Abstract
Background: Artemisinin-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.
Methods: The 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.
Results: The 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.
Conclusions: Areas 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.
| Original language | English |
|---|---|
| Article number | 279 |
| Pages (from-to) | 1-11 |
| Number of pages | 11 |
| Journal | Malaria Journal |
| Volume | 17 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2 Aug 2018 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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