COVID-19 Transmission: Bangladesh Perspective - Masud M A; Md Hamidul Islam; Khondaker A. Mamun; Byul Nim Kim; Sangil Kim (Mathematics, 2020)
The sudden emergence of the COVID-19 pandemic has tested the strength of the public health system of the most developed nations and created a “new normal”. Many nations are struggling to curb the epidemic in spite of expanding testing facilities. In this study, we consider the case of Bangladesh, and fit a simple compartmental model holding a feature to distinguish between identified infected and infectious with time series data using least square fitting as well as the likelihood approach; prior to which, dynamics of the model were analyzed mathematically and the identifiability of the parameters has also been confirmed. The performance of the likelihood approach was found to be more promising and was used for further analysis. We performed fitting for different lengths of time intervals starting from the beginning of the outbreak, and examined the evolution of the key parameters from Bangladesh’s perspective. In addition, we deduced profile likelihood and <semantics>95%</semantics> confidence interval for each of the estimated parameters. Our study demonstrates that the parameters defining the infectious and quarantine rates change with time as a consequence of the change in lock-down strategies and expansion of testing facilities. As a result, the value of the basic reproduction number <semantics>R0</semantics> was shown to be between <semantics>1.5</semantics> and 12. The analysis reveals that the projected time and amplitude of the peak vary following the change in infectious and quarantine rates obtained through different lock-down strategies and expansion of testing facilities. The identification rate determines whether the observed peak shows the true prevalence. We find that by restricting the spread through quick identification and quarantine, or by implementing lock-down to reduce overall contact rate, the peak could be delayed, and the amplitude of the peak could be reduced. Another novelty of this study is that the model presented here can infer the unidentified COVID cases besides estimating the officially confirmed COVID cases.