scholarly journals Modeling, Control, and Prediction of the Spread of COVID-19 Using Compartmental, Logistic, and Gauss Models: A Case Study in Iraq and Egypt

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1400
Author(s):  
Mahmoud A. Ibrahim ◽  
Amenah Al-Najafi
Keyword(s):  
Logistic Regression ◽  
Transmission Rate ◽  
Control Measures ◽  
Sensitivity Analyses ◽  
Gaussian Models ◽  
Logistic Regression Models ◽  
Incidence Data ◽  
Periodic Transmission ◽  
Second Wave

In this paper, we study and investigate the spread of the coronavirus disease 2019 (COVID-19) in Iraq and Egypt by using compartmental, logistic regression, and Gaussian models. We developed a generalized SEIR model for the spread of COVID-19, taking into account mildly and symptomatically infected individuals. The logistic and Gaussian models were utilized to forecast and predict the numbers of confirmed cases in both countries. We estimated the parameters that best fit the incidence data. The results provide discouraging forecasts for Iraq from 22 February to 8 October 2020 and for Egypt from 15 February to 8 October 2020. To provide a forecast of the spread of COVID-19 in Iraq, we present various simulation scenarios for the expected peak and its timing using Gaussian and logistic regression models, where the predicted cases showed a reasonable agreement with the officially reported cases. We apply our compartmental model with a time-periodic transmission rate to predict the possible start of the second wave of the COVID-19 epidemic in Egypt and the possible control measures. Our sensitivity analyses of the basic reproduction number allow us to conclude that the most effective way to prevent COVID-19 cases is by decreasing the transmission rate. The findings of this study could therefore assist Iraqi and Egyptian officials to intervene with the appropriate safety measures to cope with the increase of COVID-19 cases.

Statistical simulation of ocean current patterns using autoregressive logistic regression models: A case study in the Gulf of Mexico

2019 ◽  
Vol 136 ◽  
pp. 1-12 ◽  
Author(s):  
Helios Chiri ◽  
Ana Julia Abascal ◽  
Sonia Castanedo ◽  
José Antonio A. Antolínez ◽  
Yonggang Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Gulf Of Mexico ◽  
Regression Models ◽  
Statistical Simulation ◽  
Ocean Current ◽  
Logistic Regression Models

Using logistic regression for persona segmentation in tourism: A case study

2020 ◽  
Vol 48 (4) ◽  
pp. 1-16
Author(s):  
Rui Kang
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Tourism Industry ◽  
Opinion Leaders ◽  
Service Users ◽  
User Characteristics ◽  
Logistic Regression Models ◽  
Integrated Method ◽  
Tourism Motivation

I introduced a method for persona segmentation in the tourism industry to identify representative subgroups with different motivations or goals. Data from 496 key opinion leaders of groups representing 7,965 travel service users were analyzed with a logistic regression model of user characteristics and tourism motivation. I found that logistic regression is an integrated method of persona segmentation that balances precision and accuracy, and yields replicable and valid results. Three subgroups for persona segmentation based on logistic regression models are proposed.

scholarly journals Mathematical epidemiologic and simulation modelling of first wave COVID-19 in Malaysia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Rezal Kamel Ariffin ◽  
Kathiresan Gopal ◽  
Isthrinayagy Krishnarajah ◽  
Iszuanie Syafidza Che Ilias ◽  
Mohd Bakri Adam ◽  
...  
Keyword(s):  
Transmission Rate ◽  
Mainland China ◽  
Movement Control ◽  
Simulation Modelling ◽  
Sir Model ◽  
Control Measures ◽  
Cumulative Number ◽  
Severity Of The Disease ◽  
Control Order ◽  
Second Wave

AbstractSince the first coronavirus disease 2019 (COVID-19) outbreak appeared in Wuhan, mainland China on December 31, 2019, the geographical spread of the epidemic was swift. Malaysia is one of the countries that were hit substantially by the outbreak, particularly in the second wave. This study aims to simulate the infectious trend and trajectory of COVID-19 to understand the severity of the disease and determine the approximate number of days required for the trend to decline. The number of confirmed positive infectious cases [as reported by Ministry of Health, Malaysia (MOH)] were used from January 25, 2020 to March 31, 2020. This study simulated the infectious count for the same duration to assess the predictive capability of the Susceptible-Infectious-Recovered (SIR) model. The same model was used to project the simulation trajectory of confirmed positive infectious cases for 80 days from the beginning of the outbreak and extended the trajectory for another 30 days to obtain an overall picture of the severity of the disease in Malaysia. The transmission rate, β also been utilized to predict the cumulative number of infectious individuals. Using the SIR model, the simulated infectious cases count obtained was not far from the actual count. The simulated trend was able to mimic the actual count and capture the actual spikes approximately. The infectious trajectory simulation for 80 days and the extended trajectory for 110 days depicts that the inclining trend has peaked and ended and will decline towards late April 2020. Furthermore, the predicted cumulative number of infectious individuals tallies with the preparations undertaken by the MOH. The simulation indicates the severity of COVID-19 disease in Malaysia, suggesting a peak of infectiousness in mid-March 2020 and a probable decline in late April 2020. Overall, the study findings indicate that outbreak control measures such as the Movement Control Order (MCO), social distancing and increased hygienic awareness is needed to control the transmission of the outbreak in Malaysia.

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