Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar
Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar
Abstract As the national reference laboratory for febrile illness in Madagascar, we processed samples from the first epidemic wave of COVID-19, between March and September 2020. We fit generalized additive models to cycle threshold (Ct) value data from our RT-qPCR platform, demonstrating a peak in high viral load, low-Ct value infections temporally coincident with peak epidemic growth rates estimated in real time from publicly-reported incidence data and retrospectively from our own laboratory testing data across three administrative regions. We additionally demonstrate a statistically significant effect of duration of time since infection onset on Ct value, suggesting that Ct value can be used as a biomarker of the stage at which an individual is sampled in the course of an infection trajectory. As an extension, the population-level Ct distribution at a given timepoint can be used to estimate population-level epidemiological dynamics. We illustrate this concept by adopting a recently-developed, nested modeling approach, embedding a within-host viral kinetics model within a population-level Susceptible-Exposed-Infectious-Recovered (SEIR) framework, to mechanistically estimate epidemic growth rates from cross-sectional Ct distributions across three regions in Madagascar. We find that Ct-derived epidemic growth estimates slightly precede those derived from incidence data across the first epidemic wave, suggesting delays in surveillance and case reporting. Our findings indicate that public reporting of Ct values could offer an important resource for epidemiological inference in low surveillance settings, enabling forecasts of impending incidence peaks in regions with limited case reporting.
Razanazatovo Norosoa、Raharimanga Vaomalala、Razafindratsimandresy Richter、Bernardson Barivola、Randrianarisoa Mirella、Randremanana Rindra、H¨|raud Jean-Michel、Rabarison Joelinotahiana Hasina、Rakotondramanga Jean-Marius、Andriamandimby Soa Fy、Brook Cara E.、Mangahasimbola Reziky、Dussart Philippe、Nasolo Fr¨|d¨|rick Stanley、Rabetombosoa Roger Mario、Randrianarisoa Santatra、Razanajatovo Iony Manitra、Rasambainarivo Fidisoa
Virology Unit, Institut Pasteur de MadagascarEpidemiology and Clinical Research Unit, Institut Pasteur de MadagascarVirology Unit, Institut Pasteur de MadagascarEpidemiology and Clinical Research Unit, Institut Pasteur de MadagascarEpidemiology and Clinical Research Unit, Institut Pasteur de MadagascarEpidemiology and Clinical Research Unit, Institut Pasteur de MadagascarVirology Unit, Institut Pasteur of DakarVirology Unit, Institut Pasteur de MadagascarEpidemiology and Clinical Research Unit, Institut Pasteur de MadagascarVirology Unit, Institut Pasteur de MadagascarDepartment of Ecology and Evolution, University of ChicagoEpidemiology and Clinical Research Unit, Institut Pasteur de MadagascarVirology Unit, Institut Pasteur de MadagascarVirology Unit, Institut Pasteur de MadagascarEpidemiology and Clinical Research Unit, Institut Pasteur de MadagascarDepartment of Veterinary Sciences and Medicine, University of AntananarivoVirology Unit, Institut Pasteur de MadagascarDepartment of Ecology and Evolutionary Biology, Princeton University
医学研究方法基础医学预防医学
COVID-19LMICMadagascarAfricacycle threshold valuecross-sectional data
Razanazatovo Norosoa,Raharimanga Vaomalala,Razafindratsimandresy Richter,Bernardson Barivola,Randrianarisoa Mirella,Randremanana Rindra,H¨|raud Jean-Michel,Rabarison Joelinotahiana Hasina,Rakotondramanga Jean-Marius,Andriamandimby Soa Fy,Brook Cara E.,Mangahasimbola Reziky,Dussart Philippe,Nasolo Fr¨|d¨|rick Stanley,Rabetombosoa Roger Mario,Randrianarisoa Santatra,Razanajatovo Iony Manitra,Rasambainarivo Fidisoa.Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar[EB/OL].(2025-03-28)[2025-08-02].https://www.medrxiv.org/content/10.1101/2021.07.06.21259473.点此复制
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