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Champagne, C.

Publications and source records attributed to Champagne, C..

3 recordsLinked to original sources

Estimating mosquito bionomics parameters with a hierarchical Bayesian model

BackgroundThe malaria transmission potential and the vulnerability of Anopheles mosquitoes to different vector control methods depend, among other factors, on the endophily, endophagy, anthropophagy and survival of each species. Local information on these bionomic parameters is generally unavailable. MethodsTo address this, we estimated species-specific values of these parameters using an augmented version of the global database of bionomics data by Massey et al. (2016). We applied inclusion and exclusion criteria to select eligible studies with relevant experimental designs that minimise bias from collection methods for parous, sac, endophagy, and endophily rates as well as for the resting duration. For the human blood index (HBI), we separated data from indoor and outdoor collections. We fitted hierarchical Bayesian models with levels based on Anopheles taxonomy to estimate these quantities. Based on the estimated bionomics, we quantified the expected vectorial capacity reduction after the introduction of a pyrethroid-pyrrole insecticide-treated net (ITN) for 57 Anopheles species. ResultsWe identified 26 eligible studies for endophagy and 61 for the parous rate, leading to a Bayesian posterior average for the Anopheles genus of 42% (95% credible interval: 18-70) and 55% (32-77) respectively. HBI values widely varied depending on the location of collection, except for some species showing strong anthropophilic behaviours. Resting duration was estimated to be 2.1 days (1.2 - 4.8) at the genus level. Few studies were available to estimate the sac and endophily rates, which prevented us from deriving precise estimates for the whole Anopheles genus. Our estimates of the vectorial capacity reduction following the introduction of a pyrrole-pyrethroid ITN ranged between 48% and 76% across species, highlighting the important differences among mosquito species in vulnerability to vector control interventions. ConclusionThis work demonstrates how data from both Anopheles species complexes and individual species can be leveraged to generate species-specific estimates of bionomic parameters, capturing the local characteristics and behaviour of malaria vectors. The dataset is readily updatable as new data become available. However, more frequent and standardised field surveys are still needed to accurately characterise local vector behaviour.

zoology↗

AnophelesModel: An R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact

In recent decades, field and semi-field studies of malaria transmission have gathered geographic-specific information about mosquito ecology, behaviour and their sensitivity to interventions. Mathematical models of malaria transmission can incorporate such data to infer the likely impact of vector control interventions and hence guide malaria control strategies in various geographies. To facilitate this process and make model predictions of intervention impact available for different geographical regions, we developed AnophelesModel. AnophelesModel is an online, open-access, R package that directly allows incorporating generated entomological data for adjustment of models to assess intervention scenarios according to species and location-specific characteristics. In addition, it includes a previously published, comprehensive, curated database of field entomological data from over 50 Anopheles species, field data on mosquito and human behaviour, and on estimates of vector control effectiveness. Using the input data, the package parameterizes a discrete-time, state transition model of the mosquito oviposition cycle and infers species-specific impacts of various interventions on vectorial capacity. In addition, it offers formatted outputs ready to use in downstream analyses and by other models of malaria transmission for accurate representation of the vector-specific components. Using AnophelesModel, we show how the key implications for intervention impact change for various vectors and locations. The package facilitates quantitative comparisons of likely intervention impacts in different geographical settings varying in vector compositions, and can thus guide towards more robust and efficient malaria control recommendations. The AnophelesModel R package is available under a GPL-3.0 license at https://github.com/SwissTPH/AnophelesModel.

ecology↗

Dengue modeling in rural Cambodia: statistical performance versus epidemiological relevance

Dengue dynamics are shaped by the complex interplay between several factors, including vector seasonality, interaction between four virus serotypes, and inapparent infections. However, paucity or quality of data do not allow for all of these to be taken into account in mathematical models. In order to explore separately the importance of these factors in models, we combined surveillance data with a local-scale cluster study in the rural province of Kampong Cham (Cambodia), in which serotypes and asymptomatic infections were documented. We formulate several mechanistic models, each one relying on a different set of hypotheses, such as explicit vector dynamics, transmission via asymptomatic infections and coexistence of several virus serotypes. Models are confronted with the observed time series using Bayesian inference, through Markov chain Monte Carlo. Model selection is then performed using statistical information criteria, but also by studying the coherence of epidemiological characteristics (reproduction numbers, incidence proportion, dynamics of the susceptible class) in each model. Considering the available data, our analyses on transmission dynamics in a rural endemic setting highlight both the importance of using two-strain models with interacting effects and the lack of added value of incorporating vector and explicit asymptomatic components.

epidemiology↗