Understanding COVID-19 spreading through simulation modeling and scenarios comparison: preliminary results
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Since late 2019 the world is facing the rapid spreading of a novel viral disease (SARS-CoV-2) provoked by the coronavirus 2 infection (COVID-19), declared pandemic last 12 March 2020. As of 27 March 2020, there were more than 500,000 confirmed cases and 23,335 deaths worldwide. In those places with a rapid growth in numbers of sick people in need of hospitalization and intensive care, this demand has over-saturate the medical facilities and, in turn, rise the mortality rate.
In the absence of a vaccine, classical epidemiological measures such as testing, quarantine and physical distancing are ways to reduce the growing speed of new infections. Thus, these measures should be a priority for all governments in order to minimize the morbidity and mortality associated to this disease.
System dynamics is widely used in many fields of the biological sciences to study and explain changing systems. The system dynamics approach can help us understand the rapid spread of an infectious disease such as COVID-19 and also generate scenarios to test the effect of different control measures.
The aim of this study is to provide an open model (using STELLA® from Iseesystems) that can be customized to any area/region and by any user, allowing them to evaluate the different behavior of the COVID-19 dynamics under different scenarios. Thus, our intention is not to generate a model to accurately predict the evolution of the disease nor to supplant others more robust -official and non-official-from governments and renowned institutions. We believe that scenarios comparison can be an effective tool to convince the society of the need of a colossal and unprecedented effort to reduce new infections and ultimately, fatalities.
Article activity feed
-
SciScore for 10.1101/2020.03.30.20047043: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Although the model has limitations, since it is a simplification of the reality, the results of the calibrated model (Figure 3) fits quite well with the official data of infected people with COVID-10 in Spain until 24 March. Until today, it is difficult to obtain the most probable R0 after 14 March because of the time lag of detected …
SciScore for 10.1101/2020.03.30.20047043: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Although the model has limitations, since it is a simplification of the reality, the results of the calibrated model (Figure 3) fits quite well with the official data of infected people with COVID-10 in Spain until 24 March. Until today, it is difficult to obtain the most probable R0 after 14 March because of the time lag of detected patients due to the incubation period (set at 6 days). Therefore, further time is needed to quantify the effect of mitigation measures in the R0 reduction. We think that the real amount of infected people would reach, at least, 2.1 times the official numbers, calculated from a 3.4% fatality rate from days 24 and 25 March (Figure 4). If the fatality rate estimation changed, the real numbers of infected people would rise up consequently. It is important to remark that not knowing the real numbers of infected people does not significantly affect the power of the approach we propose here, since the comparison of different scenarios (e.g. reducing R0 after a day or another) uses the same base model. Hence, we can say that the measure that works better in the model, would work better in the real world as well. Besides, the results of this model would improve if we could adjust all the parameters to the real situation. Then, we could build a more robust model. In fact, our intention is to generate further versions of this model with whenever more accurate parameters are available. Fatality numbers followed a smooth curve until 24 March, when the increas...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
