Reconstruction of historical malaria transmission in Senegal using multiplex serocatalytic models

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 ( Pf AMA1) and Glutamate-Rich Protein ( Pf GlurpR2). 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 ( Pf AMA1 + Pf GlurpR2) 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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Cite

BibTeX RIS

BibTeX

@article{baudemont2026reconstruction,
  title = {{Reconstruction of historical malaria transmission in Senegal using multiplex serocatalytic models}},
  author = {Gaëlle Baudemont and Thomas Obadia and Laura Garcia and Camille Lambert and Françoise Donnadieu and Fatoumata Diene Sarr and Joseph Faye and Cheikh Sokhna and Inès Vigan-Womas and Aissatou Toure-Balde and Chris Drakeley and Makhtar Niang and Michael White},
  journal = {PLoS Computational Biology},
  year = {2026},
  month = jul,
  doi = {10.1371/journal.pcbi.1013630},
  url = {https://pasteur.hal.science/pasteur-05736192v1},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - Reconstruction of historical malaria transmission in Senegal using multiplex serocatalytic models
AU  - Gaëlle Baudemont
AU  - Thomas Obadia
AU  - Laura Garcia
AU  - Camille Lambert
AU  - Françoise Donnadieu
AU  - Fatoumata Diene Sarr
AU  - Joseph Faye
AU  - Cheikh Sokhna
AU  - Inès Vigan-Womas
AU  - Aissatou Toure-Balde
AU  - Chris Drakeley
AU  - Makhtar Niang
AU  - Michael White
PY  - 2026
DA  - 2026/07/27
JO  - PLoS Computational Biology
DO  - 10.1371/journal.pcbi.1013630
UR  - https://pasteur.hal.science/pasteur-05736192v1
AB  - 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 ( Pf AMA1) and Glutamate-Rich Protein ( Pf GlurpR2). 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 ( Pf AMA1 + Pf GlurpR2) outperforming all singleplex models (KruskalWallis p < 0.01). Finally, models with three antigens did not provide more accurate estimation than models with two antigens.
SN  - 1553-734X
SN  - 1553-7358
ER  -

Abstract

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