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 had been much less studied in this devastating pandemic. This study aims to assess the impact of Omicron variant in these six countries and estimate the infection fatality rate (IFR) and the transmission rate β(t) in the unit of basic reproduction number R 0 (t) in these six South Asia, Southeast Asia and Oceania countries.Method: We propose a Susceptible-Vaccinated-Exposed-Infectious-Hospitalized-Death-Recovered model with a time-varying transmission rate to fit the multiple waves of COVID-19 pandemic in the aforementioned six countries. The level of immune evasion and the intrinsic transmissibility advantage of Omicron variant is also estimated in this model.Results: We fit our model to the reported cases well. We estimate the IFR (in the range of 0.016% to 0.136%) and the transmission rate β(t) in the unit of basic reproduction number R 0 (t) (in the range of 0 to 9) in the six countries. The multiple pandemic waves in each country are observed in our simulation results.Conclusions: The invasion of Omicron variant caused the new pandemic waves in the six countries. The higher R 0 (t) suggests the intrinsic transmissibility advantage of Omicron variant. Our model simulation forecast implies that the Omicron pandemic wave may be mitigated due to the increasing immunized population and the vaccine coverage.

Influenza is an infectious disease with obvious periodic changes over time. It is of great practical significance to explore the non-environment-related factors that cause this regularity for influenza control and individual protection. In this paper, based on the randomness of population number and the heterogeneity of population contact, we have established a stochastic infectious disease model about influenza based on the degree of the network, and obtained the power spectral density function by using the van Kampen expansion method of the master equation. The relevant parameters are obtained by fitting the influenza data of sentinel hospitals. The results of the numerical analysis show that: (1) for the infected, the infection period of patients who go to the sentinel hospitals is particularly different from the others who do not; (2) for all the infected, there is an obvious nonlinear relationship between their infection period and the visiting rate of the influenza sentinel hospitals, the infection rate and the degree. Among them, only the infection period of patients who do not go to the sentinel hospitals decreased monotonously with the infection rate (increased monotonously with the visiting rate), while the rest had a non-monotonic relationship.