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Steps in Outbreak Investigation

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Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Introduction To Survival Analysis

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Related Experiment Video

Updated: May 28, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Time-Series Analysis and Age-Stratified Forecasting of Diarrheal Disease in Rwanda Using SARIMA Models.

Theos Dieudonne Benimana1,2, Martin Habimana3, Jean de Dieu Harerimana4

  • 1National Health Intelligence Center, Ministry of Health, Kigali P.O. Box 84, Rwanda.

Tropical Medicine and Infectious Disease
|May 26, 2026
PubMed
Summary

Diarrheal disease in Rwanda shows persistent seasonal surges, disproportionately affecting children under five. Forecasting models predict this pattern will continue, highlighting the need for age-specific public health strategies.

Keywords:
RwandaSARIMAdiarrheaforecastingseasonal variationtime series

Related Experiment Videos

Last Updated: May 28, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Epidemiology
  • Public Health
  • Time Series Analysis

Background:

  • Diarrheal disease is a significant cause of illness and death in Rwanda, with seasonal peaks straining health services.
  • National forecasting for age-stratified diarrheal disease burden has been underutilized.

Purpose of the Study:

  • To analyze historical diarrheal disease data in Rwanda.
  • To develop and validate age-stratified seasonal forecasting models for diarrheal disease incidence.
  • To inform public health preparedness and planning.

Main Methods:

  • Utilized Rwanda Health Management Information System (HMIS) data (2015-2025) for monthly diarrhea cases, stratified by age.
  • Developed Seasonal Autoregressive Integrated Moving Average (SARIMA) models for forecasting (2026-2027).
  • Validated models using cross-validation (RMSE, MAE, AIC, BIC, MAPE) and residual diagnostics, benchmarking against other models.

Main Results:

  • Over 6.3 million diarrhea cases were recorded (2015-2025), with nearly half in under-fives.
  • While absolute cases were higher in those aged ≥5 years, the risk was consistently higher in under-fives.
  • Strong annual seasonality with peaks from August-November was observed, predicted to persist.

Conclusions:

  • Findings support a seasonal preparedness framework (pre-peak, peak, post-peak).
  • Age-differentiated planning signals are crucial, as disease burden and risk vary significantly between age groups.
  • Emphasizes that burden and risk are not interchangeable across age groups for diarrheal disease.