EASIMES
Environment analysis and surveillance to improve malaria elimination strategies
Objective of the program & themes
This project aims to reinforce the microstratification and active surveillance tools used by Malaria Control Programs to allocate resources and target interventions during their control and elimination efforts.
This objective will be achieved by:
- quantifying the risk of malaria associated with the different types of forested environments in relation with human activities,
- quantifying the probability of vector presence and abundance according to environment type,
- combining non-malaria and malaria indicators in predictive scores/categories and maps which can be used from regional to local level,
- developing a malaria environmental surveillance system to help health officers assess in space and time the suitable conditions of malaria transmission and better monitor control actions.
This project mitigates forest transmission, by investigating the relationship between the forest environment and malaria, optimizing approaches for active surveillance, by developing a regional early warning system which can be generalized at national scale.
Research questions and operational translation
The main research questions that will be answered through this project are:
- What types of forest environments and what types of human activities conducted in these environments are associated locally with higher incidence of malaria?
- What types of environments are associated with presence of malaria vectors (i.e. which environments are more receptive than others)?
- Can environmental changes (seasonal or structural) predict changes in malaria risk or in receptivity?
- Among villages with zero-incidence, where is malaria most likely to be re-introduced?
- How far do human activities in forests stop having a significant impact on malaria incidence in villages?
The answers to these questions will be translated into: a) maps of malaria risk and of vector receptivity, accounting for environment types and human activities, as well as their seasonal changes; b) scores/classification allowing to include receptivity, environment and activity risk-factors in the microstratification (currently only based on annual parasite index (API)) and refine resource allocation and intervention targeting at regional and local level; c) evidence necessary to design interventions targeting specific populations-at-risk currently unincluded in the elimination effort.
This innovative approach will rely on an ongoing village-level malaria surveillance system and high-resolution remote sensing data and direct ground observation of environment (this project) and of entomological fauna. As a result, predictive algorithms will be developed to allow all levels of the health system (from township to national malaria control program) to prioritize allocation of interventions: to specific activity groups, locations, and seasons; and also to define how widespread each intervention should be.
Période du projet :
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Membre(s) SESSTIM du projet :
Membre(s) hors SESSTIM du projet :
Pr François Nosten, Shoklo Malaria Research Unit (SMRU), Mahidol University
Commanditaires :
RAI2E - Towards elimination of Malaria / UNOPS / Global Fund
Partenaires :
SMRU: Gilles Delmas, Victor Chaumeau
SESSTIM: Jordi Landier, Jean Gaudart
ESPACE-DEV: Vincent Herbreteau, Morgan Mangeas
Problématique:
Mapping and predicting risk of malaria in the Greater Mekong Subregion: detection of high receptivity and vulnerability environments to improve surveillance and intervention
Filling the knowledge-gap on forest transmission to improve the effectiveness of forest-malaria elimination
In the Greater Mekong Subregion (GMS), malaria transmission is strongly linked to activity in forested environments. The association between forest and malaria appears in large scale ecological and spatial studies where aggregated data (usually at health facility level or higher) show an overlap between persistence of malaria and borderland mountainous forested regions. This association also appears in individual risk factor studies where male sex, age >15 years, and “going to the forest” is consistently observed as a risk factor, pointing at occupation-related risks. These studies are most frequently case-control or cross-sectional studies, which compare travel and activity questionnaires between malaria-infected and non-infected participants. Yet neither type of studies can fully and accurately cover the diversity of “forest environments” to which individuals are exposed, in terms of ecosystem, of degree of human-made alterations in it, of types of activities conducted, and of seasonal variations.
Despite this broad association with forested environments, persistence of malaria transmission requires the presence of specific vectors and sufficient density of human hosts, over periods of time long enough to allow completion of the parasite life cycle. It is therefore likely that not all forested ecosystems in the GMS present the same risk of malaria. Likewise, activities ranging from forest-fringe or forest clearing farming and rubber tapping, to hunting, collecting forest products, logging or mining are probably not associated with the same risks.
Still, beyond the broad “forest-goer” label, there is little understanding of where, when, and during which activities forest-going populations are locally at highest risk of malaria. Likewise, because of the wide extent of forest environments and the difficulty to identify actual transmission sites, entomological surveys are often conducted in villages, where the vectorial population might differ significantly [6]. It is therefore difficult to target at-risk sites and populations for control and elimination activities. As the incidence of malaria continues to decrease, targeting of interventions (programmatic or reactive) is increasingly relevant, but often only based on clinical incidence signals, such as Annual Parasite Index. Some GMS countries, such as Cambodia, now include predictions from mathematical models in their resource-allocation plans, but due to the lack of understanding of local risk predictors, such plans are likely to be limited to high administrative divisions. It is therefore crucial to document accurately the influence of environmental parameters, from the local to the national scale, on the risk of malaria, to improve resource allocation and effectiveness of the elimination effort.
Méthode :
This research work will be conducted in 4 steps, with an early phase dedicated to P. falciparum followed by a similar analysis for P. vivax during year 2:
- Development of an accurate land-use/land-cover (LULC) database from remote-sensing data confirmed by field surveys (ground-truthing). This LULC map will characterize the environments around villages and where people move: types of environments, types of human modifications (logging, cultivation, mining…) and seasonal changes (flooding, crops, and vegetation growth).
- Exploitation of LULC and integration with malaria data (human incidence/prevalence; vector diversity and abundance) to analyze and predict malaria risk.
- Exploitation of environmental profiles defined from the LULC database to target entomological collections. Development of a vector risk map.
- Integration of malaria- and vector-risk data to inform microstratification on potential risks of persistence or resurgence of malaria. Development of field-level scores for local categorization of malaria risk and of an environment surveillance system to facilitate decision-making.
Resultats:
1. Development of a Land-use/land-cover (LULC) database for Kayin State.
Impact: quantify the diversity and volume of each environment type and human activity in the forests of a typical GMS setting (Eastern Myanmar); describe the range of distance to permanent population centers; identification of scattered population areas not accessing malaria post services; replicable methodology available for other areas of the GMS, and tentatively applied in other locations along the Myanmar-Thailand border (other funding).
2. Risk maps and risk categories to assist micro-stratification and targeting interventions for malaria elimination and prevention of resurgence.
Impact: refine the targeting of malaria elimination activities within METF (mass screening and treatment campaigns, enhanced vector control, reactive interventions). The scores/categorization algorithms will also be made available to other organizations involved in malaria control and elimination in Myanmar and the GMS to be tested/included.
3. Environment surveillance system
Impact: anticipate evolution of malaria and pre-program resource allocation; develop tools that can be generalized to other GMS countries (for data analysis and result display/help to decision making).
4. Improved understanding of the relationship between human-modified environments, human activities and risk of malaria or vector populations
Impact: fill the gaps in knowledge of malaria transmission in the GMS and improve elimination strategies.
