Using random testing in a feedback-control loop to manage a safe exit from the COVID-19 lockdown

Müller, Markus; Pm, Derlet; Mudry, Christopher; Aeppli, G.

doi:10.1101/2020.04.09.20059360

Cited by 15 publications

(13 citation statements)

References 13 publications

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“…We performed simulations to address this question, presenting different scenarios in order to determine an efficient strategy, by considering the period and intensity of interventions. The interventions can be applied at a single moment in time and kept until a decrease of the number of cases is observed, or a combination of interventions can be enforced at different time intervals, as proposed in other works 25,26 , depending on testing and monitoring capacities and/or local social-economical conditions. Our results show that, when faced with an already collapsed system, only vigorous measures (that reduce the transmission rate by at least 50%) enforced over at least two months or, alternatively, measures capable of reducing transmission by at least 75% over a 2week period, are capable of re-establishing hospitalization operation capacity.…”

Section: Discussionmentioning

confidence: 99%

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

et al. 2021

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COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R0. Finally, we discuss our results in light of epidemiological data that became available after the initial analyses.

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Section: Discussionmentioning

confidence: 99%

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

et al. 2021

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“…Such patients typically do not suffer from sampling bias and detailed enough investigations will limit the number of missed controls. Such investigations can be performed on a limited sample 28 . Another limitation of our estimate is that epidemiological investigations are not perfect, as such, some controlled individuals might be missed.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the number of undiagnosed infected in an outbreak using source of infection measurements

Melka

Louzoun

2020

Preprint

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Multiple studies have been conducted to predict the impact and duration of the current COVID-19 epidemics. Most of those studies rely on parameter calibration using the published number of confirmed cases. Unfortunately, this number is usually incomplete and biased due to the lack of testing capacities, and varying testing protocols. An essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals. This number is crucial for the determination of transmission prevention strategies and it provides statistics on the epidemic dynamics. To estimate the number of undiagnosed infected individuals, we studied the relation between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected. We simulated multiple models currently used to study the COVID-19 pandemic and computed the relation between these two fractions in all those models. Across most models currently used and for most realistic model parameters, the relation between the two fractions is consistently linear and model independent. This relation can be used to estimate the number of undiagnosed infected, with no explicit epidemiological model. We apply this method to measure the number of undiagnosed infected in Israel. Since the fraction of confirmed cases with a known source can be obtained from epidemiological investigations in any country, one can estimate the total number of infected individuals in the same country.

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“…Clearly, if a country has not enough tests, a random smart-testing strategy is required. By testing a much smaller number of randomly selected people per day, it is possible to obtain information on the local spreading rate [30].…”

Section: Conclusion VI Conclusionmentioning

confidence: 99%

Covid-19 testing strategies and lockdowns: the European closed curves, analysed by “skew-normal” distributions, the forecasts for the UK, Sweden, and the USA, and the ongoing outbreak in Brazil

Leo

2020

Preprint

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As the number of Covid-19 infections worldwide overtakes 6 millions of Total Confirmed Cases (TCC), the data reveal almost closed outbreaks in many European countries. Using the European data as a basis for our analysis, we study the spreading rate of Covid-19 and model the Daily Confirmed Cases and Deaths per Million (DCCpM and DDpM) curves by using ``skew-normal'' probability density functions. The use of these asymmetrical distributions allows to get a more realistic prediction of the end of the disease in each country and to evaluate the effectiveness of the local authorities strategies in facing the European outbreak. The initial stage of the Brazilian disease is compared with the early phase of the European one. This is done by using the weekly spreading rate of Covid-19. For Sweden, UK, and USA, we shall give a forecast for the end of pandemic and for Brazil the prediction of the peak of DDpM. We also discuss additional factors that could play an important role in the fight against Covid-19, such as the fast response of the local authorities, the testing strategies, the number of beds in the intensive care units, and, last but not least, the measures of isolation adopted. The Brazilian mitigation measures can be placed between the strict lockdown of many European countries and the Swedish approach, but clearly much comparable to the European ones (in particular to the Netherlands). Methods. For Brazil, the weekly spreading rates of Covid-19, as more people are getting infected, was used to compare the outbreak in these countries with the ones of the European countries when they were at the same stage of infection. In the early stage of the disease, normal distributions have been used to obtain what we call a dynamic prediction of the peaks. After reaching the peak of daily infections and/or deaths, skew-normal distributions are required to correctly fit the asymmetrical DCCpM and DDpM curves and get a realistic forecast of the pandemic end. Findings. The European data analysis shows that the spreading rate of Covid-19 increased similarly for all countries in its initial stage, but it changed as the number of TCCpM in each country grew. This was caused by the different timely action of the authorities in adopting isolation measures and/or massive testing strategies. The early stage of the outbreak in the USA and Brazil shows for their $\boldsymbol{\alpha}$ factor (DCCpM) a behaviour similar to Italy and Sweden, respectively. For the $\boldsymbol{\beta}$ factor (DDpM), the American spreading is similar to the one of Switzerland, whereas the Brazilian factor is greater than the ones of Portugal, Germany, and Austria (which showed, in terms of TDpM, the best results in Europe) but, at the moment, it is lower than the other European countries. Interpretation. The fitting skew parameters used to model the DCCpM and DDpM curves allow a more realistic prediction of the end of the pandemic and give us the possibility to compare the mitigation measures adopted by the local authorities by analysing their respective skew normal parameters (mean, mode, standard deviation, and skewness). In Europe, Sweden and the UK show the greatest asymmetries, a kind of marathon instead of the sprint of other European countries (as observed by Swedish authorities). This also happens for the USA. The Brazilian weekly spreading rate for deaths is lower than most of the European countries at the same stage of the outbreak.

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Using random testing in a feedback-control loop to manage a safe exit from the COVID-19 lockdown

Cited by 15 publications

References 13 publications

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

Evaluation of the number of undiagnosed infected in an outbreak using source of infection measurements

Covid-19 testing strategies and lockdowns: the European closed curves, analysed by “skew-normal” distributions, the forecasts for the UK, Sweden, and the USA, and the ongoing outbreak in Brazil

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