Optimal Interruption of P. vivax Malaria Transmission Using Mass Drug Administration

Anwar, Md Nurul, Hickson, Roslyn I., Mehra, Somya, Price, David J., McCaw, James M., Flegg, Mark B., and Flegg, Jennifer A. (2023) Optimal Interruption of P. vivax Malaria Transmission Using Mass Drug Administration. Bulletin of Mathematical Biology, 85 (6). 43.

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Plasmodium vivax is the most geographically widespread malaria-causing parasite resulting in significant associated global morbidity and mortality. One of the factors driving this widespread phenomenon is the ability of the parasites to remain dormant in the liver. Known as ‘hypnozoites’, they reside in the liver following an initial exposure, before activating later to cause further infections, referred to as ‘relapses’. As around 79–96% of infections are attributed to relapses from activating hypnozoites, we expect it will be highly impactful to apply treatment to target the hypnozoite reservoir (i.e. the collection of dormant parasites) to eliminate P. vivax. Treatment with radical cure, for example tafenoquine or primaquine, to target the hypnozoite reservoir is a potential tool to control and/or eliminate P. vivax. We have developed a deterministic multiscale mathematical model as a system of integro-differential equations that captures the complex dynamics of P. vivax hypnozoites and the effect of hypnozoite relapse on disease transmission. Here, we use our multiscale model to study the anticipated effect of radical cure treatment administered via a mass drug administration (MDA) program. We implement multiple rounds of MDA with a fixed interval between rounds, starting from different steady-state disease prevalences. We then construct an optimisation model with three different objective functions motivated on a public health basis to obtain the optimal MDA interval. We also incorporate mosquito seasonality in our model to study its effect on the optimal treatment regime. We find that the effect of MDA interventions is temporary and depends on the pre-intervention disease prevalence (and choice of model parameters) as well as the number of MDA rounds under consideration. The optimal interval between MDA rounds also depends on the objective (combinations of expected intervention outcomes). We find radical cure alone may not be enough to lead to P. vivax elimination under our mathematical model (and choice of model parameters) since the prevalence of infection eventually returns to pre-MDA levels.

Item ID: 78930
Item Type: Article (Research - C1)
ISSN: 1522-9602
Keywords: Mass drug administration, Multi-scale model, P. vivax dynamics, Radical cure, Superinfection
Copyright Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Funders: Australian Research Council (ARC), National Health and Medical Research Council of Australia (NHMRC)
Projects and Grants: ARC DP170103076, ARC DP210101920, NHMRC Australian Centre of Research Excellence in Malaria Elimination, ARC DP1200100747, ARC FT210100034
Date Deposited: 14 Jun 2023 00:54
FoR Codes: 49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490102 Biological mathematics @ 50%
49 MATHEMATICAL SCIENCES > 4903 Numerical and computational mathematics > 490304 Optimisation @ 10%
42 HEALTH SCIENCES > 4202 Epidemiology > 420205 Epidemiological modelling @ 40%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280118 Expanding knowledge in the mathematical sciences @ 50%
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280112 Expanding knowledge in the health sciences @ 50%
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