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Linked within-host and between-host models and data for infectious diseases: a systematic review.

ABSTRACT: The observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that is, how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links between them, is necessary for a holistic understanding of the spread of infectious diseases. One approach to combining these scales is through mathematical modeling. We conducted a systematic review of the published literature on multi-scale mathematical models of disease transmission (as defined by combining within-host and between-host scales) to determine the extent to which mathematical models are being used to understand across-scale transmission, and the extent to which these models are being confronted with data. Following the PRISMA guidelines for systematic reviews, we identified 24 of 197 qualifying papers across 30 years that include both linked models at the within and between host scales and that used data to parameterize/calibrate models. We find that the approach that incorporates both modeling with data is under-utilized, if increasing. This highlights the need for better communication and collaboration between modelers and empiricists to build well-calibrated models that both improve understanding and may be used for prediction.

SUBMITTER: Childs LM

PROVIDER: S-EPMC6589080 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Linked within-host and between-host models and data for infectious diseases: a systematic review.

Childs Lauren M LM El Moustaid Fadoua F Gajewski Zachary Z Kadelka Sarah S Nikin-Beers Ryan R Smith John W JW Walker Melody M Johnson Leah R LR

PeerJ 20190619

The observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that is, how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links ...[more]

PMID: 31249734

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Project description:ObjectiveThe objective of this study is to conduct a systematic review of multi-scale HIV immunoepidemiological models to improve our understanding of the synergistic impact between the HIV viral-immune dynamics at the individual level and HIV transmission dynamics at the population level.BackgroundWhile within-host and between-host models of HIV dynamics have been well studied at a single scale, connecting the immunological and epidemiological scales through multi-scale models is an emerging method to infer the synergistic dynamics of HIV at the individual and population levels.MethodsWe reviewed nine articles using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework that focused on the synergistic dynamics of HIV immunoepidemiological models at the individual and population levels.ResultsHIV immunoepidemiological models simulate viral immune dynamics at the within-host scale and the epidemiological transmission dynamics at the between-host scale. They account for longitudinal changes in the immune viral dynamics of HIV+ individuals, and their corresponding impact on the transmission dynamics in the population. They are useful to analyze the dynamics of HIV super-infection, co-infection, drug resistance, evolution, and treatment in HIV+ individuals, and their impact on the epidemic pathways in the population. We illustrate the coupling mechanisms of the within-host and between-host scales, their mathematical implementation, and the clinical and public health problems that are appropriate for analysis using HIV immunoepidemiological models.ConclusionHIV immunoepidemiological models connect the within-host immune dynamics at the individual level and the epidemiological transmission dynamics at the population level. While multi-scale models add complexity over a single-scale model, they account for the time varying immune viral response of HIV+ individuals, and the corresponding impact on the time-varying risk of transmission of HIV+ individuals to other susceptibles in the population.

Project description:Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological 'macroparasite' models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as 'hosts' and viral particles as 'macroparasites', and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell's multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.

Project description:Understanding mechanisms by which genetic variants predispose to complications of infectious diseases can lead to important benefits including the development of biomarkers to prioritize vaccination or prophylactic therapy. Family studies, candidate genes in animal models, and the absence of well-defined risks where the complications are rare all can point to genetic predisposition. The most common approach to assessing genetic risk is to conduct an association study, which is a case control study using either a candidate gene approach or a genome wide approach. Although candidate gene variants may focus on potentially causal variants, because other variants across the genome are not tested these studies frequently cannot be replicated. Genome wide association studies need a sizable sample and usually do not identify causal variants but variants which may be in linkage disequilibrium to the actual causal variant. There are many pitfalls that can lead to bias in such studies, including misclassification of cases and controls, use of improper phenotypes, and genotyping errors. These studies have been limited to common genes and rare variants may not be detected. As the use of next generation sequencing becomes more common, it can be anticipated that more variants will be confirmed. The purpose of this review article is to address the issue of genomics in infectious diseases with an emphasis on the host. Although there are a plentitude of studies that focus on the molecular characteristics of pathogens, there are far fewer studies that address the role of human genetics in the predisposition to infection or more commonly its complications. This paper will review both the approaches used to study host genetics in humans and the pitfalls associated with some of these methods. The focus will be on human disease and therefore discussion of the use of animal models will be limited to those where there are genes that have been replicated in humans. The paper will focus on common genetic variants that account for complex traits such as infectious diseases using examples from flaviviruses.

Project description:INTRODUCTION:Timely notification of infectious diseases is crucial for prompt response by public health services. Adequate notification systems facilitate timely notification. A systematic literature review was performed to assess outcomes of studies on notification timeliness and to determine which aspects of notification systems are associated with timely notification. METHODOLOGY:Articles reviewing timeliness of notifications published between 2000 and 2017 were searched in Pubmed and Scopus. Using a standardized notification chain, timeliness of reporting system for each article was defined as either sufficient (≥ 80% notifications in time), partly sufficient (≥ 50-80%), or insufficient (< 50%) according to the article's predefined timeframe, a standardized timeframe for all articles, and a disease specific timeframe. Electronic notification systems were compared with conventional methods (postal mail, fax, telephone, email) and mobile phone reporting. RESULTS:48 articles were identified. In almost one third of the studies with a predefined timeframe (39), timeliness of notification systems was either sufficient or insufficient (11/39, 28% and 12/39, 31% resp.). Applying the standardized timeframe (45 studies) revealed similar outcomes (13/45, 29%, sufficient notification timeframe, vs 15/45, 33%, insufficient). The disease specific timeframe was not met by any study. Systems involving reporting by laboratories most often complied sufficiently with predefined or standardized timeframes. Outcomes were not related to electronic, conventional notification systems or mobile phone reporting. Electronic systems were faster in comparative studies (10/13); this hardly resulted in sufficient timeliness, neither according to predefined nor to standardized timeframes. CONCLUSION:A minority of notification systems meets either predefined, standardized or disease specific timeframes. Systems including laboratory reporting are associated with timely notification. Electronic systems reduce reporting delay, but implementation needs considerable effort to comply with notification timeframes. During outbreak threats, patient, doctors and laboratory testing delays need to be reduced to achieve timely detection and notification. Public health authorities should incorporate procedures for this in their preparedness plans.

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Linked within-host and between-host models and data for infectious diseases: a systematic review.

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Linked within-host and between-host models and data for infectious diseases: a systematic review.

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