Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies
Abstract Background Most malaria-endemic countries are implementing a change in anti-malarial drug policy to artemisinin-based combination therapy (ACT). The impact of different drug choices and implementation strategies is uncertain. Data from many epidemiological studies in different levels of mal...
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ftdoajarticles:oai:doaj.org/article:245f29d21dd6471cb261a9b04dd55e4a 2023-05-15T15:15:03+02:00 Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies Dondorp Arjen M Hastings Ian M Yeung Shunmay Pongtavornpinyo Wirichada Day Nicholas PJ White Nicholas J 2008-11-01T00:00:00Z https://doi.org/10.1186/1475-2875-7-229 https://doaj.org/article/245f29d21dd6471cb261a9b04dd55e4a EN eng BMC http://www.malariajournal.com/content/7/1/229 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-7-229 1475-2875 https://doaj.org/article/245f29d21dd6471cb261a9b04dd55e4a Malaria Journal, Vol 7, Iss 1, p 229 (2008) Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2008 ftdoajarticles https://doi.org/10.1186/1475-2875-7-229 2022-12-31T06:54:19Z Abstract Background Most malaria-endemic countries are implementing a change in anti-malarial drug policy to artemisinin-based combination therapy (ACT). The impact of different drug choices and implementation strategies is uncertain. Data from many epidemiological studies in different levels of malaria endemicity and in areas with the highest prevalence of drug resistance like borders of Thailand are certainly valuable. Formulating an appropriate dynamic data-driven model is a powerful predictive tool for exploring the impact of these strategies quantitatively. Methods A comprehensive model was constructed incorporating important epidemiological and biological factors of human, mosquito, parasite and treatment. The iterative process of developing the model, identifying data needed, and parameterization has been taken to strongly link the model to the empirical evidence. The model provides quantitative measures of outcomes, such as malaria prevalence/incidence and treatment failure, and illustrates the spread of resistance in low and high transmission settings. The model was used to evaluate different anti-malarial policy options focusing on ACT deployment. Results The model predicts robustly that in low transmission settings drug resistance spreads faster than in high transmission settings, and treatment failure is the main force driving the spread of drug resistance. In low transmission settings, ACT slows the spread of drug resistance to a partner drug, especially at high coverage rates. This effect decreases exponentially with increasing delay in deploying the ACT and decreasing rates of coverage. In the high transmission settings, however, drug resistance is driven by the proportion of the human population with a residual drug level, which gives resistant parasites some survival advantage. The spread of drug resistance could be slowed down by controlling presumptive drug use and avoiding the use of combination therapies containing drugs with mismatched half-lives, together with reducing malaria transmission ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 7 1 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
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English |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Dondorp Arjen M Hastings Ian M Yeung Shunmay Pongtavornpinyo Wirichada Day Nicholas PJ White Nicholas J Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background Most malaria-endemic countries are implementing a change in anti-malarial drug policy to artemisinin-based combination therapy (ACT). The impact of different drug choices and implementation strategies is uncertain. Data from many epidemiological studies in different levels of malaria endemicity and in areas with the highest prevalence of drug resistance like borders of Thailand are certainly valuable. Formulating an appropriate dynamic data-driven model is a powerful predictive tool for exploring the impact of these strategies quantitatively. Methods A comprehensive model was constructed incorporating important epidemiological and biological factors of human, mosquito, parasite and treatment. The iterative process of developing the model, identifying data needed, and parameterization has been taken to strongly link the model to the empirical evidence. The model provides quantitative measures of outcomes, such as malaria prevalence/incidence and treatment failure, and illustrates the spread of resistance in low and high transmission settings. The model was used to evaluate different anti-malarial policy options focusing on ACT deployment. Results The model predicts robustly that in low transmission settings drug resistance spreads faster than in high transmission settings, and treatment failure is the main force driving the spread of drug resistance. In low transmission settings, ACT slows the spread of drug resistance to a partner drug, especially at high coverage rates. This effect decreases exponentially with increasing delay in deploying the ACT and decreasing rates of coverage. In the high transmission settings, however, drug resistance is driven by the proportion of the human population with a residual drug level, which gives resistant parasites some survival advantage. The spread of drug resistance could be slowed down by controlling presumptive drug use and avoiding the use of combination therapies containing drugs with mismatched half-lives, together with reducing malaria transmission ... |
format |
Article in Journal/Newspaper |
author |
Dondorp Arjen M Hastings Ian M Yeung Shunmay Pongtavornpinyo Wirichada Day Nicholas PJ White Nicholas J |
author_facet |
Dondorp Arjen M Hastings Ian M Yeung Shunmay Pongtavornpinyo Wirichada Day Nicholas PJ White Nicholas J |
author_sort |
Dondorp Arjen M |
title |
Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies |
title_short |
Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies |
title_full |
Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies |
title_fullStr |
Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies |
title_full_unstemmed |
Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies |
title_sort |
spread of anti-malarial drug resistance: mathematical model with implications for act drug policies |
publisher |
BMC |
publishDate |
2008 |
url |
https://doi.org/10.1186/1475-2875-7-229 https://doaj.org/article/245f29d21dd6471cb261a9b04dd55e4a |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 7, Iss 1, p 229 (2008) |
op_relation |
http://www.malariajournal.com/content/7/1/229 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-7-229 1475-2875 https://doaj.org/article/245f29d21dd6471cb261a9b04dd55e4a |
op_doi |
https://doi.org/10.1186/1475-2875-7-229 |
container_title |
Malaria Journal |
container_volume |
7 |
container_issue |
1 |
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1766345441817919488 |