Olivo Miotto
2023
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Executive Summary
Recent advancements in sequencing and genotyping technologies have made it possible to obtain comprehensive genomic data from large numbers of individuals, thus enabling the development of the field of Genetic Epidemiology: the study of genetic variation in populations. This has major impact in the control of infectious diseases, where evolutionary forces cause rapid changes in pathogen populations, which can now be monitored and studied through the use of high-throughput methods such as next-generation sequencing (NGS). In malaria, this field has advanced rapidly in the last decade, during which it has become a tool for understanding, monitoring and controlling the emergence in the parasite population of antimalarial drug resistance, and in particular artemisinin resistance, a global threat to malaria control and elimination efforts. Such advances have been particularly beneficial in the Greater Mekong Subregion (GMS) where markers of artemisinin and piperaquine resistance have been discovered, and the dynamics of evolution and spread have been revealed, chiefly through the use of high-throughput genetic epidemiology.
The decreasing costs of high-throughput technologies, their wider availability, and advances in the laboratory techniques, have supported a continuous increase in the volume of processed samples: the MalariaGEN P. falciparum Community Project published its first analysis of 212 parasite genomes in 2012;1 ten years later, version 7 of this data resource contains over 20,000 samples. 2 Such scaling-up has encouraged denser and more frequent sampling of the parasite across endemic regions, leading to Genetic Surveillance projects able to estimate epidemiological parameters, such as the prevalence of drug resistance, with increasingly high spatial and temporal resolution. The construction of surveillance datasets makes it possible to monitor changes in prevalence, track gene flow and the spread of strains, and study epidemiological phenomena such as outbreaks.
These new information streams have great potential to provide strategic knowledge about the evolution of parasite populations, and inform interventions aimed at elimination. In particular, they derive their value from a study of parasite data, which is considerably easier and cheaper to collect than accurate clinical data from human patients (e.g. detecting a drug resistance mutation is much simpler that monitoring a patient for several days to investigate the efficacy of the drug). However, if this new information is to have a high impact, it must be used by decision-makers in the public health bodies that coordinate malaria control and elimination operations. These bodies are typically Health Ministry departments, which we refer to as National Malaria Control Programmes (NMCPs), although department names may vary from country to country. NMCP staff are typically trained in public health, but are rarely experts in genetics, by the very nature of their job. It is therefore paramount that genetic data from surveillance and genetic epidemiology be translated to information that is relevant to their decision-making domain (e.g. estimating of the risk of an antimalarial drug failure is more informative to NMCPs than the presence of a mutation in a sample). In addition, NMCP officers will rarely be familiar with the sort of phenomena and patterns that genetic epidemiologists find informative, and findings must be interpreted if they are to be actionable. At the same time, most genetic epidemiologists have limited understanding of NMCP activities and requirements, making it difficult to make their messages impactful and usable.
The present document aims at helping to bridge these gaps, and encouraging the translation of genetic data into public health information, to support malaria elimination. We describe a framework in which public health activities and genetic epidemiology activities are separately identified, formally described, and connected to each other by the translational activities that must take place to add value to the genetic data. The framework is produced and maintained by the GenRe-Mekong project, and leverages on the project’s experience working with several NMCPs in the GMS, providing genetic 3 data from large-scale genetic surveillance at public health facilities. 3 We built on an earlier catalogue of genetic epidemiology use cases relevant to malaria elimination, 4 which we have reorganized, re elaborated and extended, providing implementation details describing the technologies, methods and techniques, as well as the sampling strategies and outputs that may be useful to the programmes. Crucially, we treated these Genetic Epidemiology Use Cases as only one half of the framework, since they do not by themselves address the knowledge gap between genetic epidemiology and public health decision making. To address this, we produced a catalogue of Programme Use Cases, which capture programme activities within which the outputs of Genetic Epidemiology Use Cases can provide value. Within the Programme Use Cases, we strive to identify what decisions are made; what information will influence the decisions; which Genetic Epidemiology Use Cases can produce such information; and what form the outputs produced must take, in order to be meaningful and actionable by the programme officers. The conceptual methodology presented will inform researchers designing epidemiological studies and genetic surveillance; assessors of project proposals; and Control Programmes aiming to understand how technology contributes to their control and elimination activities. We hope that, by bringing together scientific analyses with public health activities, we will stimulate the growth of large-scale open repositories of cross-border epidemiological data, which will benefit future elimination efforts.
