Migration rate estimation in an epidemic network

Núñez-López, M.; Ramos, L. Alarcon; Hernández, Juan González

doi:10.1016/j.apm.2020.08.025

Cited by 8 publications

(2 citation statements)

References 65 publications

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“…Thirteen studies (13/34, 38.2%) included spatial parameters within the model equations that aimed to account for connectivity [67,[258][259][260][261][262][263][264][265][266][267][268][269]. Unlike movement matrices, these were directly incorporated into the model equations to update the population within a given compartment, or as a proxy for another process.…”

Section: Spatial Parametersmentioning

confidence: 99%

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Lee

Jarvis

Edmunds

et al. 2021

J. R. Soc. Interface.

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Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

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Section: Spatial Parametersmentioning

confidence: 99%

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Lee

Jarvis

Edmunds

et al. 2021

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…New daily cases of COVID-19 follow a very highly volatile pattern, i.e, deceasing-increasing arbitrarily [ 22 , 23 ]. To implement fruitful public health measures in a scheduled time within a certain resource according to geographical state [ 24 ], it is utmost essential to study the diffusion or force of infection among the wider population. In order to explain this observation, entropy helps us to determine the heterogeneity of a daily number of cases and time of highest diffusion or force of infection [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of COVID-19 transmission with comorbidity: a data driven modelling based approach

et al. 2021

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An outbreak of the COVID-19 pandemic is a major public health disease as well as a challenging task to people with comorbidity worldwide. According to a report, comorbidity enhances the risk factors with complications of COVID-19. Here, we propose and explore a mathematical framework to study the transmission dynamics of COVID-19 with comorbidity. Within this framework, the model is calibrated by using new daily confirmed COVID-19 cases in India. The qualitative properties of the model and the stability of feasible equilibrium are studied. The model experiences the scenario of backward bifurcation by parameter regime accounting for progress in susceptibility to acquire infection by comorbidity individuals. The endemic equilibrium is asymptotically stable if recruitment of comorbidity becomes higher without acquiring the infection. Moreover, a larger backward bifurcation regime indicates the possibility of more infection in susceptible individuals. A dynamics in the mean fluctuation of the force of infection is investigated with different parameter regimes. A significant correlation is established between the force of infection and corresponding Shannon entropy under the same parameters, which provides evidence that infection reaches a significant proportion of the susceptible.

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Dynamical analysis of the SIS epidemic model in cluster events

Han

Wei

et al. 2021

Applied Mathematical Modelling

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Migration rate estimation in an epidemic network

Cited by 8 publications

References 65 publications

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Dynamics of COVID-19 transmission with comorbidity: a data driven modelling based approach

Dynamical analysis of the SIS epidemic model in cluster events

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