Implications of population-level immunity for the emergence of artemisinin-resistant malaria: a mathematical model

Abstract

BackgroundArtemisinin-resistant Plasmodium falciparum has emerged in the Greater Mekong Subregion, an area of relatively low transmission, but has yet to be reported in Africa. A population-based mathematical model was used to investigate the relationship between P. falciparum prevalence, exposure-acquired immunity and time-to-emergence of artemisinin resistance. The possible implication for the emergence of resistance across Africa was assessed.MethodsThe model included human and mosquito populations, two strains of malaria (“wild-type”, “mutant”), three levels of human exposure-acquired immunity (none, low, high) with two types of immunity for each level (sporozoite/liver stage immunity and blood-stage/gametocyte immunity) and drug pressure based on per-capita treatment numbers.ResultsThe model predicted that artemisinin-resistant strains may circulate up to 10 years longer in high compared to low P. falciparum prevalence areas before resistance is confirmed. Decreased time-to-resistance in low prevalence areas was explained by low genetic diversity and immunity, which resulted in increased probability of selection and spread of artemisinin-resistant strains. Artemisinin resistance was estimated to be established by 2020 in areas of Africa with low (< 10%) P. falciparum prevalence, but not for 5 or 10 years later in moderate (10–25%) or high (> 25%) prevalence areas, respectively.ConclusionsAreas of low transmission and low immunity give rise to a more rapid expansion of artemisinin-resistant parasites, corroborating historical observations of anti-malarial resistance emergence. Populations where control strategies are in place that reduce malaria transmission, and hence immunity, may be prone to a rapid emergence and spread of artemisinin-resistant strains and thus should be carefully monitored.Electronic supplementary materialThe online version of this article (10.1186/s12936-018-2418-y) contains supplementary material, which is available to authorized users.

Emergence and clonal expansion of in vitro artemisinin-resistant Plasmodium falciparum kelch13 R561H mutant parasites in Rwanda

“…We hypothesize that early signs of clinical ART-R can lie undetected in populations with high levels of immunity, calling into question the relevance of the current clinical metrics used to detect ART-R in Africa. This hypothesis is supported by data from population-based mathematical modeling 19 that showed that ART-R parasites might be able to circulate up to 10 years longer without detection in high-transmission areas than in low-transmission areas.…”

“…In addition to the risk of imported resistance 18 , the likelihood of resistance emerging locally in Africa has increased in areas where control measures have reduced the disease transmission intensity. The resulting attenuation in naturally acquired human immunity can increase the frequency of symptomatic infections and the need for treatment, while decreasing parasite genetic diversity and reducing competition between sensitive and resistant parasites 19 . To date, the efficacy of ACTs has remained high outside Southeast Asia (SEA) 2 .…”

Molecular assays for antimalarial drug resistance surveillance: A target product profile

“…The development of a surveillance system included in the local health system could be envisioned; samples would be collected at health posts, centres or hospitals and sent to reference laboratories for analysis and validation, while clinical data could be shared through electronic-based information system [ 86 ]. Combined with local epidemiological data; drug usage and treatment efficacy data, WGS data could provide valuable information for modelling and predicting the spread of antimalarial drug resistance [ 87 ]. The recent development of MinION nanopore portable sequencer and its application to molecular markers of resistance could facilitate as well sample analysis at point of care, while the data analysis could still be performed in the central reference laboratory [ 48 , 50 ].…”

Early transmission of sensitive strain slows down emergence of drug resistance in Plasmodium vivax

“…Mathematical models provide an understanding of drug resistance to guide the development of malaria programs [21], through both deterministic and stochastic models simulating the implementation of monotherapies and combination therapies [22]. Models also showed the contribution on the emergence of resistant strains due to factors: fitness cost, selection of resistant parasites after treatment, changes in transmission settings, efficiency in drug dose and the role of asymptomatics [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. These previous works inferred that sub-optimal doses, high treatment coverage, and lower levels of immunity have a direct relation to drug resistance.…”