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Tedim, S., Afreixo, V., Felgueiras, M., Leitão, R. P., Pinheiro, S. J. & Silva, C. J.  (2024). Evaluating COVID-19 in Portugal: Bootstrap confidence interval. AIMS Mathematics. 9 (2), 2756-2765

T. Sofia et al.,  "Evaluating COVID-19 in Portugal: Bootstrap confidence interval", in AIMS Mathematics, vol. 9, no. 2, pp. 2756-2765, 2024

@article{sofia2024_1734632558395,
	author = "Tedim, S. and Afreixo, V. and Felgueiras, M. and Leitão, R. P. and Pinheiro, S. J. and Silva, C. J. ",
	title = "Evaluating COVID-19 in Portugal: Bootstrap confidence interval",
	journal = "AIMS Mathematics",
	year = "2024",
	volume = "9",
	number = "2",
	doi = "10.3934/math.2024136",
	pages = "2756-2765",
	url = "https://www.aimspress.com/article/doi/10.3934/math.2024136"
}

TY  - JOUR
TI  - Evaluating COVID-19 in Portugal: Bootstrap confidence interval
T2  - AIMS Mathematics
VL  - 9
IS  - 2
AU  - Tedim, S.
AU  - Afreixo, V.
AU  - Felgueiras, M.
AU  - Leitão, R. P.
AU  - Pinheiro, S. J.
AU  - Silva, C. J. 
PY  - 2024
SP  - 2756-2765
SN  - 2473-6988
DO  - 10.3934/math.2024136
UR  - https://www.aimspress.com/article/doi/10.3934/math.2024136
AB  - In this paper, we consider a compartmental model to fit the real data of confirmed active cases with COVID-19 in Portugal, from March 2, 2020 until September 10, 2021 in the Primary Care Cluster in Aveiro region, ACES BV, reported to the Public Health Unit. The model includes a deterministic component based on ordinary differential equations and a stochastic component based on bootstrap methods in regression. The main goal of this work is to take into account the variability underlying the data set and analyse the estimation accuracy of the model using a residual bootstrapped approach in order to compute confidence intervals for the prediction of COVID-19 confirmed active cases. All numerical simulations are performed in R environment ( version. 4.0.5). The proposed algorithm can be used, after a suitable adaptation, in other communicable diseases and outbreaks. 
ER  -