Malaria Epidemiology at the Clone Level

Abstract

Genotyping to distinguish between parasite clones is nowadays a standard in many molecular epidemiological studies of malaria. It has become crucial in drug trials and to follow individual clones in epidemiological studies, and to understand how drug resistance emerges and spreads. Here, we will review the applications of the increasingly available genotyping tools and whole genome sequencing data, and argue for a better integration of population genetics findings into malaria control strategies.

Monitoring of Plasmodium falciparum and Plasmodium vivax using microsatellite markers indicates limited changes in population structure after substantial transmission decline in Papua New Guinea

“…Genomic surveillance of parasite populations has emerged as a promising and high-resolution approach for malaria monitoring (Arnott, Barry, & Reeder, 2012;Barry, Waltmann, Koepfli, Barnadas, & Mueller, 2015;Dalmat, Naughton, Kwan-Gett, Slyker, & Stuckey, 2019;Koepfli & Mueller, 2017; malEra Consultative Group on Monitoring & Surveillance, 2011). These approaches go beyond traditional epidemiological measures of malaria disease burden and infection prevalence by identifying local transmission dynamics (e.g.…”

“…endemic, epidemic, imported infections) and the connectivity (gene flow) between parasite populations in different endemic areas (Anderson et al, 2000;Fola et al, 2017;Noviyanti et al, 2015;Vardo-Zalik et al, 2013;Waltmann et al, 2018). Regional surveys reveal source and sink populations and parasite migration in a country and can help to predict whether and where targeted interventions would be effective and the spatial scale required (Auburn & Barry, 2017;Koepfli & Mueller, 2017). Fine-scale population genetic surveys also identify local drivers contributing to sustained transmission such as particular human social and economic interactions Delgado-Ratto et al, 2016;Koepfli & Mueller, 2017).…”

“…Regional surveys reveal source and sink populations and parasite migration in a country and can help to predict whether and where targeted interventions would be effective and the spatial scale required (Auburn & Barry, 2017;Koepfli & Mueller, 2017). Fine-scale population genetic surveys also identify local drivers contributing to sustained transmission such as particular human social and economic interactions Delgado-Ratto et al, 2016;Koepfli & Mueller, 2017). While parasite population genetics and genomics is becoming more popular and accessible, the impact on control programs has been limited, and to date few studies have systematically assessed the long-term impact of malaria control using these approaches (Batista, Barbosa, Da Silva Bastos, Viana, & Ferreira, 2015;Bei et al, 2018;Branch et al, 2011;Chenet, Taylor, Blair, Zuluaga, & Escalante, 2015; R. F. Daniels et al, 2015;Gatei et al, 2010;Gunawardena, Ferreira, Kapilananda, Wirth, & Karunaweera, 2014;Iwagami et al, 2012;Salgueiro, Vicente, Figueiredo, & Pinto, 2016;Vardo-Zalik et al, 2013).…”

“…In the WHO Western Pacific Region (WPR), the malaria mortality rate declined by 58% over the period 2010-2015, however infection prevalence in Papua New Guinea (PNG) remains the highest in this region (and outside the African continent), contributing 81% of malaria cases and 86% of malaria deaths in 2017 in the region (World Malaria Report 2017World Malaria Report 2018 primarily due to P. falciparum and P. vivax infections (Kattenberg, 2018;Koepfli et al, 2017;World Malaria Report 2018. In 2003, renewed political and financial will to strengthen malaria control worldwide resulted in a new national malaria control campaign to quickly achieve high levels of long-lasting insecticidal mosquito nets (LLIN) ownership and usage in PNG ( Hetzel et al, 2012;Hetzel et al, 2014).…”

“…This resulted in a significant increase in ownership of bed nets across the country by 2010 (any type 80%; LLINs 65%) (Hetzel et al, 2012;Hetzel et al, 2014) and the average malaria incidence rate in sentinel sites dropped from 13/1,000 population to 2/1,000 (range 0.6-3.3/1000 post-LLIN) (Hetzel et al, 2016). Prevalence of malaria infections decreased significantly post-LLIN on a provincial level as determined by light microscopy ( Hetzel et al, 2016), as well as with in-depth studies using molecular diagnostics on smaller geographic scales in the East Sepik (ESP) and Madang Provinces (MAD) on the hyperendemic north coast (Arnott et al, 2013;Barry et al, 2013;Kattenberg, 2018;Koepfli et al, 2017;Koepfli et al, 2015). P. falciparum prevalence by LDR-FMA or qPCR, dropped from 38% pre-LLIN to 12% post-LLIN and P. vivax prevalence decreased from 28% to 13% (Kattenberg, 2018;Koepfli et al, 2017).…”