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Background: This paper aims to present open-source computer simulation programs developed to simulate, track, and estimate the COVID-19 outbreak. Methods: The programs included two separate parts, one set of programs built in Simulink with a block diagram display, and another one coded as a script in MATLAB R2020b. The mathematical model used in this package was the suspectable-infected-removed (SIR), suspectable-exposed-infected-removed (SEIR), and susceptible-exposed-infected-recovereddeceased (SEIRD) models represented by a set of differential-algebraic equations. It can be easily modified to develop new models for the problem. A generalized method was adopted to simulate worldwide outbreaks in an efficient, fast, and simple way. Results: To get a good tracking of the virus spread, a sum of sigmoid functions was proposed to capture any dynamic changes in the data. The parameters used for the input (infection and recovery rate functions) were computed using the parameter estimation tool in MATLAB. Several statistical methods were applied for the rate function, including linear, Mean±SD and root mean square (RMS). In addition, an adaptive neuro-fuzzy inference system (ANFIS) was employed and proposed to train the model and predict its output. Conclusion: This procedure is presented in such a way that it can be generalized and applied in other parts and applications of estimating the scenarios of an event, including the potential of several models, including suspectable-infected-removed (SIR), which is sensitive to pollution, etc. This program can be used as an educational tool or for research studies and this article promises some lasting contributions in the field of COVID-19. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set [ABSTRACT FROM AUTHOR]

Background: In the auricular maps introduced over the past 50 years by the French and Chinese schools, most organs and systems overlap consistently. One exception is the reproductive system, which shows a markedly different somatotopic representation--for example, for the uterus and the ovary. Objective: To identify the distribution of points with increased tenderness to pressure or with reduced electrical resistance, on the outer ear of a group of women undergoing hysteroscopy. Methods: For diagnostic purposes the auricles of 78 women were examined before and after hysteroscopy using a pain-pressure test and electrical skin resistance test. The points identified were transcribed onto a graphic system called Sectogram. Spatial cluster analysis was used to identify the statistically significant clusters of sectors with a higher concentration of points appearing after hysteroscopy. Results: The points identified after hysteroscopy tend to be concentrated in specific areas not previously recognised and which only partially overlap with the French and Chinese representation of the uterus. Conclusion: When auricular acupuncture is applied to reduce discomfort during hysteroscopy, particular attention must be paid when choosing the points/areas to be stimulated, which are not only those indicated in the Chinese or French maps. [ABSTRACT FROM AUTHOR]