On Some Stochastic Epidemic Models

In this article, we present some stochastic non-linear epidemic models related to Covid-19. It is very hard to get exact solutions for the non-linear such models, but we have successfully obtained exact solutions of some suitable non-linear stochastic cases. The general stochastic logistic model is solved;and some properties related to functions of Wiener process are studied.

Chest computed tomography severity score and its correlation with age group, sex, and laboratory test results among coronavirus disease 2019 health-care workers

Background Detection of the viral nucleic acid is the cornerstone to diagnose the novel coronavirus disease 2019 (COVID-19). Due to the limited resources, the clinical and laboratory biomarkers will help in the early and differential diagnosis of severe acute respiratory syndrome coronavirus 2 infection and predict the prognosis of the disease. These findings in patients with pneumonia include fatigue, dry cough, low-grade fever, along with normal white blood cell count, decreased lymphocyte count, and high C-reactive protein (CRP) and D-dimer levels. Chest computed tomography (CT) severity score relies on the opacification of lungs as a proof for disease extension. Several studies have settled the importance of CT chest in the diagnosis and follow up of COVID-19 patients. Otherwise, many scientific societies have disclaimed the routine CT screening of these patients. Hence, it is important to correlate the CT severity score in positive PCR COVID-19 patients to their laboratory findings to minimize the need of frequent CT chest as a tool of follow-up. Patients and methods The study included 198 positive PCR for COVID-19 health-care workers including physicians, nurses, employees, and workers of Ain Shams University Hospitals, who sought medical advice at the Chest OPC or the ER triage or through teleconsultations. Patients underwent history taking, laboratory workup including complete blood count with differential, serum ferritin, CRP, D-dimer levels, and high-resolution CT chest. Results The study included 198 health-care workers including physicians, nurses, employees, and workers of Ain Shams University Hospitals with 106 of them being females and 92 males. The age range of the included patients is from 21 years up to 85 years. The cases were classified according to their CT severity score into normal CT chest with 47.5% of cases, mild CT findings representing 21.2%, 34 patients with moderate findings in CT, and those with severe score were 28 patients. A significant relation was found between the age and CT severity score with P value less than 0.001. The severity score was higher in cases with lower total leukocyte count and lymphocytes with significant relation and the P value was less than 0.001. The median CRP and ferritin level show a highly significant relation with the CT severity score. A highly significant relationship was found between severity score and D-dimer level of patients with a P value of less than 0.001. Conclusion This work sets a semiquantitative framework to assess and follow up the severity of COVID-19 disease. This score could be possibly used to facilitate the clinical triage of COVID-19 moderate to severe patients, requiring admission in hospitals in relation to laboratory findings. Also, it could be used to evaluate the severity of lung involvement in patients objectively and quickly. However, it was proposed that the use of laboratory results is of value in the follow up of the cases to limit the exposure to radiations.

ARIMA model estimation based on genetic algorithm for COVID-19 mortality rates

This paper presents a forecasting model for the mortality rates of COVID-19 in six of the top most affected countries depending on the hybrid Genetic Algorithm and Autoregressive Integrated Moving Average (GA-ARIMA). It was aimed to develop an advanced and reliable predicting model that provides future forecasts of possible confirmed cases and mortality rates (Total Deaths per 1 million Population of COVID-19) that could help the public health authorities to develop plans required to resolve the crisis of the pandemic threat in a timely and efficient manner. The study focused on predicting the mortality rates of COVID-19 because the mortality rate determines the prevalence of highly contagious diseases. The Genetic algorithm (GA) has the capability of improving the forecasting performance of the ARIMA model by optimizing the ARIMA model parameters. The findings of this study revealed the high prediction accuracy of the proposed (GA-ARIMA) model. Moreover, it has provided better and consistent predictions compared to the traditional ARIMA model and can be a reliable method in predicting expected death rates as well as confirmed cases of COVID-19. Hence, it was concluded that combining ARIMA with GA is further accurate than ARIMA alone and GA can be an alternative to find the parameters and model orders for the ARIMA model. © 2021 World Scientific Publishing Company.