Treatment

Although effective treatment for malaria is now available, approximately half of the global population remain at risk of the disease particularly in developing countries. To design effective malaria control strategies there is need to understand the pattern of malaria heterogeneity in an area. Therefore, the main objective of this study was to explore the spatial and spatio-temporal pattern of malaria cases in Zimbabwe based on malaria data aggregated at district level from 2011 to 2016.

Malaria is a vector-borne disease caused by protozoan parasites of the genus Plasmodium. According to the World Health Organization, it is one of the most serious infectious diseases threatening more than 3 billion people worldwide. In recent years, targeted covalent inhibitors (TCIs) have gained a lot of attention and several TCI-based drugs have been approved across different therapeutic areas.

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC).

We have recently identified three molecules (tilorone, quinacrine and pyronaridine tetraphosphate) which all demonstrated efficacy in the mouse model of infection with mouse-adapted Ebola virus (EBOV) model of disease and had similar in vitro inhibition of an Ebola pseudovirus (VSV-EBOV-GP), suggesting they interfere with viral entry.

Malaria remains a global health threat for centuries. In recent years, a rising resistance of Plasmodium falciparum to current standard artemisinin-based combination therapies (ACTs) leads to increasing treatment failures and requires for optimized treatment. Here, we intend to make a systematic review and meta-analysis of optimizing treatment for malaria, so as to find a potential optimal treatment.