The impact of asymptomatic COVID-19 infections on future pandemic waves

Fox, Spencer J.; Pasco, Remy; Tec, Mauricio; Du, Zhanwei; Lachmann, Michael; Scott, James G.; Meyers, Lauren Ancel

doi:10.1101/2020.06.22.20137489

Cited by 13 publications

(11 citation statements)

References 42 publications

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“…To highlight our model results, we can see that the asymptomatic infectious are as numerous as symptomatic ones, but are characterized by a larger uncertainty, due to the lack of information in the data (Figs 3C, 3D , S4 , S7 , S10 , S13 , S16 and S19 ). This is in agreement with recent papers [ 11 , 24 , 42 , 43 ], which emphasize that the growth of the COVID-19 epidemic is driven by silent infections. This has also been highlighted in Ireland where it has been estimated that during the second epidemic wave the ratio of silent infections to known reported cases was approximately 1:1 [ 44 ].…”

Section: Discussionsupporting

confidence: 93%

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

et al. 2021

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The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).

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Section: Discussionsupporting

confidence: 93%

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

et al. 2021

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“…To highlight our model results, we can see that the asymptomatic infectious are as numerous as symptomatic ones, but are characterized by a larger uncertainty, due to the lack of information in the data (Figs 3C-3D and figures in the Supporting Information ). This is in agreement with recent papers [11,24,38,39], which emphasize that the growth of the COVID-19 epidemic is driven by silent infections. This has also been highlighted in Ireland where it has been estimated that during the second epidemic wave the ratio of silent infections to known reported cases was approximately 1:1 [40].…”

Section: Discussionsupporting

confidence: 93%

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

Cazelles

Champagne

Nguyen-Van-Yen

et al. 2021

Preprint

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The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted with Bayesian inference (PMCMC) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data, without any specific hypothesis on its evolution. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). In this way, we can appropriately assess the effect of the mitigation strategies implemented to control the epidemic waves in France and in Ireland.

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“…Our approach resolves these issues by incorporating daily testing capacity as part of the model when estimating parameters from serology and case data. Models without explicit consideration of this capacity have difficulty estimating the proportion of cases that are symptomatic from these data ( 36 ), suggesting that including testing is crucial.…”

Section: Discussionmentioning

confidence: 99%

Quantifying asymptomatic infection and transmission of COVID-19 in New York City using observed cases, serology, and testing capacity

Subramanian

Pascual

2021

Proc. Natl. Acad. Sci. U.S.A.

216

148

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The contributions of asymptomatic infections to herd immunity and community transmission are key to the resurgence and control of COVID-19, but are difficult to estimate using current models that ignore changes in testing capacity. Using a model that incorporates daily testing information fit to the case and serology data from New York City, we show that the proportion of symptomatic cases is low, ranging from 13 to 18%, and that the reproductive number may be larger than often assumed. Asymptomatic infections contribute substantially to herd immunity, and to community transmission together with presymptomatic ones. If asymptomatic infections transmit at similar rates as symptomatic ones, the overall reproductive number across all classes is larger than often assumed, with estimates ranging from 3.2 to 4.4. If they transmit poorly, then symptomatic cases have a larger reproductive number ranging from 3.9 to 8.1. Even in this regime, presymptomatic and asymptomatic cases together comprise at least 50% of the force of infection at the outbreak peak. We find no regimes in which all infection subpopulations have reproductive numbers lower than three. These findings elucidate the uncertainty that current case and serology data cannot resolve, despite consideration of different model structures. They also emphasize how temporal data on testing can reduce and better define this uncertainty, as we move forward through longer surveillance and second epidemic waves. Complementary information is required to determine the transmissibility of asymptomatic cases, which we discuss. Regardless, current assumptions about the basic reproductive number of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) should be reconsidered.

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The impact of asymptomatic COVID-19 infections on future pandemic waves

Cited by 13 publications

References 42 publications

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

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

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

Quantifying asymptomatic infection and transmission of COVID-19 in New York City using observed cases, serology, and testing capacity

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