Using ancient sedimentary DNA to forecast ecosystem trajectories under climate change

Ecosystem response to climate change is complex. In order to forecast ecosystem dynamics, we need high-quality data on changes in past species abundance that can inform process-based models. Sedimentary ancient DNA (sedaDNA) has revolutionised our ability to document past ecosystems' dynamics. It provides time series of increased taxonomic resolution compared to microfossils (pollen, spores), and can often give species-level information, especially for past vascular plant and mammal abundances. Time series are much richer in information than contemporary spatial distribution information, which have been traditionally used to train models for predicting biodiversity and ecosystem responses to climate change. Here, we outline the potential contribution of sedaDNA to forecast ecosystem changes. We showcase how species-level time series may allow quantification of the effect of biotic interactions in ecosystem dynamics, and be used to estimate dispersal rates when a dense network of sites is available. By combining palaeo-time series, process-based models, and inverse modelling, we can recover the biotic and abiotic processes underlying ecosystem dynamics, which are traditionally very challenging to characterise. Dynamic models informed by sedaDNA can further be used to extrapolate beyond current dynamics and provide robust forecasts of ecosystem responses to future climate change. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient's characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient's ICS response in the next year based on the patient's characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

A modal age at death approach to forecasting adult mortality

Recent studies have shown that there are some advantages to forecasting mortality with indicators other than age-specific death rates. The mean, median, and modal ages at death can be directly estimated from the age-at-death distribution, as can information on lifespan variation. The modal age at death has been increasing linearly since the second half of the twentieth century, providing a strong basis from which to extrapolate past trends. The aim of this paper is to develop a forecasting model that is based on the regularity of the modal age at death and that can also account for changes in lifespan variation. We forecast mortality at ages 40 and above in 10 West European countries. The model we introduce increases forecast accuracy compared with other forecasting models and provides consistent trends in life expectancy and lifespan variation at age 40 over time.

Pole balancing on the fingertip: model-motivated machine learning forecasting of falls

Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available. Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data. Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%-70% of real human stick falls up to 2.35 s in advance. Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.

Spatiotemporal analysis and forecasting of lumpy skin disease outbreaks in Ethiopia based on retrospective outbreak reports

Introduction

Lumpy skin disease is a viral disease that affects cattle belonging to genus Capripoxvirus (Poxviridae) and lead to significant economic losses.

Objective

The objective of this study was to evaluate the distribution of lumpy skin disease (LSD) outbreaks and predict future patterns based on retrospective outbreak reports in Ethiopia.

Methods

Data were collected through direct communication with regional laboratories and a hierarchical reporting system from the Peasant Associations to Ministry of Agriculture. Time-series data for the LSD outbreaks were analyzed using classical additive time-series decomposition and STL decomposition. Four models (ARIMA, SARIMA, ETS, STLF) were also used to forecast the number of LSD outbreaks that occurred each month for the years (2021-2025) after the models' accuracy test was performed. Additionally, the space-time permutation model (STP) were also used to study retrospective space-time cluster analysis of LSD outbreaks in Ethiopia.

Results

This study examined the geographical and temporal distribution of LSD outbreaks in Ethiopia from 2008 to 2020, reporting a total of 3,256 LSD outbreaks, 14,754 LSD-positive cases, 7,758 deaths, and 289 slaughters. It also covered approximately 68% of Ethiopia's districts, with Oromia reporting the highest LSD outbreaks. In the LSD's temporal distribution, the highest peak was reported following the rainy season in September to December and its lowest peak in the dry months of April and May. Out of the four models tested for forecasting, the SARIMA (3, 0, 0) (2, 1, 0) [12] model performed well for the validation data, while the STLF+Random Walk had a robust prediction for the training data. Thus, the SARIMA and STLF+Random Walk models produced a more accurate forecast of LSD outbreaks between 2020 and 2025. From retrospective Space-Time Cluster Analysis of LSD, eight possible clusters were also identified, with five of them located in central part of Ethiopia.

Conclusion

The study's time series and ST-cluster analysis of LSD outbreak data provide valuable insights into the spatial and temporal dynamics of the disease in Ethiopia. These insights can aid in the development of effective strategies to control and prevent the spread of the disease and holds great potential for improving efforts to combat LSD in the country.

Peripherally inserted central-related upper extremity deep vein thrombosis and machine learning

OBJECTIVE

To establish a prediction model of upper extremity deep vein thrombosis (UEDVT) associated with peripherally inserted central catheter (PICC) based on machine learning (ML), and evaluate the effect.

METHODS

452 patients with malignant tumors who underwent PICC implantation in West China Hospital from April 2021 to December 2021 were selected through convenient sampling. UEDVT was detected by ultrasound. Machine learning models were established using the least absolute contraction and selection operator (LASSO) regression algorithm: Seeley scale model (ML-Seeley-LASSO) and ML model. The information of patients with and without UEDVT was randomly allocated to the training set and test set of the two models, and the prediction effect of machine learning and existing prediction tools was compared.

RESULTS

Machine learning training set and test set were better than Seeley evaluation results, and ML-Seeley-LASSO performance in training set was better than ML-LASSO. The performance of ML-LASSO in the test set is better than that of ML-Seeley-LASSO. The use of ML model (ML-LASSO and ML-Seeley-LASSO) in PICC-related UEDVT shows good effectiveness (the area under the subject's working characteristic curve is 0.856, 0.799), which is superior to the currently used Seeley assessment tool.

CONCLUSION

The risk of PICC-related UEDVT can be estimated and predicted relatively accurately by using the method of ML modeling, so as to effectively reduce the incidence of PICC-related UEDVT in the future.

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain

Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.

[Characteristics of Ozone Concentration in Shanghai and Its Associated Atmospheric Circulation Background During Summer Half-years from 2006 to 2021]

It is of great importance to scientifically evaluate the impact of weather and climate conditions on the occurrence of O3 pollution in order to improve the accuracy of O3 pollution forecasts， as well as to reasonably control and reduce the adverse effects of O3 pollution. The characteristics of O3 concentration and climate background were analyzed based on daily O3 concentration data， meteorological factors， and NCEP/NCER reanalysis data from 2006 to 2021 in Shanghai. In addition， the differences in atmospheric circulation situations during years with anomalous O3 concentrations were compared and diagnosed from the perspective of climatology. Additionally， the monthly O3 concentration prediction model （seasonal autoregressive integrated moving average with exogenous regressors， SARIMAX） was further established by adding the key meteorological factors. The results indicated that both the whole-year average and summer half-year average O3 concentrations in Shanghai were increasing with fluctuation， and the summer half-year average was much higher than the annual average， up to 36.2%. Furthermore， there was a significant negative correlation between O3 concentration and wind speed （correlation coefficient of -0.826） and a significant positive correlation with the frequency of static wind and the number of days in which the low cloud cover was less than 20% （correlation coefficients of 0.836 and 0.724， respectively）. The monthly mean O3 concentration had a clear periodicity， showing a pattern with a high concentration in the middle period （April to September） and a low concentration at the beginning and end of the periods. High O3 concentration years （2013-2021） were accompanied by more polluted days， lower average wind speed， more small wind （≤1.5 m·s-1） days， more days of low cloud cover of less than 20%， more days of high temperature， higher direct solar radiation， and more sunshine hours. When the location of the stronger West Pacific subtropical high was westward and southward in the summer half-year， Shanghai was influenced by an anomalous westerly wind， which was not conducive to the transportation of clean air from the sea to Shanghai and thus led to the high concentration of O3 pollution. When the long wave radiation emitted from the ground was low in the summer half-year， it was favorable for the increase in ground temperature and caused a high concentration of O3 pollution. Adding direct solar radiation， maximum temperature， and wind speed as exogenous variables to the monthly O3 forecast model could significantly improve the effectiveness of the monthly forecast， with the root mean square error decreasing by 47.7% （from 22 to 11.5） and the correlation coefficient increasing by 11.2% （from 0.819 to 0.911）， which could be applied to the practical prediction of monthly O3 concentration.

Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation

Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus, and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005-2020), historical and projected climate and population data (2005-2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.

Phenological Mapping of Invasive Insects: Decision Support for Surveillance and Management

Readily accessible and easily understood forecasts of the phenology of invasive insects have the potential to support and improve strategic and tactical decisions for insect surveillance and management. However, most phenological modeling tools developed to date are site-based, meaning that they use data from a weather station to produce forecasts for that single site. Spatial forecasts of phenology, or phenological maps, are more useful for decision-making at area-wide scales, such as counties, states, or entire nations. In this review, we provide a brief history on the development of phenological mapping technologies with a focus on degree-day models and their use as decision support tools for invasive insect species. We compare three different types of phenological maps and provide examples using outputs of web-based platforms that are presently available for real-time mapping of invasive insects for the contiguous United States. Next, we summarize sources of climate data available for real-time mapping, applications of phenological maps, strategies for balancing model complexity and simplicity, data sources and methods for validating spatial phenology models, and potential sources of model error and uncertainty. Lastly, we make suggestions for future research that may improve the quality and utility of phenological maps for invasive insects.

Monitoring and Prediction of Siberian Silk Moth Dendrolimus sibiricus Tschetv. (Lepidoptera: Lasiocampidae) Outbreaks Using Remote Sensing Techniques

The feasibility of risk assessment of a Siberian silk moth (Dendrolimus sibiricus Tschetv.) outbreak was analyzed by means of landscape and weather characteristics and tree condition parameters. Difficulties in detecting forest pest outbreaks (especially in Siberian conditions) are associated with the inability to conduct regular ground surveillance in taiga territories, which generally occupy more than 2 million km2. Our analysis of characteristics of Siberian silk moth outbreak zones under mountainous taiga conditions showed that it is possible to distinguish an altitudinal belt between 400 and 800 m above sea level where an outbreak develops and trees are damaged. It was found that to assess the resistance of forest stands to pest attacks, researchers can employ new parameters: namely, characteristics of a response of remote sensing variables to changes in land surface temperature. Using these parameters, it is possible to identify in advance (2-3 years before an outbreak) forest stands that are not resistant to the pest. Thus, field studies in difficult-to-access taiga forests are not needed to determine these parameters, and hence the task of monitoring outbreaks of forest insects is simplified substantially.

Short-term resilience to climate-induced temperature increases for equatorial sea turtle populations

Projection models are being increasingly used to manage threatened taxa by estimating their responses to climate change. Sea turtles are particularly susceptible to climate change as they have temperature-dependent sex determination and increased sand temperatures on nesting beaches could result in the 'feminisation' of hatchling sex ratios for some populations. This study modelled likely long-term trends in sand temperatures and hatchling sex ratios at an equatorial nesting site for endangered green turtles (Chelonia mydas) and critically endangered hawksbill turtles (Eretmochelys imbricata). A total of 1078 days of sand temperature data were collected from 28 logger deployments at nest depth between 2018 and 2022 in Papua New Guinea (PNG). Long-term trends in sand temperature were generated from a model using air temperature as an environmental proxy. The influence of rainfall and seasonal variation on sand temperature was also investigated. Between 1960 and 2019, we estimated that sand temperature increased by ~0.6°C and the average hatchling sex ratio was relatively balanced (46.2% female, SD = 10.7). No trends were observed in historical rainfall anomalies and projections indicated no further changes to rainfall until 2100. Therefore, the sex ratio models were unlikely to be influenced by changing rainfall patterns. A relatively balanced sex ratio such as this is starkly different to the extremely female-skewed hatchling sex ratio (>99% female) reported for another Coral Sea nesting site, Raine Island (~850 km West). This PNG nesting site is likely rare in the global context, as it is less threatened by climate-induced feminisation. Although there is no current need for 'cooling' interventions, the mean projected sex ratios for 2020-2100 were estimated 76%-87% female, so future interventions may be required to increase male production. Our use of long-term sand temperature and rainfall trends has advanced our understanding of climate change impacts on sea turtles.

Research on atrial fibrillation mechanisms and prediction of therapeutic prospects: focus on the autonomic nervous system upstream pathways

Atrial fibrillation (AF) is the most common clinical arrhythmia disorder. It can easily lead to complications such as thromboembolism, palpitations, dizziness, angina, heart failure, and stroke. The disability and mortality rates associated with AF are extremely high, significantly affecting the quality of life and work of patients. With the deepening of research into the brain-heart connection, the link between AF and stroke has become increasingly evident. AF is now categorized as either Known Atrial Fibrillation (KAF) or Atrial Fibrillation Detected After Stroke (AFDAS), with stroke as the baseline. This article, through a literature review, briefly summarizes the current pathogenesis of KAF and AFDAS, as well as the status of their clinical pharmacological and non-pharmacological treatments. It has been found that the existing treatments for KAF and AFDAS have limited efficacy and are often associated with significant adverse reactions and a risk of recurrence. Moreover, most drugs and treatment methods tend to focus on a single mechanism pathway. For example, drugs targeting ion channels primarily modulate ion channels and have relatively limited impact on other pathways. This limitation underscores the need to break away from the "one disease, one target, one drug/measurement" dogma for the development of innovative treatments, promoting both drug and non-drug therapies and significantly improving the quality of clinical treatment. With the increasing refinement of the overall mechanisms of KAF and AFDAS, a deeper exploration of physiological pathology, and comprehensive research on the brain-heart relationship, it is imperative to shift from long-term symptom management to more precise and optimized treatment methods that are effective for almost all patients. We anticipate that drugs or non-drug therapies targeting the central nervous system and upstream pathways can guide the simultaneous treatment of multiple downstream pathways in AF, thereby becoming a new breakthrough in AF treatment research.

[China's Energy Consumption and Carbon Peak Path Under Different Scenarios]

Accurately predicting energy consumption and carbon emission is important for China to make energy and carbon emission policy formulation more scientific and to achieve the goal of carbon peak before 2030 and carbon neutrality before 2060. Since energy demand is affected by numerous complex factors, it is hard to capture the dynamically developing rules of energy consumption comprehensively. Therefore, a novel two-layer decomposition-ensemble forecasting approach that was optimized by an improved particle swarm optimization algorithm based on simulation anneal and position disturbance strategy (IPSO) was proposed. Firstly, trend decomposition (TD) was utilized to break energy consumption time series down into a trend and a non-trend subseries. Then, empirical mode decomposition (EMD) was adopted to break the non-trend subseries down into several intrinsic mode functions (IMFs) and a residuum subseries. Subsequently, the aforementioned trend subseries, intrinsic mode functions, and residuum series were respectively modeled for prediction. The trend subseries was predicted using the multivariate linear regression model (MLR), which was optimized using IPSO. Both IMFs and residuum series were predicted using long short-term memory (LSTM). Finally, the final prediction of energy consumption was obtained by integrating the forecasting results of these subseries. According to China's energy consumption empirical analysis, the proposed IPSO-MLR-LSTM forecasting model based on the two-layer decomposition-ensemble approach using TD-EMD combined the advantages of TD, EMD, IPSO, and LSTM, which could comprehensively extract the developing rules of energy consumption by implementing a deeper decomposition strategy. Therefore, it is feasible and effective to apply the proposed forecasting model for energy consumption prediction. Finally, the energy consumption and carbon emissions of China under different energy consumption structure, economic growth, population, energy efficiency, and household energy consumption per capita scenarios in 2021-2035 were predicted. Then, some relevant policies and suggestions were put forward based on the forecasting results.

An integrative phenology and climatic suitability model for emerald ash borer

Introduction

Decision support models that predict both when and where to expect emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), are needed for the development and implementation of effective management strategies against this major invasive pest of ash (Fraxinus species) in North America and other regions such as Europe. We present a spatialized model of phenology and climatic suitability for EAB for use in the Degree-Days, Risk, and Phenological event mapping (DDRP) platform, which is an open-source decision support tool to help detect, monitor, and manage invasive threats.

Methods

We evaluated the model using presence records from three geographic regions (China, North America, and Europe) and a phenological dataset consisting primarily of observations from the northeastern and midwestern United States. To demonstrate the model, we produced phenological event maps for a recent year and tested for trends in EAB's phenology and potential distribution over a recent 20-year period.

Results

Overall, the model exhibited strong performance. Presence was correctly estimated for over 99% of presence records and predicted dates of adult phenological events corresponded closely with observed dates, with a mean absolute error of ca. 7 days and low estimates of bias. Climate stresses were insufficient to exclude EAB from areas with native Fraxinus species in North America and Europe; however, extreme weather events, climate warming, and an inability for EAB to complete its life cycle may reduce suitability for some areas. Significant trends toward earlier adult emergence over 20 years occurred in only some areas.

Discussion

Near real-time model forecasts for the conterminous United States are available at two websites to provide end-users with decision-support for surveillance and management of this invasive pest. Forecasts of adult emergence and egg hatch are particularly relevant for surveillance and for managing existing populations with pesticide treatments and parasitoid introductions.

Modeling the Chlorine Series from the Treatment Plant of Drinking Water in Constanta, Romania

Ensuring good drinking water quality, which does not damage the population's health, should be a priority of decision factors. Therefore, water treatment must be carried out to remove the contaminants. Chlorination is one of the most used treatment procedures. Modeling the free chlorine residual concentration series in the water distribution network provides the water supply managers with a tool for predicting residual chlorine concentration in the networks. With regard to this idea, this article proposes alternative models for the monthly free chlorine residual concentration series collected at the Palas Constanta Water Treatment Plant, in Romania, from January 2013 to December 2018. The forecasts based on the determined models are provided, and the best results are highlighted.

Ionospheric Weather at Two Starlink Launches during Two-Phase Geomagnetic Storms

The launch of a series of Starlink internet satellites on 3 February 2022 (S-36), and 7 July 2022 (S-49), coincided with the development of two-phase geomagnetic storms. The first launch S-36 took place in the middle of the moderate two-phase space weather storm, which induced significant technological consequences. After liftoff on 3 February at 18:13 UT, all Starlink satellites reached an initial altitude of 350 km in perigee and had to reach an altitude of ~550 km after the maneuver. However, 38 of 49 launched spacecrafts did not reach the planned altitude, left orbit due to increased drag and reentered the atmosphere on 8 February. A geomagnetic storm on 3-4 February 2022 has increased the density of the neutral atmosphere up to 50%, increasing drag of the satellites and dooming most of them. The second launch of S-49 at 13:11 UT on 7 July 2022 was successful at the peak of the two-phase geomagnetic storm. The global ionospheric maps of the total electron content (GIM-TEC) have been used to produce the ionospheric weather GIM-W index maps and Global Electron Content (GEC). We observed a GEC increment from 10 to 24% for the storm peak after the Starlink launch at both storms, accompanying the neutral density increase identified earlier. GIM-TEC maps are available with a lag (delay) of 1-2 days (real-time GIMs have a lag less than 15 min), so the GIMs forecast is required by the time of the launch. Comparisons of different GIMs forecast techniques are provided including the Center for Orbit Determination in Europe (CODE), Beijing (BADG and CASG) and IZMIRAN (JPRG) 1- and 2-day forecasts, and the Universitat Politecnica de Catalunya (UPC-ionSAT) forecast for 6, 12, 18, 24 and 48 h in advance. We present the results of the analysis of evolution of the ionospheric parameters during both events. The poor correspondence between observed and predicted GIM-TEC and GEC confirms an urgent need for the industry-science awareness of now-casting/forecasting/accessibility of GIM-TECs during the space weather events.

An Application of Spatio-temporal Modeling to Finite Population Abundance Prediction

Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be a lack of resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey.

Forecasting daily confirmed COVID-19 cases in Algeria using ARIMA models

BACKGROUND

COVID-19 has become a threat worldwide, affecting every country.

AIMS

This study aimed to identify COVID-19 cases in Algeria using times series models for forecasting the disease.

METHODS

Confirmed COVID-19 daily cases data were obtained from 21 March 2020 to 26 November 2020 from the Algerian Ministry of Health. Forecasting was done using the Autoregressive Integrated Moving Average (ARIMA) models (0,1,1) with Minitab 17 software.

RESULTS

Observed cases during the forecast period were accurately predicted and placed within prediction intervals generated by ARIMA. Forecasted values of COVID-19 positive cases, recoveries and deaths showed an accurate trend, which corresponded to actual cases reported during 252, 253 and 254 days. Results were strengthened by variations of less than 5% between forecast and observed cases in 100% of forecasted data.

CONCLUSION

ARIMA models with optimally selected covariates are useful tools for predicting COVID-19 cases in Algeria.

The global, regional and national burden of type 2 diabetes mellitus in the past, present and future: a systematic analysis of the Global Burden of Disease Study 2019

Aim

To report the global, regional, and national burden of type 2 diabetes mellitus (T2DM) in 2019, assess its trends in the past, and forecast its trends in the future.

Methods

The main data source was the Global Burden of Disease 2019 database. We assessed the changes in T2DM burden from 1990 to 2019 with joinpoint regression analysis. Age-period-cohort analysis was used to forecast the T2DM incidence and mortality rate from 2020 to 2034.

Results

The burden of T2DM has increased from 1990 to 2019 generally. The low-middle socio-demographic index (SDI) region had the highest increase in age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), age-standardized mortality rate (ASMR), and age-standardized disability-adjusted life years (ASDR) due to T2DM. Nationally, the increase in ASIR (r=0.151, p=0.046) and the decrease in ASMR (r=0.355, p<0.001) were positively correlated with SDIs. In 2019, the global ASIR, ASPR, ASMR, ASDR due to T2DM were 259.9 (95% UI 240.3-281.4), 5282.9 (95% UI 4853.6-5752.1), 18.5 (95% UI 17.2-19.7), and 801.5 (95% UI 55477000-79005200) per 100,000 population, respectively. Additionally, the ASIR (r=0.153, p=0.030) and ASPR (r=0.159, p=0.024) of T2DM were positively correlated with SDIs, while ASMR (r=-0.226, p=0.001) and ASDR (r=-0.171, p=0.015) due to T2DM were negatively correlated with SDIs. The ASIR was estimated to increase to 284.42, and ASMR was estimated to increase to 19.1 from 2030 to 2034, per 100,000 population.

Conclusion

Globally, the burden of T2DM has increased in the past and was forecast to continue increasing. Greater investment in T2DM prevention is needed.

Paris Agreement could prevent regional mass extinctions of coral species

Coral reef ecosystems are expected to undergo significant declines over the coming decades as oceans become warmer and more acidic. We investigate the environmental tolerances of over 650 Scleractinian coral species based on the conditions found within their present-day ranges and in areas where they are currently absent but could potentially reach via larval dispersal. These "environmental envelopes" and connectivity constraints are then used to develop global forecasts for potential coral species richness under two emission scenarios, representing the Paris Agreement target ("SSP1-2.6") and high levels of emissions ("SSP5-8.5"). Although we do not directly predict coral mortality or adaptation, the projected changes to environmental suitability suggest considerable declines in coral species richness for the majority of the world's tropical coral reefs, with a net loss in average local richness of 73% (Paris Agreement) to 91% (High Emissions) by 2080-2090 and particularly large declines across sites in the Great Barrier Reef, Coral Sea, Western Indian Ocean, and Caribbean. However, at the regional scale, we find that environmental suitability for the majority of coral species can be largely maintained under the Paris Agreement target, with 0%-30% potential net species lost in most regions (increasing to 50% for the Great Barrier Reef) as opposed to 80%-90% losses under High Emissions. Projections for subtropical areas suggest that range expansion will give rise to coral reefs with low species richness (typically 10-20 coral species per region) and will not meaningfully offset declines in the tropics. This work represents the first global projection of coral species richness under oceanic warming and acidification. Our results highlight the critical importance of mitigating climate change to avoid potentially massive extinctions of coral species.

Exploring potential applications of measles and rubella microarray patches (MR-MAPs): use case identification

Introduction

Innovative vaccine products will be critical in helping to address the existing implementation barriers that have prevented the achievement of the measles and rubella (MR) vaccine coverage targets. Overcoming those barriers will be necessary to achieve the "Immunization Agenda 2030" goals. Microarray patches (MAPs), an innovative needle-free delivery device currently in clinical development, can be a potential game changer in this respect and contribute to the equitable delivery of vaccines in low- and middle-income countries and pandemic preparedness and response. Developing in-depth knowledge of the most desired and impactful uses of MRMAPs can prove critical to identifying the critical attributes of the target product profile, informing policy and adoption decisions, and helping to evaluate the potential public health and economic value of this technology. The first step in this process is the definition of the potential use cases for MR-MAPs, i.e., where and how this product is most likely to be used within the immunization programme.

Methods

By applying a design-based user-centric approach, we implemented a three-step process, including a desk review, a survey, and interviews, to define the most relevant use cases for MR MAPS.

Results

Six use cases have been identified as relevant across all different countries and immunization programme designs and validated by experts.

Discussion

The identified use cases have already informed the demand estimate for MR-MAPs and provided the foundation for developing an initial full vaccine value assessment. We believe that, in the future, they will be highly valuable in ensuring that the roll-out of this promising innovation is designed in a way that maximizes the impact, particularly in populations and countries that are most in need.

Projections of future burden of pharmacologically treated type 2 diabetes and associated life expectancies by income in Finland: a multi-state modeling study

The burden of type 2 diabetes (T2D) differs between socioeconomic groups. The present study combines ongoing and plausible trends in T2D incidence and survival by income to forecast future trends in cases of T2D and life expectancy with and without T2D up to year 2040. Using Finnish total population data for those aged 30 years on T2D medication and mortality in 1995-2018, we developed and validated a multi-state life table model using age-, gender-, income- and calendar year-specific transition probabilities. We present scenarios based on constant and declining T2D incidence and on the effect of increasing and decreasing obesity on T2D incidence and mortality states up to 2040. With constant T2D incidence at 2019-level, the number of people living with T2D would increase by about 26% between 2020 and 2040. The lowest income group could expect more rapid increases in the number with T2D compared to the highest income group (30% vs. 23% respectively). If the incidence of T2D continues the recent declining trend, we predict about 14% fewer cases. However, if obesity increases two-fold, we predict 15% additional T2D cases. Unless, we reduce the obesity-related excess risk, the number of years lived without T2D could decrease up to 6 years for men in the lowest income group. Under all plausible scenarios, the burden of T2D is set to increase and it will be unequally distributed among socioeconomic groups. An increasing proportion of life expectancy will be spent with T2D.

The burden of cardiovascular diseases attributable to metabolic risk factors and its change from 1990 to 2019: a systematic analysis and prediction

Background

Metabolic disorders are the most important risk factors for cardiovascular diseases (CVDs). The purpose of this study was to systematically analyze and summarize the most recent data by age, sex, region, and time, and to forecast the future burden of diseases.

Methods

Data on the burden of CVDs associated with metabolic risk factors were obtained from the Global Burden of Disease (GBD) Study 2019; and then the burden of disease was assessed using the numbers and age-standardized rates (ASR) of deaths, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) and analyzed for temporal changes, differences in age, region, sex, and socioeconomic aspects; finally, the burden of disease was predicted using an autoregressive integrated moving average (ARIMA) model.

Results

From 1990 to 2019, the numbers of deaths, DALYs, YLDs, and YLLs attributed to metabolic risk factors increased by 59.3%, 51.0%, 104.6%, and 47.8%, respectively. The ASR decreased significantly. The burden of metabolic risk factor-associated CVDs was closely related to socioeconomic position and there were major geographical variations; additionally, men had a significantly greater disease burden than women, and the peak shifted later based on the age group. We predicted that the numbers of deaths and DALYs would reach 16.5 million and 324.8 million, respectively, by 2029.

Conclusions

The global burden of CVDs associated with metabolic risk factors is considerable and still rising, and more effort is needed to intervene in metabolic disorders.

Analysis of the efficacy and influencing factors of preoperative P-SOX neoadjuvant chemotherapy regimen for progressive gastric cancer-construction of a clinical prediction model

Preoperative neoadjuvant chemotherapy is one of the most common treatments for patients with advanced gastric cancer that cannot be completely removed by surgery. Nab-paclitaxel is a nano-formulation of paclitaxel that has been shown to be effective in treating stomach cancer. In addition, oxaliplatin + S-1 (SOX) has been a first-line chemotherapy regimen for gastric cancer, and it has the effect of tumor downstaging, improving the R0 resection rate, and reducing the postoperative recurrence rate, but the side effects are significant. During the application of oxaliplatin, obvious gastrointestinal reactions such as nausea and vomiting can be observed. There may also be blood system side effects such as leukopenia and thrombocytopenia, as well as serious adverse reactions such as peripheral neuropathy. Therefore, we reduced the amount of oxaliplatin in SOX and added nab-paclitaxel on the basis of this, in order to increase the efficacy while reducing the side effects of SOX regimen. We selected 192 patients with advanced gastric cancer admitted to the Department of Gastrointestinal Oncology of Qinghai University Hospital from July 2019 to February 2022, and all were treated with nab-paclitaxel plus oxaliplatin + S-1 neoadjuvant chemotherapy regimen, and underwent further surgery after chemotherapy. The tumor regression grade (TRG grade) and response evaluation criteria of solid tumor 1.1 (RECIST1.1) were taken as the dependent variables. According to TRG classification, 120 patients were effective (grade 0, 1, 2 = 62.50%, age: 55.63 ± 9.02 years), 72 patients were ineffective (grade 3 = 37.50%, 55.82 ± 9.21 years), and the effective rate of chemotherapy was 62.50%. According to RECIST1.1, 116 patients were effective (CR + PR = 60.42%, mean age 55.84 ± 9.02 years), 76 patients were ineffective (SD + PD = 39.58%, 55.47 ± 9.19 years), and the effective rate was 60.42%. The factors p < 0.2 in univariate logistic regression analysis were included in multivariate logistic regression analysis, and p < 0.05 was the statistical difference, and statistically significant factors were screened out for modeling and plotted the nomogram. Among them, in the tumor regression grade, the final factors related to effective chemotherapy are the degree of differentiation, cT. stage, tumor diameter, chemotherapy cycle, and the final factors related to effective chemotherapy in the solid tumor response evaluation criteria are the degree of differentiation, cT. stage, tumor diameter. Therefore, we conclude that the regimen of nab-paclitaxel combined with oxaliplatin and S-1 has certain positive significance in the treatment of advanced gastric cancer.

Six decades of North American bird banding records reveal plasticity in migration phenology

The timing of avian migration has evolved to exploit critical seasonal resources, yet plasticity within phenological responses may allow adjustments to interannual resource phenology. The diversity of migratory species and changes in underlying resources in response to climate change make it challenging to generalize these relationships. We use bird banding records during spring and fall migration from across North America to examine macroscale phenological responses to interannual fluctuations in temperature and long-term annual trends in phenology. In total, we examine 19 species of North American wood warblers (family Parulidae), summarizing migration timing from 2,826,588 banded birds from 1961 to 2018 across 46 sites during spring and 124 sites during fall. During spring, warmer spring temperatures at banding locations translated to earlier median passage dates for 16 of 19 species, with an average 0.65-day advancement in median passage for every 1°C increase in temperature, ranging from 0.25 to 1.26 days °C^-1 . During the fall, relationships were considerably weaker, with only 3 of 19 species showing a relationship with temperature. In those three cases, later departure dates were associated with warmer fall periods. Projecting these trends forward under climate scenarios of temperature change, we forecast continued spring advancements under shared socioeconomic pathways from 2041 to 2060 and 2081 to 2100 and more muted and variable shifts for fall. These results demonstrate the capacity of long-distance migrants to respond to interannual fluctuations in temperatures, at least during the spring, and showcase the potential of North American bird banding data understanding phenological trends across a wide diversity of avian species.

[Meteorological Characteristics, Influence Analysis and Prediction of PM2.5 Concentration in Taiyuan City]

Based on the pollutant concentration data of Taiyuan City from 2016 to 2020 and the surface meteorological data of the national benchmark meteorological observation station in the same period, the variation characteristics of PM_2.5 concentration in Taiyuan City and the effects of meteorological conditions such as humidity, precipitation, wind, and mixing layer thickness on PM_2.5 concentration were analyzed. At the same time, the causes of pollutant concentration changes were discussed, and the PM_2.5 concentration prediction model based on the LSTM neural network was established. The results showed that the number of days of heavy pollution in Taiyuan City from 2016 to 2020 was the highest in winter, of which the maximum number of days in 2017 was 28 days. The PM_2.5 concentration was generally high in autumn and winter and low in spring and summer. The PM_2.5 concentration on weekends was higher than that on weekdays. The daily variation in PM_2.5 concentration roughly presented a bimodal distribution, which appeared around 09:00 and 23:00 to 01:00 the following day. Except for relative humidity and winter temperature, other air pressure, wind speed, and PM concentration showed negative correlations in the four seasons. The pollution sources affecting the increase in PM_2.5 concentration in Taiyuan City were mainly located in the NE-ENE-E direction, and the pollution in the northwest was not relatively apparent. In flood season, when the precipitation reached the level of moderate rain (rainfall ≥ 10 mm), it had an obvious effect on the reduction of PM_2.5 concentration. The increase in atmospheric mixing layer height was very beneficial to the diffusion and dilution of PM_2.5 in the vertical direction. The strong northwest air flow in winter, low relative humidity, high pressure control on the ground, and high height of the mixing layer belonged to the cluster most conducive to the reduction in PM_2.5 concentration. Using the LSTM model for modeling, the R² of PM_2.5 concentration prediction was as high as 0.95, which was significantly better than that of the traditional tree model and linear regression model (R²<0.60). The residual of the prediction results was close to the normal distribution, of which the absolute error of 84.2% prediction results was less than 20 μg·m^-3, and the MAE, MAPE, and RMSE of the model were 38.17, 17.19%, and 20.6, respectively.

A nomogram based on radiological features of MRI for predicting the risk of severe pain in patients with osteoarthritis of the knee

Methods

This study aimed to develop and validate a nomogram for predicting the risk of severe pain in patients with knee osteoarthritis. A total of 150 patients with knee osteoarthritis were enrolled from our hospital, and nomogram was established through a validation cohort (n = 150). An internal validation cohort (n = 64) was applied to validate the model.

Results

Eight important variables were identified using the Least absolute shrinkage and selection operator (LASSO) and then a nomogram was developed by Logistics regression analysis. The accuracy of the nomogram was determined based on the C-index, calibration plots, and Receiver Operating Characteristic (ROC) curves. Decision curves were plotted to assess the benefits of the nomogram in clinical decision-making. Several variables were employed to predict severe pain in knee osteoarthritis, including sex, age, height, body mass index (BMI), affected side, Kellgren-Lawrance (K-L) degree, pain during walking, pain going up and down stairs, pain sitting or lying down, pain standing, pain sleeping, cartilage score, Bone marrow lesion (BML) score, synovitis score, patellofemoral synovitis, bone wear score, patellofemoral bone wear, and bone wear scores. The LASSO regression results showed that BMI, affected side, duration of knee osteoarthritis, meniscus score, meniscus displacement, BML score, synovitis score, and bone wear score were the most significant risk factors predicting severe pain.

Conclusions

Based on the eight factors, a nomogram model was developed. The C-index of the model was 0.892 (95% CI: 0.839-0.945), and the C-index of the internal validation was 0.822 (95% CI: 0.722-0.922). Analysis of the ROC curve of the nomogram showed that the nomogram had high accuracy in predicting the occurrence of severe pain [Area Under the Curve (AUC) = 0.892] in patients with knee osteoarthritis (KOA). The calibration curves showed that the prediction model was highly consistent. Decision curve analysis (DCA) showed a higher net benefit for decision-making using the developed nomogram, especially in the >0.1 and <0.86 threshold probability intervals. These findings demonstrate that the nomogram can predict patient prognosis and guide personalized treatment.

A quintuple helix model for foresight: Analyzing the developments of digital technologies in order to outline possible future scenarios

The challenge of contemporary society is that of planning possible paths for the future. In the current scenario of hyperconnection, men and technologies and human and artificial intelligence are intertwined in such complex ways as to generate multiple possible futures up to the limit of the capacity of imagination. In particular, it is precisely the frontier of digital and technological changes that obliges social actors and socio-economic institutions to know how to intercept the dynamism of the transformations taking place, supporting the ability to imagine a desirable future, which goes in the intelligent direction of sustainability, of wellbeing and the ethical responsibility of one's actions. In this perspective, the reflection on the so-called future studies is inserted, which becomes a necessity, especially in times of change: If the rhythm of change increases, we need to look further, but future studies are also a philosophy of thought because the future is already part of our present life in the form of anticipation of the future; and this is all the more true as social changes are improvised and systemic complexity increasingly turbulent. Based on these statements, this study aims to analyze how the triple helix model-or rather the quintuple helix model-can be a reference paradigm for social and technological forecasting in a systemic attempt to look at the future of science, digital technology, society, economy, and their interactions, in order to promote social, economic and environmental benefits. From the social perspective, the model could provide guidance to improve the anticipatory profile of organizations and communities, helping to understand-in a short time-what the present actions will be: Predict, discover, and anticipate united in active participation, communication, knowledge, and action become so essential in the processes of production, as in the past it was the accumulation of capital, and also the ethical sensitivity begins to play an increasingly critical role.

The Predictive Factors of Hospital Bankruptcy-An Exploratory Study

The United States healthcare industry has witnessed a number of hospitals declare bankruptcy. This has a meaningful impact on local communities with vast implications on access, cost, and quality of care available. In our research, we seek to determine what contemporary structural and operational factors influence a bankruptcy outcome, and craft predictive models to guide healthcare leaders on how to best avoid bankruptcy in the future. In this exploratory study we performed, a single-year cross-sectional analysis of short-term acute care hospitals in the United States and subsequently developed three predictive models: logistic regression, a linear support vector machine (SVM) model with hinge function, and a perceptron neural network. Data sources include Definitive Healthcare and Becker's Hospital Review 2019 report with 3121 observations of 32 variables with 27 observed bankruptcies. The three models consistently indicate that 18 variables have a significant impact on predicting hospital bankruptcy. Currently, there is limited literature concerning financial forecasting models and knowledge detailing the factors associated with hospital bankruptcy. By having tailored knowledge of predictive factors to establish a sound financial structure, healthcare institutions at large can be empowered to take proactive steps to avoid financial distress at the organizational level and ensure long-term financial viability.

Modeling the impact of the Omicron infection wave in Germany

In November 2021, the first infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VOC) B.1.1.529 ('Omicron') was reported in Germany, alongside global reports of reduced vaccine efficacy (VE) against infections with this variant. The potential threat posed by its rapid spread in Germany was, at the time, difficult to predict. We developed a variant-dependent population-averaged susceptible-exposed-infected-recovered infectious-disease model that included information about variant-specific and waning VEs based on empirical data available at the time. Compared to other approaches, our method aimed for minimal structural and computational complexity and therefore enabled us to respond to changes in the situation in a more agile manner while still being able to analyze the potential influence of (non-)pharmaceutical interventions (NPIs) on the emerging crisis. Thus, the model allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs), the efficacy of contact reduction strategies, and the success of the booster vaccine rollout campaign. We expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity, nevertheless, even without additional NPIs. The projected figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid-February and mid-March. Most surprisingly, our model showed that early, strict, and short contact reductions could have led to a strong 'rebound' effect with high incidences after the end of the respective NPIs, despite a potentially successful booster campaign. The results presented here informed legislation in Germany. The methodology developed in this study might be used to estimate the impact of future waves of COVID-19 or other infectious diseases.

The COVID-19 pandemic and Bitcoin: Perspective from investor attention

The response of the Bitcoin market to the novel coronavirus (COVID-19) pandemic is an example of how a global public health crisis can cause drastic market adjustments or even a market crash. Investor attention on the COVID-19 pandemic is likely to play an important role in this response. Focusing on the Bitcoin futures market, this paper aims to investigate whether pandemic attention can explain and forecast the returns and volatility of Bitcoin futures. Using the daily Google search volume index for the "coronavirus" keyword from January 2020 to February 2022 to represent pandemic attention, this paper implements the Granger causality test, Vector Autoregression (VAR) analysis, and several linear effects analyses. The findings suggest that pandemic attention is a granger cause of Bitcoin returns and volatility. It appears that an increase in pandemic attention results in lower returns and excessive volatility in the Bitcoin futures market, even after taking into account the interactive effects and the influence of controlling other financial markets. In addition, this paper carries out the out-of-sample forecasts and finds that the predictive models with pandemic attention do improve the out-of-sample forecast performance, which is enhanced in the prediction of Bitcoin returns while diminished in the prediction of Bitcoin volatility as the forecast horizon is extended. Finally, the predictive models including pandemic attention can generate significant economic benefits by constructing portfolios among Bitcoin futures and risk-free assets. All the results demonstrate that pandemic attention plays an important and non-negligible role in the Bitcoin futures market. This paper can provide enlightens for subsequent research on Bitcoin based on investor attention sparked by public emergencies.

Ultrasonic hemodynamic changes of superficial temporal artery graft in different angiogenesis outcomes of Moyamoya disease patients treated with combined revascularization surgery

Objective

Combined bypass is commonly used in adult Moyamoya disease (MMD) for revascularization purposes. The blood flow from the external carotid artery system supplied by the superficial temporal artery (STA), middle meningeal artery (MMA), and deep temporal artery (DTA) can restore the impaired hemodynamics of the ischemic brain. In this study we attempted to evaluate the hemodynamic changes of the STA graft and predict the angiogenesis outcomes in MMD patients after combined bypass surgery by using quantitative ultrasonography.

Methods

We retrospectively studied Moyamoya patients who were treated by combined bypass between September 2017 and June 2021 in our hospital. We quantitatively measured the STA with ultrasound and recorded the blood flow, diameter, pulsatility index (PI) and resistance index (RI) to assess graft development preoperatively and at 1 day, 7 days, 3 months, and 6 months after surgery. All patients received both pre- and post- operative angiography evaluation. Patients were divided into either well- or poorly-angiogenesis groups according to the transdural collateral formation status on angiography at 6 months after surgery (W group or P group). Patients with matshushima grade A or B were divided into W group. Patients with matshushima grade C were divided into P group, indicating a poor angiogenesis development.

Results

A total of 52 patients with 54 operated hemispheres were enrolled, including 25 men and 27 women with an average age of 39 ± 14.3 years. Compared to preoperative values, the average blood flow of an STA graft at day 1 postoperation increased from 16.06 ± 12.47 to 117.47± 73.77 (mL/min), diameter increased from 1.14 ± 0.33 to 1.81 ± 0.30 (mm), PI dropped from 1.77 ± 0.42 to 0.76 ± 0.37, and RI dropped from 1.77 ± 0.42 to 0.50 ± 0.12. According to the Matsushima grade at 6 months after surgery, 30 hemispheres qualified as W group and 24 hemispheres as P group. Statistically significant differences were found between the two groups in diameter (p = 0.010) as well as flow (p = 0.017) at 3 months post-surgery. Flow also remained significantly different at 6 months after surgery (p = 0.014). Based on GEE logistic regression evaluation, the patients with higher levels of flow post-operation were more likely to have poorly-compensated collateral. ROC analysis showed that increased flow of ≥69.5 ml/min (p = 0.003; AUC = 0.74) or a 604% (p = 0.012; AUC = 0.70) increase at 3 months post-surgery compared with the pre-operative value is the cut-off point which had the highest Youden's index for predicting P group. Furthermore, a diameter at 3 months post-surgery that is ≥0.75 mm (p = 0.008; AUC = 0.71) or 52% (p =0.021; AUC = 0.68) wider than pre-operation also indicates a high risk of poor indirect collateral formation.

Conclusions

The hemodynamic of the STA graft changed significantly after combined bypass surgery. An increased flow of more than 69.5 ml/min at 3 months was a good predictive factor for poor neoangiogenesis in MMD patients treated with combined bypass surgery.

Exploring associations among morphometric measurements, genetic group of sire, and performance of beef on dairy calves

Sire selection for beef on dairy crosses plays an important role in livestock systems as it may affect future performance and carcass traits of growing and finishing crossbred cattle. The phenotypic variation found in beef on dairy crosses has raised concerns from meat packers due to animals with dairy-type carcass characteristics. The use of morphometric measurements may help to understand the phenotypic structures of sire progeny for selecting animals with greater performance. In addition, due to the relationship with growth, these measurements could be used to early predict the performance until the transition from dairy farms to sales. The objectives of this study were 1) to evaluate the effect of different beef sires and breeds on the morphometric measurements of crossbred calves including cannon bone (CB), forearm (FA), hip height (HH), face length (FL), face width (FW) and growth performance; and (2) to predict the weight gain from birth to transition from dairy farms to sale (WG) and the body weight at sale (BW) using such morphometric measurements obtained at first days of animals' life. CB, FA, HH, FL, FW, and weight at 7 ± 5 d (BW7) (Table 1) were measured on 206 calves, from four different sire breeds [Angus (AN), SimAngus (SA), Simmental (SI), and Limousin (LI)], from five farms. To evaluate the morphometric measurements at the transition from dairy farms to sale and animal performance 91 out of 206 calves sourced from four farms, and offspring of two different sires (AN and SA) were used. To predict the WG and BW, 97 calves, and offspring of three different sires (AN, SA, and LI) were used. The data were analyzed using a mixed model, considering farm and sire as random effects. To predict WG and BW, two linear models (including or not the morphometric measurements) were used, and a leave-one-out cross-validation strategy was used to evaluate their predictive quality. The HH and BW7 were 7.67% and 10.7% higher (P < 0.05) in SA crossbred calves compared to AN, respectively. However, the ADG and adjusted body weight to 120 d were 14.3% and 9.46% greater (P < 0.05) in AN compared to SA. The morphometric measurements improved the model's predictive performance for WG and BW. In conclusion, morphometric measurements at the first days of calves' life can be used to predict animals' performance in beef on dairy. Such a strategy could lead to optimized management decisions and greater profitability in dairy farms.

Forecasting Hospital Visits Due to Influenza Based on Emergency Department Visits for Fever: A Feasibility Study on Emergency Department-Based Syndromic Surveillance

This study evaluated the use of chief complaint data from emergency departments (EDs) to detect the increment of influenza cases identified from the nationwide medical service usage and developed a forecast model to predict the number of patients with influenza using the daily number of ED visits due to fever. The National Health Insurance Service (NHIS) and the National Emergency Department Information System (NEDIS) databases from 2015 to 2019 were used. The definition of fever included having an initial body temperature ≥ 38.0 °C at an ED department or having a report of fever as a patient's chief complaint. The moving average number of visits to the ED due to fever for the previous seven days was used. Patients in the NHIS with the International Classification of Diseases-10 codes of J09, J10, or J11 were classified as influenza cases, with a window duration of 100 days, assuming the claims were from the same season. We developed a forecast model according to an autoregressive integrated moving average (ARIMA) method using the data from 2015 to 2017 and validated it using the data from 2018 to 2019. Of the 29,142,229 ED visits from 2015 to 2019, 39.9% reported either a fever as a chief complaint or a ≥38.0 °C initial body temperature at the ED. ARIMA (1,1,1) (0,0,1)₇ was the most appropriate model for predicting ED visits due to fever. The mean absolute percentage error (MAPE) value showed the prediction accuracy of the model. The correlation coefficient between the number of ED visits and the number of patients with influenza in the NHIS up to 14 days before the forecast, with the exceptions of the eighth, ninth, and twelfth days, was higher than 0.70 (p-value = 0.001). ED-based syndromic surveillances of fever were feasible for the early detection of hospital visits due to influenza.

Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005-2022): Multiple Change Point Analysis and Time Series Forecast

LSD is an important transboundary disease affecting the cattle industry worldwide. The objectives of this study were to determine trends and significant change points, and to forecast the number of LSD outbreak reports in Africa, Europe, and Asia. LSD outbreak report data (January 2005 to January 2022) from the World Organization for Animal Health were analyzed. We determined statistically significant change points in the data using binary segmentation, and forecast the number of LSD reports using auto-regressive moving average (ARIMA) and neural network auto-regressive (NNAR) models. Four significant change points were identified for each continent. The year between the third and fourth change points (2016-2019) in the African data was the period with the highest mean of number of LSD reports. All change points of LSD outbreaks in Europe corresponded with massive outbreaks during 2015-2017. Asia had the highest number of LSD reports in 2019 after the third detected change point in 2018. For the next three years (2022-2024), both ARIMA and NNAR forecast a rise in the number of LSD reports in Africa and a steady number in Europe. However, ARIMA predicts a stable number of outbreaks in Asia, whereas NNAR predicts an increase in 2023-2024. This study provides information that contributes to a better understanding of the epidemiology of LSD.

A Climatology of Long-Duration High 2-MeV Electron Flux Periods in the Outer Radiation Belt

Since the advent of the Space Age, the importance of understanding and forecasting relativistic electron fluxes in the Earth's radiation belts has been steadily growing due to the threat that such particles pose to satellite electronics. Here, we provide a model of long-duration periods of high time-integrated 2-MeV electron flux deep inside the outer radiation belt, based on the significant correlation obtained in 2001-2017 between time-integrated electron flux measured by satellites and a measure of the preceding time-integrated homogenized aa _H geomagnetic index. We show that this correlation is likely due to a stronger cumulative chorus wave-driven acceleration of relativistic electrons and a stronger cumulative inward radial diffusion of such electrons during periods of higher time-integrated geomagnetic activity. Return levels of 2-MeV electron flux are provided based on Extreme Value analysis of time-integrated geomagnetic activity over 1868-2017, in rough agreement with estimates based on 20-year data sets of measured flux. A high correlation is also found between our measure of time-integrated geomagnetic activity averaged over each solar cycle and averaged sunspot numbers, potentially paving the way for forecasts of time-integrated relativistic electron flux during future solar cycles based on predictions of solar activity.

Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010-2020

Thailand is one of the countries where foot and mouth disease outbreaks have resulted in considerable economic losses. Forecasting is an important warning technique that can allow authorities to establish an FMD surveillance and control program. This study aimed to model and forecast the monthly number of FMD outbreak episodes (n-FMD episodes) in Thailand using the time-series methods, including seasonal autoregressive integrated moving average (SARIMA), error trend seasonality (ETS), neural network autoregression (NNAR), and Trigonometric Exponential smoothing state−space model with Box−Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and hybrid methods. These methods were applied to monthly n-FMD episodes (n = 1209) from January 2010 to December 2020. Results showed that the n-FMD episodes had a stable trend from 2010 to 2020, but they appeared to increase from 2014 to 2020. The outbreak episodes followed a seasonal pattern, with a predominant peak occurring from September to November annually. The single-technique methods yielded the best-fitting time-series models, including SARIMA(1,0,1)(0,1,1)12, NNAR(3,1,2)12,ETS(A,N,A), and TBATS(1,{0,0},0.8,{<12,5>}. Moreover, SARIMA-NNAR and NNAR-TBATS were the hybrid models that performed the best on the validation datasets. The models that incorporate seasonality and a non-linear trend performed better than others. The forecasts highlighted the rising trend of n-FMD episodes in Thailand, which shares borders with several FMD endemic countries in which cross-border trading of cattle is found common. Thus, control strategies and effective measures to prevent FMD outbreaks should be strengthened not only in Thailand but also in neighboring countries.

Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs

A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg–Marquardt algorithm is used to train all the models and the transfer functions used are tansig and purelin in the hidden and output layers, respectively. The model’s performance is evaluated on a four-node star network and is measured in terms of the MSE and R2 values. The four stability prediction models demonstrate good performances and depict the best training and prediction ability.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

Forecasting of Flash Floods Peak Flow for Environmental Hazards and Water Harvesting in Desert Area of El-Qaa Plain, Sinai

Water resources in arid and semi-arid regions are limited where the demands of agriculture, drinking and industry are increasing, especially in drought areas. These regions are subjected to climate changes (CC) that affect the watershed duration and water supplies. Estimations of flash flooding (FF) volume and discharge are required for future development to meet the water demands in these water scarcity regions. Moreover, FF in hot deserts is characterized by low duration, high velocity and peak discharge with a large volume of sediment. Today, the trends of flash flooding due to CC have become very dangerous and affect water harvesting volume and human life due to flooding hazards. The current study forecasts the peak discharges and volumes in the desert of El-Qaa plain in Southwestern Sinai, Egypt, for drought and wet seasons by studying the influence of recurrence intervals for 2, 5, 10, 25, 50 and 100 years. Watershed modeling system software (WMS) is used and applied for the current study area delineation. The results show that the predictions of peak discharges reached 0, 0.44, 45.72, 195.45, 365.91 and 575.30 cubic meters per s (m³ s⁻¹) while the volumes reached 0, 23, 149.80, 2,896,241.40, 12,664,963.80 and 36,681,492.60 cubic meters (m³) for 2, 5, 10, 25, 50 and 100 years, respectively, which are precipitation depths of 15.20, 35.30, 50.60, 70.70, 85.90 and 101 mm, respectively. Additionally, the average annual precipitation reached 13.37 mm, with peak flow and volume reaching 0 m³ s⁻¹ where all of water harvesting returned losses. Moreover, future charts and equations were developed to estimate the peak flow and volume, which are useful for future rainwater harvesting and the design of protection against flooding hazards in drought regions due to CC for dry and wet seasons. This study provides relevant information for hazard and risk assessment for FF in hot desert regions. The study recommends investigating the impact of recurrence intervals on sediment transport in these regions.

Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study

Background

Personalized feedback is an effective behavior change technique frequently incorporated into mobile health (mHealth) apps. Innovations in data science create opportunities for leveraging the wealth of user data accumulated by mHealth apps to generate personalized health forecasts. One Drop’s digital program is one of the first to implement blood glucose forecasts for people with type 2 diabetes. The impact of these forecasts on behavior and glycemic management has not been evaluated to date.

Objective

This study sought to evaluate the impact of exposure to blood glucose forecasts on blood glucose logging behavior, average blood glucose, and percentage of glucose points in range.

Methods

This retrospective cohort study examined people with type 2 diabetes who first began using One Drop to record their blood glucose between 2019 and 2021. Cohorts included those who received blood glucose forecasts and those who did not receive forecasts. The cohorts were compared to evaluate the effect of exposure to blood glucose forecasts on logging activity, average glucose, and percentage of glucose readings in range, after controlling for potential confounding factors. Data were analyzed using analysis of covariance (ANCOVA) and regression analyses.

Results

Data from a total of 1411 One Drop users with type 2 diabetes and elevated baseline glucose were analyzed. Participants (60.6% male, 795/1311; mean age 50.2 years, SD 11.8) had diabetes for 7.1 years on average (SD 7.9). After controlling for potential confounding factors, blood glucose forecasts were associated with more frequent blood glucose logging (P=.004), lower average blood glucose (P<.001), and a higher percentage of readings in range (P=.03) after 12 weeks. Blood glucose logging partially mediated the relationship between exposure to forecasts and average glucose.

Conclusions

Individuals who received blood glucose forecasts had significantly lower average glucose, with a greater amount of glucose measurements in a healthy range after 12 weeks compared to those who did not receive forecasts. Glucose logging was identified as a partial mediator of the relationship between forecast exposure and week-12 average glucose, highlighting a potential mechanism through which glucose forecasts exert their effect. When administered as a part of a comprehensive mHealth program, blood glucose forecasts may significantly improve glycemic management among people living with type 2 diabetes.

Development of Temporal Model for Forecasting of Helicoverpa armigera (Noctuidae: Lepidopetra) Using Arima and Artificial Neural Networks

Helicoverpa armigera (Hübner) (Noctuidae: Lepidopetra) is a polyphagous pest of major crops grown in India. To prevent the damage caused by H. armigera farmers rely heavily on insecticides of diverse groups on a regular basis which is not a benign practice, environmentally and economically. To provide more efficient and accurate information on timely application of insecticides, this research was aimed to develop a forecast model to predict population dynamics of pod borer using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN). The data used in this study were collected from the randomly installed sex pheromone traps at International Crops Research Institute for the Semi-arid Tropics (ICRISAT), Patancheru, Hyderabad. Several ARIMA (p, d, q) (P, D, Q) and ANN models were developed using the historical trap catch data. ARIMA model (1,0,1), (1,0,2) with minimal BIC, RMSE, MAPE, MAE, and MASE values and higher R2 value (0.53) was selected as the best ARIMA fit model, and neural network (7-30-1) was found to be the best fit to predict the catches of male moths of pod borer from September 2021 to August 2023. A comparative analysis performed between the ARIMA and ANN, shows that the ANN based on feed forward neural networks is best suited for effective pest prediction. With the developed ARIMA model, it would be easier to predict H. armigera adult population dynamics round the year and timely intervention of control measures can be followed by appropriate decision-making schedule for insecticide application.

"Ready for the future?" - Status of national and cross-country horizon scanning systems for medicines in European countries

Background: Horizon scanning aims to systematically identify upcoming health technologies and thus allows policy-makers to be better prepared for the entry of new medicines with possibly high price tags into the national health system. The aim of this study is to survey the existence of national and cross-national horizon scanning systems for medicines in European countries.

Methods: Experts working in public authorities (members of the Pharmaceutical Pricing and Reimbursement Information/PPRI network) in the WHO European region participated in surveys in 2014 and 2019 and informed about the status of horizon scanning in their country (response rate: 14 and 44 countries, respectively). Identified advanced horizon scanning systems as of 2019 were further investigated based on a literature review.

Results: In 2019, six countries (Iceland, Italy, the Netherlands, Norway, Sweden, United Kingdom) reported systematic use of horizon scanning for some new medicines, and four countries (Austria, Denmark, France, Ireland) had some horizon scanning activities ongoing. No systematic use of horizon scanning was reported from the remaining 34 countries. The findings of the survey undertaken five years earlier were similar, with even fewer systems in place. A recent development is the establishment of cross-country initiatives of governments that aim, among others, to jointly perform horizon scanning; the International Horizon Scanning Initiative (IHSI) initiated by the Beneluxa collaboration is the most advanced undertaking in this respect. Countries with systematic use tend to have horizon scanning fully integrated in a system for the management of new medicines, and they use horizon scanning outcomes to inform decisions as to whether or not a Health Technology Assessment will be conducted and price negotiations be started. Differences between existing horizon scanning systems mainly concern the timings of scanning and reporting, the sources for the inputs and the accessibility of the findings.

Conclusion: There appears to be a discrepancy between the perceived importance of horizon scanning based on some eye-opening examples in the past and its actual implementation in European health systems. The latter is likely attributable to horizon scanning being resource-intensive. The establishment of new national and international horizon scanning systems offers the opportunity to investigate their impact on sustainable access to affordable medicines from the start.

Overview and cross-validation of COVID-19 forecasting univariate models

Researchers have been working with different models to forecast COVID-19 cases. Many of their estimates are not accurate. This study aims to propose the best model to forecast COVID-19 cumulative cases using a machine learning technic. It is a work that focused on time series univariate models because there are too many debates about the quality of the pandemic data. To increase the likelihood of the findings, we avoided many variables modeling and proposed a robust process to forecast COVID-19 cumulative cases. It will help international institutions to take optimal decisions about the world economy and response to the pandemic. Consequently, we used the data titled “Coronavirus Pandemic (COVID-19)” from “Our World in Data” about cases from 22 January 2020 to 30 November 2020. We computed Error Trend Season (ETS), Exponential smoothing with multiplicative error-trend, and ARIMA on the training data sets. In addition, we calculated the Mean Absolute Percentage Error (MAPE) per model. Among those models, we notice that ETS (with additive error-trend and no season) has the smallest MAPE statistics compared to the others. The findings revealed that with the ETS model we need at least 100 days to have good forecasts with a MAPE threshold of 1%.

Forecasting Frequent Alcohol Use among Adolescents in HBSC Countries: A Bayesian Framework for Making Predictions

(1) Aim: To summarize alcohol trends in the last 30 years (1985/6–2017/8) among 15-year-olds in Health Behaviour in School-aged Children (HBSC) countries (overall sample size: 413,399 adolescents; 51.55% girls) and to forecast the potential evolution in the upcoming 2021/22 HBSC survey. (2) Methods: Using 1986–2018 prevalence data on weekly alcohol consumption among 15-year-olds related to 40 HBSC countries/regions, a Bayesian semi-parametric hierarchical model was adopted to estimate trends making a clusterization of the countries, and to give estimates for the 2022 HBSC survey. (3) Results: An overall declining trend in alcohol consumption was observed over time in almost all the countries. However, compared to 2014, some countries showed a new increase in 2018 and 2021/22 estimates forecast a slight increase in the majority of countries, pointing out a potential bounce after a decreasing period in frequent drinking habits. (4) Conclusions: The clusterization suggested a homogenization of consumption habits among HBSC countries. The comparison between 2022 observed and expected data could be helpful to investigate the effect of risk behaviour determinants, including the pandemic impact, occurring between the last two waves of the survey.

Copper Demand: a Framework Based on Scenario Analysis and Stock Dynamics

In a world of finite metallic minerals, demand forecasting is crucial for managing the stocks and flows of these critical resources. Previous studies have projected copper supply and demand at the global level and the regional level of EU and China. However, no comprehensive study exists for the U.S., which has displayed unique copper consumption and dematerialization trends. In this study, we adapted the stock dynamics approach to forecast the U.S. copper in-use stock (IUS), consumption, and end-of-life (EOL) flows from 2016 to 2070 under various U.S.-specific scenarios. Assuming different socio-technological development trajectories, our model results are consistent with a stabilization range of 215-260 kg/person for the IUS. This is projected along with steady growth in the annual copper consumption and EOL copper generation driven mainly by the growing U.S. population. This stabilization trend of per capita IUS indicates that future copper consumption will largely recuperate IUS losses, allowing 34-39% of future demand to be met potentially by recycling 43% of domestic EOL copper. Despite the recent trends of "dematerialization", adaptive policies still need to be designed for enhancing the EOL recovery, especially in light of a potential transitioning to a "green technology" future with increased electrification dictating higher copper demand.

Modeling the Number of Confirmed Cases and Deaths from the COVID-19 Pandemic in the UK and Forecasting from April 15 to May 30, 2020

OBJECTIVE

The UK is one of the epicenters of coronavirus disease (COVID-19) in the world. As of April 14, there have been 93 873 confirmed patients of COVID-19 in the UK and 12 107 deaths with confirmed infection. On April 14, it was reported that COVID-19 was the cause of more than half of the deaths in London.

METHODS

The present paper addresses the modeling and forecasting of the outbreak of COVID-19 in the UK. This modeling must be accomplished through a 2-part time series model to study the number of confirmed cases and deaths. The period we aimed at a forecast was 46 days from April 15 to May 30, 2020. All the computations and simulations were conducted on Matlab R2015b, and the average curves and confidence intervals were calculated based on 100 simulations of the fitted models.

RESULTS

According to the obtained model, we expect that the cumulative number of confirmed cases will reach 282 000 with an 80% confidence interval (242 000 to 316 500) on May 30, from 93 873 on April 14. In addition, it is expected that, over this period, the number of daily new confirmed cases will fall to the interval 1330 to 6450 with the probability of 0.80 by the point estimation around 3100. Regarding death, our model establishes that the real case fatality rate of the pandemic in the UK approaches 11% (80% confidence interval: 8%-15%). Accordingly, we forecast that the total death in the UK will rise to 35 000 (28 000-50 000 with the probability of 80%).

CONCLUSIONS

The drawback of this study is the shortage of observations. Also, to conduct a more exact study, it is possible to take the number of the tests into account as an explanatory variable besides time.

Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia

With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia’s official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.

Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US

A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data - involving an autoregressive model with autoregressive integrated moving average (ARIMA) - and innovative web search queries - involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provided a more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.