The ambitious international commitment towards eradicating malaria by 2040 requires highly efficacious and optimally targeted interventions. The diversity of human Plasmodium parasite species complicates this, requiring species-specific approaches which are adapted to their underlying biology, pathology and epidemiology. Although these characteristics are relatively well known for Plasmodium falciparum and Plasmodium vivax, other human malaria species – Plasmodium ovale and Plasmodium malariae – are poorly understood. Addressing these knowledge gaps is therefore timely and necessary.
This DPhil project aims to (i) expand our understanding of the epidemiology of P. malariae and P. ovale malaria, and (ii) assess their public health significance. An evidence base of reports will be collated, including population surveys, health facility case reports, imported traveller infections etc, which will be used to characterise the species’ epidemiology, and in particular, their ecological drivers of transmission, geographic distributions and global clinical burden. The nature of the appropriate mapping strategy will be determined as a function of the available data. Environmental suitability models together with core epidemiological analyses such as age-risk models and analyses of interactions between Plasmodium species will support these objectives. The need for species-specific interventions will be assessed and mapped to identify gaps where existing approaches may be inadequate to ensure efficacy against all malaria parasite species.
The Malaria Atlas Project (MAP; www.map.ox.ac.uk) is a dynamic and multi-disciplinary research group, including four current DPhil students, hosted by the Oxford Big Data Institute (BDI). The group’s principal objective is to develop cutting-edge approaches to model the cartography of malaria. Collectively, the team studies a diverse range of malaria-focussed topics (see website).
This project would involve systematic literature reviews, GIS projects, geospatial modelling and meta-analyses of collated epidemiological datasets. The MAP group organises a weekly Journal Club which the student would be expected to be an active participant of, and the department and university also host an active programme of seminars and training opportunities which the student would be encouraged to attend. Specific training opportunities within and outside the university would be discussed as needed.
Project reference number: 1010
|Professor Peter Gething||Big Data Institute||Oxford University, Henry Wellcome Building of Genomic Medicine||GBRfirstname.lastname@example.org|
|Rosalind Howes||Nuffield Department of Medicine||Big Data Institute, University of Oxford||GBR|
|Katherine Battle||Nuffield Department of Medicine||Big data Institute, University of Oxford||GBR|
Quantitatively mapping the spatial distributions of infectious diseases is key to both investigating their epidemiology and identifying populations at risk of infection. Important advances in data quality and methodologies have allowed for better investigation of disease risk and its association with environmental factors. However, incorporating dynamic human behavioural processes in disease mapping remains challenging. For example, connectivity among human populations, a key driver of pathogen dispersal, has increased sharply over the past century, along with the availability of data derived from mobile phones and other dynamic data sources. Future work must be targeted towards the rapid updating and dissemination of appropriately designed disease maps to guide the public health community in reducing the global burden of infectious disease. Hide abstract
The mapping of malaria risk has a history stretching back over 100 years. The last decade, however, has seen dramatic progress in the scope, rigour and sophistication of malaria mapping such that its global distribution is now probably better understood than any other infectious disease. In this minireview we consider the main factors that have facilitated the recent proliferation of malaria risk mapping efforts and describe the most prominent global-scale endemicity mapping endeavours of recent years. We describe the diversification of malaria mapping to span a wide range of related metrics of biological and public health importance and consider prospects for the future of the science including its key role in supporting elimination efforts. Hide abstract
Plasmodium malariae causes malaria in humans throughout the tropics and subtropics. Plasmodium ovale curtisi and Plasmodium ovale wallikeri are sympatric sibling species common in sub-Saharan Africa and also found in Oceania and Asia. Although rarely identified as the cause of malaria cases in endemic countries, PCR detection has confirmed all three parasite species to be more prevalent, and persistent, than previously thought. Chronic, low-density, multispecies asymptomatic infection is a successful biological adaptation by these Plasmodium spp., a pattern also observed among malaria parasites of wild primates. Current whole-genome analyses are illuminating the species barrier separating the ovale parasite species and reveal substantial expansion of subtelomeric gene families. The evidence for and against a quiescent pre-erythrocytic form of P. malariae is reviewed. Hide abstract
Important strides have been made within the past decade toward malaria elimination in many regions, and with this progress, the feasibility of eradication is once again under discussion. If the ambitious goal of eradication is to be achieved by 2040, all species of infecting humans will need to be targeted with evidence-based and concerted interventions. In this perspective, the potential barriers to achieving global malaria elimination are discussed with respect to the related diversities in host, parasite, and vector populations. We argue that control strategies need to be reorientated from a sequential attack on each species, dominated by to one that targets all species in parallel. A set of research themes is proposed to mitigate the potential setbacks on the pathway to a malaria-free world. Hide abstract