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. 2024 Sep 13;20(9):e1011609.
doi: 10.1371/journal.pcbi.1011609. eCollection 2024 Sep.

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

Monica Golumbeanu  1   2 , Olivier Briët  1   2 , Clara Champagne  1   2 , Jeanne Lemant  1   2 , Munir Winkel  1   2 , Barnabas Zogo  3 , Maximilian Gerhards  1   2 , Marianne Sinka  4 , Nakul Chitnis  1   2 , Melissa Penny  1   2   5   6 , Emilie Pothin  1   2 , Tom Smith  1   2
Affiliations

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

Monica Golumbeanu et al. PLoS Comput Biol. .

Abstract

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 quantifies the impact of vector control interventions depending on mosquito 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 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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of the AnophelesModel R package and its components.
The package integrates various types of input data (first panel) to parameterise an existing model of the mosquito feeding cycle [32] (middle panel). This model represents the feeding cycle states with letters A through E and transition probabilities PAi–PEi between consecutive states. The dotted, grey arrows indicate that mosquitoes can die at each stage. Blue lightning symbols indicate the probabilities affected by the vector control interventions included in the package. This model is used to infer the species-specific effects of vector control interventions, including the decay of these effects over time and their impact on vectorial capacity (third panel).
Fig 2
Fig 2. Examples of the key types of data available within the AnophelesModel database which can be used to estimate the impact of vector control interventions.
The package provides (A) entomological parameters, (B) mosquito biting patterns, (C) human activity patterns, and (D) intervention properties, which can be used to parameterise an entomological model of the mosquito feeding cycle. Examples are provided for An. gambiae and An. farauti in Kenya and Papua New Guinea (PNG) settings, respectively. In panel (A), the arrows indicate the bars corresponding to the two mosquito species. In panel (B), hourly biting rates are shown for both species, while the grey area highlights the time when people sleep under a net. Panel (C) displays the hourly proportions of people located indoors and outdoors for each geographical setting. Panel (D) summarizes the observed variation in physical properties of LLINs in a Kenya-like setting [9] for each semester (every 6 months) during 3 years. Data sources of all data types are specified in the “Design and Implementation” section.
Fig 3
Fig 3. Estimated effects of LLINs deployment for An. gambiae and An. farauti.
Mosquito, human and intervention data are combined in the AnophelesModel package to estimate the different types of intervention decay throughout time (A), as well as the resulting mean reduction in vectorial capacity for varying LLINs deployment coverages (here equivalent to LLINs usage) (B). The time units in panel (A) are defined by 100 equally distanced interpolation points across the duration of the interventions (i.e., 3 years for LLINs, with dotted lines marking each semester). The ribbons in panel (B) correspond to the variation of the vectorial capacity estimated based on the confidence intervals of the mosquito bionomics parameters (details on uncertainty propagation provided in Section D in S1 Text).
Fig 4
Fig 4. Estimated impact of various interventions for An. gambiae (solid curves) and An. farauti (dashed curves).
The impact was estimated for the three types of interventions available in the package (IRS, LLINs and House screening), as well as for the combination of IRS and LLINs. The ribbons correspond to the variation of the reduction in vectorial capacity estimated based on the confidence intervals of the mosquito bionomics parameters.
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