UCSF

Malaria is a parasite infection transmitted by mosquitoes that infected 229 million cases and resulted in 409,000 deaths worldwide in 2019. In the Greater Mekong Sub-region (GMS), resistance to primary malaria treatments has emerged and is threatening to set back recent control successes. As countries ambition to eliminate malaria by 2030, infections clusters in forest-going populations that are increasingly targeted for prevention and treatment efforts by national control programs across the GMS. Yet, as pointed out by a recent review of the literature on forest-goers, a more detailed characterization of forest-going population is needed to accelerate malaria elimination in the GMS.

In chapter 1, we evaluated the association between deforestation and malaria incidence in northern and southern Lao PDR. Our approach leveraged surveillance records collected by the national program and high-resolution forest data to characterize the importance of forest-going population on malaria transmission in the GMS. Our results highlighted the challenges to transition from Plasmodium falciparum to Plasmodium vivax elimination and suggest programs may benefit from monitoring areas of on-going deforestation using remotely sensed data.

In chapter 2, three population-based surveys and one rolling survey among forest-goers from a randomized controlled trial in southern Lao PDR were combined to estimate the size of forest-going populations. Population size estimates (PSEs) were produced at three different time points and the capture-recapture methodology was used to estimate the total number of forest-goers in the study area over the study period. This study highlighted an important seasonality in malaria risk behaviors among forest-goers and illustrates population size estimation methods that can be replicated to support national control programs in the GMS.

In chapter 3, GPS logging devices were leveraged to measure and describe fine-scale mobility patterns of forest-goers recruited in a focal test and treat (FTAT) active case detection intervention conducted in southern Lao PDR. Combining clustering analyses and machine learning regressions, our results assessed the diversity within forest-going trips but did not translate into a clear segmentation of forest-goers’ role in malaria transmission in the GMS.

Taken together, this work characterizes the importance, size and mobility of forest-going populations in Lao PDR. These results are key for national control programs across the GMS to assess and meet their 2030 malaria elimination goals.