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Dose-response relationships for environmentally mediated infectious disease transmission models.

ABSTRACT: Environmentally mediated infectious disease transmission models provide a mechanistic approach to examining environmental interventions for outbreaks, such as water treatment or surface decontamination. The shift from the classical SIR framework to one incorporating the environment requires codifying the relationship between exposure to environmental pathogens and infection, i.e. the dose-response relationship. Much of the work characterizing the functional forms of dose-response relationships has used statistical fit to experimental data. However, there has been little research examining the consequences of the choice of functional form in the context of transmission dynamics. To this end, we identify four properties of dose-response functions that should be considered when selecting a functional form: low-dose linearity, scalability, concavity, and whether it is a single-hit model. We find that i) middle- and high-dose data do not constrain the low-dose response, and different dose-response forms that are equally plausible given the data can lead to significant differences in simulated outbreak dynamics; ii) the choice of how to aggregate continuous exposure into discrete doses can impact the modeled force of infection; iii) low-dose linear, concave functions allow the basic reproduction number to control global dynamics; and iv) identifiability analysis offers a way to manage multiple sources of uncertainty and leverage environmental monitoring to make inference about infectivity. By applying an environmentally mediated infectious disease model to the 1993 Milwaukee Cryptosporidium outbreak, we demonstrate that environmental monitoring allows for inference regarding the infectivity of the pathogen and thus improves our ability to identify outbreak characteristics such as pathogen strain.

SUBMITTER: Brouwer AF

PROVIDER: S-EPMC5400279 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Dose-response relationships for environmentally mediated infectious disease transmission models.

Brouwer Andrew F AF Weir Mark H MH Eisenberg Marisa C MC Meza Rafael R Eisenberg Joseph N S JNS

PLoS computational biology 20170407 4

Environmentally mediated infectious disease transmission models provide a mechanistic approach to examining environmental interventions for outbreaks, such as water treatment or surface decontamination. The shift from the classical SIR framework to one incorporating the environment requires codifying the relationship between exposure to environmental pathogens and infection, i.e. the dose-response relationship. Much of the work characterizing the functional forms of dose-response relationships h ...[more]

PMID: 28388665

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Project description:BackgroundMathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical models to guide public health interventions lies in the ability to reliably estimate model parameters and their corresponding uncertainty. Here, we present and illustrate a simple computational method for assessing parameter identifiability in compartmental epidemic models.MethodsWe describe a parametric bootstrap approach to generate simulated data from dynamical systems to quantify parameter uncertainty and identifiability. We calculate confidence intervals and mean squared error of estimated parameter distributions to assess parameter identifiability. To demonstrate this approach, we begin with a low-complexity SEIR model and work through examples of increasingly more complex compartmental models that correspond with applications to pandemic influenza, Ebola, and Zika.ResultsOverall, parameter identifiability issues are more likely to arise with more complex models (based on number of equations/states and parameters). As the number of parameters being jointly estimated increases, the uncertainty surrounding estimated parameters tends to increase, on average, as well. We found that, in most cases, R0 is often robust to parameter identifiability issues affecting individual parameters in the model. Despite large confidence intervals and higher mean squared error of other individual model parameters, R0 can still be estimated with precision and accuracy.ConclusionsBecause public health policies can be influenced by results of mathematical modeling studies, it is important to conduct parameter identifiability analyses prior to fitting the models to available data and to report parameter estimates with quantified uncertainty. The method described is helpful in these regards and enhances the essential toolkit for conducting model-based inferences using compartmental dynamic models.

Project description:More than 70 million people worldwide are still infected with the hepatitis C virus 30 years after its discovery, underscoring the need for a vaccine. To develop an effective prophylactic vaccine, detailed knowledge of the correlates of protection and an immunocompetent surrogate model are needed. In this study, we describe the minimum dose required for robust equine hepacivirus (EqHV) infection in equids and examined how this relates to duration of infection, seroconversion, and transcriptomic responses. To investigate mechanisms of hepaciviral persistence, immune response, and immune- mediated pathology, we inoculated eight EqHV naive horses with doses ranging from 1 to 2 to 1.3 106 RNA copies per inoculation. We characterized infection kinetics, pathology, and transcriptomic responses via new generation sequencing. The minimal infec- tious dose of EqHV in horses was estimated at 13 RNA copies, whereas 6 to 7 copies were insufficient to cause infection. Peak viremia did not correlate with infectious dose, while seroconversion and duration of infection appeared to be affected. Notably, sero- conversion was undetectable in the low-dose infections within the surveillance period (40 to 50 days). In addition, transcriptomic analysis revealed a nearly dose dependent effect, with greater immune activation and inflammatory response observed in high-dose infections than in low-dose infections. Interestingly, inoculation with 6 to 7 copies of RNA that did not result in productive infection was associated with a strong immune response, similar to that observed in the high-dose infections. IMPORTANCE We demonstrated that the EqHV dose of infection plays an important role for inducing immune responses, possibly linked to early clearance in high-dose and prolonged viremia in low-dose infections. In particular, pathways associated with innate and adaptive immune responses, as well as inflammatory responses, were more strongly upregulated in high-dose infections than in lower doses. Hence, inoculation with low doses may enable EqHV to evade strong immune responses in the early phase and therefore promote robust, long-lasting infection.

Project description:Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.

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Dose-response relationships for environmentally mediated infectious disease transmission models.

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Dose-response relationships for environmentally mediated infectious disease transmission models.

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