Time Series Research Articles

Acute coronary syndrome rates by age and sex before and during the COVID-19 pandemic in Israel: nationwide study.

There have been reports of sharp declines in acute coronary syndrome (ACS) during the COVID-19 pandemic. The study aims to assess nationwide ACS emergency department (ED) visit rates across age and sex subgroups and the general population, with a comparison before and throughout the pandemic's various phases. A multiple interrupted time series analysis was used to assess 61 349 ACS nationwide hospital visits from January 2018 to December 2021 at monthly intervals. The study period was divided into three periods: January 2018-February 2020 (pre-pandemic period); March 2020-January 2021 (early-pandemic period); February 2021-December 2021 (late-pandemic period). Segmented regression with a seasonally adjusted autoregressive moving average structure was used to build predictive models with an estimated reference trendline (counterfactual). Over 11 months of the early-pandemic period (lockdowns), the largest decrease in visits was seen in women aged 65 and above, of 18.4% [incidence rate ratio (IRR) 0.82; 95% confidence interval (CI) 0.77-0.86]. The lowest decrease was observed in men aged 25-64, of 7.2% (IRR 0.93; 0.91-0.94). During the late-pandemic period, which included high vaccination coverage and no lockdowns, the largest further decrease was in women aged 25-64 of 20.1% (IRR 0.80; 0.75-0.84) on average. The pandemic influenced ACS ED visits variably, with substantial declines during phases of high COVID-19 morbidity and mortality. Older individuals, particularly women, demonstrated the largest decrease in ACS ED visits, highlighting the need for tailored public health strategies to maintain public confidence in access to critical care during future health emergencies.

Synergistic sunspot forecasting: a fusion of time series analysis and machine learning

Synergistic sunspot forecasting: a fusion of time series analysis and machine learning

Feature derivation and classification of auditory-motor coupling dynamics in healthy and neurologically impaired adults.

The use of auditory stimuli in rehabilitation to target walking has been evidenced in persons with neurological conditions. The methodologies focus on the synchronisation of persons' steps to auditory stimuli showing that the type of stimuli and tempi significantly affect the synchronisation. However, the dynamic of the interaction over time between the motor system and the auditory stimuli, i.e., when steps are aligned (termed as locking) and not aligned (termed as unlocking) to the beat of the stimuli, remains unclear. Quantifying these dynamics would assist in the development of personalised rehabilitation. Nevertheless, it is currently challenging given the variability of responses per individual over time. We propose a methodological solution to quantify the dynamics of the step-to-beat coupling over time within an experimental paradigm where healthy (n = 7) and neurological impaired (n = 6) participants walk three minutes to music and metronomes at various tempi. We applied window partitioning within the time series to account for the changing pattern. To classify data into locked and unlocked events, features of fluctuation and trend were derived on which two statistical tests (circular statistical test and slope test) were done, respectively. Based on the ground truth, the performance of our proposed method yielded high accuracy (91%), precision (90%) and recall (97%). The standard deviation of the inter-step intervals was then modelled across the label and experimental factors. The proposed method is suitable for quantifying fine-grained observation of the dynamics of auditory-motor coupling in adult healthy and neurological impaired participants, with the potential of designing personalised rehabilitation.

Urban Heat and Burden of Hyponatremia.

Hyponatremia (sodium level <135 mEq/L), the most prevalent electrolyte disorder in clinical practice, is associated with considerable clinical and economic burdens. Despite its recognized effect and indication of seasonal patterns, there is a research gap regarding heat-related hyponatremia. Addressing this issue is crucial, especially regarding projected increases in environmental temperature, particularly in urban areas. To comprehensively analyze the association of heat in an urban setting with hyponatremia prevalence. This cross-sectional study using retrospective time series analysis was conducted among all adult patients (age ≥18 years) presenting to the Charité-Universitätsmedizin Berlin between March 1, 2000, and August 31, 2023, with a blood sodium measurement. The daily heat index, which accounts for both outdoor air temperature and relative humidity. The primary outcome measure was the daily number of hyponatremia cases, further categorized by severity. Differences in prevalence across age groups (adult patients, aged 18-65 years; and older patients, aged >65 years) and sexes were also examined. A total of 7 135 688 sodium measurements from 2 028 537 hospital visits were analyzed. The mean (SD) age at admission was 57.8 (17.8) years, and 51.7% of patients were male. A clear seasonal pattern of heat-related hyponatremia was evident among older patients, especially for moderate (sodium level, 125-129 mEq/L) and severe hyponatremia (sodium level, <125 mEq/L), where the cumulative risk over a lag period of 5 days reached a maximum relative risk (RR) of 1.26 (95% CI, 1.07-1.48) when the heat index reached 30 °C, compared with the temperature at which hyponatremia occurred least frequently. Older women were disproportionately more likely to experience hyponatremia compared with older men, with a cumulative RR of 1.10 (95% CI, 1.03-1.18) at a heat index of 26 °C. The highest RR was on lag day 0 for all subgroups (older patients: RR, 1.04 [95% CI, 1.00-1.08]; moderate and severe hyponatremia in older patients: RR, 1.05 [1.01-1.10]; and older women: RR, 1.07 [95% CI, 1.01-1.12]) and significantly increased when the heat index exceeded 15 °C (RR, 1.01 [95% CI, 1.00-1.02]). This cross-sectional study of patients with sodium measurements suggests that older people, especially women, were vulnerable to heat-related hyponatremia. Environmental heat was associated with an immediate exacerbation of hyponatremia. This finding highlights the importance of implementing prevention strategies to mitigate heat-related hyponatremia, as an increased burden in the future due to climate change is likely.

Revisiting the standard for modeling functional brain network activity: Application to consciousness.

Functional connectivity (FC) of resting-state fMRI time series can be estimated using methods that differ in their temporal sensitivity (static vs. dynamic) and the number of regions included in the connectivity estimation (derived from a prior atlas). This paper presents a novel framework for identifying and quantifying resting-state networks using resting-state fMRI recordings. The study employs a linear latent variable model to generate spatially distinct brain networks and their associated activities. It specifically addresses the atlas selection problem, and the statistical inference and multivariate analysis of the obtained brain network activities. The approach is demonstrated on a dataset of resting-state fMRI recordings from monkeys under different anesthetics using static FC. Our results suggest that two networks, one fronto-parietal and cingular and another temporo-parieto-occipital (posterior brain) strongly influences shifts in consciousness, especially between anesthesia and wakefulness. Interestingly, this observation aligns with the two prominent theories of consciousness: the global neural workspace and integrated information theories of consciousness. The proposed method is also able to decipher the level of anesthesia from the brain network activities. Overall, we provide a framework that can be effectively applied to other datasets and may be particularly useful for the study of disorders of consciousness.

The Causal Relation within Air–Sea Interaction as Inferred from Observations

Abstract The intricate cause–effect relations in air–sea interaction are investigated using a quantitative causal inference formalism. The formalism is first validated with a classical stochastic coupled model and then applied to the observational time series of sea surface temperature (SST) and air–sea turbulent surface heat flux (SHF). We identify an overall asymmetry of causality between the two variables, namely, the causality from SHF to SST is significantly larger than that from SST to SHF over most of the global oceans. Geographically, the coupling is strongest in the tropics and gets weaker substantially in the extratropics. In the midlatitude ocean, SST makes higher contributions to the SHF variability in frontal regions. We further show that the identified causality is space and time scale dependent. The dominance of SHF driving SST occurs at basin scales, whereas the dominance of SST driving SHF mostly occurs at scales smaller than 10°. The causalities in both directions get larger with increasing time scale and are less asymmetric at longer time scales. The seasonality of the causality is also discussed here.

ExTAD: Embedding Exchange Inspired Time Series Anomaly Detection With Modal Consistency

ExTAD: Embedding Exchange Inspired Time Series Anomaly Detection With Modal Consistency

Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts.

Macroeconomics As A Determinant Of Demand For Oil And Gas Exports And Non-oil And Gas In East Nusa Tenggara Region By Timor Leste

International trade is a key component in enhancing welfare and economic growth, as well as generating foreign exchange needed for development. East Nusa Tenggara (NTT), with its significant potential in the oil and gas, as well as non-oil and gas sectors, has considerable export opportunities to Timor Leste. However, the effectiveness of NTT's exports is heavily influenced by various macroeconomic factors, such as exchange rates, per capita income, and government spending in both Indonesia and Timor Leste. The variation in resources and production capacity in NTT offers opportunities to meet Timor Leste's demand, but changes in macroeconomic conditions play a critical role in determining the competitiveness of NTT's export products. This study employs a quantitative approach with multiple linear regression analysis to examine the impact of macroeconomic variables on the demand for oil and gas, as well as non-oil and gas exports from NTT to Timor Leste. The sample used consists of annual time series data from 2021 to 2023, including the Rupiah exchange rate against the US Dollar, Timor Leste's per capita income, and government spending in Timor Leste. The results show that the exchange rate has a significant negative impact on export demand, while government spending has a positive effect. Timor Leste's per capita income does not significantly affect export demand. These findings are expected to contribute to policymakers in improving NTT's export competitiveness and volume, ultimately having a positive impact on the region's economic welfare.ipsum

Challenges in Observation of Ultrafine Particles: Addressing Estimation Miscalculations and the Necessity of Temporal Trends.

Ultrafine particles (UFPs) pose a significant health risk, making comprehensive assessment essential. The influence of emission sources on particle concentrations is not only constrained by meteorological conditions but often intertwined with them, making it challenging to separate these effects. This study utilized valuable long-term particle number and size distribution (PNSD) data from 2018 to 2023 to develop a tree-based machine learning model enhanced with an interpretable component, incorporating temporal markers to characterize background or time series residuals. Our results demonstrated that, differing from PM2.5, which is significantly shaped by planetary boundary layer height, wind speed plays a crucial role in determining the particle number concentration (PNC), showing strong regional specificity. Furthermore, we systematically identified and analyzed anthropogenically influenced periodic trends. Notably, while Aitken mode observations are initially linked to traffic-related peaks, both Aitken and nucleation modes contribute to concentration peaks during rush hour periods on short-term impacts after deweather adjustment. Pollutant baseline concentrations are largely driven by human activities, with meteorological factors modulating their variability, and the secondary formation of UFPs is likely reflected in temporal residuals. This study provides a flexible framework for isolating meteorological effects, allowing more accurate assessment of anthropogenic impacts and targeted management strategies for UFP and PNC.

Predictive Abilities of Machine Learning and Deep Learning Approaches for Exchange Rate Prediction

This study evaluates the efficacy of forecasting models in predicting USD/TRY exchange rate fluctuations. We assess Support Vector Machine (SVM), XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models with 96 and 21 feature sets. Data from 01.01.2010 to 30.04.2024 were sourced from Bloomberg, CBRT, and BDDK. Findings indicate that LSTM and GRU models outperform traditional models, with GRU showing the highest predictive accuracy. SVM performs poorly with highdimensional data, while XGBoost offers moderate predictive power but lacks in capturing intricate patterns. This study highlights the importance of model and feature selection in financial time series forecasting and underscores the advantages of advanced neural networks. The results provide valuable insights for analysts and policymakers in developing robust economic forecasting models.

Near Real Time Crop Health Status Monitoring and Mapping using Sentinel Satellite Image in Menjar Shenkora District, Ethiopia

Agricultural monitoring systems must provide timely and standardized information on crop production, status, and yield, from sub-regional to national scales. Accurate monitoring and mapping of vegetation condition and health are vital for managing crops, assessing damage, and predicting yields. Crop health monitoring is one of the important items for tracking the general health status of any crop. In this regard, remote sensing and GIS play a crucial role for monitoring crop health, providing current information that traditional methods like field surveys and sampling questionnaires struggle to obtain. Effective cropland mapping techniques are essential for regular crop monitoring. This type of monitoring demands frequents continuous data with high time and space resolution. Near real-time crop monitoring uses technologies like the Sentinel-2 satellite mission, offering a consistent 5-day revisit cycle and freely accessible data. This opens new doors for delivering timely updates and monitoring parcel-based crop health and conditions in real-time. Therefore, this study used satellite images, Global Positioning System (GPS) collected data, and parcel-based socioeconomic data. GPS and socioeconomic data were employed to validate the satellite-based near real-time crop monitoring results. Vegetation Condition Index (VCI) and the Normalized Difference Vegetation Index (NDVI) were used to evaluate crop health at different stages of the growing season and to generate time series data for crop phenology respectively. NDVI time series data was used to generate crop phenology information for four main crops: Teff, wheat, onion, and sorghum. The crop type maps for these crops at the study sites were validated with an overall accuracy of 79.26% and a Kappa value of 0.737. Additionally, the results from the current research and the field-collected data were consistent in providing information about the onset, greening, maturity, and senescence dates of each crop. These findings highlight the effectiveness of the satellite-based system for real-time agricultural crop monitoring using Sentinel-2 observations across various sites and time frames. Moreover, it helps to fill the gaps of traditional crop monitoring methods with those based on satellite technology. This system is particularly valuable for early warning purpose in areas like the current study site, where conventional crop monitoring methods and inputs are limited.

Entropy as a Tool for the Analysis of Stock Market Efficiency During Periods of Crisis

In the article, we analyse the problem of the efficiency market hypothesis using entropy in moments of transition from a normal economic situation to crises or slowdowns in European, Asian and US stock markets and the economy in the years 2007–2023 (2008–2009, U.S. financial sector crises; 2020–2021, Pandemic period; and the 2022–2023 period of Russia’s attack on Ukraine). The following hypothesis is put forward in the article: In periods of economic slowdown and economic crises, the entropy of prices and return rates decreases. According to the principles of physics, in an isolated system, entropy increases and decreases at the moment of external intervention, similar to finance, where during crises and economic slowdowns, there is interference from governments introducing new regulations and intervening in financial markets. The article uses the Shannon entropy method. This measure, as a statistical measure, does not require the assumption of stationarity of time series or a known probability distribution, unlike classical statistical methods. Our results confirm decreased entropy in stock markets during crisis.

Do geopolitical uncertainty and inflation transmit opportunities or threats to green energy consumption?

Purpose This study aims to examine the impact of geopolitical uncertainty and inflation on green energy consumption. In addition, it aims to reveal the cause-and-effect relationship among these variables. Design/methodology/approach The authors exert the panel dynamic ordinary least squares (OLS) approach of Kao and Chiang (2000) and the Granger non-causality method of Dumitrescu and Hurlin (2011). On the other hand, current study relies on the annual time series data of 29 countries from 1985 to 2022. Findings The panel dynamic OLS results confirm a long-run relationship between geopolitical uncertainty, inflation and green energy consumption. On the other hand, inflation negatively impacts green energy consumption, with high inflation levels potentially halting the transition. Conversely, geopolitical uncertainty shows no significant effect on green energy use, indicating a reliance on traditional energy sources. Moreover, the current investigation reveals the unidirectional causality from green energy consumption to inflation, while no short-run causality exists from inflation as well as geopolitical uncertainty to green energy consumption in the selected countries. Research limitations/implications The authors drew conclusions about the potential impact of geopolitical uncertainty and inflation on green energy uses by considering the macro-level data. However, this investigation further be enhanced by looking at such issues through the micro perspective by understating the thought and perspective of public on the implication of geopolitical uncertainty and inflation on green energy uses. Such micro-level, country-specific study would definitely help us to generate more concrete ideas about such themes. Practical implications The empirical findings have important implications for policymakers. Results suggest that high inflation negatively impacts green energy use, so policymakers must consider implementing measures to control the inflation while promoting green energy uses for economic prosperity and environmental sustainability. In the short run, green energy use leads to inflation. Therefore, macroeconomic policymakers can implement various subsidy policies for the general public to mitigate the short-term inflationary impact of green energy use. Originality/value This investigation is novel in three ways. First, it explores the impact of geopolitical uncertainty and inflation on green energy use, using Caldara and Iacoviello’s (2022) geopolitical risk index as a proxy of geopolitical uncertainty. Second, it uses panel data econometric analysis, a method that most previous studies have not contemplated for this type of investigation. Finally, by including both advanced and emerging economies, it provides valuable insights for policymakers to develop effective strategies related to green energy consumption for achieving sustainable economic development.

Peeking into the future: inferring mechanics in dynamical tissues.

Cells exert forces on each other and their environment, shaping the tissue. The resulting mechanical stresses can be determined experimentally or estimated computationally using stress inference methods. Over the years, mechanical stress inference has become a non-invasive, low-cost computational method for estimating the relative intercellular stresses and intracellular pressures of tissues. This mini-review introduces and compares the static and dynamic modalities of stress inference, considering their advantages and limitations. To date, most software has focused on static inference, which requires only a single microscopy image as input. Although applicable in quasi-equilibrium states, this approach neglects the influence that cell rearrangements might have on the inference. In contrast, dynamic stress inference relies on a time series of microscopy images to estimate stresses and pressures. Here, we discuss both static and dynamic mechanical stress inference in terms of their physical, mathematical, and computational foundations and then outline what we believe are promising avenues for in silico inference of the mechanical states of tissues.

Differential Analysis Based on Different Decomposition Methods of Time Series

A detailed analysis of four popular time series decomposition methods: Classical, X11, SEATS, and STL is provided in this study, with a focus on their applications, strengths, and limitations. Understanding seasonal patterns in time series data is an important aspect in several fields such as economics or environmental science. The R package seasonal was employed to conduct the decompositions on the official data of National Oceanic and Atmospheric Administration (NOAA) Climate Data from January 1980 till July 2024. It offers a detailed analysis of the performance of each method in isolating trends, seasonal components, and residuals. Findings reveal that although all methods can be used for time series data decomposition, their success is influenced by data nature and particular analytical tasks. For instance, SEATS is robust enough to handle irregularities while Classical decomposition wins when it comes to simple understanding. Similarly, X11 methods were known for lack of flexibility as well as recurring seasonal changes sensitivity, where STL is particularly efficient for varying seasonal patterns datasets with nonlinearity trends. From the findings, it can be concluded that it is important to choose the appropriate decomposition method to increase reliability and make appropriate adjustments in the forecasts incorporating time series data.

CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany

Abstract. Comprehensive large-sample hydrological datasets, particularly the CAMELS datasets (Catchment Attributes and MEteorology for Large-sample Studies), have advanced hydrological research and education in recent years. These datasets integrate extensive hydro-meteorological observations with landscape features, such as geology and land use, across numerous catchments within a national framework. They provide harmonised large-sample data for various purposes, such as assessing the impacts of climate change or testing hydrological models on a large number of catchments. Furthermore, these datasets are essential for the rapid progress of data-driven models in hydrology in recent years. Despite Germany's extensive hydro-meteorological measurement infrastructure, it has lacked a consistent, nationwide hydrological dataset, largely due to its decentralised management across different federal states. This fragmentation has hindered cross-state studies and made the preparation of hydrological data labour-intensive. The introduction of CAMELS-DE represents a step forward in bridging this gap. CAMELS-DE includes 1582 streamflow gauges with hydro-meteorological time series data covering up to 70 years (median length of 46 years and a minimum length of 10 years), from January 1951 to December 2020. It includes consistent catchment boundaries with areas ranging from 5 to 15 000 km2 along with detailed catchment attributes covering soil, land cover, hydrogeologic properties, and data on human influences. Furthermore, it includes a regionally trained long short-term memory (LSTM) network and a locally trained HBV (Hydrologiska Byråns Vattenbalansavdelning) model that were used as quality control and that can be used to fill gaps in discharge data or act as baseline models for the development and testing of new hydrological models. Given the large number of catchments, including numerous relatively small ones (636 catchments &lt; 100 km2), and the time series length of up to 70 years (166 catchments with 70 years of discharge data), CAMELS-DE is one of the most comprehensive national CAMELS datasets available and offers new opportunities for research, particularly in studying long-term trends and runoff formation in small catchments and in analysing catchments with strong human influences. This article describes CAMELS-DE version 1.0, which is available at https://doi.org/10.5281/zenodo.13837553 (Dolich et al., 2024).

Analysis of Outdoor and Indoor Radon Concentration Time Series Recorded with RadonEye Monitors

Consumer-grade economical radon monitors are becoming increasingly popular in private and institutional use, in the contexts of both Citizen Science and traditional research. Although originally designed for screening indoor radon levels in view of radon regulation and decisions about mitigation or remediation—motivated by the health hazard posed by high radon concentrations—researchers are increasingly exploring their potential in some environmental studies. For long time, radon has been used as a tracer for investigating atmospheric transport processes. This paper focuses on RadonEye, currently the most sensitive among low-cost monitors available on the market, and specifically, its potential use for monitoring very low radon concentrations. It has two objectives: firstly, discussing issues of statistics of low count rates, and secondly, analyzing radon concentration time series acquired with RadonEyes outdoors and in low-radon indoor spaces. Regarding the first objective, among other things, the inference radon concentration reported to expected true is discussed. The second objective includes the application of autoregressive methods and fractal statistics to time series analysis. The overall result is that radon dynamics can be well captured using this “low-tech” approach. Statistical results are plausible; however, few results are available in the literature for comparison, particularly concerning fractal methods. The paper may therefore be seen as an incentive for further research in this direction.

The Impact of Educational Attainment on U.S. Unemployment Rates: A Regression and Log Difference Analysis

This study investigates U.S. unemployment rates among individuals aged 25 and older, segmented by four distinct levels of educational attainment. Understanding the interplay between education and employment is crucial for effective policy-making and long-term economic planning. To capture these dynamics, a regression model is utilized to derive equations representing the patterns observed in four separate time series, each corresponding to a specific educational group. In addition to this, logarithmic difference analysis is applied to compare these time series, resulting in the creation of three new plots that help further elucidate the relationships between educational attainment and unemployment rates. The analysis of these new plots reveals a narrowing disparity in unemployment rates across various educational levels, suggesting a trend toward convergence. Over time, this indicates a potential reduction in gaps based on educational background. The findings provide valuable insights into the evolving U.S. labor market, highlighting the influence of education on employment outcomes. These insights offer important implications for future policies aimed at reducing unemployment disparities and fostering equitable job opportunities across different educational levels.

Socio-Demographic Determinants of Mortality in Bangladesh: A Time Series Analysis

Understanding the factors influencing the death rate in Bangladesh is essential for effective policy-making and addressing demographic challenges. This study aims to examine both the short-term dynamics and long-term equilibrium relationships between socio-demographic factors and the death rate of Bangladesh over a given period. It focuses on four key factors: adolescence fertility rate (AFR), life expectancy rate at birth, birth rate, and infant mortality rate (IMR). Taking an annual time series dataset from 1975 to 2021 from the World Bank (WB), the study uses the Johansen co-integration test, vector error correction model (VECM), and Granger causality test to analyze the relationships between these factors and death rate. The findings reveal short-term and long-term equilibrium links between the socio-demographic parameters and death rate. A substantial negative correlation exists between death rate and AFR and life expectancy. Additionally, a positive correlation is observed between the death rate and birth rate, and IMR. These findings provide valuable insights for decision-makers to address the death rate in Bangladesh, bridging a research gap and guiding effective socio-demographic policies. International Journal of Statistical Sciences, Vol. 24(2), November, 2024, pp 35-54