@article{antia96,
  title = {Models of the Within-Host Dynamics of Persistent Mycobacterial Infections.},
  author = {Antia, R and Koella, J C and Perrot, V},
  year = {1996},
  month = mar,
  journal = {Proceedings. Biological sciences},
  volume = {263},
  number = {1368},
  pages = {257--263},
  issn = {0962-8452},
  doi = {10.1098/rspb.1996.0040},
  abstract = {We use mathematical models to investigate the within-host dynamics of mycobacterial infections. In particular, we investigate the mechanisms by which bacteria such as Mycobacterium tuberculosis and Mycobacterium leprae persist at low densities for extended periods, and attain high densities much later. We suggest that the persistence of bacteria in face of immune pressure may result from the bacteria having a very slow growth rate, or having a dormant stage. We show that whereas these mechanisms may lead to long-term persistence, this will be obtained at relatively low densities. We then suggest that the long-term persistence of bacteria may result in the loss of immunity because of the deletion of specific T-cells arriving from the thymus, and the exhaustion of the specific T-cells as these cells reach the Hayflick limit and die. This loss of immunity will allow the bacteria to attain a high density. We propose experiments capable of testing our models and discuss the implications of the models for the treatment of infected hosts.},
  citation-subset = {IM},
  completed = {1997-01-07},
  issn-linking = {0962-8452},
  nationality = {England},
  nlm-id = {101245157},
  owner = {NLM},
  pmid = {8920248},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2016-10-18},
  keywords = {Biological,drug therapy,etiology,growth \& development,Humans,immunology,Leprosy,Mathematics,microbiology,Models,Mycobacterium Infections,Mycobacterium leprae,Mycobacterium tuberculosis,Time Factors,Tuberculosis},
  timestamp = {2018.05.15}
}

@article{baccam06,
  title = {Kinetics of Influenza {{A}} Virus Infection in Humans.},
  author = {Baccam, Prasith and Beauchemin, Catherine and Macken, Catherine A and Hayden, Frederick G and Perelson, Alan S},
  year = {2006},
  month = aug,
  journal = {Journal of virology},
  volume = {80},
  number = {15},
  pages = {7590--7599},
  issn = {0022-538X},
  doi = {10.1128/JVI.01623-05},
  abstract = {Currently, little is known about the viral kinetics of influenza A during infection within an individual. We utilize a series of mathematical models of increasing complexity, which incorporate target cell limitation and the innate interferon response, to examine influenza A virus kinetics in the upper respiratory tracts of experimentally infected adults. The models were fit to data from an experimental H1N1 influenza A/Hong Kong/123/77 infection and suggest that it is important to include the eclipse phase of the viral life cycle in viral dynamic models. Doing so, we estimate that after a delay of approximately 6 h, infected cells begin producing influenza virus and continue to do so for approximately 5 h. The average lifetime of infected cells is approximately 11 h, and the half-life of free infectious virus is approximately 3 h. We calculated the basic reproductive number, R(0), which indicated that a single infected cell could produce approximately 22 new productive infections. This suggests that antiviral treatments have a large hurdle to overcome in moderating symptoms and limiting infectiousness and that treatment has to be initiated as early as possible. For about 50\% of patients, the curve of viral titer versus time has two peaks. This bimodal behavior can be explained by incorporating the antiviral effects of interferon into the model. Our model also compared well to an additional data set on viral titer after experimental infection and treatment with the neuraminidase inhibitor zanamivir, which suggests that such models may prove useful in estimating the efficacies of different antiviral therapies for influenza A infection.},
  chemicals = {Antiviral Agents, Guanidines, Pyrans, Sialic Acids, Interferons, Zanamivir},
  citation-subset = {IM},
  completed = {2006-08-18},
  issn-linking = {0022-538X},
  nationality = {United States},
  nlm-id = {0113724},
  owner = {NLM},
  pii = {80/15/7590},
  pmc = {PMC1563736},
  pmid = {16840338},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2016-12-03},
  keywords = {Administration,Adult,Antiviral Agents,drug therapy,Guanidines,H1N1 Subtype,Human,Humans,Influenza,Influenza A Virus,Interferons,Intranasal,isolation \& purification,Kinetics,Models,Nose,pharmacology,physiology,Pyrans,Sialic Acids,Theoretical,therapeutic use,virology,Zanamivir},
  timestamp = {2018.04.09}
}

@article{beauchemin08,
  title = {Modeling Amantadine Treatment of Influenza {{A}} Virus in Vitro.},
  author = {Beauchemin, Catherine A A and McSharry, James J and Drusano, George L and Nguyen, Jack T and Went, Gregory T and Ribeiro, Ruy M and Perelson, Alan S},
  year = {2008},
  month = sep,
  journal = {Journal of theoretical biology},
  volume = {254},
  number = {2},
  pages = {439--451},
  issn = {1095-8541},
  doi = {10.1016/j.jtbi.2008.05.031},
  abstract = {We analyzed the dynamics of an influenza A/Albany/1/98 (H3N2) viral infection, using a set of mathematical models highlighting the differences between in vivo and in vitro infection. For example, we found that including virion loss due to cell entry was critical for the in vitro model but not for the in vivo model. Experiments were performed on influenza virus-infected MDCK cells in vitro inside a hollow-fiber (HF) system, which was used to continuously deliver the drug amantadine. The HF system captures the dynamics of an influenza infection, and is a controlled environment for producing experimental data which lend themselves well to mathematical modeling. The parameter estimates obtained from fitting our mathematical models to the HF experimental data are consistent with those obtained earlier for a primary infection in a human model. We found that influenza A/Albany/1/98 (H3N2) virions under normal experimental conditions at 37 degrees C rapidly lose infectivity with a half-life of approximately 6.6+/-0.2 h, and that the lifespan of productively infected MDCK cells is approximately 13 h. Finally, using our models we estimated that the maximum efficacy of amantadine in blocking viral infection is approximately 74\%, and showed that this low maximum efficacy is likely due to the rapid development of drug resistance.},
  chemicals = {Amantadine},
  citation-subset = {IM},
  completed = {2008-12-05},
  issn-linking = {0022-5193},
  mid = {NIHMS70313},
  nationality = {England},
  nlm-id = {0376342},
  owner = {NLM},
  pii = {S0022-5193(08)00279-8},
  pmc = {PMC2663526},
  pmid = {18653201},
  pubmodel = {Print-Electronic},
  pubstatus = {ppublish},
  revised = {2016-11-22},
  keywords = {Amantadine,Animals,Biological,Cell Death,Cell Line,Dogs,Drug Resistance,drug therapy,H3N2 Subtype,H5N1 Subtype,Influenza A virus,Influenza A Virus,instrumentation,Kidney,methods,Models,Orthomyxoviridae Infections,therapeutic use,Time Factors,Viral,Virion,virology,Virology,Virulence},
  timestamp = {2018.04.09}
}

@article{beauchemin11,
  title = {A Review of Mathematical Models of Influenza {{A}} Infections within a Host or Cell Culture: Lessons Learned and Challenges Ahead},
  author = {Beauchemin, Catherine AA and Handel, Andreas},
  year = {2011},
  journal = {BMC public health},
  volume = {11},
  number = {1},
  pages = {S7},
  publisher = {{BioMed Central}},
  owner = {andreas},
  timestamp = {2018.04.09}
}

@book{bolker08,
  title = {Ecological Models and Data in r},
  author = {Bolker, Benjamin M.},
  year = {2008},
  month = jul,
  pages = {--},
  publisher = {{Princeton University Press}},
  issn = {0691125228},
  owner = {andreas},
  refid = {6225},
  timestamp = {2012.09.17}
}

@article{canini14a,
  title = {Impact of Different Oseltamivir Regimens on Treating Influenza {{A}} Virus Infection and Resistance Emergence: Insights from a Modelling Study.},
  author = {Canini, Laetitia and Conway, Jessica M and Perelson, Alan S and Carrat, Fabrice},
  year = {2014},
  month = apr,
  journal = {PLoS computational biology},
  volume = {10},
  number = {4},
  pages = {e1003568},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1003568},
  abstract = {Several studies have proven oseltamivir to be efficient in reducing influenza viral titer and symptom intensity. However, the usefulness of oseltamivir can be compromised by the emergence and spread of drug-resistant virus. The selective pressure exerted by different oseltamivir therapy regimens have received little attention. Combining models of drug pharmacokinetics, pharmacodynamics, viral kinetics and symptom dynamics, we explored the efficacy of oseltamivir in reducing both symptoms (symptom efficacy) and viral load (virological efficacy). We simulated samples of 1000 subjects using previously estimated between-subject variability in viral and symptom dynamic parameters to describe the observed heterogeneity in a patient population. We simulated random mutations conferring resistance to oseltamivir. We explored the effect of therapy initiation time, dose, intake frequency and therapy duration on influenza infection, illness dynamics, and emergence of viral resistance. Symptom and virological efficacies were strongly associated with therapy initiation time. The proportion of subjects shedding resistant virus was 27-fold higher when prophylaxis was initiated during the incubation period compared with no treatment. It fell to below 1\% when treatment was initiated after symptom onset for twice-a-day intakes. Lower doses and prophylaxis regimens led to lower efficacies and increased risk of resistance emergence. We conclude that prophylaxis initiated during the incubation period is the main factor leading to resistance emergence.},
  chemicals = {Antiviral Agents, Oseltamivir},
  citation-subset = {IM},
  completed = {2014-12-08},
  issn-linking = {1553-734X},
  nationality = {United States},
  nlm-id = {101238922},
  owner = {NLM},
  pii = {PCOMPBIOL-D-13-01641},
  pmc = {PMC3990489},
  pmid = {24743564},
  pubmodel = {Electronic-eCollection},
  pubstatus = {epublish},
  revised = {2016-10-19},
  keywords = {Antiviral Agents,drug effects,Drug Resistance,drug therapy,Human,Humans,Influenza,Influenza A virus,isolation \& purification,Models,Oseltamivir,pharmacology,Theoretical,therapeutic use,Viral,virology},
  timestamp = {2018.06.20}
}

@article{chatterjee12,
  title = {Mathematical Modelling of {{HCV}} Infection: What Can It Teach Us in the Era of Direct-Acting Antiviral Agents?},
  author = {Chatterjee, Anushree and Guedj, Jeremie and Perelson, Alan S},
  year = {2012},
  journal = {Antiviral therapy},
  volume = {17},
  number = {6 Pt B},
  pages = {1171--1182},
  issn = {2040-2058},
  doi = {10.3851/IMP2428},
  abstract = {HCV infection is a major cause of chronic liver disease and affects nearly 170 million people worldwide. Whereas the previous standard of care with pegylated interferon and ribavirin had a modest effectiveness, the recent approval of two highly potent protease inhibitors and the ongoing development of dozens of direct-acting antiviral agents (DAAs) constitute a major milestone for HCV therapy. Mathematical modelling of viral kinetics under treatment has played an instrumental role in improving our understanding of virus pathogenesis and in guiding drug development. Here, we review the current state of HCV kinetic modelling, and challenges to the standard biphasic viral decline model that arise when fitting viral kinetic models to data obtained with DAAs.},
  chemicals = {Antiviral Agents, Protease Inhibitors},
  citation-subset = {IM},
  completed = {2013-08-13},
  issn-linking = {1359-6535},
  mid = {NIHMS460955},
  nationality = {England},
  nlm-id = {9815705},
  owner = {NLM},
  pmc = {PMC3641583},
  pmid = {23186606},
  pubmodel = {Print-Electronic},
  pubstatus = {ppublish},
  revised = {2017-02-20},
  keywords = {Antiviral Agents,Biological,Chronic,complications,drug effects,Drug Resistance,drug therapy,Hepacivirus,Hepatitis C,Humans,Kinetics,Models,pharmacology,physiology,Protease Inhibitors,therapeutic use,Viral,Viral Load,virology,Virus Replication},
  timestamp = {2018.06.20}
}

@article{dixit04,
  title = {Modelling How Ribavirin Improves Interferon Response Rates in Hepatitis {{C}} Virus Infection.},
  author = {Dixit, Narendra M and {Layden-Almer}, Jennifer E and Layden, Thomas J and Perelson, Alan S},
  year = {2004},
  month = dec,
  journal = {Nature},
  volume = {432},
  number = {7019},
  pages = {922--924},
  issn = {1476-4687},
  doi = {10.1038/nature03153},
  abstract = {Nearly 200 million individuals worldwide are currently infected with hepatitis C virus (HCV). Combination therapy with pegylated interferon and ribavirin, the latest treatment for HCV infection, elicits long-term responses in only about 50\% of patients treated. No effective alternative treatments exist for non-responders. Consequently, significant efforts are continuing to maximize response to combination therapy. However, rational therapy optimization is precluded by the poor understanding of the mechanism(s) of ribavirin action against HCV. Ribavirin alone induces either a transient early decline or no decrease in HCV viral load, but in combination with interferon it significantly improves long-term response rates. Here we present a model of HCV dynamics in which, on the basis of growing evidence, we assume that ribavirin decreases HCV infectivity in an infected individual in a dose-dependent manner. The model quantitatively predicts long-term response rates to interferon monotherapy and combination therapy, fits observed patterns of HCV RNA decline in patients undergoing therapy, reconciles conflicting observations of the influence of ribavirin on HCV RNA decline, provides key insights into the mechanism of ribavirin action against HCV, and establishes a framework for rational therapy optimization.},
  chemicals = {RNA, Viral, Ribavirin, Interferons},
  citation-subset = {IM},
  completed = {2005-01-03},
  issn-linking = {0028-0836},
  nationality = {England},
  nlm-id = {0410462},
  owner = {NLM},
  pii = {nature03153},
  pmid = {15602565},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2013-11-21},
  keywords = {Biological,Combination,Dose-Response Relationship,Drug,drug effects,drug therapy,Drug Therapy,genetics,Hepacivirus,Hepatitis C,Humans,Interferons,metabolism,Models,pharmacology,physiology,Ribavirin,RNA,therapeutic use,Viral,Viral Load,virology},
  timestamp = {2018.06.20}
}

@article{dobrovolny13,
  title = {Assessing Mathematical Models of Influenza Infections Using Features of the Immune Response},
  author = {Dobrovolny, Hana M and Reddy, Micaela B and Kamal, Mohamed A and Rayner, Craig R and Beauchemin, Catherine AA},
  year = {2013},
  journal = {PloS one},
  volume = {8},
  number = {2},
  pages = {e57088},
  publisher = {{Public Library of Science}},
  owner = {andreas},
  timestamp = {2018.04.09}
}

@book{efron93,
  title = {An Introduction to the Bootstrap},
  author = {Efron, Bradley and Tibshirani, Robert},
  year = {1993},
  publisher = {{Chapman \& Hall}},
  address = {{New York}},
  date-modified = {2008-03-06 11:48:58 -0500},
  owner = {andreas},
  timestamp = {2018.07.02}
}

@article{guedj10,
  title = {A Perspective on Modelling Hepatitis {{C}} Virus Infection.},
  author = {Guedj, J and Rong, L and Dahari, H and Perelson, A S},
  year = {2010},
  month = dec,
  journal = {Journal of viral hepatitis},
  volume = {17},
  number = {12},
  pages = {825--833},
  issn = {1365-2893},
  doi = {10.1111/j.1365-2893.2010.01348.x},
  abstract = {By mathematically describing early hepatitis C virus (HCV) RNA decay after initiation of interferon (IFN)-based antiviral therapy, crucial parameters of the in vivo viral kinetics have been estimated, such as the rate of production and clearance of free virus, and the rate of loss of infected cells. Furthermore, by suggesting mechanisms of action for IFN and ribavirin mathematical modelling has provided a means for evaluating and optimizing treatment strategies. Here, we review recent modelling developments for understanding complex viral kinetics patterns, such as triphasic HCV RNA declines and viral rebounds observed in patients treated with pegylated interferon and ribavirin. Moreover, we discuss new modelling approaches developed to interpret the viral kinetics observed in clinical trials with direct-acting antiviral agents, which induce a rapid decline of wild-type virus but also engender a higher risk for emergence of drug-resistant variants. Lastly, as in vitro systems have allowed a better characterization of the virus lifecycle, we discuss new modelling approaches that combine the intracellular and the extracellular viral dynamics.},
  chemicals = {Antiviral Agents, Interferon-alpha, RNA, Viral, Recombinant Proteins, Polyethylene Glycols, interferon alfa-2b, interferon alfa-2a, Ribavirin, peginterferon alfa-2b, peginterferon alfa-2a},
  citation-subset = {IM},
  completed = {2011-03-16},
  issn-linking = {1352-0504},
  mid = {NIHMS279191},
  nationality = {England},
  nlm-id = {9435672},
  owner = {NLM},
  pii = {JVH1348},
  pmc = {PMC3070361},
  pmid = {20723038},
  pubmodel = {Print-Electronic},
  pubstatus = {ppublish},
  revised = {2017-02-20},
  keywords = {Antiviral Agents,Biological,blood,drug effects,Drug Resistance,drug therapy,Hepacivirus,Hepatitis C,Humans,Interferon-alpha,Kinetics,Models,pharmacokinetics,pharmacology,physiology,Polyethylene Glycols,Recombinant Proteins,Ribavirin,RNA,therapeutic use,Viral,virology},
  timestamp = {2018.06.20}
}

@article{handel07,
  title = {Neuraminidase Inhibitor Resistance in Influenza: Assessing the Danger of Its Generation and Spread.},
  author = {Handel, Andreas and Longini, Jr, Ira M and Antia, Rustom},
  year = {2007},
  month = dec,
  journal = {PLoS computational biology},
  volume = {3},
  number = {12},
  pages = {e240},
  publisher = {{Department of Biology, Emory University, Atlanta, Georgia, United States of America. ahandel@emory.edu}},
  doi = {10.1371/journal.pcbi.0030240},
  abstract = {Neuraminidase Inhibitors (NI) are currently the most effective drugs against influenza. Recent cases of NI resistance are a cause for concern. To assess the danger of NI resistance, a number of studies have reported the fraction of treated patients from which resistant strains could be isolated. Unfortunately, those results strongly depend on the details of the experimental protocol. Additionally, knowing the fraction of patients harboring resistance is not too useful by itself. Instead, we want to know how likely it is that an infected patient can generate a resistant infection in a secondary host, and how likely it is that the resistant strain subsequently spreads. While estimates for these parameters can often be obtained from epidemiological data, such data is lacking for NI resistance in influenza. Here, we use an approach that does not rely on epidemiological data. Instead, we combine data from influenza infections of human volunteers with a mathematical framework that allows estimation of the parameters that govern the initial generation and subsequent spread of resistance. We show how these parameters are influenced by changes in drug efficacy, timing of treatment, fitness of the resistant strain, and details of virus and immune system dynamics. Our study provides estimates for parameters that can be directly used in mathematical and computational models to study how NI usage might lead to the emergence and spread of resistance in the population. We find that the initial generation of resistant cases is most likely lower than the fraction of resistant cases reported. However, we also show that the results depend strongly on the details of the within-host dynamics of influenza infections, and most importantly, the role the immune system plays. Better knowledge of the quantitative dynamics of the immune response during influenza infections will be crucial to further improve the results.},
  langid = {english},
  medline-pst = {ppublish},
  pii = {07-PLCB-RA-0281},
  pmc = {PMC2134965},
  pmid = {18069885},
  keywords = {antagonists /\&/ inhibitors,Antiviral Agents,Biological,Computer Simulation,Disease Outbreaks,Drug Resistance,drug therapy/epidemiology/immunology/transmission/virology,Human,Humans,Immunological,immunology,Influenza,Influenza A virus,methods,Models,Neuraminidase,physiology,prevention /\&/ control,Risk Assessment,Risk Factors,therapeutic use,Viral,Virus Replication}
}

@article{handel09b,
  title = {Exploring the Role of the Immune Response in Preventing Antibiotic Resistance.},
  author = {Handel, Andreas and Margolis, Elisa and Levin, Bruce R},
  year = {2009},
  month = feb,
  journal = {Journal of theoretical biology},
  volume = {256},
  number = {4},
  pages = {655--662},
  issn = {1095-8541},
  doi = {10.1016/j.jtbi.2008.10.025},
  abstract = {For many bacterial infections, drug resistant mutants are likely present by the time antibiotic treatment starts. Nevertheless, such infections are often successfully cleared. It is commonly assumed that this is due to the combined action of drug and immune response, the latter facilitating clearance of the resistant population. However, most studies of drug resistance emergence during antibiotic treatment focus almost exclusively on the dynamics of bacteria and the drug and neglect the contribution of immune defenses. Here, we develop and analyze several mathematical models that explicitly include an immune response. We consider different types of immune responses and investigate how each impacts the emergence of resistance. We show that an immune response that retains its strength despite a strong drug-induced decline of bacteria numbers considerably reduces the emergence of resistance, narrows the mutant selection window, and mitigates the effects of non-adherence to treatment. Additionally, we show that compared to an immune response that kills bacteria at a constant rate, one that trades reduced killing at high bacterial load for increased killing at low bacterial load is sometimes preferable. We discuss the predictions and hypotheses derived from this study and how they can be tested experimentally.},
  chemicals = {Anti-Bacterial Agents},
  citation-subset = {IM},
  completed = {2009-04-02},
  issn-linking = {0022-5193},
  mid = {NIHMS918709},
  nationality = {England},
  nlm-id = {0376342},
  owner = {NLM},
  pii = {S0022-5193(08)00575-4},
  pmc = {PMC5814249},
  pmid = {19056402},
  pubmodel = {Print-Electronic},
  pubstatus = {ppublish},
  revised = {2018-02-17},
  keywords = {Anti-Bacterial Agents,Bacteria,Bacterial,Bacterial Infections,Cellular,Drug Administration Schedule,drug effects,Drug Resistance,drug therapy,Genetic,genetics,growth \& development,Humans,Immunity,Immunological,immunology,Microbial,microbiology,Models,Mutation,Patient Compliance,Selection,therapeutic use}
}

@article{handel10,
  title = {Towards a Quantitative Understanding of the Within-Host Dynamics of Influenza {{A}} Infections.},
  author = {Handel, Andreas and Longini, Jr, Ira M and Antia, Rustom},
  year = {2010},
  month = jan,
  journal = {Journal of The Royal Society Interface},
  volume = {7},
  number = {42},
  pages = {35--47},
  publisher = {{Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA 30602, USA. ahandel@uga.edu}},
  doi = {10.1098/rsif.2009.0067},
  abstract = {Although the influenza A virus has been extensively studied, a quantitative understanding of the infection dynamics is still lacking. To make progress in this direction, we designed several mathematical models and compared them with data from influenza A infections of mice. We find that the immune response (IR) plays an important part in the infection dynamics. Both an innate and an adaptive IR are required to provide adequate explanation of the data. In contrast, regrowth of epithelial cells did not seem to be an important mechanism on the time scale of the infection. We also find that different model variants for both innate and adaptive responses fit the data well, indicating the need for additional data to allow further model discrimination.},
  langid = {english},
  medline-pst = {ppublish},
  pii = {rsif.2009.0067},
  pmc = {PMC2839376},
  pmid = {19474085},
  keywords = {Animal,Animals,Computer Simulation,Disease Models,Epithelial Cells,Host-Pathogen Interactions,Human,Humans,Immunity,Immunological,immunology,immunology/pathogenicity/physiology,immunology/virology,Influenza,Influenza A virus,Innate,Mice,Models}
}

@article{handel13,
  title = {A Multi-Scale Analysis of Influenza {{A}} Virus Fitness Trade-Offs Due to Temperature-Dependent Virus Persistence.},
  author = {Handel, Andreas and Brown, Justin and Stallknecht, David and Rohani, Pejman},
  year = {2013},
  journal = {PLoS computational biology},
  volume = {9},
  number = {3},
  pages = {e1002989},
  publisher = {{Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, United States of America. ahandel@uga.edu}},
  doi = {10.1371/journal.pcbi.1002989},
  abstract = {Successful replication within an infected host and successful transmission between hosts are key to the continued spread of most pathogens. Competing selection pressures exerted at these different scales can lead to evolutionary trade-offs between the determinants of fitness within and between hosts. Here, we examine such a trade-off in the context of influenza A viruses and the differential pressures exerted by temperature-dependent virus persistence. For a panel of avian influenza A virus strains, we find evidence for a trade-off between the persistence at high versus low temperatures. Combining a within-host model of influenza infection dynamics with a between-host transmission model, we study how such a trade-off affects virus fitness on the host population level. We show that conclusions regarding overall fitness are affected by the type of link assumed between the within- and between-host levels and the main route of transmission (direct or environmental). The relative importance of virulence and immune response mediated virus clearance are also found to influence the fitness impacts of virus persistence at low versus high temperatures. Based on our results, we predict that if transmission occurs mainly directly and scales linearly with virus load, and virulence or immune responses are negligible, the evolutionary pressure for influenza viruses to evolve toward good persistence at high within-host temperatures dominates. For all other scenarios, influenza viruses with good environmental persistence at low temperatures seem to be favored.},
  langid = {english},
  medline-pst = {ppublish},
  pii = {PCOMPBIOL-D-12-01335},
  pmc = {PMC3605121},
  pmid = {23555223},
  keywords = {Animals,Biological,Birds,Computational Biology,Feces,Genetic Fitness,Host-Pathogen Interactions,Human,Humans,Influenza,Influenza A virus,Models,Orthomyxoviridae Infections,pathogenicity/physiology,physiology,Temperature,transmission/virology,virology}
}

@article{handel14,
  title = {Trade-Offs between and within Scales: Environmental Persistence and within-Host Fitness of Avian Influenza Viruses.},
  author = {Handel, Andreas and Lebarbenchon, Camille and Stallknecht, David and Rohani, Pejman},
  year = {2014},
  month = jul,
  journal = {Proceedings. Biological sciences / The Royal Society},
  volume = {281},
  number = {1787},
  publisher = {{Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.}},
  doi = {10.1098/rspb.2013.3051},
  abstract = {Trade-offs between different components of a pathogen's replication and transmission cycle are thought to be common. A number of studies have identified trade-offs that emerge across scales, reflecting the tension between strategies that optimize within-host proliferation and large-scale population spread. Most of these studies are theoretical in nature, with direct experimental tests of such cross-scale trade-offs still rare. Here, we report an analysis of avian influenza A viruses across scales, focusing on the phenotype of temperature-dependent viral persistence. Taking advantage of a unique dataset that reports both environmental virus decay rates and strain-specific viral kinetics from duck challenge experiments, we show that the temperature-dependent environmental decay rate of a strain does not impact within-host virus load. Hence, for this phenotype, the scales of within-host infection dynamics and between-host environmental persistence do not seem to interact: viral fitness may be optimized on each scale without cross-scale trade-offs. Instead, we confirm the existence of a temperature-dependent persistence trade-off on a single scale, with some strains favouring environmental persistence in water at low temperatures while others reduce sensitivity to increasing temperatures. We show that this temperature-dependent trade-off is a robust phenomenon and does not depend on the details of data analysis. Our findings suggest that viruses might employ different environmental persistence strategies, which facilitates the coexistence of diverse strains in ecological niches. We conclude that a better understanding of the transmission and evolutionary dynamics of influenza A viruses probably requires empirical information regarding both within-host dynamics and environmental traits, integrated within a combined ecological and within-host framework.},
  langid = {english},
  medline-pst = {ppublish},
  pii = {rspb.2013.3051},
  pmc = {PMC4071531},
  pmid = {24898369},
  keywords = {Animals,Biological,Biological Evolution,Bird Diseases,Ducks,Fresh Water,Genetic Fitness,genetics/physiology,Human,Humans,Influenza,Influenza A virus,Influenza in Birds,Minnesota,Models,Phenotype,Temperature,transmission/virology,veterinary,Viral Load,virology}
}

@article{handel17,
  title = {Learning Infectious Disease Epidemiology in a Modern Framework.},
  author = {Handel, Andreas},
  year = {2017},
  month = oct,
  journal = {PLoS computational biology},
  volume = {13},
  number = {10},
  pages = {e1005642},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1005642},
  citation-subset = {IM},
  completed = {2017-12-04},
  issn-linking = {1553-734X},
  nationality = {United States},
  nlm-id = {101238922},
  owner = {NLM},
  pii = {PCOMPBIOL-D-17-00604},
  pmc = {PMC5654261},
  pmid = {29049284},
  pubmodel = {Electronic-eCollection},
  pubstatus = {epublish},
  revised = {2018-02-01},
  keywords = {Communicable Diseases,Computational Biology,Computer Graphics,epidemiology,Humans,Models,Software,Theoretical,User-Computer Interface},
  timestamp = {2018.07.02}
}

@article{handel20,
  title = {Simulation Modelling for Immunologists.},
  author = {Handel, Andreas and La Gruta, Nicole L. and Thomas, Paul G.},
  year = {2020},
  month = mar,
  journal = {Nature reviews. Immunology},
  volume = {20},
  number = {3},
  pages = {186--195},
  issn = {1474-1741},
  doi = {10.1038/s41577-019-0235-3},
  abstract = {The immune system is inordinately complex with many interacting components determining overall outcomes. Mathematical and computational modelling provides a useful way in which the various contributions of different immunological components can be probed in an integrated manner. Here, we provide an introductory overview and review of mechanistic simulation models. We start out by briefly defining these types of models and contrasting them to other model types that are relevant to the field of immunology. We follow with a few specific examples and then review the different ways one can use such models to answer immunological questions. While our examples focus on immune responses to infection, the overall ideas and descriptions of model uses can be applied to any area of immunology.},
  citation-subset = {IM},
  completed = {2020-05-01},
  issn-linking = {1474-1733},
  nationality = {England},
  nlm-id = {101124169},
  owner = {NLM},
  pii = {10.1038/s41577-019-0235-3},
  pmid = {31804613},
  pubmodel = {Print-Electronic},
  pubstate = {ppublish},
  revised = {2020-05-01},
  keywords = {Allergy and Immunology,Computer Simulation,Humans,Immunity,Immunological,immunology,Models}
}

@book{hastie11,
  title = {The Elements of Statistical Learning: {{Data}} Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)},
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
  year = {2011},
  publisher = {{Springer}},
  isbn = {0-387-84857-6},
  owner = {andreas},
  timestamp = {2014.06.25}
}

@article{hayden96,
  title = {Safety and Efficacy of the Neuraminidase Inhibitor {{GG167}} in Experimental Human Influenza.},
  author = {Hayden, F G and Treanor, J J and Betts, R F and Lobo, M and Esinhart, J D and Hussey, E K},
  year = {1996},
  journal = {JAMA},
  volume = {275},
  number = {4},
  pages = {295--299},
  issn = {0098-7484},
  abstract = {The current study evaluated whether intranasal administration of the sialic acid analog 4-guanidino-Neu5Ac2en (GG167), an inhibitor of influenza virus neuraminidase, was effective and safe in either preventing or treating experimental human influenza. Four randomized, double-blind, placebo-controlled trials involving three prophylaxis limbs, two early treatment limbs, and one delayed treatment limb were conducted. Isolation in individual rooms. Susceptible (serum hemagglutination-inhibition antibody titer \textexclamdown{} or = 1:8) adult volunteers (n = 166) were inoculated intranasally with 10(5) TCID50 influenza A/Texas/91 (H1N1) virus. GG167, 3.6 to 16 mg, was administered intranasally two or six times daily beginning 4 hours before inoculation (prophylaxis) or 1 or 2 days afterward (early or delayed treatment). Virological measures were frequency of infection based on viral shedding and/or seroconversion (prophylaxis) or quantitative viral shedding based on titers and duration of virus recovery (treatment). Clinical measures were the frequency of febrile illness and symptom severity scores. Intranasal GG167 was well tolerated for both prophylaxis and therapy. For all dose groups combined, GG167 prophylaxis was 82\% effective in preventing laboratory evidence of infection and 95\% effective in preventing febrile illness (P \textexclamdown{} .01 vs placebo). Early treatment with GG167 reduced peak viral titers by 2.0 log10, the median duration of viral shedding by 3 days, and the frequency of febrile illness by 85\% (P \textexclamdown{} .05 for each comparison). Other measures of illness were reduced by approximately 50\% to 70\% in the GG167 dosing groups. Twice daily dosing was as effective as six times daily. Direct respiratory administration of the selective neuraminidase inhibitor GG167 appears safe and effective for both prevention and early treatment of experimental influenza. Influenza virus neuraminidase is important for viral replication in humans.},
  chemicals = {Enzyme Inhibitors, Guanidines, Pyrans, Sialic Acids, Neuraminidase, Zanamivir},
  citation-subset = {AIM, IM},
  completed = {1996-02-14},
  issn-linking = {0098-7484},
  nationality = {United States},
  nlm-id = {7501160},
  owner = {NLM},
  pmid = {8544269},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2016-10-17},
  keywords = {Administration,administration \& dosage,Adult,adverse effects,antagonists \& inhibitors,Double-Blind Method,drug effects,drug therapy,Enzyme Inhibitors,enzymology,Female,Guanidines,Human,Humans,Influenza,Influenza A virus,Intranasal,Male,Neuraminidase,prevention \& control,Pyrans,Sialic Acids,therapeutic use,Virus Shedding,Zanamivir},
  timestamp = {2018.05.11}
}

@article{heffernan05a,
  title = {Perspectives on the Basic Reproductive Ratio.},
  author = {Heffernan, J M and Smith, R J and Wahl, L M},
  year = {2005},
  month = sep,
  journal = {Journal of the Royal Society, Interface},
  volume = {2},
  number = {4},
  pages = {281--293},
  issn = {1742-5689},
  doi = {10.1098/rsif.2005.0042},
  abstract = {The basic reproductive ratio, R0, is defined as the expected number of secondary infections arising from a single individual during his or her entire infectious period, in a population of susceptibles. This concept is fundamental to the study of epidemiology and within-host pathogen dynamics. Most importantly, R0 often serves as a threshold parameter that predicts whether an infection will spread. Related parameters which share this threshold behaviour, however, may or may not give the true value of R0. In this paper we give a brief overview of common methods of formulating R0 and surrogate threshold parameters from deterministic, non-structured models. We also review common means of estimating R0 from epidemiological data. Finally, we survey the recent use of R0 in assessing emerging diseases, such as severe acute respiratory syndrome and avian influenza, a number of recent livestock diseases, and vector-borne diseases malaria, dengue and West Nile virus.},
  citation-subset = {IM},
  completed = {2006-08-16},
  created = {2006-07-19},
  issn-linking = {1742-5662},
  nationality = {England},
  nlm = {PMC1578275},
  nlm-id = {101217269},
  owner = {NLM},
  pii = {KJ2U12Q2WMNF2W1E},
  pmc = {PMC1578275},
  pmid = {16849186},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2016-10-20},
  keywords = {Animals,Biological,Communicable Diseases,Computer Simulation,Disease Outbreaks,Disease Susceptibility,Disease Transmission,Emerging,Epidemiologic Methods,epidemiology,Humans,Incidence,Infectious,methods,Models,Population Dynamics,Prevalence,Risk Assessment,Risk Factors,statistics \& numerical data},
  timestamp = {2017.02.20}
}

@book{hilborn97,
  title = {The Ecological Detective : Confronting Models with Data},
  author = {Hilborn, Ray and Mangel, Marc},
  year = {1997},
  series = {Monographs in Population Biology 28},
  publisher = {{Princeton University Press}},
  address = {{Princeton, N.J.}},
  date-modified = {2008-03-06 11:53:09 -0500},
  owner = {andreas},
  timestamp = {2018.06.25}
}

@article{hoare08,
  title = {Sampling and Sensitivity Analyses Tools ({{SaSAT}}) for Computational Modelling.},
  author = {Hoare, Alexander and Regan, David G and Wilson, David P},
  year = {2008},
  journal = {Theoretical Biology and Medical Modelling},
  volume = {5},
  pages = {4},
  publisher = {{National Centre in HIV Epidemiology and Clinical Research, The University of New South Wales, Sydney, New South Wales, 2010, Australia. ahoare@nchecr.unsw.edu.au}},
  doi = {10.1186/1742-4682-5-4},
  pii = {1742-4682-5-4},
  pmid = {18304361},
  timestamp = {2009.04.10}
}

@article{kochin10,
  title = {On the Control of Acute Rodent Malaria Infections by Innate Immunity.},
  author = {Kochin, Beth F and Yates, Andrew J and {de Roode}, Jacobus C and Antia, Rustom},
  year = {2010},
  month = may,
  journal = {PloS one},
  volume = {5},
  number = {5},
  pages = {e10444},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0010444},
  abstract = {Does specific immunity, innate immunity or resource (red blood cell) limitation control the first peak of the blood-stage parasite in acute rodent malaria infections? Since mice deficient in specific immunity exhibit similar initial dynamics as wild-type mice it is generally viewed that the initial control of parasite is due to either limitation of resources (RBC) or innate immune responses. There are conflicting views on the roles of these two mechanisms as there is experimental evidence supporting both these hypotheses. While mathematical models based on RBC limitation are capable of describing the dynamics of primary infections, it was not clear whether a model incorporating the key features of innate immunity would be able to do the same. We examine the conditions under which a model incorporating parasite and innate immunity can describe data from acute Plasmodium chabaudi infections in mice. We find that innate immune response must decay slowly if the parasite density is to fall rather than equilibrate. Further, we show that within this framework the differences in the dynamics of two parasite strains are best ascribed to differences in susceptibility to innate immunity, rather than differences in the strains' growth rates or their propensity to elicit innate immunity. We suggest that further work is required to determine if innate immunity or resource limitation control acute malaria infections in mice.},
  citation-subset = {IM},
  completed = {2010-09-07},
  issn-linking = {1932-6203},
  nationality = {United States},
  nlm-id = {101285081},
  owner = {NLM},
  pmc = {PMC2865546},
  pmid = {20463903},
  pubmodel = {Electronic},
  pubstatus = {epublish},
  revised = {2016-10-19},
  keywords = {Animals,Immunity,Immunological,immunology,Innate,Malaria,Mice,Models,parasitology,Plasmodium chabaudi,prevention \& control,veterinary}
}

@article{li14,
  title = {Modeling Inoculum Dose Dependent Patterns of Acute Virus Infections.},
  author = {Li, Yan and Handel, Andreas},
  year = {2014},
  month = apr,
  journal = {Journal of theoretical biology},
  volume = {347},
  pages = {63--73},
  issn = {1095-8541},
  doi = {10.1016/j.jtbi.2014.01.008},
  abstract = {Inoculum dose, i.e. the number of pathogens at the beginning of an infection, often affects key aspects of pathogen and immune response dynamics. These in turn determine clinically relevant outcomes, such as morbidity and mortality. Despite the general recognition that inoculum dose is an important component of infection outcomes, we currently do not understand its impact in much detail. This study is intended to start filling this knowledge gap by analyzing inoculum dependent patterns of viral load dynamics in acute infections. Using experimental data for adenovirus and infectious bronchitis virus infections as examples, we demonstrate inoculum dose dependent patterns of virus dynamics. We analyze the data with the help of mathematical models to investigate what mechanisms can reproduce the patterns observed in experimental data. We find that models including components of both the innate and adaptive immune response are needed to reproduce the patterns found in the data. We further analyze which types of innate or adaptive immune response models agree with observed data. One interesting finding is that only models for the adaptive immune response that contain growth terms partially independent of viral load can properly reproduce observed patterns. This agrees with the idea that an antigen-independent, programmed response is part of the adaptive response. Our analysis provides useful insights into the types of model structures that are required to properly reproduce observed virus dynamics for varying inoculum doses. We suggest that such models should be taken as basis for future models of acute viral infections.},
  citation-subset = {IM},
  completed = {2014-10-29},
  issn-linking = {0022-5193},
  nationality = {England},
  nlm-id = {0376342},
  owner = {NLM},
  pii = {S0022-5193(14)00017-4},
  pmid = {24440713},
  pubmodel = {Print-Electronic},
  pubstatus = {ppublish},
  revised = {2014-03-10},
  keywords = {Biological,CD8-Positive T-Lymphocytes,Immune response dynamics,Immunity,immunology,Innate,Models,physiopathology,Viral Load,Virus Diseases,Within-host model},
  timestamp = {2018.05.15}
}

@article{marino08,
  title = {A Methodology for Performing Global Uncertainty and Sensitivity Analysis in Systems Biology},
  author = {Marino, Simeone and Hogue, Ian B. and Ray, Christian J. and Kirschner, Denise E.},
  year = {2008},
  month = sep,
  journal = {J. Theor. Biol},
  volume = {254},
  number = {1},
  pages = {178--196},
  doi = {10.1016/j.jtbi.2008.04.011},
  ljournal = {Journal of Theoretical Biology},
  timestamp = {2010.10.25}
}

@article{miao11a,
  title = {On Identifiability of Nonlinear {{ODE}} Models and Applications in Viral Dynamics},
  author = {Miao, Hongyu and Xia, Xiaohua and Perelson, Alan S. and Wu, Hulin},
  year = {2011},
  journal = {SIAM Review},
  volume = {53},
  number = {1},
  pages = {3},
  issn = {00361445},
  doi = {10.1137/090757009},
  urldate = {2011-05-25},
  timestamp = {2014.01.02}
}

@article{neumann98,
  title = {Hepatitis {{C}} Viral Dynamics in Vivo and the Antiviral Efficacy of Interferon-Alpha Therapy.},
  author = {Neumann, A U and Lam, N P and Dahari, H and Gretch, D R and Wiley, T E and Layden, T J and Perelson, A S},
  year = {1998},
  month = oct,
  journal = {Science (New York, N.Y.)},
  volume = {282},
  number = {5386},
  pages = {103--107},
  issn = {0036-8075},
  abstract = {To better understand the dynamics of hepatitis C virus and the antiviral effect of interferon-alpha-2b (IFN), viral decline in 23 patients during therapy was analyzed with a mathematical model. The analysis indicates that the major initial effect of IFN is to block virion production or release, with blocking efficacies of 81, 95, and 96\% for daily doses of 5, 10, and 15 million international units, respectively. The estimated virion half-life (t1/2) was, on average, 2.7 hours, with pretreatment production and clearance of 10(12) virions per day. The estimated infected cell death rate exhibited large interpatient variation (corresponding t1/2 = 1.7 to 70 days), was inversely correlated with baseline viral load, and was positively correlated with alanine aminotransferase levels. Fast death rates were predictive of virus being undetectable by polymerase chain reaction at 3 months. These findings show that infection with hepatitis C virus is highly dynamic and that early monitoring of viral load can help guide therapy.},
  chemicals = {Antiviral Agents, Interferon-alpha, RNA, Viral, Recombinant Proteins, interferon alfa-2b, Alanine Transaminase},
  citation-subset = {IM, X},
  completed = {1998-10-16},
  issn-linking = {0036-8075},
  nationality = {United States},
  nlm-id = {0404511},
  owner = {NLM},
  pmid = {9756471},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2016-11-24},
  keywords = {administration \& dosage,Alanine Transaminase,Antiviral Agents,Biological,blood,Cell Death,Dose-Response Relationship,Drug,Half-Life,Hepacivirus,Hepatitis C,Humans,immunology,Interferon-alpha,Kinetics,Models,physiology,Recombinant Proteins,Regression Analysis,RNA,therapeutic use,therapy,Viral,Viral Load,Viremia,Virion,virology,Virus Replication},
  timestamp = {2018.06.20}
}

@book{otto11,
  title = {A Biologist's Guide to Mathematical Modeling in Ecology and Evolution},
  author = {Otto, Sarah P and Day, Troy},
  year = {2011},
  publisher = {{Princeton University Press}},
  owner = {andreas},
  timestamp = {2018.05.15}
}

@article{perelson13,
  title = {Modeling the Within-Host Dynamics of {{HIV}} Infection},
  author = {Perelson, Alan S and Ribeiro, Ruy M},
  year = {2013},
  journal = {BMC biology},
  volume = {11},
  number = {1},
  pages = {96},
  publisher = {{BioMed Central}},
  owner = {andreas},
  timestamp = {2018.04.09}
}

@article{powers03,
  title = {Modeling Viral and Drug Kinetics: Hepatitis {{C}} Virus Treatment with Pegylated Interferon Alfa-2{{b}}},
  author = {Powers, Kimberly A and Dixit, Narendra M and Ribeiro, Ruy M and Golia, Preeti and Talal, Andrew H and Perelson, Alan S},
  year = {2003},
  journal = {Seminars in Liver Disease},
  volume = {23 Suppl 1},
  pages = {13--18},
  doi = {10.1055/s-2003-41630},
  date-modified = {2008-03-06 11:51:30 -0500},
  pmid = {12934163},
  timestamp = {2005.12.23}
}

@article{roberts07,
  title = {The Pluses and Minuses of {{R0}}.},
  author = {Roberts, M G},
  year = {2007},
  month = oct,
  journal = {Journal of the Royal Society, Interface},
  volume = {4},
  number = {16},
  pages = {949--961},
  issn = {1742-5689},
  doi = {10.1098/rsif.2007.1031},
  abstract = {The concept of the basic reproduction number (R0) occupies a central place in epidemic theory. The value of R0 determines the proportion of the population that becomes infected over the course of a (modelled) epidemic. In many models, (i) an endemic infection can persist only if R0\textquestiondown 1, (ii) the value of R0 provides a direct measure of the control effort required to eliminate the infection, and (iii) pathogens evolve to maximize their value of R0. These three statements are not universally true. In this paper, some exceptions to them are discussed, based on the extensions of the SIR model.},
  citation-subset = {IM},
  completed = {2007-11-13},
  created = {2007-09-13},
  issn-linking = {1742-5662},
  nationality = {England},
  nlm = {PMC2075534},
  nlm-id = {101217269},
  owner = {NLM},
  pii = {P368100327733485},
  pmc = {PMC2075534},
  pmid = {17490942},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2016-10-20},
  keywords = {Animal Diseases,Animals,Biological,Communicable Diseases,Disease Transmission,epidemiology,Infectious,Models,Statistical,statistics \& numerical data,Wild},
  timestamp = {2017.02.20}
}

@book{saltelli04,
  title = {Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models},
  author = {Saltelli, Andrea and Tarantola, Stefano and Campolongo, Francesca and Ratto, Marco},
  year = {2004},
  publisher = {{John Wiley \& Sons}}
}

@article{schirm20,
  title = {A Biomathematical Model of Immune Response and Barrier Function in Mice with Pneumococcal Lung Infection},
  author = {Schirm, Sibylle and Ahnert, Peter and Berger, Sarah and Nouailles, Geraldine and Wienhold, Sandra-Maria and {M{\"u}ller-Redetzky}, Holger and Suttorp, Norbert and Loeffler, Markus and Witzenrath, Martin and Scholz, Markus},
  year = {2020},
  month = dec,
  journal = {PLOS ONE},
  volume = {15},
  number = {12},
  pages = {e0243147},
  publisher = {{Public Library of Science}},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0243147},
  urldate = {2022-06-15},
  abstract = {Pneumonia is one of the leading causes of death worldwide. The course of the disease is often highly dynamic with unforeseen critical deterioration within hours in a relevant proportion of patients. Besides antibiotic treatment, novel adjunctive therapies are under development. Their additive value needs to be explored in preclinical and clinical studies and corresponding therapy schedules require optimization prior to introduction into clinical practice. Biomathematical modeling of the underlying disease and therapy processes might be a useful aid to support these processes. We here propose a biomathematical model of murine immune response during infection with Streptococcus pneumoniae aiming at predicting the outcome of different treatment schedules. The model consists of a number of non-linear ordinary differential equations describing the dynamics and interactions of the pulmonal pneumococcal population and relevant cells of the innate immune response, namely alveolar- and inflammatory macrophages and neutrophils. The cytokines IL-6 and IL-10 and the chemokines CCL2, CXCL1 and CXCL5 are considered as major mediators of the immune response. We also model the invasion of peripheral blood monocytes, their differentiation into macrophages and bacterial penetration through the epithelial barrier causing blood stream infections. We impose therapy effects on this system by modelling antibiotic therapy and treatment with the novel C5a-inactivator NOX-D19. All equations are derived by translating known biological mechanisms into equations and assuming appropriate response kinetics. Unknown model parameters were determined by fitting the predictions of the model to time series data derived from mice experiments with close-meshed time series of state parameters. Parameter fittings resulted in a good agreement of model and data for the experimental scenarios. The model can be used to predict the performance of alternative schedules of combined antibiotic and NOX-D19 treatment. We conclude that we established a comprehensive biomathematical model of pneumococcal lung infection, immune response and barrier function in mice allowing simulations of new treatment schedules. We aim to validate the model on the basis of further experimental data. We also plan the inclusion of further novel therapy principles and the translation of the model to the human situation in the near future.},
  langid = {english},
  keywords = {Alveolar macrophages,Antibiotics,Blood,Cytokine therapy,Cytokines,Immune response,Monocytes,Neutrophils},
  file = {C\:\\Users\\Andreas\\Zotero\\storage\\Z27EQ4FK\\article.html}
}

@article{smith11,
  title = {Influenza {{A}} Virus Infection Kinetics: Quantitative Data and Models.},
  author = {Smith, Amber M and Perelson, Alan S},
  year = {2011},
  month = jul,
  journal = {Wiley Interdiscip Rev Syst Biol Med},
  volume = {3},
  number = {4},
  pages = {429--445},
  publisher = {{Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA.}},
  doi = {10.1002/wsbm.129},
  langid = {english},
  medline-pst = {ppublish},
  owner = {andreas},
  pmid = {21197654},
  timestamp = {2011.07.14}
}

@article{talal06,
  title = {Pharmacodynamics of {{PEG-IFN}} Alpha Differentiate {{HIV}}/{{HCV}} Coinfected Sustained Virological Responders from Nonresponders.},
  author = {Talal, Andrew H and Ribeiro, Ruy M and Powers, Kimberly A and Grace, Michael and Cullen, Constance and Hussain, Musaddeq and Markatou, Marianthi and Perelson, Alan S},
  year = {2006},
  month = may,
  journal = {Hepatology},
  volume = {43},
  number = {5},
  pages = {943--953},
  pmid = {16761329},
  timestamp = {2008.10.28}
}
