Said Rutabayiro Ngoga
Dr Said Rutabayiro Ngoga,He is Division Head, Rwanda Information Society Authority (RISA).He got his PhD at Blekinge Institute of Technology (BTH-Sweden) in 2014 within the main area of Telecommunication Systems. Special emphasis was on dynamic spectrum access as an enabling technology for increasing the efficiency of wireless communications by using cognitive radio networking. He is a Visiting Lecturer in the School of Engineering, College of Science and Technology and University of Rwanda and the Postgraduate Coordinator in the School of ICT. Dr Ngoga research interests are within the main areas of wireless networks, cognitive radio networks, and recent developmental applications of ICT such as e-government and IoT. Dr Ngoga has published several works in peer reviewed journals. Malaria still continues to impose a substantial burden on global public health. In Rwanda, there is a serious problem of the disease resurgence since 2012. The resurgence has been attributed to different factors including inappropriate use of insecticide-treated nets, spread of insecticide resistance in mosquito vectors, and drug-resistant Plasmodium among others. Therefore, coordinated endeavors have been stepped up in Rwanda in response to malaria resurgence. These include proper use of the insecticide-treated nets (ITNs), the plan to introduce the G2 and PBO nets to overcome pyrethroid resistance and establishment of a research centre to monitor the emergence and prevalence of Anopheles mosquitoes resistant to insecticides used for malaria control. Other efforts focused on strengthening surveillance systems. However, malaria resurgence is a complex phenomenon that requires a multi-disciplinary approach to contribute the disease effective treatment and management of the disease. The present study will integrate diagnostics of mixed species malaria infection and real time malaria surveillance. Dr Ngoga will focus of the following objectives: (i) to evaluate the existence of non-P. falciparum malaria and potential for mixed-species infections in Rwanda; (ii) to assess a diagnostic test suitable for accurately detect different Plasmodium species in clinical samples for ultimate use in low- resource settings; (iii) to analyze transcriptome (RNA-seq) of whole blood samples infected with mixed parasite species to gain insight into inter-species interactions of parasites, (iv) to develop a model that allow to represent malaria data in a format knowledgeable to policy makers, (v) to develop a query language based on the underlying data model (vi) to deploy a surveillance tool that implement the underlying query language allowing health professionals/policy makers to formulate a wide range of queries and retrieve feedbacks within a reasonable amount of time and minimum efforts.