The shape of the contact–density function matters when modelling parasite transmission in fluctuating populations

Borremans, Benny; Reijniers, Jonas; Hens, Niel; Leirs, Herwig

doi:10.1098/rsos.171308

Cited by 29 publications

(40 citation statements)

References 48 publications

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“…K = 0.8–0.9 or K = 0.1–0.2; Figure ). Furthermore, a recent modelling study showed that even in cases where nonlinear (sigmoid) and linear transmission functions were so similar that they would be difficult to statistically differentiate, the functions had distinct impacts on the probabilities of pathogen invasion and persistence (Borremans, Reijniers, Hens, et al, ). Therefore, infectious disease modelling would benefit more careful and creative study designs that tease apart underlying transmission mechanisms and their relationships to host density, and from innovation in statistical or other methodological tools that improve our abilities to confidently compare and choose transmission functions, like our new contactr r package.…”

Section: Discussionmentioning

confidence: 99%

“…The more that density varies, the more that choosing the correct transmission function matters (Borremans, Reijniers, Hens, & Leirs, ). This choice is often simplified into a dichotomy between the two linear, canonical transmission functions: density‐dependent transmission (hereafter DD transmission) and density‐independent or frequency‐dependent transmission (hereafter FD transmission; Figure ; also see Appendix ).…”

Section: Introductionmentioning

confidence: 99%

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Systematic review of modelling assumptions and empirical evidence: Does parasite transmission increase nonlinearly with host density?

et al. 2020

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Host–parasite dynamics are impacted by the relationship between host density and parasite transmission, and thus, all epidemiological models contain a central transmission–density function. Recent theoretical work demonstrates that this central parasite transmission function might be best represented by a nonlinear continuum from one linear extreme to another: density‐dependent transmission at low host densities to density‐independent transmission at high host densities. But how often are nonlinear transmission functions used, and when are they better at describing transmission in real host–parasite systems? To quantify existing modelling practices, we systematically reviewed seven representative ecology journals, finding 262 studies containing host–parasite models that contained linear and/or nonlinear transmission functions. We also reviewed the literature to find 28 experimental and observational studies that compared multiple transmission functions in real host–parasite systems, and tallied which functions were best supported in those systems. Finally, we created a flexible model simulation tool to explore and quantify the bias in model parameter estimates that is created when using an inaccurate transmission function. We found that most experimental and observational studies reported that nonlinear transmission–density functions outperformed simple linear transmission–density functions, supporting recent theoretical work. In contrast, most studies containing host–parasite models assumed that host density was constant and/or used a single, linear transmission function to explain how transmission rates changed with density. Using the wrong linear function and/or using a linear function when the underlying transmission–density relationship is even slightly nonlinear can substantially bias model parameter estimates, as demonstrated by our simulations over a broad parameter space. Some modelling studies may be using linear functions in host–parasite systems where nonlinear functions are more appropriate. If true, these models would yield substantially biased parameter estimates. To avoid such biases that compromise ecological understanding and prediction, we recommend that future studies compare multiple transmission functions, including nonlinear options, whenever possible.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Systematic review of modelling assumptions and empirical evidence: Does parasite transmission increase nonlinearly with host density?

et al. 2020

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“…Mastomys natalensis has a promiscuous mating system and is not territorial or aggressive towards conspecifics (Kennis, Sluydts, Leirs, & Hooft, ). Home range overlap is generally high and increases significantly with abundance, suggesting contact rates to be density‐dependent, probably nonlinearly (Borremans et al, , , ). Given that MORV transmission is most likely density‐dependent, infection is predominantly acute, and the immune response is lifelong, it is surprising that MORV can persist during low host density periods, when host density would be expected to be below the N T (Goyens, Reijniers, Borremans, & Leirs, ).…”

Section: Methodsmentioning

confidence: 99%

“…Given the difference in persistence probability, understanding the role of these different parasite-host characteristics is an important prerequisite for the development of wildlife disease control programs (e.g. to predict the effectiveness of culling to eliminate a disease; Morters et al, 2012;Borremans, Reijniers, Hens, & Leirs, 2017 in natural conditions, we performed a small laboratory experiment in which wild-caught rodents were caged and sampled for 8 weeks.…”

Section: Introductionmentioning

confidence: 99%

Density dependence and persistence of Morogoro arenavirus transmission in a fluctuating population of its reservoir host

Mariën

Borremans

Verhaeren

et al. 2019

Journal of Animal Ecology

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A key aim in wildlife disease ecology is to understand how host and parasite characteristics influence parasite transmission and persistence. Variation in host population density can have strong impacts on transmission and outbreaks, and theory predicts particular transmission–density patterns depending on how parasites are transmitted between individuals. Here, we present the results of a study on the dynamics of Morogoro arenavirus in a population of multimammate mice (Mastomys natalensis). This widespread African rodent, which is also the reservoir host of Lassa arenavirus in West Africa, is known for its strong seasonal density fluctuations driven by food availability. We investigated to what degree virus transmission changes with host population density and how the virus might be able to persist during periods of low host density. A seven‐year capture–mark–recapture study was conducted in Tanzania where rodents were trapped monthly and screened for the presence of antibodies against Morogoro virus. Observed seasonal seroprevalence patterns were compared with those generated by mathematical transmission models to test different hypotheses regarding the degree of density dependence and the role of chronically infected individuals. We observed that Morogoro virus seroprevalence correlates positively with host density with a lag of 1–4 months. Model results suggest that the observed seasonal seroprevalence dynamics can be best explained by a combination of vertical and horizontal transmission and that a small number of animals need to be infected chronically to ensure viral persistence. Transmission dynamics and viral persistence were best explained by the existence of both acutely and chronically infected individuals and by seasonally changing transmission rates. Due to the presence of chronically infected rodents, rodent control is unlikely to be a feasible approach for eliminating arenaviruses such as Lassa virus from Mastomys populations.

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“…In addition to assumptions about the likely values of a few poorly understood model parameters, our approach relies on a relatively simple mathematical model that simplifies population age structure, assumes density dependent infection and spatially homogenous transmission rates. Relaxing these assumptions could, in principle, alter our quantitative estimates for some model parameters (Borremans et al 2017). Finally, our estimates of parameters are based on data collected over 14 years ago, creating the possibility that ecological, sociological, or evolutionary change in Lassa virus, the reservoir M. natalensis , or human populations with which the reservoir is commensal have caused values of important parameters to change.…”

Section: Discussionmentioning

confidence: 99%

Bayesian estimation of Lassa virus epidemiological parameters: implications for spillover prevention using wildlife vaccination

Nuismer

Remien

Basinski

et al. 2019

Preprint

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32Lassa virus is a significant burden on human health throughout its endemic region in 33 West Africa, with most human infections the result of spillover from the primary rodent 34 reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a 35 Bayesian methodology for estimating epidemiological parameters of Lassa virus within its 36 rodent reservoir and for generating probabilistic predictions for the efficacy of rodent 37 vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to 38 integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa 39 virus surveillance data from rodent populations. Using simulated data, we show that our 40 method accurately estimates key model parameters, even when surveillance data are available 41 from only a relatively small number of points in space and time. Applying our method to 42 previously published data from two villages in Guinea estimates the time-averaged of Lassa 0 43 virus to be 1.658 and 1.453 for rodent populations in the villages of Bantou and Tanganya, 44 respectively. Using the posterior distribution for model parameters derived from these 45 Guinean populations, we evaluate the likely efficacy of vaccination programs relying on 46 distribution of vaccine-laced baits. Our results demonstrate that effective and durable 47 reductions in the risk of Lassa virus spillover into the human population will require repeated 48 distribution of large quantities of vaccine. 49 3 50 Author Summary 51 Lassa virus is a chronic source of illness throughout West Africa, and is considered to be a threat 52 for widespread emergence. Because most human infections result from contact with infected 53 rodents, interventions that reduce the number of rodents infected with Lassa virus represent 54 promising opportunities for reducing the public health burden of this disease. Evaluating how 55 well alternative interventions are likely to perform is complicated by our relatively poor 56 understanding of viral epidemiology within the reservoir population. Here we develop a novel 57 statistical approach that couples mathematical models and viral surveillance data from rodent 58 populations to robustly estimate key epidemiological parameters. Applying our method to 59 existing data from Guinea yields well-resolved parameter estimates and allows us to simulate a 60 variety of rodent vaccination programs. Together, our results demonstrate that rodent 61 vaccination alone is unlikely to be an effective tool for reducing that public health burden of 62 Lassa fever within West Africa. 63 65 Lassa virus is a zoonotic pathogen endemic to West Africa where it poses a significant 66 burden on human health (Buckley et al. 1970). Although only a relatively small proportion of 67 human cases result in severe symptoms and mortality, human infection is common, with 8-52% 68 of the human population within Sierra Leone seropositive (McCormick et al. 1987; Bausch et al. 69 2001; Dan-Nwafor et al. 2019), indicative of past...

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The shape of the contact–density function matters when modelling parasite transmission in fluctuating populations

Cited by 29 publications

References 48 publications

Systematic review of modelling assumptions and empirical evidence: Does parasite transmission increase nonlinearly with host density?

Systematic review of modelling assumptions and empirical evidence: Does parasite transmission increase nonlinearly with host density?

Density dependence and persistence of Morogoro arenavirus transmission in a fluctuating population of its reservoir host

Bayesian estimation of Lassa virus epidemiological parameters: implications for spillover prevention using wildlife vaccination

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