Lavigne2021 - Non-spatial model of viral infection dynamics and interferon response of well-mixed viral infection

ABSTRACT: This ordinary differential equation model is described in the following article: "Autocrine and paracrine interferon signalling as ‘ring vaccination’ and ‘contact tracing’ strategies to suppress virus infection in a host" G. Michael Lavigne, Hayley Russell, Barbara Sherry and Ruian Ke DOI: 10.1098/rspb.2020.3002 Comment: This model is based on the ordinary differential equations of the non-spatial model of well-mixed viral infection stated in the manuscript (Eq. 2.1 in the article). Abstract: The innate immune response, particularly the interferon response, represents a first line of defence against viral infections. The interferon molecules produced from infected cells act through autocrine and paracrine signalling to turn host cells into an antiviral state. Although the molecular mechanisms of IFN signalling have been well characterized, how the interferon response collectively contribute to the regulation of host cells to stop or suppress viral infection during early infection remain unclear. Here, we use mathematical models to delineate the roles of the autocrine and the paracrine signalling, and show that their impacts on viral spread are dependent on how infection proceeds. In particular, we found that when infection is well-mixed, the paracrine signalling is not as effective; by contrast, when infection spreads in a spatial manner, a likely scenario during initial infection in tissue, the paracrine signalling can impede the spread of infection by decreasing the number of susceptible cells close to the site of infection. Furthermore, we argue that the interferon response can be seen as a parallel to population-level epidemic prevention strategies such as ‘contact tracing’ or ‘ring vaccination’. Thus, our results here may have implications for the outbreak control at the population scale more broadly.

SUBMITTER: Emilia Chen  

PROVIDER: BIOMD0000001021 | BioModels | 2024-09-02

REPOSITORIES: BioModels