Organik Gübre

• The Interdisplinary Research Group in Public Health of the Doctoral School of the University of Burundi is an interdisciplinary research team working on different public health challenges, and Building local capacities in epidemiology, biomedical sciences, epidemic preparedness and response, and public health in Burundi.

  Given the multidisciplinary nature of public health challenges, the interdisciplinary research group provides a research environment where several areas of expertise and technical supports are made available to researchers and policymakers in public health domain.

  The main priority areas for the research group are:

  • Epidemiology of Infectious and Emerging Diseases
  • Infection Prevention and Control
  • Epidemic Prevention, Preparedness and Response
  • Epidemiology of Non-Communicable Diseases
  • Evaluation and Quality Assurance of Health Systems
  • Mother-Child Health
  • Antimicrobial Resistance (AMR)
  • Biosecurity
  • Research and Development

•  

(1) Malaria:
    a. Eco-climatic and environmental determinants of malaria transmission in Burundi: implications for vector control
    b. Modeling the impact of climate change in malaria transmission dynamics: case of Burundi
    c. Modeling optimal interventions for Malaria control in Burundi
(2) Malnutrition:
    a. Malaria and malnutrition in children in Burundi
(3) Non-Communicable Diseases
    a. Temporal trends of Non-Communicable Diseases in Burundi

(4) Risk mapping for infectiousous diseases
    a. Building risk mapping models for zoonotic diseases in Burundi

(5) Outbreak preparedness and response

    a. Modelling transmission dynamics of Mpox in Burundi, DRC, and Rwanda

·  Investigating HRP2 deletions in Burundi

·  Investigating malaria drug resistance in Burundi

·  AI4Mpox in Burundi, DRC and Rwanda

Malaria surveillance Tool for National Malaria Control Program in Burundi

Five year projections of malaria cases in Burudi for programatic exercise

Course overview

In the last two decades the Bayesian approach has become increasingly popular in virtually all application areas. The approach is especially known for its capability to tackle complex statistical modeling tasks. The aim of this course is to introduce the participants smoothly into Bayesian statistical methods, from basic concepts to hierarchical models, model building and model testing. Numerous biostatistical examples (e.g. meta-analyses, longitudinal studies including growth curve modelling, analysis of clinical trials, etc.) illustrate the theoretical concepts. The course is scheduled into classroom teaching and computer exercises, and uses the software packages WinBUGS, OpenBUGS, JAGS and but also their interfaces with R making use of R2WinBUGS, R2OpenBUGS and RJAGS. The course is based on a recently published Wiley book of Lesaffre and Lawson, entitled Bayesian Biostatistics. Each participant will receive a copy of this book, included in the course fee.

The course assumes a good knowledge of regression techniques (linear, logistic, etc.) and some knowledge of models for correlated data. Experience with R is beneficial though not essential; however programming skills are required for the course.

Course overview

R is an open-source, statistical software platform that is growing in popularity due to its rapidly expanding amount of libraries containing cutting-edge statistical functions, as well as the user-friendly, built-in communication tools in RStudio. Course participants will not only be introduced to the basics of programming in R, but they will also learn how import and clean data in addition to visualising and reporting results. Specific topics that will be covered include: importing data; reshaping/tidying data; merging/joining datasets; handling dates, transforming numeric and categorical variables and summarizing data with plots and tables.

Course Outline

Participants will learn the basics of:

• Base R programming and coding style
• Data importation into and exportation out of RStudio
• Data cleaning/tidying into a format suitable for analysis (using tidyverse packages like dplyr and tidyr)
• Data analysis using basic descriptive statistics
• Data exploration and visualization using ggplot 2 and a combination of R packages for plotting and summarising data and a sneak peek into creating reproducible, dynamic reports using RMarkdown

Target audience

The course is suitable for graduate students or professionals who are familiar with statistical analysis and have managed or interacted with datasets using other software platforms

DOCTORAL SCHOOL MONTH: 1st EDITION 2022

CONFERENCE ON MALARIA

VENUE: DOCTORAL SCHOOL CAMPUS

DATE: 27 APRIL 2022

*: Presentation are scheduled for 20 minutes, remaining time for each presentation slot will be dedicated to Q&A.

Zoom: https://us06web.zoom.us/meeting/register/tZIqd-2vqj4uG90uhgyc7njhaYnjBQdLgLm

(1) Malaria:
    a. Eco-climatic and environmental determinants of malaria transmission in Burundi: implications for vector control
    b. Modeling the impact of climate change in malaria transmission dynamics: case of Burundi
    c. Modeling optimal interventions for Malaria control in Burundi
(2) Malnutrition:
    a. Malaria and malnutrition in children in Burundi
(3) Non-Communicable Diseases
    a. Temporal trends of Non-Communicable Diseases in Burundi

(5) Outbreak preparedness and response

    a. Modelling transmission dynamics of Mpox in Burundi, DRC, and Rwanda