Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation

Wen, Conghua; Wei, Junwei; Feei, Zheng; He, Mu; Zhao, Shi; Ji, Jiayu; He, Daihai

doi:10.1016/j.idm.2022.02.001

Cited by 2 publications

(1 citation statement)

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“…Some efforts have been made in the literature to characterize the epidemic’s evolution, especially during the first stages of the pandemic, especially in China. In a particular example, Wen and colleagues [ 30 ] delve into heterogeneous epidemic modeling within enclosed spaces. Their Bayesian estimation approach offers nuanced insights into the progression of diseases in spatially confined settings.…”

Section: Introductionmentioning

confidence: 99%

Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models

Sciannameo,

Azzolina,

Lanera

et al. 2023

Healthcare

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The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.

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Section: Introductionmentioning

confidence: 99%