Modelling effectiveness of COVID-19 pandemic control policies using an Area-based SEIR model with consideration of infection during interzonal travel

The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model

“…First, the model is based on the reality of intercity transportation networks and the theory of correlation between intercity population mobility and infectious diseases, whereas traditional infectious disease models only consider transmission within a single city or case output from an epidemic center. Examples include the SIR-M proposed by Muley et al ( Muley et al, 2021 ) traditional room models such as the SEIR developed by Liu et al (Jielun Liu et al, 2022 ) and the deep-learning models proposed by Chimmula and Zhang ( Chimmula & Zhang, 2020 ). These studies were mostly set in a closed space and only looked at the evolutionary pattern of the epidemic within the city, making it difficult to obtain information on intercity transmission.…”

“…The first group contains the classical infectious disease models based on SIR (Susceptible-Infected-Recovered) and SEIR (Susceptible-Exposed-Infected-Recovered) ( Kermack & McKendrick, 1939 ; Lekone & Finkenstädt, 2006 ; Jielun Liu et al, 2022 ; Shulgin et al, 1998 ). These models can be a good fit for urban epidemics, describing quantitatively the development and elimination of an epidemic through indicators such as peak, peak period and basic regeneration number ( He et al, 2020 ; Yang et al, 2020 ).…”

“…To determine the effects of different intervention measures on the spread of COVID-19, scholars have varied the SIR and SEIR models by introducing different populations. For example, Nguyen et al (2022) established the SIR-M model by adding the mobile population M to the SIR model, and used this to study the evolution of the epidemic in Melbourne, Australia, under different prevention and control measures; Liu et al (2022) then proposed the SEPIR model based on the SEIR model but considering a pre-symptomatic population P. They used their model to evaluate the effects of stay-at-home requirements and travel restrictions on the spread of cases in the Tokyo urban agglomeration; Chuan et al (2020) added the quarantine population Q to develop the SEQIR model, and used it to evaluate the effectiveness of quarantine measures in India at the time. Other models along similar lines include SEIQRD ( Ghostine et al, 2021 ), SEAHIR ( Leontitsis et al, 2021 ), MSEIR ( X. Chen, Zhang, Wang, Gallaher, & Zhu, 2021b ) and SEIRD ( Maugeri et al, 2020 ).…”

STG-Net: A COVID-19 prediction network based on multivariate spatio-temporal information

“…Singh et al [5] proposed a generalized SIR (GSIR) prediction framework that captures different waves of different times through GSIR pandemic data and captures the impact of government decisions through dynamic modeling of parameters. Liu et al [6] developed a region-based SEIR model that considers infections within spatio-temporal regions during commuting and quarantine, demonstrating the importance of spatio-temporal restriction policies such as social distance for epidemic prevention and control. Cooper et al [7] proposed a new model based on the SIR model, which is able to dynamically track changes in community infection.…”

Mathematical model of student learning behavior with the effect of learning motivation and student social interaction

“…The SEIR model is a widely used epidemiological model to predict the increase in infection (Sampath & Bose, 2022). The SEIR model is widely used to describe the dynamic process of epidemic spread with consideration of infection exposure during travel and quarantine (Liu, Ong, et al, 2022).…”