On Modelling Microbial Infections

Morgan, Byron J. T.; Watts, Simon A.

doi:10.2307/2529985

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…The success with which these data allowed us to detect effects of demographic stochasticity and a dynamic immune system suggests that response times do indeed provide substantial information about within-host pathogen growth, despite the Morgan and Watts (1980) argument that responsetime data are insufficient for model comparison. A key difference between their work and ours, however, is that we took a Bayesian approach to estimate our model parameters.…”

Section: Discussionmentioning

confidence: 99%

“…This is important because estimating some of the model parameters from response-time data alone is very difficult (Morgan and Watts 1980). For example, unreasonably low estimates of the median threshold population size N can produce reasonable speeds of kill if the estimate of the virus growth rate parameter f is also very low.…”

Section: Bayesian Inferencementioning

confidence: 99%

“…To meet the significantly greater computing requirements of our models, we used a highly parallel computing environment, and we developed a new form of Markov chain Monte Carlo algorithm that takes advantage of this environment (Kennedy et al 2014b). Because response-time data are sometimes insufficient to choose between models (Morgan and Watts 1980), we constrained the values of some parameters by using previously collected experimental data, while estimating other parameters directly from responsetime data.…”

Section: Introductionmentioning

confidence: 99%

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Pathogen Growth in Insect Hosts: Inferring the Importance of Different Mechanisms Using Stochastic Models and Response-Time Data

Kennedy

Dukić

Dwyer

2014

The American Naturalist

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Pathogen population dynamics within individual hosts can alter disease epidemics and pathogen evolution, but our understanding of the mechanisms driving within-host dynamics is weak. Mathematical models have provided useful insights, but existing models have only rarely been subjected to rigorous tests, and their reliability is therefore open to question. Most models assume that initial pathogen population sizes are so large that stochastic effects due to small population sizes, so-called demographic stochasticity, are negligible, but whether this assumption is reasonable is unknown. Most models also assume that the dynamic effects of a host's immune system strongly affect pathogen incubation times or "response times," but whether such effects are important in real host-pathogen interactions is likewise unknown. Here we use data for a baculovirus of the gypsy moth to test models of within-host pathogen growth. By using Bayesian statistical techniques and formal model-selection procedures, we are able to show that the response time of the gypsy moth virus is strongly affected by both demographic stochasticity and a dynamic response of the host immune system. Our results imply that not all response-time variability can be explained by host and pathogen variability, and that immune system responses to infection may have important effects on population-level disease dynamics.

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Section: Discussionmentioning

confidence: 99%

Section: Bayesian Inferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pathogen Growth in Insect Hosts: Inferring the Importance of Different Mechanisms Using Stochastic Models and Response-Time Data

Kennedy

Dukić

Dwyer

2014

The American Naturalist

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“…Thus, as far as we continue to rely on observed frequencies and arbitrarily chosen specific distributions, it is essential that comparisons using different distributions are made; any assumptions should be explicitly evaluated by means of significance tests and visual assessments. Second, the biological validity of assuming specific distributions for the incubation period remains an open question [100,101], and thus, further information is needed. For example, within-host dynamics would help clarify disease onset mechanisms in the most explicit way [102].…”

Section: A Further Critique Of the Lognormal Assumptionmentioning

confidence: 99%

Early efforts in modeling the incubation period of infectious diseases with an acute course of illness

Nishiura

2007

Emerg Themes Epidemiol

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The incubation period of infectious diseases, the time from infection with a microorganism to onset of disease, is directly relevant to prevention and control. Since explicit models of the incubation period enhance our understanding of the spread of disease, previous classic studies were revisited, focusing on the modeling methods employed and paying particular attention to relatively unknown historical efforts. The earliest study on the incubation period of pandemic influenza was published in 1919, providing estimates of the incubation period of Spanish flu using the daily incidence on ships departing from several ports in Australia. Although the study explicitly dealt with an unknown time of exposure, the assumed periods of exposure, which had an equal probability of infection, were too long, and thus, likely resulted in slight underestimates of the incubation period.After the suggestion that the incubation period follows lognormal distribution, Japanese epidemiologists extended this assumption to estimates of the time of exposure during a point source outbreak. Although the reason why the incubation period of acute infectious diseases tends to reveal a right-skewed distribution has been explored several times, the validity of the lognormal assumption is yet to be fully clarified. At present, various different distributions are assumed, and the lack of validity in assuming lognormal distribution is particularly apparent in the case of slowly progressing diseases. The present paper indicates that (1) analysis using well-defined short periods of exposure with appropriate statistical methods is critical when the exact time of exposure is unknown, and (2) when assuming a specific distribution for the incubation period, comparisons using different distributions are needed in addition to estimations using different datasets, analyses of the determinants of incubation period, and an understanding of the underlying disease mechanisms.

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“…Morgan and Watts tested the Williams model against data reported by Sartwell and found that there was poor identifiability of parameters, although the model provided good fit. In particular, the time to effect data only provided good estimates of the difference between birth and death rates, μ – λ, and also the estimates of the threshold burden to effect (N), appeared implausible.…”

Section: Experimental Frameworkmentioning

confidence: 99%

Microbial Dose Response Modeling: Past, Present, and Future

Haas

2015

Environ. Sci. Technol.

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The understanding of the risk to humans from exposure to pathogens has been firmly put into a risk assessment framework. A key element of applying this approach is the understanding of the relationship between dose and response for particular pathogens. This understanding has progressed from early use of threshold concepts ("minimal infectious dose") thru multiple generations of models. Generation 1 models describe probability of response to exposed dose. Generation 2 models incorporate host factors (e.g., age) and/or pathogen factors (e.g., particle size of inhaled agents). Generation 3 models describe the rate at which effects develop, i.e. the epidemic curve. These (generation 1 through three models) have been developed and used in multiple contexts. Beyond Generation 3 lies an opportunity for the deep incorporation of in vivo physiological responses and the coupling of the individual host dynamics to the dynamics of spread of contagious diseases in the population. This would enable more direct extrapolation from controlled dosing studies to estimate population level effects. There remain also needs to understand broader categories of infectious agents, including pathogenic amoebae and fungi. More advanced models need to be validated against well-characterized human outbreak data.

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On Modelling Microbial Infections

Cited by 5 publications

References 7 publications

Pathogen Growth in Insect Hosts: Inferring the Importance of Different Mechanisms Using Stochastic Models and Response-Time Data

Pathogen Growth in Insect Hosts: Inferring the Importance of Different Mechanisms Using Stochastic Models and Response-Time Data

Early efforts in modeling the incubation period of infectious diseases with an acute course of illness

Microbial Dose Response Modeling: Past, Present, and Future

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