Forecasting influenza epidemics in China using transmission dynamic model with absolute humidity.

Background: An influenza forecasting system is critical to influenza epidemic preparedness. Low temperature has long been recognized as a condition favoring influenza epidemic, yet it fails to justify the summer influenza peak in tropics/subtropics. Recent studies have suggested that absolute humidity (AH) had a U-shape relationship with influenza survival and transmission across climate zones, indicating that a unified influenza forecasting system could be established for China with various climate conditions. Methods: Our study has generated weekly influenza forecasts by season and type/subtype in northern and southern China from 2011 to 2021, using a forecasting system combining an AH-driven susceptible-infected-recovered-susceptible (SIRS) model and the ensemble adjustment Kalman filter (EAKF). Model performance was assessed by sensitivity and specificity in predicting epidemics, and by accuracies in predicting peak timing and magnitude. Results: Our forecast system can generally well predict seasonal influenza epidemics (mean sensitivity>87.5%; mean specificity >80%). The average forecast accuracies were 82% and 60% for peak timing and magnitude at 3-6 weeks ahead for northern China, higher than those of 42% and 20% for southern China. The accuracy was generally better when the forecast was made closer to the actual peak time. Discussion: The established AH-driven forecasting system can generally well predict the occurrence of seasonal influenza epidemics in China.

Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features.

Wind speed prediction is crucial for precisely wind power forecasting and reduced maintenance costs. Highland regions, which possess a considerable wind potential, present complex meteorological conditions, making wind speed prediction challenging. Traditional weather forecasting relies on complex statistical methods and extensive prior knowledge. While recent deep learning models have improved prediction accuracy, they often assume uniform influence weight structure, limiting model effectiveness. This study introduces an enhanced Conditional Local Convolution Recurrent Network (CLCRN) model to improve spatiotemporal wind speed forecasting using multidimensional meteorological inputs such as temperature, pressure, and dew point, alongside wind components. This model addresses uniform influence model weight issue by redesigning convolution kernels to better capture local meteorological features and integrating multiple influencing factors. Our model consistently achieves lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values across various prediction intervals (3, 6, 9, and 12 h) compared to other models, supported by the meteorological station data from 2019 to 2021. Furthermore, the spatial distribution of the local convolution weights aligns with local wind velocity patterns in Inner Mongolia, enhancing model interpretability. These results demonstrate potential for practical applications in renewable energy planning and wind dynamics simulation.

Assessing the impact of the National Clean Air Programme in Uttar Pradesh's non-attainment cities: a prophet model time series analysis.

Background: Uttar Pradesh, India's largest state, faces critical pollution levels, necessitating urgent action. The National Clean Air Programme (NCAP) targets a 40% reduction in particulate pollution by 2026. This study assesses the impact of NCAP on 15 non-attainment cities in Uttar Pradesh using the Prophet forecasting model. Methods: Monthly data on AQI and PM 10 concentrations from 2016 to 2023 were sourced from the Uttar Pradesh Pollution Control Board. Significant changes in mean AQI and PM 10 levels from 2017 to 2023 were evaluated using the Friedman test. Prophet models forecast PM 10 concentrations for 2025-26, with relative percentage changes calculated and model evaluation metrics assessed. Findings: Most cities exhibited unhealthy air quality. Jhansi had the lowest AQI (72.73) in 2023, classified as 'moderate' by WHO standards. Gorakhpur consistently showed 'poor' AQI levels, peaking at 249.31 in 2019. Western Uttar Pradesh cities such as Ghaziabad, Noida, and Moradabad had significant pollution burdens. Predictions showed Bareilly with over a 70% reduction in PM 10 levels, Raebareli 58%, Moradabad 55%, Ghaziabad 48%, Agra around 41%, and Varanasi 40%, meeting NCAP targets. However, Gorakhpur and Prayagraj predicted increases in PM 10 levels by 50% and 32%, respectively. Moradabad's model showed the best performance with an R 2 of 0.81, MAE of 17.27 µ g / m 3 , and MAPE of 0.10. Interpretation: Forecasting PM 10 concentrations in Uttar Pradesh's non-attainment cities offers policymakers substantial evidence to enhance current efforts. While existing measures are in place, our findings suggest that intensified provisions may be necessary for cities predicted to fall short of meeting program targets. The Prophet model's forecasts can pinpoint these at-risk areas, allowing for targeted interventions and regional adjustments to strategies. This approach will help promote sustainable development customized to each city's specific needs. Funding: No funding was issued for this research.

Smooth multi-period forecasting with application to prediction of COVID-19 cases.

Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this paper we consider the problem of multi-period forecasting that aims to predict several horizons at once. We propose a novel approach that forces the prediction to be "smooth" across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression. This methodology was developed for real-time distributed COVID-19 forecasting. We illustrate the proposed technique with the CovidCast dataset as well as a small simulation example.

A combined neural ODE-Bayesian optimization approach to resolve dynamics and estimate parameters for a modified SIR model with immune memory.

We propose a novel hybrid approach that integrates Neural Ordinary Differential Equations (NODEs) with Bayesian optimization to address the dynamics and parameter estimation of a modified time-delay-type Susceptible-Infected-Removed (SIR) model incorporating immune memory. This approach leverages a neural network to produce continuous multi-wave infection profiles by learning from both data and the model. The time-delay component of the SIR model, expressed through a convolutional integral, results in an integro-differential equation. To resolve these dynamics, we extend the NODE framework, employing a Runge-Kutta solver, to handle the challenging convolution integral, enabling us to fit the data and learn the parameters and dynamics of the model. Additionally, through Bayesian optimization, we enhance prediction accuracy while focusing on long-term dynamics. Our model, applied to COVID-19 data from Mexico, South Africa, and South Korea, effectively learns critical time-dependent parameters and provides accurate short- and long-term predictions. This combined methodology allows for early prediction of infection peaks, offering significant lead time for public health responses.

Single-center nomogram model for sepsis complicated by acute lung injury.

OBJECTIVE: To construct and validate a nomogram model for predicting sepsis complicated by acute lung injury (ALI). METHODS: The healthcare records of 193 sepsis patients hospitalized at The Affiliated Tai'an City Central Hospital of Qingdao University from January 2022 to December 2023 were retrospectively reviewed. Among these patients, 69 were in the ALI group and 124 in the non-ALI group. A nomogram prediction model was constructed using logistic regression analysis. Its predictive performance was evaluated through various measures, including the area under the curve (AUC), calibration curve, decision curve, sensitivity, specificity, accuracy, recall rate, and precision rate. RESULTS: The predictive factors included the neutrophil/lymphocyte ratio (NLR), oxygenation index (PaO2/FiO2), tumor necrosis factor-α (TNF-α), and acute physiology and chronic health evaluation II (APACHE II). The nomogram training set achieved an AUC of 0.959 (95% CI: 0.924-0.995), an accuracy of 92.59%, a recall of 96.70%, and a precision of 92.63%. In the validation set, the AUC was 0.938 (95% CI: 0.880-0.996), with an accuracy of 89.66%, a recall of 93.94%, and a precision of 88.57%. The calibration curve demonstrated that the prediction results were consistent with the actual findings. The decision curve indicated that the model has clinical applicability. CONCLUSION: NLR, PaO2/FiO2, TNF-α, and APACHE II are closely associated with ALI in sepsis patients. A nomogram model based on these four variables shows strong predictive performance and may be used as a clinical decision-support tool to help physicians better identify high-risk groups.

Development of a deep surrogate model with spatiotemporal characteristics mining capabilities for the prediction of groundwater level in coastal areas.

Effective reflection of the spatio-temporal characteristics of time series is crucial in development of time-series-based surrogate models for hydrologic systems, especially in coastal areas. In this study, a deep learning-based surrogate modeling framework, named STA-GRU, is proposed to predict groundwater levels accurately and efficiently through incorporation of spatio-temporal attention mechanism of multivariate time series and gated recurrent neural network. Firstly, a three-dimensional groundwater flow model is developed based on GMS-MODFLOW and used to generate groundwater levels as input datasets for the STA-GRU framework. The spatio-temporal sequence window is then reconstructed, and the spatio-temporal attention mechanism is employed to assign different weights to the time series of each groundwater well and the time step of a single time series. The gated recurrent unit (GRU) is finally introduced to address the spatial and temporal characteristics of groundwater levels. The comparison between the ablation experiment and the baseline model demonstrates that the framework is efficient in reducing the conflict of non-target variables by capturing the spatiotemporal dependence of variables. The STA-GRU modeling framework developed in this study can effectively extract the spatio-temporal characteristics of the groundwater table and improve model performance. In addition, compared with the finite difference method, the STA-GRU surrogate model saves a lot of calculation and time costs to achieve accurate prediction of complex hydrological sequences. The proposed STA-GRU framework has provided an effective method for predicting groundwater levels in coastal areas.

Harnessing Flex Point Symmetry to Estimate Logistic Tumor Population Growth.

The observed time evolution of a population is well approximated by a logistic growth function in many research fields, including oncology, ecology, chemistry, demography, economy, linguistics, and artificial neural networks. Initial growth is exponential, then decelerates as the population approaches its limit size, i.e., the carrying capacity. In mathematical oncology, the tumor carrying capacity has been postulated to be dynamically evolving as the tumor overcomes several evolutionary bottlenecks and, thus, to be patient specific. As the relative tumor-over-carrying capacity ratio may be predictive and prognostic for tumor growth and treatment response dynamics, it is paramount to estimate it from limited clinical data. We show that exploiting the logistic function's rotation symmetry can help estimate the population's growth rate and carry capacity from fewer data points than conventional regression approaches. We test this novel approach against published pan-cancer animal and human breast cancer data, achieving a 30% to 40% reduction in the time at which subsequent data collection is necessary to estimate the logistic growth rate and carrying capacity correctly. These results could improve tumor dynamics forecasting and augment the clinical decision-making process.

A hybrid model of ARIMA and MLP with a Grasshopper optimization algorithm for time series forecasting of water quality.

Water quality monitoring of rivers is necessary in order to properly manage their basins so that steps can be taken to control the amount of pollutants and bring them to the allowable level. The ARIMA (autoregressive integrated moving average) model does not consider nonlinear patterns in modeling water quality components. Also, in modeling using the MLP (Multilayer Perceptrons) model, both linear and nonlinear pattern are not controlled equally. Therefore, in the present study, linear time series models (ARIMA), MLP model, and a hybrid model of MLP and ARIMA optimized by a Grasshopper optimization algorithm are used to predict water quality components in the statistical period of 2011-2019. In the proposed hybrid method, the ability of the ARIMA and the MLP model are exploited. Observational water quality data for forecasting time series in the hybrid method include dissolved oxygen, water temperature, and boron over 108 months. Since, the hybrid model is capable of realizing the nonlinear essence of complicated time series, it makes more reliable forecasts. In the hybrid model, the correlation coefficients between the observational data and the predicted values are 0.9 for dissolved oxygen, 0.91 for water temperature, and 0.91 for boron. To compare the three ARIMA, MLP, and hybrid models, the accuracy indices of each model are calculated. The results show that the hybrid model's higher accuracy compared with the other two models.

Analysis of Inverter Efficiency Using Photovoltaic Power Generation Element Parameters.

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model's performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter's efficiency compared to the inverter's power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants.

Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I.

From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (280,760-weight; 322,570-temperature; 155,373-video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication.

DeepVol: volatility forecasting from high-frequency data with dilated causal convolutions.

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve volatility predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical findings demonstrate that dilated convolutional filters are highly effective at extracting relevant information from intraday financial time-series, proving that this architecture can effectively leverage predictive information present in high-frequency data that would otherwise be lost if realised measures were precomputed. Simultaneously, dilated convolutional filters trained with intraday high-frequency data help us avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate the performance of DeepVol. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data, resulting in more accurate predictions compared to traditional methodologies and producing more accurate risk measures.

Seizure forecasting with ultra long-term EEG signals.

OBJECTIVE: The apparent randomness of seizure occurrence affects greatly the quality of life of persons with epilepsy. Since seizures are often phase-locked to multidien cycles of interictal epileptiform activity, a recent forecasting scheme, exploiting RNS data, is capable of forecasting seizures days in advance. METHODS: We tested the use of a bandpass filter to capture the universal mid-term dynamics enabling both patient-specific and cross-patient forecasting. In a retrospective study, we explored the feasibility of the scheme on three long-term recordings obtained by the NeuroPace RNS System, the NeuroVista intracranial, and the UNEEG subcutaneous devices, respectively. RESULTS: Better-than-chance forecasting was observed in 15 (83 %) of 18 patients, and in 16 (89 %) patients for daily and hourly forecast, respectively. Meaningful forecast up to 30 days could be achieved in 4 (22 %) patients for hourly forecast frequency. The cross-patient performance decreased only marginally and was patient-wise strongly correlated with the patient-specific one. Comparable performance was obtained for NeuroVista and UNEEG data sets. SIGNIFICANCE: The feasibility of cross-patient forecasting supports the universal importance of mid-term dynamics for seizure forecasting, demonstrates promising inter-subject-applicability of the scheme on ultra long-term EEG recordings, and highlights its huge potential for clinical use.

Analysis of risk factors and construction of nomogram model for arthroscopic single-row rivet repair.

BACKGROUND: The factors influencing the clinical outcome of arthroscopic rotator cuff repair are not fully understood. PURPOSE: To explore the factors related to the postoperative outcome of arthroscopic single-row rivet rotator cuff repair in patients with rotator cuff injury and to construct the related nomogram risk prediction model. METHODS: 207 patients with rotator cuff injury who underwent arthroscopic single-row rivet rotator cuff repair were reviewed. The differences of preoperative and postoperative Visual Analogue Score (VAS) scores and University of California, Los Angeles (UCLA) scores were analyzed and compared. The postoperative UCLA score of 29 points was taken as the critical point, and the patients were divided into good recovery group and poor recovery group, and binary logstic regression analysis was performed. According to the results of multivariate logistic regression analysis, the correlation nomogram model was constructed, and the calibration chart was used, AUC, C-index. The accuracy, discrimination and clinical value of the prediction model were evaluated by decision curve analysis. Finally, internal validation is performed using self-random sampling. RESULTS: The mean follow-up time was 29.92 ± 17.20 months. There were significant differences in VAS score and UCLA score between preoperative and final follow-up (p < 0.05); multivariate regression analysis showed: Combined frozen shoulder (OR = 3.890, 95% CI: 1.544 ∼ 9.800), massive rotator cuff tear (OR = 3.809, 95%CI: 1.218 ∼ 11.908), More rivets number (OR = 2.118, 95%CI: 1.386 ∼ 3.237), lower preoperative UCLA score (OR = 0.831, 95%CI: 0.704-0.981) were adverse factors for the postoperative effect of arthroscopic rotator cuff repair. Use these factors to build a nomogram. The nomogram showed good discriminant and predictive power, with AUC of 0.849 and C-index of 0.900 (95% CI: 0.845 ∼ 0.955), and the corrected C index was as high as 0.836 in internal validation. Decision curve analysis also showed that the nomogram could be used clinically when intervention was performed at a threshold of 2%∼91%. CONCLUSION: Combined frozen shoulders, massive rotator cuff tears, and increased number of rivets during surgery were all factors associated with poor outcome after arthroscopic rotator cuff repair, while higher preoperative UCLA scores were factors associated with good outcome after arthroscopic rotator cuff repair. This study provides clinicians with a new and relatively accurate nomogram model.

Platform for precise, personalised glucose forecasting through continuous glucose and physical activity monitoring and deep learning.

Emerging research has demonstrated the advantage of continuous glucose monitoring for use in artificial pancreas and diabetes management in general. Recent studies demonstrate that glucose level forecasting using deep learning can help avoid postprandial hyperglycemia (≥ 180 mg/dL) or hypoglycemia (≤70 mg/dL) from delayed or increased insulin dosing in artificial pancreas. In this paper, a novel hybrid deep learning framework with integration of content-based attention learning is presented, to effectively predict the glucose measurements with prediction horizons (PH) = 15, 30 and, 60 minutes for T1D and T2D patients based on past data. We also present a complete cloud-based system and mobile app used for collecting CGM sensor, physical activity data, CHO values and insulin measurements to perform glucose forecasts using the proposed model running on Cloud. This model was validated using clinical data of individual with Type 1 diabetes (OhioT1DM) and individual with Type 2 diabetes. The mean absolute relative difference (MARD) was 12.33±3.15, 7.14±1.76% for PH=60 and, 30 min respectively on OhioT1DM clinical Dataset. The root mean squared error (RMSE) was 29.41±5.92 mg/dL and 17.19±3.22 mg/dL and the mean absolute error (MAE) was 21.96±4.67 mg/dL and 12.58±2.34 mg/dL for PH=60 and, 30 min respectively on the same clinical dataset. It was observed that inclusion of physical activity leads to improved glucose forecasting accuracy. Furthermore, all these results were obtained by training the model on only 8 days of clinical data of a single patient, followed by testing on clinical data on the following days. The results indicate that training on a single patient data may lead to better personalisation and better glucose forecasting results compared to existing works.

Correlation change analysis and NDVI prediction in the Yellow River Basin of China using complex networks and GRNN-PSRLSTM.

The Normalized Difference Vegetation Index (NDVI) is affected by various environmental factors, and its relationship with these factors is complex. In order to explore the complex relationship between NDVI and environmental factors, this paper adopts the complex network method to construct a correlation fluctuation network and analyze the interaction between them. It is found that temperature, precipitation, soil moisture, sunshine duration, and PM2.5 are all correlated with NDVI to varying degrees, and their combined correlation with NDVI varies over time. The correlation typically takes 3 to 6 months to change, and it tends to persist to some extent. Moreover, we fuse a generalized regression neural network (GRNN) with a long-short-term memory (LSTM) network combining phase space reconstruction (PSR) to propose a GRNN-PSRLSTM prediction model. The model achieves the prediction of monthly NDVI using the five environmental factors of the fluctuation network. The results indicate that the averages of root mean squared error (RMSE) and mean absolute percentage error (MAPE) predicted by the GRNN-PSRLSTM model in the nine provinces are 0.0232 and 0.0564 respectively. This model performs better in the assessment metrics for monthly NDVI forecasts. These findings are significant for evaluating vegetation changes and have some theoretical value for the ecological protection of the Yellow River Basin.

An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems.

The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO- λ ) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.

Predicting public mental health needs in a crisis using social media indicators: a Singapore big data study.

Mental health issues have increased substantially since the onset of the COVID-19 pandemic. However, health policymakers do not have adequate data and tools to predict mental health demand, especially amid a crisis. Using time-series data collected in Singapore, this study examines if and how algorithmically measured emotion indicators from Twitter posts can help forecast emergency mental health needs. We measured the mental health needs during 549 days from 1 July 2020 to 31 December 2021 using the public's daily visits to the emergency room of the country's largest psychiatric hospital and the number of users with "crisis" state assessed through a government-initiated online mental health self-help portal. Pairwise Granger-causality tests covering lag length from 1 day to 5 days indicated that forecast models using Twitter joy, anger and sadness emotions as predictors perform significantly better than baseline models using past mental health needs data alone (e.g., Joy Intensity on IMH Visits, χ2 = 14·9, P < ·001***; Sadness Count on Mindline Crisis, χ2 = 4·6, P = ·031*, with a one-day lag length). The findings highlight the potential of new early indicators for tracking emerging public mental health needs.

Transferability of ecological forecasting models to novel biotic conditions in a long-term experimental study.

Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long-term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty into forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts.

Long- and short-run asymmetric impacts of climate variation on tuberculosis based on a time series study.

Distinguishing between long-term and short-term effects allows for the identification of different response mechanisms. This study investigated the long- and short-run asymmetric impacts of climate variation on tuberculosis (TB) and constructed forecasting models using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL). TB showed a downward trend, peaking in March-May per year. A 1 h increment or decrement in aggregate sunshine hours resulted in an increase of 32 TB cases. A 1 m/s increment and decrement in average wind velocity contributed to a decrement of 3600 and 5021 TB cases, respectively (Wald long-run asymmetry test [WLR] = 13.275, P < 0.001). A 1% increment and decrement in average relative humidity contributed to an increase of 115 and 153 TB cases, respectively. A 1 hPa increment and decrement in average air pressure contributed to a decrease of 318 and 91 TB cases, respectively (WLR = 7.966, P = 0.005). ∆temperature(-), ∆(sunshine hours)( -), ∆(wind velocity)(+) and ∆(wind velocity)(-) at different lags had a meaningful short-run effect on TB. The NARDL outperformed the ARDL in forecasting. Climate variation has significant long- and short-run asymmetric impacts on TB. By incorporating both dimensions of effects into the NARDL, the accuracy of the forecasts and policy recommendations for TB can be enhanced.