Twice evasions of Omicron variants explain the temporal patterns in six Asian and Oceanic countries

Abstract

Background The ongoing coronavirus 2019 (COVID-19) pandemic has emerged and caused multiple pandemic waves in the following six countries: India, Indonesia, Nepal, Malaysia, Bangladesh and Myanmar. Some of the countries have been much less studied in this devastating pandemic. This study aims to assess the impact of the Omicron variant in these six countries and estimate the infection fatality rate (IFR) and the reproduction number $${R}_{0}$$ R 0 in these six South Asia, Southeast Asia and Oceania countries. Methods We propose a Susceptible-Vaccinated-Exposed-Infectious-Hospitalized-Death-Recovered model with a time-varying transmission rate $$\beta (t)$$ β ( t ) to fit the multiple waves of the COVID-19 pandemic and to estimate the IFR and $${R}_{0}(t)$$ R 0 ( t ) in the aforementioned six countries. The level of immune evasion and the intrinsic transmissibility advantage of the Omicron variant are also considered in this model. Results We fit our model to the reported deaths well. We estimate the IFR (in the range of 0.016 to 0.136%) and the reproduction number $${R}_{0}(t)$$ R 0 ( t ) (in the range of 0 to 9) in the six countries. Multiple pandemic waves in each country were observed in our simulation results. Conclusions The invasion of the Omicron variant caused the new pandemic waves in the six countries. The higher $${R}_{0}(t)$$ R 0 ( t ) suggests the intrinsic transmissibility advantage of the Omicron variant. Our model simulation forecast implies that the Omicron pandemic wave may be mitigated due to the increasing immunized population and vaccine coverage.

Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency

“…One of the problems is the difficulty of an accurate prediction of the numbers of reported and unreported cases for the COVID-19 pandemic, and similar pandemics for different age classes [146] . Second, the change of Reproduction number in the presence of rapid evolutionary changes of viral agents in variants and sub-variants should be considered to improve the accurateness of epidemiologic modelling of prediction [87] , [147] , [148] . Finally, a lot of confounding and situational factors should be considered for designing accurate measures of preparedness and prediction for future pandemics.…”

Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia

“…The potential impact of immune imprinting also motivates further studies to understand the effects and limitations of boosters ( 117 ). Reduced effectiveness of vaccines against Omicron strains might also shift initial targets for vaccination coverage in a population upwards, obstructing the relaxation of non-pharmacological interventions ( 118 ). Ongoing viral transmission from viral shedding from convalescent individuals, in addition to cessation of mask usage, may contribute to future surges of the disease.…”

The optimisation of public health emergency governance: a simulation study based on COVID-19 pandemic control policy

“…Estimates for during the COVID-19 pandemic in Wuhan, China, range from 3.11 to 6.47 [ 37 – 39 ], or 1.4 to 6.47 [ 40 ]. With the virus mutations, some scholars have estimated the transmission characteristics of the Omicron variant in five countries including India, Indonesia, Malaysia, Bangladesh, and Myanmar, and found that the range of reproduction number is between 0 and 9 [ 41 ]. The range of basic reproduction number for each series of viruses is shown in Table 1 .…”

“…Some scholars have estimated IFR, finding that the IFR of the early virus is approximately between 0.5% and 1% [ 45 , 46 ]. The infection fatality rate of the Omicron variant in India, Indonesia, Malaysia, Bangladesh, and Myanmar is estimated to be between 0.016% and 0.136% [ 41 ]. The range of IFR is detailed in Table 1 .…”