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Prescriptive temporal modeling approach using climate variables to forecast dengue incidence in Córdoba, Colombia.
Math Biosci Eng
December 2024
Department of Mathematics, New Mexico Tech, New Mexico 87801, USA.
We present a modeling strategy to forecast the incidence rate of dengue in the department of Córdoba, Colombia, thereby considering the effect of climate variables. A Seasonal Autoregressive Integrated Moving Average model with exogenous variables (SARIMAX) model is fitted under a cross-validation approach, and we examine the effect of the exogenous variables on the performance of the model. This study uses data of dengue cases, precipitation, and relative humidity reported from years 2007 to 2021.
View Article and Find Full Text PDFMonitoring air quality index with EWMA and individual charts using XGBoost and SVR residuals.
MethodsX
June 2025
Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
Article Synopsis
- PM2.5 air pollution in urban areas like Jakarta is a major health concern, often exceeding safe levels due to rapid urbanization, prompting this study to evaluate specific modeling techniques.
- The study investigates autocorrelation in air quality data and finds that while the Support Vector Regression (SVR) effectively handles autocorrelation, the combination of XGBoost and an Exponentially Weighted Moving Average (EWMA) chart shows superior monitoring performance.
- The integration of XGBoost and EWMA successfully identified changes in air quality, detecting only one out-of-control point during the monitoring phases, thus improving accuracy and reducing false alarms in air quality assessments.
Understanding the stroke burden in Saudi Arabia: Trends from 1990 to 2019 and forecasting through time series analysis.
Neurosciences (Riyadh)
January 2025
From the Department of Family and Community Medicine (Mahfouz, Ghazy), College of Medicine, King Khalid University, Abha, Kingdom of Saudi Arabia, from Alexandria Directorate of Health Affairs (Abdelmoneim), Egyptian Ministry of Health and Population, Alexandria, Department of Public Health and Community Medicine (Abdu), Faculty of Medicine, Mansoura University, Mansoura, from Public Health and Community Medicine (AboElela, Shiba), Faculty of Medicine for Girls, Al-Azhar University, Cairo, Neuroscience Center (Alhazzani), King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
Objectives: To describe age-standardized incidence and disability-adjusted life years (DALYs) of ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH) in the Kingdom of Saudi Arabia (KSA) from 1990 to 2019 and forecast these variables using the Global Burden of Diseases (GBD) data over the next years (2020-2030).
Methods: Poisson regression models were employed to identify significant changes in incidence rate ratios (IRRs) and DALY rates for different stroke types. For time series models, the autoregressive integrated moving average (ARIMA) and exponential smoothing state space (ETS) models were used for forecasting.
Global surgery and climate change: how global surgery can prioritise both the health of the planet and its people.
BMC Surg
January 2025
Global Surgery Division, Department of Surgery, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
Climate change is an emerging global health crisis, disproportionately affecting low- and middle-income countries (LMICs) where health outcomes are increasingly compromised by environmental stressors such as pollution, natural disasters, and human migration. With a focus on promoting health equity, Global Surgery advocates for expanding access to surgical care and enhancing health outcomes, particularly in resource-limited and disaster-affected areas like LMICs. The healthcare industry-and more specifically, surgical care-significantly contributes to the global carbon footprint, primarily through resource-intensive settings, i.
View Article and Find Full Text PDFInvestigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence.
Nutrients
January 2025
Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates.
Background: Artificial Intelligence (AI) technologies are now essential as the agenda of nutrition research expands its scope to look at the intricate connection between food and health in both an individual and a community context. AI also helps in tracing and offering solutions in dietary assessment, personalized and clinical nutrition, as well as disease prediction and management, such as cardiovascular diseases, diabetes, cancer, and obesity. This review aims to investigate and assess the different applications and roles of AI in nutrition and research and understand its potential future impact.
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