Data Science
Introduction
These projects have 3 overarching aims. First is to identify strengths and weaknesses in TIBA’s capacity to manage data (information) as a contribution towards improving national health systems. The second aim is to help build data science and e-health capacity across our partnerships. TIBA’s focus is ultimately to improve the health of African populations. The third aim is to strengthen partner capacity in regard of the 4th Industrial revolution requirements where automation as well as generation and analysis of big data will become very significant. The projects are as a result of the lessons learned through the analysis of the Rapid Impact projects. Each project is supported with a £20,000 funding and would be completed by February 2021.
Key outcomes from these projects include
- developing an eHealth platform for data capturing and monitoring of NTDs;
- exploration of large-scale e-health datasets generated by TIBA partners;
- storage and management of malaria data;
- eHealth Surveillance System;
- acquiring high capacity servers for data storage and analysis;
- digitalise archived and previously collected data to support decision making.
Projects
Botswana |
Identifying strengths and weaknesses in capacity to manage data. |
Ghana |
Building the capacity of local TIBA partners for ethical use of data and big data management and analysis. |
Kenya |
Workshop on dealing with missing data in health research. |
Rwanda |
A Big data platform for strengthening malaria surveillance program in Rwanda. |
South Africa |
Development of an eHealth Surveillance System for Ingwavuma Community in uMkhanyakude District, KwaZulu-Natal, South Africa. |
Sudan |
Strengthening data management and analysis. |
Tanzania |
Improving Lymphatic Filariasis morbidity mapping and provision of care in Rural Tanzania using Mobile Communication Technology in Tanzania. |
Uganda |
Capacity building and infrastructural development for improved trypanosomiasis and other diseases data management. |
Zimbabwe |
Capacity building in data science. |