Modelling the transmission dynamics of H9N2 avian influenza viruses in a live bird market

Abstract

H9N2 avian influenza viruses (AIVs) are a major concern for the poultry sector and human health in countries where this subtype is endemic. By fitting a model simulating H9N2 AIV transmission to data from a field experiment, we characterise the epidemiology of the virus in a live bird market in Bangladesh. Many supplied birds arrive already exposed to H9N2 AIVs, resulting in many broiler chickens entering the market as infected, and many indigenous backyard chickens entering with pre-existing immunity. Most susceptible chickens become infected within one day spent at the market, owing to high levels of viral transmission within market and short latent periods, as brief as 5.3 hours. Although H9N2 AIV transmission can be substantially reduced under moderate levels of cleaning and disinfection, effective risk mitigation also requires a range of additional interventions targeting markets and other nodes along the poultry production and distribution network.

Dynamic Modeling of Antibody Repertoire Reshaping in Response to Viral Infections

“…Here, dynamic data refers not to ELISA data at different time points but rather the variation in ELISA data within the same sample testing process, encompassing more data information, including the situation of antibody (Kon) values, while the final ELISA result can only reflect the equilibrium constant (Kd). This parameter testing group includes only two parameters, the mean and variance of ln( Kon), and through the use of MCMC [56][57][58] , we can accurately estimate their values. Once we obtain the numerical values of the first category of parameters, we can further estimate the second category of parameters based on ELISA results of patients during the virus infection process.…”