Mathematical analysis of COVID-19 via new mathematical model

Abdullah, .; Ahmad, Saeed; Owyed, Saud; Abdel‐Aty, Abdel‐Haleem; Mahmoud, Emad E.; Shah, Kamal; Alrabaiah, Hussam

doi:10.1016/j.chaos.2020.110585

Cited by 48 publications

(36 citation statements)

References 41 publications

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“…perhaps individuals were unaware that cell phone usage could be a risk) [ [20] , [21] ]. In summary, observational results from this DETER data set can be used to inform analyses of infectious disease transmission with epidemiologic models [ [22] , [23] , [24] , [25] ]. Furthermore, results can be used to advance more effective public health policies and guidelines that include cleaning regimens for public environmental objects and the removal or relocation of frequently touched objects to help limit the spread of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Spring 2020 COVID-19 community transmission behaviours around New York City medical facilities

Lee

Laefer

2021

Infection Prevention in Practice

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BACKGROUND Epidemiological studies have long been used to develop infection transmission prevention, but exact patterns of touch behaviours and transportation choices (COVID-19 community spread contributors) were previously unknown. AIM To investigate individual risk behaviour levels with respect to local COVID-19 infection levels. METHODS A longitudinal field study recorded behaviours of individuals leaving medical facilities following the New York State’s PAUSE order. A subset of that data was analyzed herein (4,793 records, 16 facilities, 23 rd March – 17 th May 2020). Touched objects and transportation choices were compared over time using chi-square tests (p < 0.05 significance threshold). FINDINGS Over eight weeks, touching progressively decreased but for-hire vehicle usage increased. In week 1, 60.4% of subjects touched at least one object: a building’s door handle (21.8%); traffic light, railing, or parking meter (5.6%); shared object (19.7%, e.g. vehicle’s door handle); personal object (13.9%, e.g. cell phone); or themselves (0.7%). Certain touch points, however, remained. Throughout the study, public transportation ridership remained steady (about 20%); for-hire car usage increased from 0% in week one to 7% in week eight, mirroring a 7% decrease in personal vehicle usage (from 34% to 27%). Touching and transportation patterns varied significantly by facility. CONCLUSIONS Inconsistent trends in risk related behaviour were documented in the eight weeks following a NYC PAUSE order. Namely, while overall touching decreased 25%, there was no appreciable change in cell phone usage, and for-hire vehicles usage increased 7%.

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Section: Discussionmentioning

confidence: 99%

Spring 2020 COVID-19 community transmission behaviours around New York City medical facilities

Lee

Laefer

2021

Infection Prevention in Practice

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“…Several epidemiological models [1] , [10] , [23] , [38] , [43] , [44] , [49] , [51] , [57] have been developed to study the transmission of the COVID-19 virus. As it is well known, the generalized logistic equation is widely used in interpreting the aggregate number of COVID-19 infection trajectories in several countries [49] , [50] , [57] .…”

Section: Numerical Simulationmentioning

confidence: 99%

“…Several advances have been made in the field of mathematical modeling in studying the behavior of certain dynamical systems whose state evolves with time [1] , [10] , [23] , [38] , [43] , [44] , [49] , [51] , [54] , [56] , [57] . Most of these dynamical processes in nature are far from equilibrium and so using stationary distribution to describe and analyze the evolution of such processes has a great limitation [8] , [9] , [30] , [55] .…”

Section: Introductionmentioning

confidence: 99%

Time-dependent probability distribution for number of infection in a stochastic SIS model: case study COVID-19

Otunuga

2021

Chaos, Solitons & Fractals

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We derive the time-dependent probability distribution for the number of infected individuals at a given time in a stochastic Susceptible-Infected-Susceptible (SIS) epidemic model. The mean, variance, skewness, and kurtosis of the distribution are obtained as a function of time. We study the effect of noise intensity on the distribution and later derive and analyze the effect of changes in the transmission and recovery rates of the disease. Our analysis reveals that the time-dependent probability density function exists if the basic reproduction number is greater than one. It converges to the Dirac delta function on the long run (entirely concentrated on zero) as the basic reproduction number tends to one from above. The result is applied using published COVID-19 parameters and also applied to analyze the probability distribution of the aggregate number of COVID-19 cases in the United States for the period: January 22, 2020-March 23, 2021. Findings show that the distribution shifts concentration to the right until it concentrates entirely on the carrying infection capacity as the infection growth rate increases or the recovery rate reduces. The disease eradication and disease persistence thresholds are calculated.

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“…Some mathematical models were constructed to analyze the spreads of viruses [18] . During the outbreak of COVID-19, modeling attracted a special attention to many pharmacists, mathematician, chemists, biologists, epidemiologists [1] , [2] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] . This can be an effective approach to study, simulate and predict the mechanism and transmission of the disease.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of COVID-19 transmission dynamics between healthcare workers and community

Masandawa¹,

Mirau²,

Mbalawata³

2021

Results in Physics

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Corona-virus disease 2019 (COVID-19) is an infectious disease that has affected different groups of humankind such as farmers, soldiers, drivers, educators, students, healthcare workers and many others. The transmission rate of the disease varies from one group to another depending on the contact rate. Healthcare workers are at a high risk of contracting the disease due to the high contact rate with patients. So far, there exists no mathematical model which combines both public control measures (as a parameter) and healthcare workers (as an independent compartment). Combining these two in a given mathematical model is very important because healthcare workers are protected through effective use of personal protective equipment, and control measures help to minimize the spread of COVID-19 in the community. This paper presents a mathematical model named SWE HR; susceptible individuals (S), healthcare workers (W), exposed (E), symptomatic infectious ( ), asymptomatic infectious ( ), hospitalized (H), recovered (R). The value of basic reproduction number for all parameters in this study is 2.8540. In the absence of personal protective equipment and control measure in the public , the value of which implies the presence of the disease. When and were introduced in the model, basic reproduction number is reduced to 0.4606, indicating the absence of disease in the community. Numerical solutions are simulated by using Runge–Kutta fourth-order method. Sensitivity analysis is performed to presents the most significant parameter. Furthermore, identifiability of model parameters is done using the least square method. The results indicated that protection of healthcare workers can be achieved through effective use of personal protective equipment by healthcare workers and minimization of transmission of COVID-19 in the general public by the implementation of control measures. Generally, this paper emphasizes the importance of using protective measures.

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Mathematical analysis of COVID-19 via new mathematical model

Cited by 48 publications

References 41 publications

Spring 2020 COVID-19 community transmission behaviours around New York City medical facilities

Spring 2020 COVID-19 community transmission behaviours around New York City medical facilities

Time-dependent probability distribution for number of infection in a stochastic SIS model: case study COVID-19

Mathematical modeling of COVID-19 transmission dynamics between healthcare workers and community

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