ArticlePLoS computational biology2026
Reconstruction of historical malaria transmission in Senegal using multiplex serocatalytic models.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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13 authors.
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Abstract
The advent of multiplexing technologies, allowing antibodies to hundreds of antigens to be measured in a single test, has led to enormous increases in the amount of data generated by serological surveys. New modelling methods are required to exploit this data. This study extends serocatalytic models to consider up to three antibody responses targeting the same pathogen simultaneously. These models were fitted to data from cross-sectional serological surveys of Plasmodium falciparum malaria in the Senegalese villages of Dielmo and Ndiop, and model predictions were validated against 22 years of longitudinal epidemiological data. The most accurate reconstruction of historical clinical incidence of P. falciparum was provided by a combination of antibodies to Apical Membrane Antigen 1 (PfAMA1) and Glutamate-Rich Protein (PfGlurpR2). This model estimated a 76% (95% CrI: 61% - 86%) drop in transmission in 2004 (95% CrI: 2001 - 2008) coinciding with changing anti-malarial treatment. Multiplex serocatalytic models provided more accurate estimates of past clinical incidence than singleplex serocatalytic models, with the most accurate multiplex model (PfAMA1 + PfGlurpR2) outperforming all singleplex models (KruskalWallis p < 0.01). Finally, models with three antigens did not provide more accurate estimation than models with two antigens.
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