of vector control for best efficiency. METHODS: The forecasting algorithm uses a mixture of purely spatial, purely temporal and spatio-temporal data to derive dynamic risk maps for dengue transmission. LASSO-based regression was used for the prediction models and separate sub-models were constructed for each forecast window. Data were divided into training and testing sets for out-of-sample validation. Neighbourhoods were categorised as high or low risk based on the forecast number of cases within the cell. The predictive accuracy of the categorisation was measured. RESULTS: Close concordance between the projections and the eventual incidence of dengue were observed. The average Matthew's correlation coefficient for a classification of the upper risk decile (operational capacity) is similar to the predictive performance at the optimal 30% cut-off. The quality of the spatial predictive algorithm as a classifier shows areas under the curve at all forecast windows being above 0.75 and above 0.80 within the next month. CONCLUSIONS: Spatially resolved forecasts of geographically structured diseases like dengue can be obtained at a neighbourhood level in highly urban environments at a precision that is suitable for guiding control efforts. The same method can be adapted to other urban and even rural areas, with appropriate adjustment to the grid size and shape.