The Data clerk will support entry of beneficiary level data into electronic and paper-based systems to always ensure establishment of high-quality records.
Key duties and responsibilities
• Review data tools submitted by the community team for completeness.
• Check source documents/ filled forms for accuracy and completeness.
• Support safe storage of beneficiary records and always promote confidentiality.
• Establish systems that allow for easy retrieval and archiving of OVC files within CSO records rooms.
• Support field data collection exercises whenever necessary.
• Prepare, compile and sort filled forms for data entry.
• Obtain further information for incomplete documents and update data whenever necessary.
• Accurately enter data from source documents/ forms into Database.
• Analyze any data entry errors and address with district staff and M&E
The Data clerk will support entry of beneficiary level data into electronic and paper-based systems to always ensure establishment of high-quality records.
The Data clerk will support entry of beneficiary level data into electronic and paper-based systems to always ensure establishment of high-quality records.
In a multicenter-project run by the Infectious Diseases Institute (IDI) and Charité Universitaetsmedizin Berlin including further clinical and research institutions in Rwanda and DR Congo, a total of 3,600 malaria patients will be recruited at nine sentinel sites and in two recruitment rounds. Mutations of the parasite Plasmodium falciparum predicting artemisinin resistance (K13 gene, MDR1 gene) will be genotyped. Alongside sample collection, patients will be interviewed, questionnaire-based, on potential determinants of emerging artemisinin resistance, including, among others, preferred health care procedures in case of sickness, sources of antimalarial drug consumption, herbal remedies, socio-economic status. The analysis of determinants of artemisinin resistance will be conducted for the whole sample population and stratified by country.
The student is invited to do their MSc thesis within the ARMEA project. Formal supervision at IDI will be provided by Dr Benard S. Bagaya, and additional laboratory training and supervision, as well as quality control will be provided by staff at CHUB, Rwanda. The student will be included in the international ARMEA consortium and is invited to attend a 2-week training in Rwanda. Continuous online mentoring is provided for education in molecular techniques and analysis from the team at CHUB, Rwanda, and at Charité, Germany.
Background
The Infectious Diseases Institute (IDI) is part of the Centers for Antimicrobial Optimization Network (CAMO-Net) awarded by Wellcome Trust (#226692/Z/22/Z) which is a multidisciplinary global collaboration with institutions in other countries (UK, India, South Africa and Brazil) to address the impact of Antimicrobial Resistance (AMR) on human health. The CAMO-Net Uganda hub is seeking a highly motivated and skilled individual with expertise in machine learning, statistics, and predictive modeling to join our research team. The successful candidate will complement ongoing efforts to develop and implement machine learning models aimed at predicting clinical outcomes in patients with bacterial infections. This role is ideal for someone passionate about applying computational techniques to solve real-world healthcare challenges, particularly in the context of infectious diseases.
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