Genetic surveillance of Plasmodium falciparum populations following treatment policy revisions in the Greater Mekong Subregion

Abstract

Genetic surveillance of Plasmodium falciparum (Pf) can track antimalarial-resistant strains, to inform decision-making by National Malaria Control Programmes (NMCPs). The GenRe-Mekong project prospectively collected 5982 samples in the Greater Mekong Subregion (GMS) between 2017 and 2022, genotyping drug resistance markers, and barcodes that recapitulate genetic variation. Genotypes were analyzed with the grcMalaria R package, first described in this paper, to translate genetic epidemiology data into actionable visual information. Since 2020, Pf incidences decreased rapidly, accompanied by a decline of dihydroartemisinin-piperaquine (DHA-PPQ) resistant lineages, previously dominant in the eastern GMS. The frequency of plasmepsin2/3 amplifications, conferring piperaquine resistance, dropped from 62% in 2017-2019 to 2% in 2022, coinciding with a switch in frontline therapy in Cambodia, Thailand, and Vietnam. While regional artemisinin resistance levels remained high, no evidence of emerging mefloquine resistance was found. Routine genetic surveillance proved valuable in monitoring rapid parasite population changes in response to public health interventions, providing actionable information for NMCPs.

Fig. 1. P. falciparum (Pf) samples genotyped by GenRe-Mekong during the study period.

a Quarterly counts of Pf samples collected and genotyped by the GenRe-Mekong project, presented as bar charts. No routine surveillance was carried out by GenRe-Mekong in Cambodia in 2018 and 2019. Malaria prevalence, calculated as the proportion of confirmed Pf cases over the total number of tested cases, as reported by WHO^–, is shown as a black line in each chart. The prevalence scales differ between countries. b Spatial distributions by province of Pf samples collected between 2017 and 2022. Markers are colored by country, and marker size represents the number of samples from the province (N). Source data are provided as a Source Data file.

Fig. 2. Spatiotemporal patterns of predicted resistance to DHA-PPQ and artemisinin, from 2017 to 2022.

Predicted resistance to a dihydroartemisinin-piperaquine (DHA-PPQ) and b artemisinin at provincial level. Left: 2017–2019, middle: 2020–2021, right: January–December 2022. Resistance to artemisinin was predicted based on the presence of nonsynonymous mutations in the kelch13 gene, while resistance to DHA-PPQ was inferred from both kelch13 mutations and plasmepsin2/3 gene amplification (Supplementary Table 1). Marker colors reflects resistance prevalence, ranging from 0 to 1, where 0 means no parasites were predicted to be resistant, and 1 means 100% of the parasites in the province carried the relevant resistance markers. A marker appears when at least two samples were processed from the province. Source data are provided as a Source Data file.

Fig. 3. Predicted regional antimalarial resistance from 2017 to 2022.

Panels show the trend in proportions of samples predicted to be resistant to a artemisinin, piperaquine and mefloquine, b sulfadoxine and pyrimethamine and c chloroquine. Resistance was predicted based on established molecular markers: nonsynonymous mutations in the kelch13 gene for artemisinin, amplifications in plasmepsin2/3 for piperaquine, amplifications in mdr1 for mefloquine, the dhps 437G mutation for sulfadoxine, the dhfr 108N mutation for pyrimethamine, and the crt 76T mutation for chloroquine (Supplementary Table 1). Data are presented as proportion of resistant samples (resistant/total), with error bars indicating 95% confidence intervals calculated using the Wilson score interval with continuity correction. Samples with undetermined status—due to missing genotypes or mixed infections—were excluded from the analysis. For each drug, sample size (N) for the years 2017 to 2022 were as follows: artemisinin (N = 894, 1683, 1179, 521, 370, 270); piperaquine (N = 1177, 1289, 1207, 476, 261, 316); mefloquine (N = 696, 1240, 1192, 471, 377, 317); sulfadoxine (N = 1171, 1739, 1249, 551, 330, 239); pyrimethamine (N = 1225, 1724, 1237, 512, 363, 238); and chloroquine (N = 1124, 1728, 1255, 555, 368, 282). Source data are provided as a Source Data file.

Fig. 4. Predicted levels of piperaquine resistance in five endemic provinces of Vietnam.

The bar chart shows the quarterly numbers of samples (left axis) with wild-type (WT, navy) and plasmepsin2/3 gene amplification (red), against the derived proportion of piperaquine resistance (PPQ-R) samples in five endemic provinces in Vietnam (right axis). Purple shaded area shows 95% confidence interval of the PPQ-R proportion estimate. Q1: January–March; Q2: April–June; Q3: July–September; Q4: October–December. The bar below the graph shows first-line treatment policy for uncomplicated P. falciparum in Vietnam, showing the timeline of transition from dihydroartemisinin-piperaquine (DHA-PPQ) to pyronaridine-artesunate (AS-PYR). *AS-PYR was adopted in 5 endemic provinces (Binh Phuoc, Dak Nong, Gia Lai, Dak Lak, and Phu Yen)^,. Source data are provided as a Source Data file.

Fig. 5. Prevalence of kelch13 alleles between 2017 and 2022.

The pie chart shows the proportions of kelch13 alleles in each province where samples were collected. Pie chart size represents the number of samples from the province (N). Wild-type (WT) parasites are predicted to be sensitive to artemisinin. All but two of the kelch13 alleles detected in this study have been associated with delayed parasite clearance, and thus predictive of artemisinin resistance. Since the two rare alleles G357S and G544R are not in World Health Organization’s validated marker list, their association with artemisinin resistance is undetermined. Source data are provided as a Source Data file.

Fig. 6. Proportion of the most prevalent P. falciparum clusters in each province across three periods.

Left panel: 2017–2019, middle: 2020–2021, right: January–December 2022. Clusters were identified by applying community detection algorithm to a graph of parasites sharing at least 95% genetic barcode identity, identifying 64 clusters with at least 10 members. In 2017–2019, 54 clusters were present, while 14 clusters were present in 2020–2021 and only four clusters were found in 2022. Pie chart size represents the sample size for the province (N). To improve visualization, the top six clusters in each period were assigned colors, while the remaining smaller clusters are shown in gray. White segments represent the proportions of samples that were not assigned to a cluster. In the legend, clusters are arranged in descending order of cluster size for each period. The main kelch13 variant observed in the cluster and a label denoting plasmepsin2/3 amplification (p+) or plasmepsin2/3 wild-type (p−), are shown in brackets following the cluster name. Source data are provided as a Source Data file.