• immune evasion
• mathematical model
• pathogen evolution
• phylodynamics

• COVID-19/immunology
• COVID-19/epidemiology
• COVID-19/transmission
• COVID-19/virology
• Humans
• SARS-CoV-2/genetics
• SARS-CoV-2/immunology
• Immune Evasion
• Biological Evolution
• Pandemics

• Princeton Precision Health
• Carlsbergfondet
• Princeton Catalysis Initiative
• Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley

• 92D30
• Basic reproduction number
• HIV
• multi-scale model
• opioid epidemic
• optimal control

• Humans
• Analgesics, Opioid/therapeutic use
• Models, Biological
• HIV Infections/drug therapy
• HIV Infections/epidemiology
• Antiviral Agents

• HIV
• basic reproduction number
• invasion number
• network
• opioid

• Humans
• HIV Infections/epidemiology
• Opioid Epidemic
• Analgesics, Opioid
• Models, Biological
• Epidemics
• Basic Reproduction Number
• Opioid-Related Disorders/epidemiology

• Biological Evolution
• Host-Pathogen Interactions
• Phylogeny
• RNA Viruses

• BB/T004401/1 Biotechnology and Biological Sciences Research Council

• HIV
• co-affection
• data fitting
• elasticity analysis
• identifiable
• immuno-epidemiological model
• invasion numbers
• multi-scale
• opioid addiction

• Analgesics, Opioid
• HIV Infections/epidemiology
• Humans
• Morphine/therapeutic use
• Opioid-Related Disorders/epidemiology
• Viral Load

• Cross-immunity
• Human vaccination choice
• Imitation dynamics
• Mutant invasion
• Pathogen competition

• ecology
• evolution
• diseases

• Animals
• Biological Evolution
• Disease Outbreaks
• Host-Pathogen Interactions
• Humans
• Models, Biological
• Population Dynamics
• Spatio-Temporal Analysis
• Zoonoses/epidemiology

• National Science Foundation EEID/DEB

Despite being similar in structure, functioning, and size, viral pathogens enjoy very different, usually well-defined ways of life. They occupy their hosts for a few days (influenza), for a few weeks (measles), or even lifelong (HCV), which manifests in acute or chronic infections. The various transmission routes (airborne, via direct physical contact, etc.), degrees of infectiousness (referring to the viral load required for transmission), antigenic variation/immune escape and virulence define further aspects of pathogenic lifestyles. To survive, pathogens must infect new hosts; the success determines their fitness. Infection happens with a certain likelihood during contact of hosts, where contact can also be mediated by vectors. Besides structural aspects of the host-contact network, three parameters appear to be key: the contact rate and the infectiousness during contact, which encode the mode of transmission, and third the immunity of susceptible hosts. On these grounds, what can be said about the reproductive success of viral pathogens? This is the biological question addressed in this paper. The answer extends earlier results of the author and makes explicit connection to another basic work on the evolution of pathogens. A mathematical framework is presented that models intra- and inter-host dynamics in a minimalistic but unified fashion covering a broad spectrum of viral pathogens, including those that cause flu-like infections, childhood diseases, and sexually transmitted infections. These pathogens turn out as local maxima of numerically simulated fitness landscapes. The models involve differential and integral equations, agent-based simulation, networks, and probability.

• Contact networks
• Evolution of viral infections
• Infection types
• Infectious disease modeling
• Phylodynamic patterns
• Viral fitness

• Coinfection
• multiple infection
• parasite interactions
• virulence evolution

• Aminoacyl-tRNA synthetase
• Antibiotics
• Antiviral immunity
• Infection
• Multi-tRNA synthetase complex

• Amino Acyl-tRNA Synthetases/antagonists & inhibitors
• Amino Acyl-tRNA Synthetases/metabolism
• Animals
• Anti-Bacterial Agents/administration & dosage
• Anti-Bacterial Agents/metabolism
• Antiviral Agents/administration & dosage
• Antiviral Agents/metabolism
• Communicable Diseases/drug therapy
• Communicable Diseases/metabolism
• Drug Delivery Systems/methods
• Drug Delivery Systems/trends
• Humans
• Microbial Sensitivity Tests/methods
• Protein Biosynthesis/drug effects
• Protein Biosynthesis/physiology
• Signal Transduction/drug effects
• Signal Transduction/physiology

The basic reproduction ratio, R₀, is a fundamental concept in epidemiology. It is defined as the total number of secondary infections brought on by a single primary infection, in a totally susceptible population. The value of R₀ indicates whether a starting epidemic reaches a considerable part of the population and causes a lot of damage, or whether it remains restricted to a relatively small number of individuals. To calculate R₀ one has to evaluate an integral that ranges over the duration of the infection of the host. This duration is, of course, limited by remaining host longevity. So, R₀ depends on remaining host longevity and in this paper we show that for long-lived hosts this aspect may not be ignored for long-lasting infections. We investigate in particular how this epidemiological measure of pathogen fitness depends on host longevity. For our analyses we adopt and combine a generic within- and between-host model from the literature. To find the optimal strategy for a pathogen from an evolutionary point of view, we focus on the indicator \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0^{{opt}}$$\end{document}R0opt, i.e., the optimum of R₀ as a function of its replication and mutation rates. These are the within-host parameters that the pathogen has at its disposal to optimize its strategy. We show that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0^{{opt}}$$\end{document}R0opt is highly influenced by remaining host longevity in combination with the contact rate between hosts in a susceptible population. In addition, these two parameters determine whether a killer-like or a milker-like strategy is optimal for a given pathogen. In the killer-like strategy the pathogen has a high rate of reproduction within the host in a short time span causing a relatively short disease, whereas in the milker-like strategy the pathogen multiplies relatively slowly, producing a continuous small amount of offspring over time with a small effect on host health. The present research allows for the determination of a bifurcation line in the plane of host longevity versus contact rate that forms the boundary between the milker-like and killer-like regions. This plot shows that for short remaining host longevities the killer-like strategy is optimal, whereas for very long remaining host longevities the milker-like strategy is advantageous. For in-between values of host longevity, the contact rate determines which of both strategies is optimal.

• Duration of infection
• Epidemiology
• Fitness strategy
• Host longevity
• R 0
• milker–killer dilemma

• Biological Evolution
• Humans
• Longevity
• Models, Theoretical
• Reproduction

• Zika virus
• genetic mapping
• mutagenesis
• transposons

• computational models
• infectious disease
• multi-scale

• Differential equation models
• Disease transmission
• Infectious disease modeling

To better understand the mechanisms involved in the dynamics of Johne’s disease in dairy cattle, this paper illustrates a novel way to link a within-host model for Mycobacterium avium ssp. paratuberculosis with an epidemiological model. The underlying variable in the within-host model is the time since infection. Two compartments, infected macrophages and T cells, of the within-host model feed into the epidemiological model through the direct transmission rate, disease-induced mortality rate, the vertical transmission rate, and the shedding of MAP into the environment. The epidemiological reproduction number depends on the within-host bacteria load in a complex way, exhibiting multiple peaks. A possible mechanism to account for the switch in shedding patterns of the bacteria in this disease is included in the within-host model, and its effect can be seen in the epidemiological reproduction model.

• Coverage
• Elimination
• Immunity
• Modeling
• Vaccination

• Communicable Disease Control/economics
• Communicable Disease Control/methods
• Communicable Disease Control/statistics & numerical data
• Communicable Diseases/immunology
• Health Policy
• Humans
• Immunity, Innate
• Models, Statistical
• Vaccines/economics
• Vaccines/therapeutic use

• CDF-2011-04-019 Department of Health
• MR/K021680/1 Medical Research Council
• P2C HD047879 NICHD NIH HHS
• 097830/Z/11/A-C Wellcome Trust

• avian influenza
• environmental persistence
• mathematical model
• multi-scale analysis

Pathogen evolution is influenced strongly by the host immune response. Previous studies of the effects of herd immunity on the population structure of directly transmitted, short-lived pathogens have primarily focused on the impact of competition for hosts. In contrast, for long-lived infections like HIV, theoretical work has focused on the mechanisms promoting antigenic variation within the host. In reality, successful transmission requires that pathogens balance both within- and between-host immune selection. The Opa adhesins in the bacterial Neisseria genus provide a unique system to study the evolution of the same antigens across two major pathogens: while N. meningitidis is an airborne, respiratory pathogen colonising the nasopharynx relatively transiently, N. gonorrhoeae can cause sexually transmitted, long-lived infections. We use a simple mathematical model and genomic data to show that trade-offs between immune selection pressures within- and between-hosts can explain the contrasting Opa repertoires observed in meningococci and gonococci.

Different species inhabit different sensory worlds and thus have evolved diverse means of processing information, learning and memory. In the escalated arms race with host defense, each pathogenic bacterium not only has evolved its individual cellular sensing and behavior, but also collective sensing, interbacterial communication, distributed information processing, joint decision making, dissociative behavior, and the phenotypic and genotypic heterogeneity necessary for epidemiologic success. Moreover, pathogenic populations take advantage of dormancy strategies and rapid evolutionary speed, which allow them to save co-generated intelligent traits in a collective genomic memory. This review discusses how these mechanisms add further levels of complexity to bacterial pathogenicity and transmission, and how mining for these mechanisms could help to develop new anti-infective strategies.

• biofilm
• cooperative behavior
• dormancy
• heterogeneity
• information processing
• learning
• memory
• pathogen intelligence

• drug treatment
• influenza
• vaccines
• within-host model

• Epidemiological
• Influenza A virus model
• Metapopulation
• Networks
• Viral kinetics

• Animals
• Evolution, Molecular
• Humans
• Influenza A virus/physiology
• Influenza, Human/genetics
• Influenza, Human/metabolism
• Influenza, Human/transmission
• Models, Biological

• HHSN272201000055C NIAID NIH HHS
• HH5N272201000055C HHS

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.

It has recently been suggested that for avian influenza viruses, prolonged persistence in the environment plays an important role in the transmission between birds. In such situations, influenza virus strains may face a trade-off: they need to persist well in the environment at low temperatures, but they also need to do well inside an infected bird at higher temperatures. Here, we analyze how potential trade-offs on these two scales interact to determine overall fitness of the virus. We find that the link between infection dynamics within a host and virus shedding and transmission is crucial in determining the relative advantage of good low-temperature versus high-temperature persistence. We also find that the role of virus-induced mortality, the immune response and the route of transmission affect the balance between optimal low-temperature and high-temperature persistence.

• Animals
• Birds
• Computational Biology
• Feces/virology
• Genetic Fitness/physiology
• Host-Pathogen Interactions
• Humans
• Influenza A virus/pathogenicity
• Influenza A virus/physiology
• Influenza, Human/transmission
• Influenza, Human/virology
• Models, Biological
• Orthomyxoviridae Infections/transmission
• Orthomyxoviridae Infections/virology
• Temperature

• HHSN266200700007C PHS HHS

Different viruses transmit among hosts with different degrees of efficiency. A basic reproductive number (R₀) indicates an average number of cases getting infected from a single infected case. R₀ can vary widely from a little over 1 to more than 10. Low R₀ is usually found among rapidly evolving viruses that are often under a strong positive selection pressure, while high R₀ is often found among viruses that are highly stable. The reason for the difference between antigenically diverse viruses with low R₀, such as influenza A virus, and antigenically stable viruses with high R₀, such as measles virus, is not clear and has been a subject of great interest. Optimization of transmissibility fitness considering intra-host dynamics and inter-host transmissibility was shown to result in strategies for tradeoff between transmissibility and diversity. The nature of transmission, targeting either a naïve children population or an adult population with partial immunity, has been proposed as a contributing factor for the difference in the strategies used by the two groups of viruses. The R₀ determines the levels of threshold heard immunity. Lower R₀ requires lower herd immunity to terminate an outbreak. Therefore, it can be assumed that the outbreak saturation can be reached more readily when the R₀ is low. In addition, one may assume that when the outbreak saturation is reached, herd immunity may provide a strong positive selection pressure that could possibly result in an occurrence of escape mutants. Studies of these hypotheses will give us an important insight into viral evolution. This review discusses the above hypotheses as well as some possible mechanistic explanation for the difference in transmission efficiency of viruses

• Basic reproductive number
• Transmission
• Viral infection
• Antigenic diversity
• Herd Immunity
• Selective pressure
• Influenza
• Measles

Many questions remain about P. falciparum within-host dynamics, immunity, and transmission–issues that may affect public health campaign planning. These gaps in knowledge concern the distribution of durations of malaria infections, determination of peak parasitemia during acute infection, the relationships among gametocytes and immune responses and infectiousness to mosquitoes, and the effect of antigenic structure on reinfection outcomes. The present model of intra-host dynamics of P. falciparum implements detailed representations of parasite and immune dynamics, with structures based on minimal extrapolations from first-principles biology in its foundations. The model is designed to quickly and readily accommodate gains in mechanistic understanding and to evaluate effects of alternative biological hypothesis through in silico experiments. Simulations follow the parasite from the liver-stage through the detailed asexual cycle to clearance while tracking gametocyte populations. The modeled immune system includes innate inflammatory and specific antibody responses to a repertoire of antigens. The mechanistic focus provides clear explanations for the structure of the distribution of infection durations through the interaction of antigenic variation and innate and adaptive immunity. Infectiousness to mosquitoes appears to be determined not only by the density of gametocytes but also by the level of inflammatory cytokines, which harmonizes an extensive series of study results. Finally, pre-existing immunity can either decrease or increase the duration of infections upon reinfection, depending on the degree of overlap in antigenic repertoires and the strength of the pre-existing immunity.

• Adaptive Immunity
• Animals
• Antigens, Protozoan/immunology
• Culicidae/parasitology
• Female
• Immunity, Innate
• Malaria, Falciparum/immunology
• Malaria, Falciparum/therapy
• Malaria, Falciparum/transmission
• Male
• Models, Immunological
• Parasitemia/immunology
• Parasitemia/therapy
• Parasitemia/transmission
• Plasmodium falciparum/immunology
• Plasmodium falciparum/pathogenicity
• Plasmodium falciparum/physiology
• Time Factors

• Air Pollutants
• Animals
• Disease Outbreaks
• Dust
• Influenza in Birds/transmission
• Models, Biological
• Models, Theoretical
• Netherlands
• Particulate Matter
• Poultry/virology
• Wind

Sleeping sickness is characterized by waves of the extracellular parasite Trypanosoma brucei in host blood, with infections continuing for months or years until inevitable host death. These waves reflect the dynamic conflict between the outgrowth of a succession of parasite antigenic variants and their control by the host immune system. Although a contributor to these dynamics is the density-dependent differentiation from proliferative “slender forms” to transmissible “stumpy forms,” an absence of markers discriminating stumpy forms has prevented accurate parameterization of this component. Here, we exploit the stumpy-specific PAD1 marker, which functionally defines transmission competence, to quantitatively monitor stumpy formation during chronic infections. This allows reconstruction of the temporal events early in infection. Mathematical modeling of these data describes the parameters controlling trypanosome within-host dynamics and provides strong support for a quorum-sensing-like mechanism. Our data reveal the dominance of transmission stages throughout infection, a consequence being austere use of the parasite's antigen repertoire.

There is growing interest in understanding the nature and consequences of interactions among infectious agents. Pathogen interactions can be operational at different scales, either within a co-infected host or in host populations where they co-circulate, and can be either cooperative or competitive. The detection of interactions among pathogens has typically involved the study of synchrony in the oscillations of the protagonists, but as we show here, phase association provides an unreliable dynamical fingerprint for this task. We assess the capacity of a likelihood-based inference framework to accurately detect and quantify the presence and nature of pathogen interactions on the basis of realistic amounts and kinds of simulated data. We show that when epidemiological and demographic processes are well understood, noisy time series data can contain sufficient information to allow correct inference of interactions in multi-pathogen systems. The inference power is dependent on the strength and time-course of the underlying mechanism: stronger and longer-lasting interactions are more easily and more precisely quantified. We examine the limitations of our approach to stochastic temporal variation, under-reporting, and over-aggregation of data. We propose that likelihood shows promise as a basis for detection and quantification of the effects of pathogen interactions and the determination of their (competitive or cooperative) nature on the basis of population-level time-series data.

It is becoming increasingly evident that pathogens associated with infectious diseases interact amongst themselves. Pathogen interactions can occur in a co-infected host, or in host populations where they co-circulate, and they can be cooperative or competitive. Four serotypes of dengue virus, for example, can exhibit both forms of interactions – cross protection for a temporary period and followed by long-lasting enhancement. This bears important consequences for understanding the ecology and developing control and prevention measures. Detecting such interactions in a natural host population, though, can be tricky. We show that studying the phase relation of epidemic cycles, as it has been typically done, is unreliable. We assess the ability of a likelihood based method in detecting such interactions, and find that they are accurate and robust. We propose that this framework shows promise of serving as a basis for detecting and quantifying pathogen interactions.

• Coinfection/microbiology
• Coinfection/virology
• Communicable Diseases/microbiology
• Communicable Diseases/virology
• Computational Biology
• Computer Simulation
• Microbial Interactions
• Models, Biological
• Stochastic Processes

• Bacteria/genetics
• Biological Evolution
• Genetic Drift
• Host-Pathogen Interactions
• Humans
• Infections/immunology
• Infections/microbiology
• Infections/virology
• Selection, Genetic
• Virulence
• Viruses/genetics

• hiv
• siv
• viral evolution
• acute infection
• transmitted virus

• Animals
• Cluster Analysis
• HIV/classification
• HIV/genetics
• HIV/isolation & purification
• HIV Infections/transmission
• HIV Infections/virology
• Humans
• Models, Theoretical
• Phylogeny
• Polymorphism, Genetic
• Sequence Analysis, DNA
• Sequence Homology
• Simian Acquired Immunodeficiency Syndrome/transmission
• Simian Acquired Immunodeficiency Syndrome/virology
• Simian Immunodeficiency Virus/classification
• Simian Immunodeficiency Virus/genetics
• Simian Immunodeficiency Virus/isolation & purification

• Z99 CA999999 Intramural NIH HHS