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Coronavir.org

Site de veille et d'information sur le nouveau coronavirus SARS-COV-2 et la maladie associée COVID-19

Actualités et recherches en cours

Han H, Luo Q, Mo F, Long, L, Zheng W. SARS-CoV-2 RNA more readily detected in induced sputum than in throat swabs of convalescent COVID-19 patients. Lancet Infect Dis 2020; published online March 12. https://doi.org/10.1016/S1473-3099(20)30174-2—In this Correspondence, the email address for the corresponding author should have been huanqinhan@126.com. This correction has been made to the online version as of March 18, 2020 and will be made to the...

There is increasing evidence that one of the most difficult problems in trying to control the ongoing COVID-19 epidemic is the presence of a large cohort of asymptomatic infectives. We develop a SIR-type model taking into account the presence of asymptomatic, or however undetected, infective, and the substantially long time these spend being infective and not isolated. We discuss how a SIR-based prediction of the epidemic course based on early ...

There is increasing evidence that one of the most difficult problems in trying to control the ongoing COVID-19 epidemic is the presence of a large cohort of asymptomatic infectives. We develop a SIR-type model taking into account the presence of asymptomatic, or however undetected, infective, and the substantially long time these spend being infective and not isolated. In the second part of the note, we apply our model to the COVID-19 epidemi...

There is increasing evidence that one of the most difficult problems in trying to control the ongoing COVID-19 epidemic is the presence of a large cohort of asymptomatic infectives. We develop a SIR-type model taking into account the presence of asymptomatic, or however undetected, infective, and the substantially long time these spend being infective and not isolated. In the second part of the note, we apply our model to the COVID-19 epidemi...

Thanh HN, Van TN, Thu HNT, et al. Outbreak investigation for COVID-19 in northern Vietnam. Lancet Infect Dis 2020; published online March 4. https://doi.org/10.1016/S1473-3099(20)30159-6—In the appendix for this Correspondence, the sex of case 10 should be female. This correction has been made to the online appendix as of March 18, 2020....

Online search data is routinely used to monitor the prevalence of infectious diseases, such as influenza. Previous work has focused on supervised learning solutions, where ground truth data, in the form of historical syndromic surveillance reports, can be used to train machine learning models. However, no sufficient data -- in terms of accuracy and time span -- exist to apply such approaches for monitoring the emerging COVID-19 infectious disea...

Online search data is routinely used to monitor the prevalence of infectious diseases, such as influenza. Previous work has focused on supervised learning solutions, where ground truth data, in the form of historical syndromic surveillance reports, can be used to train machine learning models. However, no sufficient data $-$in terms of accuracy and time span$-$ exist to apply such approaches for monitoring the emerging COVID-19 infectious disea...

Online search data is routinely used to monitor the nationwide prevalence of infectious diseases, such as influenza. Previous work has focused on supervised learning solutions, where ground truth information, in the form of historical syndromic surveillance reports, can be used to train machine learning models. However, no sufficient data $-$in terms of validity, representativeness, and time span$-$ exist to apply such approaches for monitoring...

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We t...

Online search data are routinely used to monitor the prevalence of infectious diseases, such as influenza. Previous work has focused on developing supervised models, where ground truth data, in the form of historical syndromic surveillance reports, can be used to train machine learning models. However, no sufficient data, in terms of accuracy and time span, exist to apply such approaches for monitoring the emerging COVID-19 infectious disease p...

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