In many public healthcare systems, particularly in resource-constrained environments, time-series forecasting models offer a practical, interpretable, and evidence-based alternative (Stacey et al., ...

Current Python alternatives for statistical models are slow, inaccurate and don't scale well. So we created a library that can be used to forecast in production environments or as benchmarks.

Gastroesophageal reflux disease (GERD) and asthma often co-occur, yet their joint trajectories and shared drivers may differ across regions. Using Global Burden of Disease (GBD) 2023 estimates ...

Gastric Cancer Mortality-to-Incidence Ratios in Latin America and the Caribbean: A Machine Learning Analysis of Socioeconomic and Clinical Research Predictors Using data from the Global Burden of ...

1 Faculty of Agriculture and Environmental Science, Somali National University, Mogadishu, Somalia. 2 Centre of research and Innovation, Amoud University, Borama, Somalia. Cereal production in Somalia ...

Objectives To project the future burden of cancer mortality in India by forecasting age-standardised mortality rates (ASMRs) for 23 major cancer types up to the year 2030, providing crucial evidence ...

Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors.

The repository provides an in-depth analysis and forecast of a time series dataset as an example and summarizes the mathematical concepts required to have a deeper understanding of Holt-Winter's model ...