Forecasting COVID-19 Pandemic: A Data-Driven Analysis

Abstract

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 coeffcient (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 signicantly affect the transmission 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.

Forecasting of COVID-19 pandemic: From integer derivatives to fractional derivatives

“…4 . The epidemic threshold, is estimated about ∼ 3.41 as of July 08, which is consistent with the prior studies of COVID-19 [26] . This crucial epidemiological parameter could surge in coming days due to scant diagnostics and ineffective non-pharmaceutical interventions.…”

“… Migration is not considered. Indeed, concerning migration, the initial time of our model is taken when the disease is already inside the region or country and that international migrations are prohibited [26] . Vital dynamic (birth and natural deaths) is ignored.…”

“…The model (2) calibration has been performed using a newly developed optimization algorithm based on trust-region-reflective (TRR) algorithm, which can be regarded as an evolution of Levenberg-Marquardt algorithm [26] . This robust optimization procedure can be used effectively for solving nonlinear least-squares problems.…”

“…Different types of mathematical models have been playing notable role in predicting the transmission dynamics of infectious diseases and different effective control measures can be designed to limit the community transmission. Several models have already been proposed to predict the evolution of COVID-19 and to study the impact of control measures imposed by different governments [1] , [26] . Although these models have similarities, each of them presents specifications related to the evolution of COVID-19 and various control measures deployed by several respective governments.…”

“…However, confined or quarantined class and isolated class have not been considered in this study. In a recent study, Nabi [26] has proposed a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-Quarantined-Hospitalized-Recovered-Dead ( SEI D I U QHRD ) compartmental mathematical model and calibrated model parameters to project the future dynamics of COVID-19 for various COVID-19 hotspots. Although he considered a quarantine compartment, he did not consider the possibility of those quarantined people to contract the virus.…”