Quantitative COVID-19 infectiousness estimate correlating with viral shedding and culturability suggests 68% pre-symptomatic transmissions

Prakash, Meher K.

doi:10.1101/2020.05.07.20094789

Cited by 14 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Regarding the transmission rate b(a), initial data of the outbreak and measurements of the viral load during the disease suggest a distribution with an early peak at two or three days followed with sharp decline (He et al, 2020;Prakash, 2020). To keep the math simple, we assume a negative exponential function that declines quickly from the peak day of infection assumed to be also the first day of infectiousness u:…”

Section: A (More Realistic) Model With Exponential Ratesmentioning

confidence: 99%

See 1 more Smart Citation

The mathematics of the reproduction number R for Covid-19: A primer for demographers

Rosero-Bixby¹,

Miller²

2020

Preprint

View full text Add to dashboard Cite

BACKGROUNDThe reproduction number R is a key indicator to characterize the dynamics of the Covid-19 pandemic and to assess the effect of control strategies that frequently have extremely high social and economic costs. Despite having an analog in demography’s net reproduction rate, demographers may not be familiar with the concept and measurement of R in the context of Covid-19. OBJECTIVETo provide a primer for demographers for understanding and estimating R for Covid-19. METHODSIn order to demystify the complexities of estimating this important indicator, we present a simple method for its calculation, following well-known procedures in demography, a discipline in which an analog of R -- the net reproduction rate -- has been routinely computed for a century. RESULTSWe show that R can be estimated as a quotient between the numbers of new cases today divided by the weighted average of cases in previous days. We present two alternative derivations for these weights: based on how risks change over time (constant vs exponential decay). We provide estimates of these weights and demonstrate their use in calculating R to trace the course of the Covid-19 pandemic in several countries. CONTRIBUTIONThis primer for demographers demonstrates a method for estimating the reproductive number R for the Covid-19 pandemic derived using familiar demographic techniques. The strengths of the proposed approach are the transparency of the assumptions and the simplicity of the procedure.

show abstract

Section: A (More Realistic) Model With Exponential Ratesmentioning

confidence: 99%

“…As before, we chose the parameters on the basis of early knowledge of the Covid-19 epidemiology mostly from the Chinese province of Hubei (Anastassopoulou et al, 2020;He et al, 2020;Park et al, 2020;Prakash, 2020; WHO, 2020).…”

Section: Equation 13mentioning

confidence: 99%

The mathematics of the reproduction number R for Covid-19: A primer for demographers

Rosero-Bixby¹,

Miller²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Despite the intense global efforts, there is no scalable treatment or vaccine yet. This problem is compounded by the fact that the epidemiology of COVID-19 is very unique [2] and that it spreads efficiently, with an estimated 44% [3] to 69% [4] of the transmissions before the onset of symptoms. Non-pharmaceutical interventions (NPI) have been the only successful method of mitigating the spread of infections [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

Sruthi

Biswal

Saraswat

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The role of complete lockdowns in reducing the reproduction ratios (Rt) of COVID-19 is now established. However, the persisting reality in many countries is no longer a complete lockdown, but restrictions of varying degrees using different choices of Non-pharmaceutical interaction (NPI) policies. A scientific basis for understanding the effectiveness of these graded NPI policies in reducing the Rt is urgently needed to address the concerns on personal liberties and economic activities. In this work, we develop a systematic relation between the degrees of NPIs implemented by the 26 cantons in Switzerland during March 9 to September 13 and their respective contributions to the Rt. Using a machine learning framework, we find that Rt which should ideally be lower than 1.0, has significant contributions in the post-lockdown scenario from the different activities - restaurants (0.0523 (CI. 0.0517-0.0528)), bars (0.030 (CI. 0.029-0.030)), and nightclubs (0.154 (CI. 0.154-0.156)). Activities which keep the land-borders open (0.177 (CI. 0.175-0.178)), and tourism related activities contributed comparably 0.177 (CI. 0.175-0.178). However, international flights with a quarantine did not add further to the Rt of the cantons. The requirement of masks in public transport and secondary schools contributed to an overall 0.025 (CI. 0.018-0.030) reduction in Rt, compared to the baseline usage even when there are no mandates. Although causal relations are not guaranteed by the model framework, it nevertheless provides a fine-grained justification for the relative merits of choice and the degree of the NPIs and a data-driven strategy for mitigating Rt.

show abstract

“…Many epidemiological predictions studied the consequences of different Rt's and highlighted the need to keep it in control [7]. However, the epidemiology of COVID-19 pandemic had been quite unique [8], as it defied the intuitions on infectiousness period, pre-symptomatic and asymptomatic transmission [9,10] of most respiratory infections, including SARS 2003. How the different non-pharmaceutical interventions can reduce the rates to an acceptable level was not clear and each government used a different combination of the different non-pharmaceutical intervention policies.…”

Section: Introductionmentioning

confidence: 99%

Predicting and interpreting COVID-19 transmission rates from the ensemble of government policies

Prakash

Sruthi

Biswal

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Several questions resonate as the governments relax their COVID-19 mitigation policies - is it too early to relax them, were the policies as effective as they could have been. Answering these questions about the past or crafting newer policy decisions in the future requires a quantification of how policy choices affect the spread of the infection. Policy landscape as well as the infection trajectories from different states and countries diverged so fast that comparing and learning from them has not been easy. In this work, we standardize and pool together the ensemble of lockdown and graded re-opening policies adopted by the 50 states of USA in any given week between 9th March and 9th August. Using artificial intelligence (AI) on this pooled data, we build a predictive model (R2training=0.79, R2test=0.76) for the weekly-averaged transmission rate of infections. Predictability conceptually raises the possibility of an evidence-based or data-driven mitigation policy-making by evaluating the relative merits of the different policy scenarios. Probing the predictions with interpretable AI highlights how factors such as the closing of bars or the use of masks influence transmission, effects which have been hard to decouple from the ensemble of policy instrument combinations. While acknowledging the limitations of our predictions as well as of the infection testing, we ask the theoretical question if the observed transmission rates in the states were as efficient as they could have been under various levels of restrictions, and if the mitigation policies of the states are ‘overdesigned’ for the success they have. The model can be further refined with a more detailed inclusion of geographies and policy compliances, as well as expanded as newer policies emerge.

show abstract

Quantitative COVID-19 infectiousness estimate correlating with viral shedding and culturability suggests 68% pre-symptomatic transmissions

Cited by 14 publications

References 18 publications

The mathematics of the reproduction number R for Covid-19: A primer for demographers

The mathematics of the reproduction number R for Covid-19: A primer for demographers

How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

Predicting and interpreting COVID-19 transmission rates from the ensemble of government policies

Contact Info

Product

Resources

About