A mathematical model developed at the University of Rochester can predict the immune responses to infection with influenza A viruses, including novel viruses such as the emergent 2009 influenza A (H1N1).
The model examines the contributions of specific sets of immune cells in fighting influenza A virus.
Also, the model helps predict when antiviral therapy would be most effective during the immune response to viral infection.
The researchers conducted a study to improve preparedness for emerging and re-emerging pathogens.
At the time of virus infection, a network of immune cells becomes immediately engaged, taking up viral particles and presenting pieces of the virus-antigens-to specialized white blood cells, thereby initiating a virus-specific response.
The responding cells include T cells-which either directly attack and eliminate virus-infected cells or help other immune cells fight the virus-as well as B cells, which produce antibodies that bind and neutralize the virus.
The mathematical model generates immune response scenarios reflecting multiple variables, including the pathogenicity of the virus, numbers of responding B and T cells and function of antigen-presenting cells, in the lungs and lymph nodes.
According to the model, prolonged viral infection limits the production of T cells and inhibits antigen presentation to immune cells.
The mathematical model confirmed previous finding by predicting that antiviral therapy is most effective in reducing the spread of the virus when given within two days after infection.
The researchers tested the accuracy of their model in mice infected with influenza A virus.
Now, they are planning to apply the model to human populations and continue to improve the model as more data become available.