Forecasting COVID-19 pandemic: A data-driven analysis
Fecha
2020Autor
Nazmoon Nabi, Khondoker
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Resumen
In this paper, a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-QuarantinedHospitalized-Recovered-Dead (SEIDIUQHRD) deterministic compartmental model has been proposed and
calibrated for interpreting the transmission dynamics of the novel coronavirus disease (COVID-19). The
purpose of this study is to give tentative predictions of the epidemic peak for Russia, Brazil, India and
Bangladesh which could become the next COVID-19 hotspots in no time by using a newly developed algorithm based on well-known Trust-region-reflective (TRR) algorithm, which is one of the robust real-time
optimization techniques. Based on the publicly available epidemiological data from late January until 10
May, it has been estimated that the number of daily new symptomatic infectious cases for the above
mentioned countries could reach the peak around the middle of June with the peak size of ∼ 15, 774
(95% CI, 12,814–16,734) symptomatic infectious cases in Russia, ∼ 26, 449 (95% CI, 25,489–31,409) cases
in Brazil, ∼ 9, 504 (95% CI, 8,378–13,630) cases in India and ∼ 2, 209 (95% CI, 2,078–2,840) cases in
Bangladesh if current epidemic trends hold. As of May 11, 2020, incorporating the infectiousness capability of asymptomatic carriers, our analysis estimates the value of the basic reproductive number (R0) was
found to be ∼ 4.234 (95% CI, 3.764–4.7) in Russia, ∼ 5.347 (95% CI, 4.737–5.95) in Brazil, ∼ 5.218 (95%
CI, 4.56–5.81) in India, ∼ 4.649 (95% CI, 4.17–5.12) in the United Kingdom and ∼ 3.53 (95% CI, 3.12–3.94)
in Bangladesh. Moreover, Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) which
is a global sensitivity analysis (GSA) method has been applied to quantify the uncertainty of our model
mechanisms, which elucidates that for Russia, the recovery rate of undetected asymptomatic carriers,
the rate of getting home-quarantined or self-quarantined and the transition rate from quarantined class
to susceptible class are the most influential parameters, whereas the rate of getting home-quarantined
or self-quarantined and the inverse of the COVID-19 incubation period are highly sensitive parameters
in Brazil, India, Bangladesh and the United Kingdom which could significantly affect the transmission
dynamics of the novel coronavirus disease (COVID-19). Our analysis also suggests that relaxing social
distancing restrictions too quickly could exacerbate the epidemic outbreak in the above-mentioned countries.
Palabras clave
Compartmental model; COVID-19; Coronavirus; Asymptomatic carrier; Quarantined class; Model calibration; SensitivityEnlace al recurso
https://doi.org/10.1016/j.chaos.2020.110046Colecciones
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