Application of machine learning algorithms in an epidemiologic study of mortality.

Purpose: Epidemiologic studies are important in assessing risk factors of mortality. Machine learning (ML) is efficient in analyzing multidimensional data to unravel dependencies between risk factors and health outcomes.

Methods: Using a representative sample from the National Health and Nutrition Examination Survey data collected from 2009 to 2016 linked to the National Death Index public-use mortality data through December 31, 2019, we applied logistic, random forests, k-Nearest Neighbors, multivariate adaptive regression splines, support vector machines, extreme gradient boosting, and super learner ML algorithms to study risk factors of all-cause mortality.

Cytokine profiles as predictors of HIV incidence using machine learning survival models and statistical interpretable techniques.

HIV remains a critical global health issue, with an estimated 39.9 million people living with the virus worldwide by the end of 2023 (according to WHO). Although the epidemic's impact varies significantly across regions, Africa remains the most affected.

Investigating the effects of cytokine biomarkers on HIV incidence: a case study for individuals randomized to pre-exposure prophylaxis vs. control.

Introduction: Understanding and identifying the immunological markers and clinical information linked with HIV acquisition is crucial for effectively implementing Pre-Exposure Prophylaxis (PrEP) to prevent HIV acquisition. Prior analysis on HIV incidence outcomes have predominantly employed proportional hazards (PH) models, adjusting solely for baseline covariates. Therefore, models that integrate cytokine biomarkers, particularly as time-varying covariates, are sorely needed.

Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches.

Symptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among children younger than 5 years in sub-Saharan African (sSA) countries. We used the most recent (2012-2022) nationally representative Demographic and Health Surveys data of 33 sSA countries.

The future of public health doctoral education in Africa: transforming higher education institutions to enhance research and practice.

The African Union and the Africa Centers for Disease Control and Prevention issued a Call to Action in 2022 for Africa's New Public Health Order that underscored the need for increased capacity in the public health workforce. Additional domestic and global investments in public health workforce development are central to achieving the aspirations of Agenda 2063 of the African Union, which aims to build and accelerate the implementation of continental frameworks for equitable, people-centred growth and development. Recognising the crucial role of higher education and research, we assessed the capabilities of public health doctoral training in schools and programmes of public health in Africa across three conceptual components: instructional, institutional, and external.