An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect

Poonia, Ramesh Chandra; Saudagar, Abdul Khader Jilani; AlTameem, Abdullah; Alkhathami, Mohammed; Khan, Muhammad Badruddin; Hasanat, Mozaherul Hoque Abul

doi:10.3390/life12050647

Cited by 15 publications

(16 citation statements)

References 33 publications

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“…Therefore, most COVID-19 epidemic models use the SEIR model [10]. According to the degree of infection and the possibility of transmission of infection, the SEIR model divides the population into four mutually exclusive sub-populations called compartments: susceptible compartment S, exposed compartment E, infected compartment I and removed compartment R [11]. The mathematical representation of SEIR model is shown in equations ( 1) to ( 4):…”

Section: Seir Modelmentioning

confidence: 99%

MaMiH: a new data assimilation framework based on macro-micro hierarchical simulation model

Ma,

Xie,

Chen

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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The widespread transmission of infectious diseases is a significant threat to human safety and development, and predicting epidemic trends is a pressing concern for scholars. Data assimilation methods can improve prediction accuracy by incorporating real observation data into system models. However, current data assimilation in epidemic models is limited to macro models, which fail to reflect individual heterogeneity realistically. Conversely, micro models have a high-dimensional parameter space, and the computational complexity of using data assimilation to adjust the state of each individual is high. To address this issue, this paper proposes a new data assimilation framework based on a macro-micro hierarchical simulation model called MaMiH. The MaMiH framework integrates the SEIR model as the macro model, reducing the computational complexity of the data assimilation process, and a complex network model as the micro model to simulate the infection status of each node within the network, thus reflecting individual heterogeneity. This paper verifies the effectiveness of the framework using the early stages of the COVID-19 pandemic as a research background.

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Section: Seir Modelmentioning

confidence: 99%

MaMiH: a new data assimilation framework based on macro-micro hierarchical simulation model

Ma,

Xie,

Chen

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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“…For instance, Ghostine et al [15] introduced an enhanced SEIQRDV model comprising susceptible, exposed, infected, quarantined, recovered, deceased, and vaccinated compartments. Poonia et al [16] presented an improved SEIRV model involving susceptible, infected, recovered, and vaccinated compartments.…”

Section: Figure 1 Sir Compartmentmentioning

confidence: 99%

“…This spread results from contaminated contact between the infected population and the healthy population, and the number of infected individuals increases as a function of the number of contaminated contacts between infected individuals and healthy individuals. This number is proportional to the size of the infected population and the size of the healthy population, and therefore to the product of these two numbers, I*S. Therefore, we can write [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]:…”

Section: Introductionmentioning

confidence: 99%

“…Symmetrically, the size of the susceptible population decreases. Therefore, it can be written [15][16][17][18][19]:…”

Section: Introductionmentioning

confidence: 99%

“…If an individual stays on average λ days, I/λ is the instantaneous value of the flow between the "infected" compartment and the "recovered" compartment, i.e., the measure of the flow of individuals who recover, leaving the "infected" compartment, towards the "recovered" compartment. This action governing infected individuals, which we can write as [15][16][17][18][19]:…”

Section: Introductionmentioning

confidence: 99%

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Simulating the COVID-19 Epidemic: A Numerical Examination of SIR, SIRID, and SIRVI Models

El Arabi,

Chafi,

Alami

2023

MMEP

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A comprehensive understanding of the propagation dynamics of COVID-19 is the paramount goal of this study. Two innovative mathematical models, namely the susceptible, infected, recovered, infected, dead (SIRID) model and the susceptible, infected, recovered, vaccinated, infected (SIRVI) model, are introduced. These models extend the conventional susceptible, infected, recovered (SIR) model by contemplating two pivotal factors: reinfections and the impact of vaccination. The SIRID model encapsulates the potential for a previously infected and recovered population to experience a secondary infection leading to death. The model forecasts crucial phases in this intricate progression: initial infection, recovery, reinfection, and subsequent fatality. Reinfections are underscored as a potentially significant driver of mortality in the SIRID model. The SIRVI model, however, integrates vaccination into the analysis, evaluating how immunization may modulate the virus's spread amid post-vaccination reinfections. In essence, the SIRVI model estimates the key stages: initial infection, recovery, vaccination, and reinfection notwithstanding immunization. This model underscores the potential for vaccination to mitigate the pandemic's severity, while also highlighting the ongoing challenges associated with reinfections. The methodologies employed to construct the SIR, SIRID, and SIRVI models stem from an adaptation of the classic SIR model to incorporate reinfections and vaccination. Each model was built using a comparable approach, albeit with additional compartments to capture the intricate interplay among various pandemic dynamics. The models' compartments (S, I, R, etc.) represent distinct population states based on disease status. The transitions between compartments illustrate the flux of individuals from one state to another. For the SIRID and SIRVI models, an innovative approach was adopted: every compartment accounts for incoming and outgoing fluxes as additions and subtractions, respectively. This allows infections, recoveries, reinfections, and deaths to be represented as dynamic variables, each with specific equations. The interactions between compartments were regulated according to inflows and outflows, capturing the complexity of viral spread, potential reinfections, and vaccination impact. Once the equations were formulated, numerical methods were employed to solve these differential equations. The model parameters were adjusted to align with real-world pandemic data, and iterations were conducted to observe various possible scenarios. This permitted detailed predictions about the pandemic's progression, considering potential reinfections and vaccination, thereby providing valuable insights for public health decision-making.

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