Developing effective subsampling strategies for malaria indicators from DHIS2

Reference Number: 
Dr. Victor Alegana and Dr. Emelda Okiro

The Kenya DHIS2 platform has promoted the availability of aggregated health data nationally. However, issues of data quality impact translation of these health data into information useful for local decision making at the county level or national-level policy. There is a need to improve understanding of data elements needs and flows within DHIS2 with a view to feedback to counties for improvement.


Main Research Question:

What are the key data elements that impact malaria data quality, and how can these be effectively sampled within the DHIS2 platform?

Specific question:

  1. What innovative ways can be implemented to improve data and information monitoring at sub-national levels?


The student will learn:

  1. Overall understanding of DHIS2 malaria data elements and indicators.
  2. Introduction to Geographic Information Systems.
  3. Introduction to spatial statistics – specificaly spatial sub-sampling for big-data science.
  4. Writing skills


Preferred training background of prospective student:

  1. Statistics
  2. Knowledge in R-Software


Application Deadline: 19 July 2019