tetano
Editor, Senior Moderator
Viruses. 2015 Oct 12;7(10):5274-304. doi: 10.3390/v7102875.
[h=1]Modeling Influenza Virus Infection: A Roadmap for Influenza Research.[/h] Boianelli A[SUP]1[/SUP], Nguyen VK[SUP]2[/SUP], Ebensen T[SUP]3[/SUP], Schulze K[SUP]4[/SUP], Wilk E[SUP]5[/SUP], Sharma N[SUP]6[/SUP], Stegemann-Koniszewski S[SUP]7[/SUP], Bruder D[SUP]8,[/SUP][SUP]9[/SUP], Toapanta FR[SUP]10[/SUP], Guzm?n CA[SUP]11[/SUP], Meyer-Hermann M[SUP]12,[/SUP][SUP]13[/SUP], Hernandez-Vargas EA[SUP]14[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Influenza A virus (IAV) infection represents a global threat causing seasonal outbreaks and pandemics. Additionally, secondary bacterial infections, caused mainly by Streptococcus pneumoniae, are one of the main complications and responsible for the enhanced morbidity and mortality associated with IAV infections. In spite of the significant advances in our knowledge of IAV infections, holistic comprehension of the interplay between IAV and the host immune response (IR) remains largely fragmented. During the last decade, mathematical modeling has been instrumental to explain and quantify IAV dynamics. In this paper, we review not only the state of the art of mathematical models of IAV infection but also the methodologies exploited for parameter estimation. We focus on the adaptive IR control of IAV infection and the possible mechanisms that could promote a secondary bacterial coinfection. To exemplify IAV dynamics and identifiability issues, a mathematical model to explain the interactions between adaptive IR and IAV infection is considered. Furthermore, in this paper we propose a roadmap for future influenza research. The development of a mathematical modeling framework with a secondary bacterial coinfection, immunosenescence, host genetic factors and responsiveness to vaccination will be pivotal to advance IAV infection understanding and treatment optimization.
[h=4]KEYWORDS:[/h] aging; coinfection; host genetic factors; influenza; mathematical models; parameters estimation; vaccinology
PMID: 26473911 [PubMed - in process] Free full text
[h=1]Modeling Influenza Virus Infection: A Roadmap for Influenza Research.[/h] Boianelli A[SUP]1[/SUP], Nguyen VK[SUP]2[/SUP], Ebensen T[SUP]3[/SUP], Schulze K[SUP]4[/SUP], Wilk E[SUP]5[/SUP], Sharma N[SUP]6[/SUP], Stegemann-Koniszewski S[SUP]7[/SUP], Bruder D[SUP]8,[/SUP][SUP]9[/SUP], Toapanta FR[SUP]10[/SUP], Guzm?n CA[SUP]11[/SUP], Meyer-Hermann M[SUP]12,[/SUP][SUP]13[/SUP], Hernandez-Vargas EA[SUP]14[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Influenza A virus (IAV) infection represents a global threat causing seasonal outbreaks and pandemics. Additionally, secondary bacterial infections, caused mainly by Streptococcus pneumoniae, are one of the main complications and responsible for the enhanced morbidity and mortality associated with IAV infections. In spite of the significant advances in our knowledge of IAV infections, holistic comprehension of the interplay between IAV and the host immune response (IR) remains largely fragmented. During the last decade, mathematical modeling has been instrumental to explain and quantify IAV dynamics. In this paper, we review not only the state of the art of mathematical models of IAV infection but also the methodologies exploited for parameter estimation. We focus on the adaptive IR control of IAV infection and the possible mechanisms that could promote a secondary bacterial coinfection. To exemplify IAV dynamics and identifiability issues, a mathematical model to explain the interactions between adaptive IR and IAV infection is considered. Furthermore, in this paper we propose a roadmap for future influenza research. The development of a mathematical modeling framework with a secondary bacterial coinfection, immunosenescence, host genetic factors and responsiveness to vaccination will be pivotal to advance IAV infection understanding and treatment optimization.
[h=4]KEYWORDS:[/h] aging; coinfection; host genetic factors; influenza; mathematical models; parameters estimation; vaccinology
PMID: 26473911 [PubMed - in process] Free full text