Estimation of the incubation period of COVID-19 using viral load data

Ejima, Keisuke; Kim, Kwang Su; Ludema, Christina; Bento, Ana I.; Iwanami, Shoya; Fujita, Yasuhisa; Ohashi, Hirofumi; Koizumi, Yoshiki; Watashi, Koichi; Aihara, Kazuyuki; Nishiura, Hiroshi; Iwami, Shingo

doi:10.1016/j.epidem.2021.100454

Cited by 52 publications

(46 citation statements)

References 24 publications

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“…Most prior work uses Ordinary Differential Equation (ODE) models to represent within-host virus dynamics, e.g., [6][7][8][9][10][11][12]. Such models are useful for studying the onset and duration of the infective period [13] and the effect of various therapeutics given at different times [14][15][16]. However, they have limited ability to fully account for dynamics in the large and complex space of the lung [12,17].…”

Section: Introductionmentioning

confidence: 99%

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

Moses

Hofmeyr

Cannon

et al. 2021

Preprint

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A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates billions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows that spatially dispersed infections lead to increased viral loads. The model shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. When the branching airway structure is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed ODE model. These results illustrate how realistic spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

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Section: Introductionmentioning

confidence: 99%

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

Moses

Hofmeyr

Cannon

et al. 2021

Preprint

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“…The best fit to the data provides the four fit parameters: the maximal viral charge is reached after 5.4 days; the exponential growth rate before this maximum is 2.8 days −1 ; the exponential decay rate after this maximum is −1.5 days −1 . (b) Histogram of the duration between contamination and symptoms [43]. The solid line is the best fit by a Weibull distribution.…”

Section: Viral Load Distributionmentioning

confidence: 99%

Risk assessment for long and short range airborne transmission of SARS-CoV-2, indoors and outdoors, using carbon dioxide measurements

Poydenot

Abdourahamane

Caplain

et al. 2021

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A quantitative analysis of the viral transmission risk in public spaces al- lows us to identify the dominant mechanisms that a proactive public health policy can act upon to reduce risk, and to evaluate the reduction of risk that can be obtained. The contribution of public spaces to the propa- gation of SARS-CoV-2 can be reduced to a level necessary for a declining epidemic, i.e. an overall reproduction rate below one. Here, we revisit the quantitative assessment of indoor and outdoor transmission risk. We show that the long-range aerosol transmission is controlled by the flow rate of fresh air and by the mask filtering quality, and is quantitatively re- lated to the CO2 concentration, regardless the room volume and the num- ber of people. The short-range airborne transmission is investigated ex- perimentally using dedicated dispersion experiments performed in two shopping malls. Exhaled aerosols are dispersed by turbulent draughts in a cone, leading to a concentration inversely proportional to the squared dis- tance and to the flow velocity. We show that the average infection dose, called the viral quantum, can be determined from epidemiological data in a manner consistent with biological experimental data. Practical implications. The results provide quantitative guidance useful for making rational public health policy decisions to prevent the dominant routes of viral transmission through reinforced ventilation, air purification, mechanical dispersion using fans, and incentivizing the wear- ing of correctly fitted, quality facial masks (surgical masks, possibly cov- ered by another fabric mask, or non-medical FFP2 masks). Taken to- gether, such measures significantly reduce the airborne transmission risk of SARS-CoV-2.

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“…To describe the temporal change in viral load of each infected individual in the school or the office, a mathematical model describing the viral dynamics of SARS-CoV-2 was employed as in our previous studies Ejima, Kim, Ludema, et al, 2021;Iwanami et al, 2021;Jeong et al, 2021;Kim et al, 2021). Briefly explaining, the model is composed of two compartments: the viral load (copies/mL) at time ‫,ݐ‬ ܸሺ‫ݐ‬ሻ, and the ratio between the number of uninfected cells at time to and that at time 0, ݂ሺ‫ݐ‬ሻ.…”

Section: Sars-cov-2 Viral Dynamics Model and Viral Load Data Generationmentioning

confidence: 99%

“…The nonlinear mixed-effect model allows estimation of population parameters while accounting for variation in parameters between individuals (Best et al, 2017;Gonçalves et al, 2020). As the time of infection is not observed for patients, we estimated the timing of infection as well Ejima, Kim, Ludema, et al, 2021). Note that the model parameters were estimated for symptomatic and asymptomatic patients (Fig.…”

Section: Sars-cov-2 Viral Dynamics Model and Viral Load Data Generationmentioning

confidence: 99%

“…Beside the screening tests, patents are further identified through symptom presence. Thus symptomatic cases could be identified either symptom onset (an incubation period of each case is randomly determined from the distribution previously estimated: ‫,67.1‪݉ሺ‬ݎ݈݊݃‬ 0.41ሻ [the median incubation period is 5.8 days] (Ejima, Kim, Ludema, et al, 2021)) or positive screening test results that come first.…”

Section: Simulation Of Screening Covid-19 Patientsmentioning

confidence: 99%

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Safely return to schools and offices: early and frequent screening with high sensitivity antigen tests effectively identifies COVID-19 patients

Jeong

Ejima

Kim

et al. 2021

Preprint

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Background: In-person interaction at school and offices offers invaluable experience to students and benefits to companies. In the midst of the pandemic, ways to safely go back to schools and offices have been argued. Centers for Disease Control and Prevention (CDC) recommends taking all precautions such as vaccination and universal indoor masking. However, even if all the precautions are implemented and transmission is perfectly prevented in the facilities, they may be infected outside of the facilities, which would be a source of transmission in the facilities. Therefore, identifying those infected outside of the facility through screening is essential to safely go back to schools or offices. However, studies investigating the effectiveness of screening are limited. Further, it is not well clarified now which screening strategy (e.g., low or high sensitivity antigen tests, intervals and the number of tests) effectively identify infected and infectious individuals to avoid transmission in facilities Methods: We assessed the effectiveness of various screening strategies in schools and offices through quantitative simulation. The effectiveness was assessed by the proportion of identified infected and infectious participants. Infection dynamics in the facility is governed by transmission dynamics of the population they belong to, and the screening is initiated at different epidemic phases: growth, peak, and declining phases. The viral load trajectory over time for each infected individual was modelled by the viral dynamics model, and the transmission process at the population level was modelled by a deterministic compartment model. The model parameters were estimated from clinical and epidemiological data. Screening strategies were varied by antigen tests with different sensitivity and schedules of screening over 10 days. Results: Under the daily screening, we found high sensitivity antigen tests (the detection limit: 63000 copies/mL) yielded 88% (95%CI 86-89) of effectiveness by the end of 10 days screening period, which is about 20% higher than that with low sensitivity antigen tests (2000000 copies/mL). Comparing screening scenarios with different schedules, we found early and frequent screening is the key to maximize the effectiveness. Sensitivity analysis revealed that less frequent tests might be an option when the number of antigen tests is limited especially when the screening is performed at the growth phase. Discussion: High sensitivity antigen tests, high frequency screening, and immediate initiation of screening are the key to safely restart educational and economic activities allowing in-person interactions. Our computational framework is useful in assessment of screening strategies by incorporating additional factors for specific situations.

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Estimation of the incubation period of COVID-19 using viral load data

Cited by 52 publications

References 24 publications

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

Risk assessment for long and short range airborne transmission of SARS-CoV-2, indoors and outdoors, using carbon dioxide measurements

Safely return to schools and offices: early and frequent screening with high sensitivity antigen tests effectively identifies COVID-19 patients

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