Progress and trends in mathematical modelling of influenza A virus infections

Beauchemin, 2011, A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead, BMC Publ Health, 11, S7, 10.1186/1471-2458-11-S1-S7 Smith, 2011, Influenza A virus infection kinetics: quantitative data and models, Wiley Interdiscip Rev Syst Biol Med, 3, 429, 10.1002/wsbm.129 Condit, 2007, Principles of virology, vol. 1, 25 Beyleveld, 2013, New-generation screening assays for the detection of anti-influenza compounds targeting viral and host functions, Antiviral Res, 100, 120, 10.1016/j.antiviral.2013.07.018 Holder, 2011, Exploring the effect of biological delays in kinetic models of influenza within a host or cell culture, BMC Publ Health, 11, 10.1186/1471-2458-11-S1-S10 Pinilla, 2012, The H275Y neuraminidase mutation of the pandemic A/H1N1 virus lengthens the eclipse phase and reduces viral output of infected cells, potentially compromising fitness in ferrets, J Virol, 86, 10651, 10.1128/JVI.07244-11 Simon, 2016, Avian influenza viruses that cause highly virulent infections in humans exhibit distinct replicative properties in contrast to human H1N1 viruses, Sci Rep, 6, 24154, 10.1038/srep24154 Paradis, 2015, Impact of the H275Y and I223V mutations in the neuraminidase of the 2009 pandemic influenza virus in vitro and evaluating experimental reproducibility, PLoS One, 10, 10.1371/journal.pone.0126115 Dobrovolny, 2013, Assessing mathematical models of influenza infections using features of the immune response, PLoS One, 8, 10.1371/journal.pone.0057088 Boianelli, 2015, Modeling influenza virus infection: a roadmap for influenza research, Viruses, 7, 5274, 10.3390/v7102875 Laurie, 2015, Interval between infections and viral hierarchy are determinants of viral interference following influenza virus infection in a ferret model, J Infect Dis, 212, 1701, 10.1093/infdis/jiv260 Cao, 2015, Innate immunity and the inter-exposure interval determine the dynamics of secondary influenza virus infection and explain observed viral hierarchies, PLoS Comput Biol, 11, 10.1371/journal.pcbi.1004334 Yan, 2017, Modelling cross-reactivity and memory in the cellular adaptive immune response to influenza infection in the host, J Theor Biol, 413, 34, 10.1016/j.jtbi.2016.11.008 Yan, 2017, Quantifying innate and adaptive immunity during influenza infection using sequential infection experiments and mathematical models, ArXiv e-prints Butler, 2014, Estimating the fitness advantage conferred by permissive mutations in recent oseltamivir-resistant A(H1N1)pdm09 influenza viruses, PLoS Pathog, 10, 10.1371/journal.ppat.1004065 Petrie, 2015, Quantifying relative within-host replication fitness in influenza virus competition experiments, J Theor Biol, 382, 259, 10.1016/j.jtbi.2015.07.003 Cao, 2016, On the role of CD8+ T cells in determining recovery time from influenza virus infection, Front Immunol, 7, 611, 10.3389/fimmu.2016.00611 Li, 2014, Modeling inoculum dose dependent patterns of acute virus infections, J Theor Biol, 347, 63, 10.1016/j.jtbi.2014.01.008 Ahmed, 2017, Mathematical analysis of a mouse experiment suggests little role for resource depletion in controlling influenza infection within host, ArXiv e-prints Petrie, 2013, Reducing uncertainty in within-host parameter estimates of influenza infection by measuring both infectious and total viral load, PLoS One, 8, 10.1371/annotation/3b815950-b0eb-4aac-9a83-e92f830f844b Brooke, 2014, Biological activities of ‘noninfectious’ influenza A virus particles, Future Virol, 9, 41, 10.2217/fvl.13.118 Brooke, 2017, Population diversity and collective interactions during influenza virus infection, J Virol, 91, 10.1128/JVI.01164-17 Frensing, 2013, Continuous influenza virus production in cell culture shows a periodic accumulation of defective interfering particles, PLoS One, 8, 10.1371/journal.pone.0072288 Liao, 2016, (In)validating experimentally derived knowledge about influenza A defective interfering particles, J R Soc Interface, 13, 20160412, 10.1098/rsif.2016.0412 Laske, 2016, Modeling the intracellular replication of influenza A virus in the presence of defective interfering RNAs, Virus Res, 213, 90, 10.1016/j.virusres.2015.11.016 Meng, 2017, Unexpected complexity in the interference activity of a cloned influenza defective interfering RNA, Virol J, 14, 138, 10.1186/s12985-017-0805-6 Xue, 2016, Propagation and characterization of influenza virus stocks that lack high levels of defective viral genomes and hemagglutinin mutations, Front Microbiol, 7, 326, 10.3389/fmicb.2016.00326 Frensing, 2015, Defective interfering viruses and their impact on vaccines and viral vectors, Biotechnol J, 10, 681, 10.1002/biot.201400429 Smith, 2016, A defective interfering influenza RNA inhibits infectious influenza virus replication in human respiratory tract cells: a potential new human antiviral, Viruses, 8, E237, 10.3390/v8080237 Dimmock, 2015, Cloned defective interfering influenza RNA and a possible pan-specific treatment of respiratory virus diseases, Viruses, 7, 3768, 10.3390/v7072796 Gould, 2017, Live attenuated influenza vaccine contains substantial and unexpected amounts of defective viral genomic RNA, Viruses, 9, E269, 10.3390/v9100269 Fonville, 2015, Influenza virus reassortment is enhanced by semi-infectious particles but can be suppressed by defective interfering particles, PLoS Pathog, 11, 10.1371/journal.ppat.1005204 Smith, 2014, Secondary bacterial infections in influenza virus infection pathogenesis, Curr Top Microbiol Immunol, 385, 327 Smith, 2013, Kinetics of coinfection with influenza A virus and Streptococcus pneumoniae, PLoS Pathog, 9, 10.1371/journal.ppat.1003238 Duvigneau, 2016, Hierarchical effects of pro-inflammatory cytokines on the post-influenza susceptibility to pneumococcal coinfection, Sci Rep, 6, 37045, 10.1038/srep37045 Shrestha, 2013, Time and dose-dependent risk of pneumococcal pneumonia following influenza: a model for within-host interaction between influenza and Streptococcus pneumoniae, J R Soc Interface, 10, 20130233, 10.1098/rsif.2013.0233 Smith, 2016, A critical, nonlinear threshold dictates bacterial invasion and initial kinetics during influenza, Sci Rep, 6, 38703, 10.1038/srep38703 Boianelli, 2016, Oseltamivir PK/PD modeling and simulation to evaluate treatment strategies against influenza-pneumococcus coinfection, Front Cell Infect Microbiol, 6, 60, 10.3389/fcimb.2016.00060 Smith, 2017, Quantifying the therapeutic requirements and potential for combination therapy to prevent bacterial coinfection during influenza, J Pharmacokinet Pharmacodyn, 44, 81, 10.1007/s10928-016-9494-9 Pinky, 2016, Coinfections of the respiratory tract: viral competition for resources, PLoS One, 11, 10.1371/journal.pone.0155589 Beggs, 2015, Determining drug efficacy parameters for mathematical models of influenza, J Biol Dyn, 9, 332 Cao, 2017, The mechanisms for within-host influenza virus control affect model-based assessment and prediction of antiviral treatment, Viruses, 9, E197, 10.3390/v9080197 Liao, 2017, Exploring virus release as a bottleneck for the spread of influenza A virus infection in vitro and the implications for antiviral therapy with neuraminidase inhibitors, PLoS One, 12, 10.1371/journal.pone.0183621 Perelson, 2012, Combination antiviral therapy for influenza: predictions from modeling of human infections, J Infect Dis, 205, 1642, 10.1093/infdis/jis265 Canini, 2014, Impact of different oseltamivir regimens on treating influenza A virus infection and resistance emergence: insights from a modelling study, PLoS Comput Biol, 10, 10.1371/journal.pcbi.1003568 Dobrovolny, 2017, Modelling the emergence of influenza drug resistance: the roles of surface proteins, the immune response and antiviral mechanisms, PLoS One, 12, 10.1371/journal.pone.0180582 Palmer, 2017, The in vivo efficacy of neuraminidase inhibitors cannot be determined from the decay rates of influenza viral titers observed in treated patients, Sci Rep, 7, 40210, 10.1038/srep40210 Deecke, 2018, Intermittent treatment of severe influenza, J Theor Biol, 442, 129, 10.1016/j.jtbi.2018.01.012 Handel, 2014, How sticky should a virus be? The impact of virus binding and release on transmission fitness using influenza as an example, J R Soc Interface, 11, 20131083, 10.1098/rsif.2013.1083 Handel, 2018, Exploring the impact of inoculum dose on host immunity and morbidity to inform model-based vaccine design, bioRxiv Murillo, 2013, Towards multiscale modeling of influenza infection, J Theor Biol, 332, 267, 10.1016/j.jtbi.2013.03.024 Handel, 2013, A multi-scale analysis of influenza A virus fitness trade-offs due to temperature-dependent virus persistence, PLoS Comput Biol, 9, 10.1371/journal.pcbi.1002989 Handel, 2014, Trade-offs between and within scales: environmental persistence and within-host fitness of avian influenza viruses, Proc Biol Sci, 281 Reperant, 2012, Linking influenza virus tissue tropism to population-level reproductive fitness, PLoS One, 7, 10.1371/journal.pone.0043115 Lukens, 2014, A large-scale immuno-epidemiological simulation of influenza A epidemics, BMC Publ Health, 14, 1019, 10.1186/1471-2458-14-1019 Canini, 2016, Heterogeneous shedding of influenza by human subjects and its implications for epidemiology and control, Sci Rep, 6, 38749, 10.1038/srep38749 Heldt, 2013, Multiscale modeling of influenza A virus infection supports the development of direct-acting antivirals, PLoS Comput Biol, 9, 10.1371/journal.pcbi.1003372 Begley, 2015, Reproducibility in science: improving the standard for basic and preclinical research, Circ Res, 116, 116, 10.1161/CIRCRESAHA.114.303819 Schwartz, 2015, Estimating epidemic parameters: application to H1N1 pandemic data, Math Biosci, 270, 198, 10.1016/j.mbs.2015.03.007 Ganusov, 2016, Strong inference in mathematical modeling: a method for robust science in the twenty-first century, Front Microbiol, 7, 1131, 10.3389/fmicb.2016.01131 Smith, 2018, Influenza virus infection model with density dependence supports biphasic viral decay, Front Microbiol, 9, 1554, 10.3389/fmicb.2018.01554