Author: Patrick Jenny; David F Jenny; Hossein Gorji; Markus Arnoldini; Wolf-Dietrich Hardt
Title: Dynamic Modeling to Identify Mitigation Strategies for Covid-19 Pandemic Document date: 2020_3_30
ID: ngsstnpr_46
Snippet: The above equations are nonlinear (due to the non-linearity of the log-normal distribution) and do not admit closed form expressions for N or T d . Yet, using Monte-Carlo sampling, the maps N −1 → k d and N −1 → T d can be plotted as shown in Fig. 8 . As indicated in Fig. 8 , increasing the detection rate by 1.7666-fold would require testing the entire susceptible population roughly once every 10 days. This testing interval would suffice .....
Document: The above equations are nonlinear (due to the non-linearity of the log-normal distribution) and do not admit closed form expressions for N or T d . Yet, using Monte-Carlo sampling, the maps N −1 → k d and N −1 → T d can be plotted as shown in Fig. 8 . As indicated in Fig. 8 , increasing the detection rate by 1.7666-fold would require testing the entire susceptible population roughly once every 10 days. This testing interval would suffice to end the pandemic, even if no other mitigation strategies were applied. It is of practical relevance that this is equivalent to testing a random fraction of 10% of the whole susceptible population every day.
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