Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.
Skip to main content

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.

20191010_134912
AToME Group Head Dr Ricardo Aguas (right) at ENDGAME project organised training in mathematical modelling and outbreak analysis for staff of the Bureau of Vector Borne Diseases (BVBD), Ministry of Public Health, Thailand in Oct 2019.

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.

Atome figure 1
Dynamics of asexual parasite densities and infectiousness derived from sexual parasite densities over time.

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.

Atome figure 2
Predicted optimal intervention package under different budget constraints for a putative African population with a mean malaria true prevalence of 10%.

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).

Atome figure 3
The first two principal components of the PCA undertaken using (A) SARS coronavirus complete spike protein nucleotide sequences, and (B) nucleotides selected by the RFA. Viral groups, defined by host species and season, are represented by ellipses of different colours: Human patient samples from 2002/2003 collected in early, mid and late epidemic phase are HP03E (green), HP03M (purple) and HP03L (yellow); 2004 Human samples are labelled HP04 (black); palm civets samples collected in 2003 and 2004 are labelled PC03 (blue) and PC04 (red); bat samples are labelled BT (magenta).

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.

Atome figure 4
Superimposed 2-dimensional antigenic maps generated from predicted antigenic distances (in colour) using a machine learning algorithm to establish an explicit genotype to phenotype map; and from HI titre data (in grey).