A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic

Cazelles, Bernard; Champagne, Clara; Nguyen-Van-Yen, Benjamin; Comiskey, Catherine; Vergu, Elisabeta; Roche, Benjamín

doi:10.1101/2021.02.04.21251167

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…In [44], a direct stochastic model is proposed for R t , assuming that its log derivative is Brownian, namely where ν is the volatility of the Brownian process to be estimated. Then we have where C is a constant depending on steady transmission characteristics and s ( t ) is the proportion of the population that is susceptible.…”

Section: Review Of Previous Modelsmentioning

confidence: 99%

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Modeling Covid-19 incidence by the renewal equation after removal of administrative bias and noise

Álvarez

Morel

2022

Preprint

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The sanitary crisis of the past two years has focused the public's attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number Rt, defined by the average number of new infections caused by a single infected individual at time t, is one of the best metrics for estimating the epidemic trend. In this paper, we give a complete observation model for sampled epidemiological incidence signals obtained through periodic administrative measurements. The model is governed by the classic renewal equation using an empirical reproduction kernel, and subject to two perturbations: a time-varying gain with a weekly period and a white observation noise. We estimate this noise model and its parameters by extending a variational inversion of the model recovering its main driving variable Rt. Using Rt, a restored incidence curve, corrected of the weekly and festive day bias, can be deduced through the renewal equation. We verify experimentally on many countries that, once the weekly and festive days bias have been corrected, the difference between the incidence curve and its expected value is well approximated by an exponential distributed white noise multiplied by a power of the magnitude of the restored incidence curve.

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Section: Review Of Previous Modelsmentioning

confidence: 99%

“…In [44], a direct stochastic model is proposed for R t , assuming that its log derivative is Brownian, namely…”

Section: Stochastic Observation Models For I T and R Tmentioning

confidence: 99%

Modeling Covid-19 incidence by the renewal equation after removal of administrative bias and noise

Álvarez

Morel

2022

Preprint

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La edad de la población en la COVID-19: controversias socio-demográficas sobre un hecho común

Rodríguez

2021

BAGE

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La pandemia COVID-19 ha traído consigo múltiples incertidumbres que afectan al individuo, a la sociedad y a los territorios, pero también una certeza poderosa, son las personas mayores quienes tienen una posición más vulnerable ante la COVID-19. La identificación de esta población sigue criterios biológicos, cronológico/ estadísticos, instrumentales o de decisión social y política, que hace posible su análisis poblacional y su uso en la definición de políticas públicas a distintos niveles. La abundante bibliografía generada durante la pandemia permitirá, mediante el análisis del contenido cualitativo a través de Atlas.ti, valorar las controversias sobre la edad de la población. La situación de partida es que los documentos estudiados son muy diversos, producidos con criterios de urgencia, sin seguir siempre los procedimientos de validación en la toma de datos, en el uso de métodos y en el contraste de evidencias. Muestran las controversias en su denominación, en la imagen que tiene la sociedad y los medios de comunicación, en las valoraciones éticas de cómo la población mayor es tratada, en la consideración de la edad para las recomendaciones sanitarias y profesionales, y en, por último, su significación en las medidas políticas en marcha durante la pandemia.

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Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise

Álvarez

Morel

2022

Biology

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The sanitary crisis of the past two years has focused the public’s attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number Rt, defined by the average number of new infections caused by a single infected individual at time t, is one of the best metrics for estimating the epidemic trend. In this paper, we provide a complete observation model for sampled epidemiological incidence signals obtained through periodic administrative measurements. The model is governed by the classic renewal equation using an empirical reproduction kernel, and subject to two perturbations: a time-varying gain with a weekly period and a white observation noise. We estimate this noise model and its parameters by extending a variational inversion of the model recovering its main driving variable Rt. Using Rt, a restored incidence curve, corrected of the weekly and festive day bias, can be deduced through the renewal equation. We verify experimentally on many countries that, once the weekly and festive days bias have been corrected, the difference between the incidence curve and its expected value is well approximated by an exponential distributed white noise multiplied by a power of the magnitude of the restored incidence curve.

show abstract

A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic

Cited by 3 publications

References 53 publications

Modeling Covid-19 incidence by the renewal equation after removal of administrative bias and noise

Modeling Covid-19 incidence by the renewal equation after removal of administrative bias and noise

La edad de la población en la COVID-19: controversias socio-demográficas sobre un hecho común

Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise

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