Mathematical Model of COVID-19 Pandemic with Double Dose Vaccination

Peter, Olumuyiwa James; Panigoro, Hasan S.; Abidemi, Afeez; Ojo, Mayowa M.; Oguntolu, Festus Abiodun

doi:10.1007/s10441-023-09460-y

Cited by 40 publications

(23 citation statements)

References 57 publications

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“…In addition to vaccine efficacy, vaccination policy must also account for factors such as vaccination program design and optimal dosing strategies. Recent research has underscored the value of modeling and simulation studies for exploring the potential impact of double doses [ 37 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…The studies of Paul et al [ 37 ] and Peter et al [ 38 ] have also shown that a full vaccination program can significantly reduce cases and impact disease eradication. Public health professionals can use this information to effectively control COVID-19 outbreaks in Bangladesh [ 37 ] and Malaysia [ 38 ]. Overall, mass vaccination exercises are crucial in achieving herd immunity and preventing further outbreaks of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

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Modeling the Spread of COVID-19 with the Control of Mixed Vaccine Types during the Pandemic in Thailand

et al. 2023

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COVID-19 is a respiratory disease that can spread rapidly. Controlling the spread through vaccination is one of the measures for activating immunization that helps to reduce the number of infected people. Different types of vaccines are effective in preventing and alleviating the symptoms of the disease in different ways. In this study, a mathematical model, SVIHR, was developed to assess the behavior of disease transmission in Thailand by considering the vaccine efficacy of different vaccine types and the vaccination rate. The equilibrium points were investigated and the basic reproduction number R0 was calculated using a next-generation matrix to determine the stability of the equilibrium. We found that the disease-free equilibrium point was asymptotically stable if, and only if, R0<1, and the endemic equilibrium was asymptotically stable if, and only if, R0>1. The simulation results and the estimation of the parameters applied to the actual data in Thailand are reported. The sensitivity of parameters related to the basic reproduction number was compared with estimates of the effectiveness of pandemic controls. The simulations of different vaccine efficacies for different vaccine types were compared and the average mixing of vaccine types was reported to assess the vaccination policies. Finally, the trade-off between the vaccine efficacy and the vaccination rate was investigated, resulting in the essentiality of vaccine efficacy to restrict the spread of COVID-19.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Modeling the Spread of COVID-19 with the Control of Mixed Vaccine Types during the Pandemic in Thailand

et al. 2023

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“…Global sensitivity analysis will be carried out, using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCCs) [94][95][96], on the WBE model {(2.2)(2.3)} to determine the parameters that have the most influence on a chosen response function. Various SARS-CoV-2 modeling studies have chosen the reproduction number of the models (a measure of the average number of new cases generated by a typical infectious individual over the duration of their infectiousness) as the response function [53,65,97,98]. In this study, we will use the cumulative viral load in wastewater (W ) as the response function.…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Mathematical assessment of wastewater-based epidemiology to predict SARS-CoV-2 cases and hospitalizations in Miami-Dade County

Pant,

Safdar,

Ngonghala

et al. 2024

Preprint

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This study presents a wastewater-based mathematical model for assessing the transmission dynamics of the SARS-CoV-2 pandemic in Miami-Dade County, Florida. The model, which takes the form of a deterministic system of nonlinear differential equations, monitors the temporal dynamics of the disease, as well as changes in viral RNA concentration in the county's wastewater system (which consists of three sewage treatment plants). The model was calibrated using the wastewater data during the third wave of the SARS-CoV-2 pandemic in Miami-Dade (specifically, the time period from July 3, 2021 to October 9, 2021). The calibrated model was used to predict SARS-CoV-2 case and hospitalization trends in the county during the aforementioned time period, showing a strong correlation (with a correlation coefficient r = 0.99) between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model's prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matches the time when the maximum observed/reported cases were recorded (which was August 14, 2021). Furthermore, the model's projection of the maximum number of cases for the week of August 14, 2021 is about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). This result is consistent with the result of numerous SARS-CoV-2 modeling studies (including other wastewater-based modeling, as well as statistical models) in the literature. Furthermore, the model accurately predicts a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function - the cumulative viral load in the wastewater. This analysis revealed that the transmission rate of infectious individuals, shedding rate of infectious individuals, recovery rate of infectious individuals, average fecal load per person per unit time and the proportion of shed viral RNA that is not lost in sewage before measurement at the wastewater treatment plant were most influential to the response function during the entire time period of the study. This study shows, conclusively, that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.

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“…To study the dynamics of COVID-19, mathematical models and optimal controls are required to predict and emphasize disease transmission, recovery, death, and other significant parameters individually for disparate countries, that is, the reported range of high too low for specific regions of the COVID-19 cases. Several modeling approaches have been used in numerous research studies [2][3][4][5][6][7]. Kamrujjaman et al (2022) [2] proposed a deterministic mathematical model for the COVID-19 outbreak and validated the model using real data from Italy from 15th Feb 2020 to 14th July 2020.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis and Optimal Control of Covid 19 Model

Firmansyah

Rangkuti

2023

Jambura J. Biomath.

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Coronavirus infection is a disease that causes death and threatens human life; for prevention, it is necessary to quarantine susceptible, exposed, and infected populations and vaccinate the entire population. This kind of quarantine and vaccination is intended to reduce the spread of coronavirus. Epidemiological models are a strategy used by public health practitioners to prevent and fight diseases. However, to be used in decision making, mathematical models must be carefully parameterized and validated using epidemiological and entomological data. Epidemiological models: susceptible, symptomatic, contagious, and recovering. In this study, sensitivity analysis and optimal control were performed to determine the relative importance of the model parameters and to minimize the number of infected populations and control measures against the spread of the disease. Sensitivity analysis was carried out using a sensitivity index to measure the relative change in the basic reproduction number for each parameter, and this control function was applied to the dynamic modeling of the spread of COVID-19 using the Pontryagin Minimum Principle. We will describe the formulation of a dynamic system for the spread of COVID-19 with optimal control and then use Pontryagin’s Minimum Principle to find optimal control solutions. In this article, COVID-19 cases in the USA and India serve as examples of the efficiency of control measures. The results obtained revealed that the parameters that became the basis for reducing the number of infected with COVID-19 for the two countries, the USA and India, are effective transmission rates from S to E, (β), transmission rates from E to I, (α), and transmission rates from S to R, (ps), which are the main parameters to watch for growth with respect to Basic Reproduction rates (R0). Finally, three controls were simulated in cases I (in the USA) and II (in India) in the interval t ∈ [0, 15]. For all controls, the effectiveness was close to 50% in India and 100% in the USA to reduce the spread of COVID 19. According to the findings, if these three controls were implemented ideally from the start of the pandemic, the number of sufferers.

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Mathematical Model of COVID-19 Pandemic with Double Dose Vaccination

Cited by 40 publications

References 57 publications

Modeling the Spread of COVID-19 with the Control of Mixed Vaccine Types during the Pandemic in Thailand

Modeling the Spread of COVID-19 with the Control of Mixed Vaccine Types during the Pandemic in Thailand

Mathematical assessment of wastewater-based epidemiology to predict SARS-CoV-2 cases and hospitalizations in Miami-Dade County

Sensitivity Analysis and Optimal Control of Covid 19 Model

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