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The increasing incidence of dengue virus (DENV) infections poses significant public health challenges in Bangladesh, demanding advanced forecasting methodologies to guide timely interventions. This study introduces a rigorous multivariate time series analysis, integrating meteorological factors with state-of-the-art machine learning (ML) models, to predict DENV case trends across different temporal scales. Leveraging a robust data pipeline, this research incorporates a strategic downscaling technique, applying the Stochastic Bayesian Downscaling (SBD) algorithm to convert monthly DENV case data to daily frequency. This approach addresses key issues in the handling of sparse datasets and missing data, offering novel insights into the potential accuracy benefits of data downscaling in time series forecasting. Among the models assessed, the decision tree demonstrated superior performance on the actual monthly data, achieving an accuracy of 74.6 % . In contrast, the random forest model outperformed others on the downscaled daily data, reaching an accuracy of 95.8 % , thereby supporting the efficacy of data downscaling for ML applications in epidemiology. Comparative analysis reveals that downscaling provided a 28.5 % improvement in accuracy and an 89.3 % reduction in mean absolute percentage error (MAPE) over non-downscaled data which has been proven to be statistically significant using the Wilcoxon signed rank test, illustrating the substantial advantages of employing downscaling for effective DENV forecasting. Based on the best-performing model, the study further projects a worst-case scenario for 2024, forecasting daily cases to peak at 1,382 ( 95 % CI: 1,341-1,423) between August and October, with a gradual decline expected by December. The findings not only underscore the critical influence of meteorological variables on DENV transmission but also advocate for the adoption of sophisticated data preprocessing techniques, such as downscaling, to enhance prediction accuracy. This research marks a significant advancement in predictive epidemiology, offering a scalable framework for DENV and other vector-borne diseases, with implications for improving public health responses in vulnerable regions globally.

The Government of Bangladesh (GoB) first implemented the cash incentive of 2 percent in July 2019 and continued the scheme with some modifications amid the pandemic to enhance remittance inflows through formal channels and ensure macroeconomic stability in the country. This study examines the impact of the cash incentive introduced by the GoB to boost remittance inflow using the Interrupted Time Series (ITS) analysis along with the Chow test for structural stability. While ITS analysis has been employed by numerous studies in the healthcare sector, but this paper uses such analysis for the first time in any type of migration study in Bangladesh. We have used ITS as it is most effective in measuring the impact of policy interventions that are expected to act either quickly after an intervention or within a stipulated time frame. The study is also the first to examine the region wise efficacy of policy intervention in the country. Monthly Remittance Inflow data from July 2013 to December 2021 has been used for the analysis. Chow test results conclude that the policy intervention had a significant impact while the ITS analysis findings demonstrated that the cash intervention significantly increased both aggregated and region-specific remittance inflows, highlighting the significance of the action. The overall findings revealed that the introduction of cash incentive in July 2019 resulted in an immediate, sustained increase of 6.68 percent in remittance inflows, with a further increase of 0.25 percent every month. Region wise analysis shows that the impact was highest in the USA & UK region and lowest in the Middle Eastern region, which signifies issues related to prevalence of hundi market, skillset of migrant workers, average monthly salary, and remittance sending costs. Our research provides policymakers with significant information to implement customized policies that ensure macroeconomic stability by enhancing remittance inflows through formal channels.

Dengue, a mosquito-borne viral disease, continues to pose severe risks to public health and economic stability in tropical and subtropical regions, particularly in developing nations like Bangladesh. The necessity for advanced forecasting mechanisms has never been more critical to enhance the effectiveness of vector control strategies and resource allocations. This study formulates a dynamic data pipeline to forecast dengue incidence based on 13 meteorological variables using a suite of state-of-the-art machine learning models and custom features engineering, achieving an accuracy of 84.02%, marking a substantial improvement over existing studies. A novel wrapper feature selection algorithm employing a custom objective function is proposed in this study, which significantly improves model accuracy by 12.63% and reduces the mean absolute percentage error by 70.82%. The custom objective function’s output can be transformed to quantify the contribution of each variable to the target variable’s variability, providing deeper insights into the workings of black box models. The study concludes that relative humidity is redundant in predicting dengue infection, while meteorological factors exhibit more significant short-term impacts compared to long-term and immediate impacts. Sunshine (hours) emerges as the meteorological factor with the most immediate impact, whereas precipitation is the most impactful predictor over both short-term (8-month lag) and long-term (26–30-month lag) periods. Forecasts for 2024 using the best-performing model predict a rise in dengue cases starting in May, peaking at 24,000 cases per month by August and persisting at high levels through October before declining to half by year-end. These findings offer critical insights into temporal climate effects on dengue transmission, aiding the development of effective forecasting systems.

Data scarcity and discontinuity are common occurrences in the healthcare and epidemiological dataset and often is needed to form an educative decision and forecast the upcoming scenario. Often to avoid these problems, these data are processed as monthly/yearly aggregate where the prevalent forecasting tools like Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and TBATS often fail to provide satisfactory results. Artificial data synthesis methods have been proven to be a powerful tool for tackling these challenges. The paper aims to propose a novel algorithm named Stochastic Bayesian Downscaling (SBD) algorithm based on the Bayesian approach that can regenerate downscaled time series of varying time lengths from aggregated data, preserving most of the statistical characteristics and the aggregated sum of the original data. The paper presents two epidemiological time series case studies of Bangladesh (Dengue, Covid-19) to showcase the workflow of the algorithm. The case studies illustrate that the synthesized data agrees with the original data regarding its statistical properties, trend, seasonality, and residuals. In the case of forecasting performance, using the last 12 years data of Dengue infection data in Bangladesh, we were able to decrease error terms up to 72.76% using synthetic data over actual aggregated data.

The continued rise in global carbon dioxide ( ) emissions challenges international climate policy, particularly the goals of the Paris Agreement. This study forecasts emissions through 2030 for the eleven highest-emitting nations–China, the United States, India, Russia, Japan, Iran, Indonesia, Saudi Arabia, Canada, South Korea, and Germany–while assessing their progress toward Nationally Determined Contributions (NDCs). Using data from 1990 to 2023, we apply a robust data pipeline comprised of six machine learning models and sequential squeeze feature selection incorporating eleven economic, industrial, and energy consumption variables. We have modelled the scenario with an average prediction accuracy of 96.21%. Results indicate that Russia is on track to exceed its reduction targets, while Germany and the United States will fall slightly short. China, India, Japan, Canada, South Korea, and Indonesia are projected to miss their commitments by significant margins. At the same time, Iran and Saudi Arabia are expected to increase emissions rather than reduce them. These findings highlight the need for strengthened energy efficiency policies, expanded renewable energy adoption, enhanced carbon pricing mechanisms, and stricter regulatory enforcement. Emerging economies require international collaboration and investment to support low-carbon transitions. This study provides a data-driven assessment of emission trajectories, emphasizing the urgency of coordinated global action, technological innovation, and adaptive policy measures to align emissions with the 1.5 warming threshold. This work represents a novel integration of multivariate machine learning modelling, data-driven feature selection, and policy-oriented emission forecasts, establishing new methodological and empirical benchmarks in climate analytics.

Dengue is the most prominent arboviral infection known to humans, especially in tropical regions of the world like Bangladesh. This is often a tricky outbreak to deal with, given its nature of seasonality, and due to the impact of climate change, variations in the length of its on‐season have been observed. This article models the dengue scenario in Bangladesh using a periodic, nonautonomous SIS vector–host model, proposes some development over the existing algorithm to determine the basic reproduction number, R 0 , for nonautonomous models, namely the “linear operator method,” and hence patriots the behavior of R 0 with respect to the length of the on‐season. Our experimentation shows that the infection transmission will be at its peak when the length of the on‐season is around 10 months. Based on the data of 2022, the current dynamic of the disease scenario in Bangladesh shows that the disease will not persist in the long run but occasional outbreaks may occur, given the right set of conditions. Finally, we conduct a sensitivity analysis of the model parameters, which shows that improving the recovery rate of the infected patient class and impeding the birth rate of the vector can effectively subdue the disease outbreak.

Prime factorization has been a buzzing topic in the field of number theory since time unknown. However, in recent years, alternative avenues to tackle this problem are being explored by researchers because of its direct application in the arena of cryptography. One of such applications is the cryptanalysis of RSA numbers, which requires prime factorization of large semiprimes. Based on numerical experiments, this paper proposes a conjecture on the distribution of digits on prime of infinite length. This paper infuses the theoretical understanding of primes to optimize the search space of prime factors by shrinking it upto 98.15%, which, in terms of application, has shown 26.50% increase in the success rate and 41.91% decrease of the maximum number of generations required by the genetic algorithm used traditionally in the literature. This paper also introduces a variation of the genetic algorithm named Sieve Method that is fine-tuned for factorization of big semi-primes, which was able to factor numbers up to 23 decimal digits with 84% success rate. Our findings shows that sieve methods on average has achieved 321.89% increase in success rate and 64.06% decrement in the maximum number of generations required for the algorithm to converge compared to the existing literatures.

Data scarcity and discontinuity are common occurrences in the healthcare and epidemiological dataset and often need help in forming an educative decision and forecasting the upcoming scenario. Often, these data are stored as monthly/yearly aggregate where the prevalent forecasting tools like Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and TBATS often fail to provide satisfactory results. Artificial data synthesis methods have been proven to be a powerful tool for tackling these challenges. The paper aims to propose a downscaling data algorithm based on the underlying distribution. Our findings show that the synthesized data is in agreement with the original data in terms of trend, seasonality, and residuals, and the synthesized data provides a stable foothold for the forecasting tools to generate a much more accurate forecast of the situation.