Analytical Tools for Malaria Elimination (AToME) group MAEMOD
Led by Research Fellow Dr Ricardo Aguas, the AToME (Analytical Tools for Malaria Elimination) Group is currently entirely dedicated to the Enhanced modelling for NMCP Decision-making to Accelerate Malaria Elimination (ENDGAME) project, funded by the Bill and Melinda Gates Foundation. Engaging with National Malaria Control Programmes (NMCP) in the Greater Mekong Sub-Region (GMS), ENDGAME calibrates malaria transmission models using epidemiological data and records from previous intervention deployments to predict the impact and cost efficiency of different elimination driven strategies.
AToME’s mission is the continued development and provision of analytical tools to national malaria control programmes in the GMS in support of their efforts to eliminate P. falciparum malaria in the next decade.
AToME uses these tools to achieve our objectives:
We continue to develop an individual based model for malaria transmission that not only encapsulates the complexities of malaria transmission between humans through the mosquito, but also takes into account details of dynamic processes occurring within each person. A particularly innovative aspect of this model is the simulation of the logistical details involved in malaria interventions implementation.
We are particularly interested in understanding how drug pharmacokinetics and pharmacodynamics translate into different evolutionary pressures for the emergence and spread of drug resistant parasites. We developed a within host model that captures the dynamics of parasite asexual and sexual replication cycles, thus allowing us to correlate the total infectiousness of certain parasite populations, given the administration of different drug regiments and combinations.
To be truly able to inform policy, models must include a cost component and they must translate the detailed logistics of malaria interventions to both a cost and a measure of impact. This then allows for optimization algorithms to explore the best combination of malaria interventions under varied resource limitations. The group is exploring the development of frameworks that facilitate the usage of these algorithms by control programmes.
Moving away from genome scan methods used for human GWAS (ultimately inappropriate for the short highly polymorphic genomes of RNA viruses), the group has demonstrated the potential of multi-class machine learning algorithms in inferring the functional genetic changes associated with phenotypic change (e.g. a virus crossing the species barrier).
These genotype to phenotype (GP) methods allow to uncover a set of features and insights that ultimately could be quite relevant in understanding viral transmission across host species. They:
- Show that even distantly related viruses within a viral family share highly conserved genetic signatures of host specificity;
- Reinforce how fitness landscapes of host adaptation are shaped by host phylogeny;
- Highlight the evolutionary trajectories of RNA viruses in rapid expansion and under great evolutionary pressure.
These methods can serve as rigorous tools of emergence potential assessment, specifically in scenarios where rapid host classification of newly emerging viruses can be more important than identifying putative functional sites.
Establishment of accurate genotype to phenotype maps allows us to explore molecular and antigenic evolution of pathogens in parallel. They also allow for rapid antigenic novelty prediction to be incorporated into molecular surveillance systems. They can help advise updates to vaccines (seasonal flu) and serve as an early warning system for zoonotic pandemics.