Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

Handel, Andreas; Rohani, Pejman

doi:10.1098/rstb.2014.0302

Cited by 109 publications

(191 citation statements)

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“…In a more recent study [39], at a given viremia level, dengue virus infected people with no detectable symptoms or whose symptoms had not yet begun were more infectious to mosquitoes than people who were bitten when they were symptomatic. Fully addressing how fever and other disease symptoms affect an individual's contribution to transmission will require combining data on human mobility with data on infectiousness at multiple time points over the course of infection [10,40].…”

Section: Discussionmentioning

confidence: 99%

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Calling in sick: impacts of fever on intra-urban human mobility

et al. 2016

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Pathogens inflict a wide variety of disease manifestations on their hosts, yet the impacts of disease on the behaviour of infected hosts are rarely studied empirically and are seldom accounted for in mathematical models of transmission dynamics. We explored the potential impacts of one of the most common disease manifestations, fever, on a key determinant of pathogen transmission, host mobility, in residents of the Amazonian city of Iquitos, Peru. We did so by comparing two groups of febrile individuals (denguepositive and dengue-negative) with an afebrile control group. A retrospective, semi-structured interview allowed us to quantify multiple aspects of mobility during the two-week period preceding each interview. We fitted nested models of each aspect of mobility to data from interviews and compared models using likelihood ratio tests to determine whether there were statistically distinguishable differences in mobility attributable to fever or its aetiology. Compared with afebrile individuals, febrile study participants spent more time at home, visited fewer locations, and, in some cases, visited locations closer to home and spent less time at certain types of locations. These multifaceted impacts are consistent with the possibility that diseasemediated changes in host mobility generate dynamic and complex changes in host contact network structure.

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Section: Discussionmentioning

confidence: 99%

“…[9]. More generally, how disease impacts host mobility and contact has been proposed as a key determinant of how within-host infection dynamics affect population-level transmission dynamics [10].…”

Section: Introductionmentioning

confidence: 99%

Calling in sick: impacts of fever on intra-urban human mobility

et al. 2016

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“…In reality, the within-host infection process determines key parameters in the disease transmission [13,12,14, Fig. 1].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the effective transmission probability [7] has been usually simplified as a single parameter that reflects collective effects of the contact rate with the infectious, the infectivity of the infectious, and the susceptibility of the susceptible; influential factors of a disease transmission such as transmission probability and contact rate were mostly fixed while they dynamically change in reality [11]. As a result, these key processes in the disease transmission are lost, especially the transient nature of the infection course as well as the dynamics of the active population portion in an epidemic [12,11].In reality, the within-host infection process determines key parameters in the disease transmission [13,12,14, Fig. 1].…”

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confidence: 99%

“…http://dx.doi.org/10.1101/133421 doi: bioRxiv preprint first posted online Nguyen et al The interplays between within-host infection and between hosts transmission led to arising attempts connecting the two levels [14,21,22,12,23,24,25,4], but the approach is still at its infancy [13]. On one hand, most of these models were conceptual and theoretical [13] or rely on assumptive and previously obtained parameter estimates [26,15,27]. We propose that this limitation can be overcome by using explicitly within-host infection model.…”

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confidence: 99%

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High-Resolution Epidemic Simulation Using Within-Host Infection and Contact Data

Nguyen

Mikolajczyk

Hernandez‐Vargas

2017

Preprint

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Background: Transmission in epidemics of infectious diseases is characterized by a high level of subject-specific elements. These include heterogeneous infection conditions, time-dependent transmission potential, and age-dependent contact structure. These insights are often lost in epidemic models using population data. Here we submit an approach that can capture these details, paving the way for studying epidemics in a more mechanistic and realistic way. Methods: Using experimental data, we formulated mathematical models of a pathogen infection dynamics from which we can simulate its transmission potential mechanistically. The models were then embedded in our implement of an age-specific contact network structure that allows to express all elements relevant to the transmission process. This approach is illustrated here with an example of Ebola virus (EBOV). Results:The results showed that within-host infection dynamics can capture EBOV's transmission parameters as good as approaches using population data. Population age-structure, contact distribution and patterns can also be captured with our network generating algorithm. This framework opens vast opportunities for the investigations of each element involved in the epidemic process. Here, estimating EBOV's reproduction number revealed a heterogeneous pattern among age-groups, prompting questions on current estimates which are not adjusted for this factor. Assessments of mass vaccination strategies showed that a time window from five months before to one week after the start of an epidemic appeared to be effective. Noticeably, compared to a non-intervention scenario, a low vaccination coverage of 33% could reduce number of cases by ten to hundred times as well as lessen the case-fatality rate. Conclusions: This is the first effort coupling directly within-host infection model into an age-structured epidemic network model, adding more realistic elements in simulating epidemic processes. Experimental data at the within-host infection are shown able to capture upfront key parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. Population of interest in epidemic assessments could be modeled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios are underway to explore multilevel aspects in infectious diseases epidemics.Keywords: high-resolution; epidemic; simulation; within-host infection; age-structure; contact network CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/133421 doi: bioRxiv preprint first posted online Nguyen et al. Page 2 of 22 BackgroundEpidemics of infectious diseases are listed among the potential catastrophes and can be potentially misused as mass destruction weapons [1]. Overwhelming research efforts have been developed to early predict the danger ...

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Mechanistic Modeling of SARS‐CoV‐2 and Other Infectious Diseases and the Effects of Therapeutics

Perelson

2021

Clin Pharma and Therapeutics

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Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS‐CoV‐2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID‐19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, “Everything should be made as simple as possible, but not simpler,” and this idea underlies the modeling we describe below.

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Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

Cited by 109 publications

References 120 publications

Calling in sick: impacts of fever on intra-urban human mobility

Calling in sick: impacts of fever on intra-urban human mobility

High-Resolution Epidemic Simulation Using Within-Host Infection and Contact Data

Mechanistic Modeling of SARS‐CoV‐2 and Other Infectious Diseases and the Effects of Therapeutics

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