Mathematical modeling for COVID-19 transmission dynamics: A case study in Ethiopia

Kifle, Zenebe Shiferaw; Obsu, Legesse Lemecha

doi:10.1016/j.rinp.2022.105191

“…For this reason, during the SARS-CoV-2 pandemic, many researchers have proposed numerous mathematical models based on various correlations. The mathematical models took into account, inter alia , the method of transmission, the impact of quarantine and isolation, pharmaceutical and non-pharmaceutical interventions ( 50 , 51 ). Most of the models developed during the SARS-CoV-2 pandemic were based on the Susceptible-infected-recovered (SIR) model and the Susceptible-Exposed-Infected-Removed (SEIR) model.…”

Section: Discussionmentioning

confidence: 99%

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Matysek

¹

,

Studnicka

²

,

Smith

³

et al. 2022

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BackgroundSince the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.MethodsBiochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman’s rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.ResultsThe parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.ConclusionThe current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

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“…Sensitivity analysis helps to identify the most influential parameters on the basic reproduction number so efforts to control the problem are directed to these parameters. Mathematically, we compute the sensitivity analysis of our model system of equations based on the classical definition [ 14 , 15 ] defined as the normalized forward sensitivity index of a variable R 0 , which depends differentiably on a parameter p , given by △ p R 0 = p / R 0 × ∂R 0 / ∂p .…”

Section: Model Analysismentioning

confidence: 99%

“…The outbreak of COVID-19 has spread to 222 countries and territories, inflicted more than 116.17 million confirmed cases, and claimed more than 2.58 million lives, as reported on March 7th, 2021 [ 12 ]. Globally, over 3.1 million new cases and just over 54,000 new deaths were reported during the week of 27 September to 3 October 2021 and more than 197 million new cases, and among these, more than 4 million individuals have died up to the end of July 2021 [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…Mathematical model is a mathematical equation that describes changes in the system with time, and it is useful to accurately predict the evolution of infectious disease (COVID-19), and this in turn helps to give an insight for health workers and government to deal on the most influential parameters in COVID-19 transmission [ 15 ]. Since COVID-19 outbreak, different scholars studied the transmission dynamics of the disease (COVID-19) by considering different scenarios [ 1 , 2 , 5 , 9 – 11 , 14 – 21 ] to curb its spread with the help of epidemiological mathematical model. However, all the above studies failed to consider the impact of awareness campaign and influx of COVID-19 asymptomatic human immigrants in the control of COVID-19 transmission.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Model and Analysis on the Impact of Awareness Campaign and Asymptomatic Human Immigrants in the Transmission of COVID-19

Anteneh

¹

,

Bazezew

²

,

Palanisamy

³

2022

BioMed Research International

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In this study, an autonomous type deterministic nonlinear mathematical model that explains the transmission dynamics of COVID-19 is proposed and analyzed by considering awareness campaign between humans and infectives of COVID-19 asymptomatic human immigrants. Unlike some of other previous model studies about this disease, we have taken into account the impact of awareness c between humans and infectives of COVID-19 asymptomatic human immigrants on COVID-19 transmission. The existence and uniqueness of model solutions are proved using the fundamental existence and uniqueness theorem. We also showed positivity and the invariant region of the model system with initial conditions in a certain meaningful set. The model exhibits two equilibria: disease (COVID-19) free and COVID-19 persistent equilibrium points and also the basic reproduction number, R 0 which is derived via the help of next generation approach. Our analytical analysis showed that disease-free equilibrium point is obtained only in the absence of asymptomatic COVID-19 human immigrants and disease (COVID-19) in the population. Moreover, local stability of disease-free equilibrium point is verified via the help of Jacobian and Hurwitz criteria, and the global stability is verified using Castillo-Chavez and Song approach. The disease-free equilibrium point is both locally and globally asymptotically stable whenever R 0 < 1 , so that disease dies out in the population. If R 0 > 1 , then disease-free equilibrium point is unstable while the endemic equilibrium point exists and stable, which implies the disease persist and reinvasion will occur within a population. Furthermore, sensitivity analysis of the basic reproduction number, R 0 with respect to model parameters, is computed to identify the most influential parameters in transmission as well as in the control of COVID-19. Finally, some numerical simulations are illustrated to verify the theoretical results of the model.

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“…Infectious diseases can exhibit complex nonlinear dynamics, and it is possible to examine, explain, and predict the transmission dynamics of infectious diseases using mathematical models [5][6][7][8][9][10][11]. An optimal control problem entails the identification of a feasible scheme, policy, program, strategy, or campaign to achieve the optimal possible outcome of a system [12].…”

Section: Introductionmentioning

confidence: 99%

Tuberculosis in Ethiopia: Optimal Intervention Strategies and Cost-Effectiveness Analysis

Mengistu

¹

,

Witbooi

²

2022

Axioms

8

0

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This paper searches for optimal strategies for the minimization of the number of high-risk latent and active tuberculosis (TB) infectious individuals using real data from Ethiopia. Optimal control theory is harnessed for investigation and analysis of the optimal combination of interventions for controlling the transmission of TB using distancing, case finding, and case holding as controls. We calculate and compare the incremental cost-effectiveness ratio (ICER) for each of the strategies to determine the most effective combination of interventions for curbing the spread of the disease. Our findings suggest that, for optimal cost-effective management of the TB disease, the government of Ethiopia must focus more on prevention strategies such as isolation of infectious people, early TB patient detection, treatment, and educational programs. The optimal strategy is quantified through simulation.

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Mathematical modeling for COVID-19 transmission dynamics: A case study in Ethiopia

Cited by 48 publications

References 28 publications

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Mathematical Model and Analysis on the Impact of Awareness Campaign and Asymptomatic Human Immigrants in the Transmission of COVID-19

Tuberculosis in Ethiopia: Optimal Intervention Strategies and Cost-Effectiveness Analysis

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