Studying the course of Covid-19 by a recursive delay approach

Abstract

In an earlier paper we proposed a recursive model for epidemics; in the present paper we generalize this model to include the asymptomatic or unrecorded symptomatic people, which we call dark people (dark sector). We call this the SEPARd-model. A delay differential equation version of the model is added; it allows a better comparison to other models. We carry this out by a comparison with the classical SIR model and indicate why we believe that the SEPARd model may work better for Covid-19 than other approaches.In the second part of the paper we explain how to deal with the data provided by the JHU, in particular we explain how to derive central model parameters from the data. Other parameters, like the size of the dark sector, are less accessible and have to be estimated more roughly, at best by results of representative serological studies which are accessible, however, only for a few countries. We start our country studies with Switzerland where such data are available. Then we apply the model to a collection of other countries, three European ones (Germany, France, Sweden), the three most stricken countries from three other continents (USA, Brazil, India). Finally we show that even the aggregated world data can be well represented by our approach.At the end of the paper we discuss the use of the model. Perhaps the most striking application is that it allows a quantitative analysis of the influence of the time until people are sent to quarantine or hospital. This suggests that imposing means to shorten this time is a powerful tool to flatten the curves.

Evidence-based pandemic preparedness: an infrastructure for population-scale genome-based based testing for COVID-19

“…The model and its validation are described in ref. 12 . Besides reflecting changes of contact rates, the model allows to build in the effects of vaccinations and the developments of new mutants.…”

Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

“…Studies leveraged data from ongoing research and guidelines from intergovernmental agencies such as the World Health Organization (WHO), which provides advice on the ways in which the virus gets spread [66]. The data qualitatively informed the selection and calibration of parameters such as the availability of tests [67], levels of preparedness and response capacity at schools [68], hygiene guidance [69], target groups [70], demographic factors [71], geographies [72], the expected amount of unrecorded symptomatic people [73], individual pre-existing conditions [74] and the cost of measures being implemented [75]. The sensitivity of those parameters to uncertainties [76], to hypothetical scenarios such as the additional impact of isolation and quarantine [77], as well as the overall efficiency of arrangements such as test-trace-isolate [1] or diagnosing-screening-surveilling arrangements [78] have thus far informed modeling architectures.…”

“…While most modeling endeavors have not necessarily discussed artificial intelligence applications to school testing, a few have conversed with pertaining concepts such as optimization towards desired performance means [73] and rule-based models [79][80][81][82].…”

A new approach to the dynamic modeling of an infectious disease

“…These choices are not derived from fits to specific data sets but rather are chosen to demonstrate the different kinds of behaviour which the epidemic trajectories can show with this model. Extensive fits of real country data to the model have been demonstrated in Reference [19]. We use numerical integration to solve (2.3), the method being second order Runge Kutta with a step size of 0.001 day.…”

“…We would like to highlight that the solution classes in this Table and in Figure 1 serve primarily to demonstrate the versatility of our model rather than provide detailed fits for policymakers etc. The latter exercise has been conducted by Kreck and Scholz [19].…”