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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8615| FORECASTING COVID-19 PANDEMIC: A DATA-DRIVEN ANALYSIS | |
| Khondoker Nazmoon Nabi | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2020.05.12.20099192 | |
| https://www.medrxiv.org/content/10.1101/2020.05.12.20099192v2 | |
| In this paper, a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-Quarantined-Hospitalized-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 a tentative prediction of the epidemic peak for Russia, Brazil, India and Bangladesh which could become the next COVID-19 hotspots in no time by using a Trust-region-reflective (TRR) algorithm which one of the well-known real data fitting 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 beginning of June with the peak size of ~ 15, 774 (95% CI, 13,814-17,734) symptomatic infectious cases in Russia, ~ 26, 449 (95% CI, 23,489-29,409) cases in Brazil, ~ 9, 504 (95% CI, 8,378-10,630) cases in India and ~ 2, 209 (95% CI, 1,878-2,540) cases in Bangladesh. As of May 11, 2020, incorporating the infectiousness capability of asymptomatic carriers, our analysis estimates the value of the basic reproduction number (R0) as of May 11, 2020 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 is 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 trans-mission dynamics of the novel coronavirus. Our analysis also suggests that relaxing social distancing restrictions too quickly could exacerbate the epidemic outbreak in the above-mentioned countries. | |
| bioRxiv | |
| 26-05-2020 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| FORECASTING COVID-19 PANDEMIC A DATA-DRIVEN ANALYSIS.pdf | 3.93 MB | Adobe PDF | Visualizar/Abrir |