Model-based assessment of Chikungunya and O’nyong-nyong virus circulation in Mali in a serological cross-reactivity context

Autor: Hozé, Nathanaël, Diarra, Issa, Sangaré, Abdoul Karim, Pastorino, Boris, Pezzi, Laura, Kouriba, Bourèma, Sagara, Issaka, Dabo, Abdoulaye, Djimdé, Abdoulaye, Thera, Mahamadou Ali, Doumbo, Ogobara K., de Lamballerie, Xavier, Cauchemez, Simon
Přispěvatelé: Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Unité des Virus Emergents (UVE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Malaria Research and Training Center [Bamako, Mali], Université de Bamako, Centre d'Infectiologie Charles Mérieux, Bamako, Mali, Laboratoire de Virologie [UNIV Corse-Inserm] (EA7310), Université Pascal Paoli (UPP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Nathanaël Hozé and Simon Cauchemez acknowledge financial support from the AXA Research Fund, the Investissement d’Avenir program, the Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases program (Grant ANR-10-LABX-62-IBEID), the INCEPTION project (PIA/ANR-16-CONV-0005), We thank the European Union’s Horizon 2020 research and innovation program under ZIKAlliance grant agreement No. 734548 and the Minister of Health and Hygiene of Mali supported this work through the subvention no. 2016/668116-0 from the Mali World Health Organization Local Office., We dedicate this article to Ogobara K. Doumbo, who initiated this project before he passed. May he rest in peace. We thank Christine Isnard from EFS, Marseille for invaluable technical contribution. We are grateful to Ismaila Thera, from MRTC, Bamako who developed the electronic database using ODK and provided the data management service for the study. We also thank the study district health officers and the study population for their cooperation. Specifically, we are indebted to Hamma Maiga, Bakary Sidibé, Modibo Diarra, Kassoum Kayentao, Souleymane Dama, Hamidou Niangaly, Amadou Bamadio, Hamadoun Diaité, Karim Traoré and Balla Diarra for their support to field investigations., ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), European Project: 734548,ZIKAlliance(2016), Limouzin, Cécile, Integrative Biology of Emerging Infectious Diseases - - IBEID2010 - ANR-10-LABX-0062 - LABX - VALID, Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs - - INCEPTION2016 - ANR-16-CONV-0005 - CONV - VALID, A global alliance for Zika virus control and prevention - ZIKAlliance - 2016-10-01 - 2019-09-30 - 734548 - VALID, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pascal Paoli (UPP)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Nature Communications
Nature Communications, 2021, 12 (1), pp.6735. ⟨10.1038/s41467-021-26707-9⟩
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
Nature Communications, Nature Publishing Group, 2021, 12 (1), pp.6735. ⟨10.1038/s41467-021-26707-9⟩
ISSN: 2041-1723
DOI: 10.1038/s41467-021-26707-9⟩
Popis: Serological surveys are essential to quantify immunity in a population but serological cross-reactivity often impairs estimates of the seroprevalence. Here, we show that modeling helps addressing this key challenge by considering the important cross-reactivity between Chikungunya (CHIKV) and O’nyong-nyong virus (ONNV) as a case study. We develop a statistical model to assess the epidemiology of these viruses in Mali. We additionally calibrate the model with paired virus neutralization titers in the French West Indies, a region with known CHIKV circulation but no ONNV. In Mali, the model estimate of ONNV and CHIKV prevalence is 30% and 13%, respectively, versus 27% and 2% in non-adjusted estimates. While a CHIKV infection induces an ONNV response in 80% of cases, an ONNV infection leads to a cross-reactive CHIKV response in only 22% of cases. Our study shows the importance of conducting serological assays on multiple cross-reactive pathogens to estimate levels of virus circulation.
O’nyong nyong and Chikungunya virus are arboviruses present in Africa but their prevalence is unknown, partly due to high antibody cross-reactivity with one another. Here, the authors develop a statistical model that accounts for cross-reactivity to characterise circulation of both viruses from seroprevalence surveys.
Databáze: OpenAIRE
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