# Spatial modeling of the population dynamics of Anopheles mosquitoes in Madagascar

Hobiniaina Anthonio Rakotoarison, Thiery Nirina Jean Jose Nepomichene,

Helene Guis, Romain Girod, Solofoarisoa Rakotoniaina, Fanjasoa

Rakotomanana, Annelise Tran

► To cite this version:

Hobiniaina Anthonio Rakotoarison, Thiery Nirina Jean Jose Nepomichene, Helene Guis, Romain Girod, Solofoarisoa Rakotoniaina, et al. Spatial modeling of the population dynamics of Anopheles mosquitoes in Madagascar. International Journal of Health Geographics, 2025, 24, 15 p. 10.1186/s12942-025-00424-8. hal-05382643

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# Spatial modeling of the population dynamics of *Anopheles* mosquitoes in Madagascar

Hobiniaina Anthonio Rakotoarison¹,²,³,⁴*, Thiery Nirina Nepomichene⁵, Hélène Guis¹,⁶,⁷,⁸, Romain Girod⁵, Solofoarisoa Rakotoniaina⁴, Fanjasoa Rakotomanana¹ and Annelise Tran²,³,⁷,⁹

**Abstract**

**Background** Malaria, whose parasites are transmitted by *Anopheles* mosquitoes, remains a major public health burden in Madagascar despite the control measures led by the National Malaria Control Program. Understanding the population dynamics of *Anopheles* mosquitoes is therefore essential to optimize malaria surveillance and control. This study aimed to develop a model incorporating environmental, climatic and agricultural determinants of *Anopheles* abundance to predict their spatiotemporal distribution.

**Methods** We developed a model of spatiotemporal dynamics for four *Anopheles* species, vectors of malaria parasite in Madagascar: *Anopheles arabiensis*, *Anopheles coustani*, *Anopheles funestus* and *Anopheles gambiae*. This model was based on the life cycle of *Anopheles* and accounted for both the aquatic and aerial phases of their development. It used a system of differential equations to estimate the number of *Anopheles* mosquitoes at each stage of development. The Ocelet language, dedicated to the modeling of spatial dynamics, was used to produce simulations based on climate and environmental data. The model explicitly integrates the agricultural calendar to adjust the environmental carrying capacity of larval habitats. Model outputs were validated with entomological data collected in Vohimasy (Farafangana districts, 2014–2017).

**Results** 24 simulation outputs, from three *Anopheles* species and eight sites, were obtained and the validation revealed a significant correlation between field observations and model predictions: the correlation coefficients obtained ranged from 0.70 to 0.76. The predicted abundance of host-seeking *Anopheles* varied seasonally influenced by precipitation, temperature and environmental carrying capacity. The model exhibited robustness across sites with diverse climates and accurately reproduced interannual dynamics. The integration of the agricultural calendar significantly reduced the overestimation of the density of host-seeking adult females.

**Conclusion** The developed *Anopheles* dynamics model provides a valuable tool for predicting mosquito abundance and distribution over time and space. It correctly predicted the abundance at villages with contrasting climates and reproduced interannual dynamics well. A distinctive aspect of this work lies in the explicit integration of seasonal agricultural practices into the estimation of larval habitat availability. This allows for a more accurate and transferable modeling of *Anopheles* population dynamics.

Full list of author information is available at the end of the article

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© The Author(s) 2025. **Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

<footnote>*Correspondence:
Hobiniaina Anthonio Rakotoarison
a.rakotoarison@gmail.com</footnote>
**Keywords** Agricultural calendar integration, *Anopheles* population dynamics, Madagascar, Mechanistic model, Spatiotemporal simulation

**Background**

Mosquitoes are among the common biting insects, and they are also the main vectors of pathogens such as viruses and parasites that can induce diseases such as dengue, Zika, chikungunya, filariasis, and malaria. The latter is the most common parasitic infection and continues to kill hundreds of thousands of people each year [1]. Malaria affects approximately 85 countries, particularly in tropical areas. In 2022, globally, the World Health Organization (WHO) estimated there were 249 million cases of malaria. Africa alone accounts for 92% of malaria cases [2].

In Madagascar, malaria continues to pose a significant public health challenge. Following a period of stability between 2018 and 2019, characterized by an incidence rate of 36.9‰, a notable increase was observed in 2020, with the incidence rising to 70.6‰, before reaching its peak in 2021 at 81.3‰ [3]. This resurgence has had a particularly pronounced impact on children aged 0 to 15 years [3]. Malaria transmission in Madagascar is heterogeneous and characterized by two epidemiological profiles [4]. In the Highlands and the subdesert South, malaria is unstable and is characterized by low transmission. In contrast, it is stable and marked by high transmission in the coastal regions. Despite the preventive and control measures implemented by the Ministry of Public Health, such as the indoor residual spraying, the distribution of long-lasting insecticidal nets, and intermittent preventive treatment for pregnant women, malaria persists in the country and remains a major public health problem.

*Plasmodium*, the pathogen responsible for malaria, is transmitted by certain species of mosquitoes of the genus *Anopheles*. Thus, malaria transmission is strongly influenced by the population dynamics of *Anopheles* [5, 6], whose aquatic stages (eggs, larvae, and pupae) as well as the aerial adult stage are affected by environmental and climatic conditions [5–7]. *Anopheles*, like all mosquitoes, are very sensitive to the temperature of the environment in which they live because they are poikilothermic, meaning that their body temperature is not stable and is highly dependent on the outside temperature [8]. Temperature also strongly impacts the length of development of the various stages of the mosquito life cycle by modulating their metabolism, which accelerates or slows down the transition between stages [9]. It also influences the development of pathogens in female *Anopheles*, particularly Plasmodium, by affecting the rates of maturation of parasitic forms, which conditions the duration of extrinsic incubation [5, 10]. Precipitation can greatly affect the

availability of breeding sites. Environmental and climatic factors therefore strongly influence the distribution and spatiotemporal dynamics of malaria [11–14]. Global and climatic changes are altering landscape structures and disrupting the population dynamics of *Anopheles* in various ways, by altering their habitats, modifying breeding conditions, and influencing their life cycle, which directly impacts the emergence and spread of malaria [15]. For example, rising temperatures accelerate the mosquito life cycle and favor parasite development at moderate temperatures. Changes in precipitation patterns can increase breeding sites or, conversely, reduce them during drought periods. Deforestation, by creating temporary pools, and urbanization, by increasing stagnant water areas, also generate new habitats conducive to vectors. Finally, extreme weather events, such as floods, contribute to the emergence of new transmission hotspots [10, 15].

The dynamics of *Anopheles* mosquito populations are a key determinant of malaria transmission, influencing its intensity, duration, and seasonality [16]. These dynamics affect both the life cycle of the vectors and their competence in transmitting Plasmodium [9, 10]. Understanding these dynamics helps to better anticipate high-risk periods and areas, tailor control strategies, and optimize targeted interventions. Modeling these dynamics is a valuable tool for improving surveillance and enhancing the effectiveness of public health actions [17].

Vector control is one of the main measures to prevent malaria transmission in Madagascar [18]. The ability to predict *Anopheles* dynamics becomes essential to help public health actors in malaria control. For example, one study showed that climatic factors strongly influence the dynamics of *Anopheles arabiensis* populations, which is crucial for assessing the effectiveness of control strategies [19]. Another model demonstrated that vector control interventions, such as insecticide-treated bed nets, affect the dynamics of *An. gambiae* populations, highlighting the importance of density-dependent regulation [20]. A further study integrated both abiotic and biotic factors to predict population dynamics and refine malaria control strategies [21]. In this context, modeling plays an important role, as it allows to describe the biology of *Anopheles*, and can act as a decision support tool for *Anopheles* surveillance and control. Several studies have resulted in the development of mosquito population dynamics models while highlighting the importance of climate and environment [19, 22–25]. Cailly et al. [22] developed a generic climate-based mosquito population model to evaluate vector control strategies, and found that climatic conditions strongly influence mosquito abundance,
requiring control strategies adapted to these variations. In 2016, Abiodun et al. [19] investigated the influence of climatic factors on the temporal dynamics of *An. arabiensis* in the KwaZulu-Natal Province, South Africa, and found that these factors significantly affect the reproduction, maturation, and survival of this population. The generic mosquito population model [22] was applied in different geographical contexts and for different mosquito species, such as *Aedes albopictus* in Reunion Island [23, 25], and Rift Valley fever mosquito vectors in Senegal [24] and Botswana [26]. To our knowledge, this study is the first to explicitly incorporate local agricultural seasonality, particularly rice cultivation calendars, into a mechanistic framework for the dynamic estimation of the environmental carrying capacity of Anopheles larval habitats, and subsequently, the population dynamics of *Anopheles*. This integration formalizes a factor hitherto not accounted for in mechanistic mosquito population models, while providing a more accurate representation of the temporal availability of breeding habitats beyond climate-only approaches. Furthermore, our study simultaneously considers four *Anopheles* species that transmit malaria in Madagascar (*An. arabiensis*, *An. coustani*, *An. funestus* and *An. gambiae*). Some biological parameters

were species-specific, while others, due to limited data or assumed similarities, were shared across species. This multi-species approach, which remains uncommon in mechanistic models, offers an improved representation of local vector dynamics without introducing excessive model complexity.

The objective of this study was to understand and predict the dynamics of *Anopheles* vectors of malaria in Madagascar by adapting the generic model developed by Cailly et al. [22] to the Malagasy context. This model accounted for both the temporal and spatial dimensions of *Anopheles* abundance.

# Methods

## Study areas

Our study areas were three districts of Madagascar with different environments and climates: Farafangana District, Maevatanana District and Morondava District (Fig. 1).

Farafangana district, in the Atsimo Atsinanana region, is located in the southeastern part of Madagascar. Its altitude varies from 0 to 172 m. It is characterized by a hot and humid tropical climate. Although rainfall persists almost year-round, a less humid season is observed from April to August when drizzle replaces the heavy thunderstorms of November to March. This region is known for its high malaria incidence. The average annual rainfall in Farafangana District can reach a value of more than 2,500 mm [27]. The average temperature ranges from 15 °C in the dry season to 29 °C in the hot season [27].

Belonging to the Betsiboka region, Maevatanana District is located in the northwestern margins of the Central Highlands of Madagascar. It reaches an altitude of 1,479 m. The climate is tropical with an alternating dry season (April to November) and wet season (December to March). The average rainfall varies from 929 mm to 1,519 mm per year, while the average annual temperature is 21 °C. Although the region experiences a dry season, malaria transmission remains a concern due to seasonal variations in mosquito breeding, particularly in wetland areas.

The district of Morondava is located in western coast of Madagascar in the Menabe region. It is characterized by gently sloping land at an altitude between 0 m and 484 m. The average annual temperature is 19 °C, while the average annual rainfall is 570 mm. Despite its more arid conditions, Morondava faces significant malaria transmission due to the presence of wetlands and rivers, which create favorable breeding grounds for mosquitoes, particularly during the wet season.

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Location of the three study areas</figcaption></figure>
# Data used

## Climatic data

Temperature and precipitation play a very important role in the life cycle of *Anopheles* [7].

The temperature and precipitation data used in this study were obtained from the International Research Institute (IRI) library (http://iridl.ldeo.columbia.edu/). These are meteorological estimates derived from remote sensing data from 2013 to 2017. Temperature data are provided in eight-day increments with a spatial resolution of 1 km, while precipitation data are available by decade with a spatial resolution of 12 km.

## Environmental data

The environment plays an important role in the life cycle of mosquitoes. The development of *Anopheles* is highly dependent on the land use, particularly wetlands that are potential breeding sites [28–30].

Landsat 8 satellite images were used to produce land use maps of the three study areas while focusing on wetlands representing potential breeding sites. These satellite images were downloaded from the United States Geological Survey (USGS) Earth Explorer website (https://earthexplorer.usgs.gov/). Wetlands (agricultural crops, water bodies, rivers and rice fields), which are potential breeding sites, were highlighted using supervised pixel-based classification. These land use types correspond to larval breeding sites of different *Anopheles* species. For rivers and water bodies, a buffer zone of two meters on each side of the edges of these two types of land use was

set up and considered hatching sites. We assumed that small hive sites, not detected by satellite imagery, were already accounted for in the wetlands.

## Description of the model

We adapted the generic mosquito dynamics model developed by Cailly et al. [22] to *Anopheles*. The initial model is a mechanistic model based on the life cycle process of mosquitoes with an aquatic phase (eggs, larvae and nymphs) and an aerial phase (adults). In this model, only female mosquitoes are considered. After emergence, the females mate and then take blood meals and lay their eggs, and the cycle starts again. The mosquito population is divided into ten compartments corresponding to the different developmental stage of mosquitoes (Fig. 2).

First, the eggs () become larvae () which then become pupae (). After emergence, the emerging females () are considered nulliparous (i.e., females that have never laid eggs) in search of a host (1h), i.e., in search of a blood meal, then engorged (1), and then in search for an oviposition site (1). After having laid eggs once, they become parous females in search of a host (2h), then engorged (2) and finally in search for an oviposition site (2) (Fig. 2). Between each compartment, the mosquitoes move from one stage to another or die. To estimate the number of individuals for each developmental stage, a system of differential equations (Additional file 1) was used [22].

<figure><img src="image_5.png" /><figcaption>**Fig. 2** Compartment model representing the mosquito population dynamic</figcaption></figure>
## Parameters and functions of the model

Transitions from one stage of *Anopheles* development to another, as well as mortality rates, are governed by parameters that do not vary in time or space (noted with Greek letters in Fig. 2) or functions (noted with Latin letters in Fig. 2) that depend on rainfall, temperature or environmental carrying capacity (i.e., the availability of breeding sites in the environment). The parameters used in the model are listed in Table 1, whereas Table 2 shows the functions used. They were defined from literature reviews and experiments. Some parameters were specific to each *Anopheles* species, while others were generalized to the *Anopheles* genus when species information was not available. The parameters that significantly influence variations in the peak of adult abundance, such as the standard environment carrying capacity, the mortality rate at emergence, the sex-ratio, and the number of eggs laid by parous females, were identified as particularly sensitive during the model’s sensitivity analysis (see [22] for details). To ensure robust results, it is essential to measure these variables with optimal precision, as they play a key role in the dynamics of the modeled populations.

Larval and pupal dynamics are dependent on the availability and the suitability of breeding sites, expressed as the environmental carrying capacity. The environmental carrying capacity function (Eq. 1) depends on precipitation. Indeed, precipitation influences the availability of breeding sites [19].

$$k_X = K_{fix}(t) + (K_{var}(t) * P_{norm}(t)), \quad X \in \{L, P\} \quad (1)$$

<table><caption>**Table 1** Parameters used in the model</caption><thead><tr><th>Notation</th><th>Definition</th><th>An. arabiensis</th><th>An. coustani</th><th>An. funestus</th><th>An. gambiae</th><th>Reference</th></tr></thead><tbody><tr><td>β<sub>1</sub></td><td>Number of eggs laid by nulliparous female/oviposition</td><td>48</td><td>72</td><td>68</td><td>71</td><td>[40, 45, 47]</td></tr><tr><td>β<sub>2</sub></td><td>Number of eggs laid by parous female/oviposition</td><td>64</td><td>96</td><td>108</td><td>105</td><td>[40, 45, 47]</td></tr><tr><td>σ</td><td>Sex-ratio at emergence</td><td>0.82</td><td>0.72</td><td>0.72</td><td>0.61</td><td>[41, 42, 46, 49]</td></tr><tr><td>γ<sub>Aem</sub></td><td>Development rate of emerging adults (day<sup>-1</sup>)</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td><td>[43]</td></tr><tr><td>γ<sub>Ah</sub></td><td>Transition rate from host-seeking to engorged adults (day<sup>-1</sup>)</td><td>2</td><td>2</td><td>2</td><td>2</td><td>[48]</td></tr><tr><td>γ<sub>Ao</sub></td><td>Transition rate from ovipositing to host-seeking adults (day<sup>-1</sup>)</td><td>2</td><td>2</td><td>2</td><td>2</td><td>[48]</td></tr><tr><td>μ<sub>E</sub></td><td>egg mortality rate (day<sup>-1</sup>)</td><td>0.1</td><td>0.1</td><td>0.1</td><td>0.1</td><td>[48]</td></tr><tr><td>μ<sub>L</sub></td><td>Larvae mortality rate (day<sup>-1</sup>)</td><td>0.08</td><td>0.08</td><td>0.08</td><td>0.08</td><td>[48]</td></tr><tr><td>μ<sub>P</sub></td><td>Pupae mortality rate (day<sup>-1</sup>)</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>[48]</td></tr><tr><td>μ<sub>em</sub></td><td>Mortality rate during emergence (day<sup>-1</sup>)</td><td>0.1</td><td>0.1</td><td>0.1</td><td>0.1</td><td>[22]</td></tr><tr><td>μ<sub>r</sub></td><td>Adult mortality rate related to seeking behaviour (day<sup>-1</sup>)</td><td>0.08</td><td>0.08</td><td>0.08</td><td>0.08</td><td>[22]</td></tr><tr><td>T<sub>E</sub></td><td>Minimal temperature needed for egg development (°C)</td><td>13.1</td><td>12.9</td><td>12.7</td><td>12.9</td><td>[44]</td></tr><tr><td>TDDE</td><td>Total number of degree-days necessary for egg development (°C)</td><td>25.4</td><td>26.6</td><td>35.6</td><td>26.6</td><td>[44, 48]</td></tr><tr><td>T<sub>Ag</sub></td><td>Minimal temperature needed for egg maturation (°C)</td><td>9.9</td><td>9.9</td><td>9.9</td><td>9.9</td><td>[48]</td></tr><tr><td>TDD<sub>Ag</sub></td><td>Total number of degree-days necessary for egg maturation (°C)</td><td>36.5</td><td>36.5</td><td>36.5</td><td>36.5</td><td>[48]</td></tr><tr><td>K</td><td>Standard carrying capacity of aquatic stages (Larvae/m<sup>2</sup>)</td><td>1914</td><td>1914</td><td>1914</td><td>1914</td><td>*</td></tr></tbody></table>

where is the precipitation-independent environmental carrying capacity; is the precipitation-dependent environmental loading capacity; and is the weekly precipitation normalized between 0 and 1. The variable *Pnorm* to represent weekly precipitation normalized between 0 and 1. This normalization was performed at the local scale (by district), over the entire study period (2013–2017), using the observed minimum and maximum weekly values. When precipitation is zero, *Pnorm* = 0, reflecting the absence of temporary breeding sites. When precipitation reaches its local maximum, *Pnorm* = 1, indicating the highest possible contribution of rainfall-dependent breeding sites. This approach allows for a flexible and biologically realistic modulation of the environmental carrying capacity related to rainfall, while ensuring comparability across sites.

For each study site the fixed and variable load capacities were estimated for each month following Eqs. 2 and 3:

$$K_{fix}(t) = \sum_{gites=1}^{4} S_{breeding sites} * K_{fix_{breeding sites}}(t) \quad (2)$$

$$K_{var} = \sum_{gites=1}^{4} S_{breeding sites} * K_{var_{breeding sites}}(t) \quad (3)$$

where *breeding s* is the surface area of the larval breeding sites and *breeding s* and *breeding s* are the fixed and variable carrying capacities of each larval site (river, agricultural crop, water body and rice field), respectively.

Considering that *Anopheles* larval sites in Madagascar are mainly dependent on rainfall, except for rice fields when they are in water (August and September), we

<footnote>* To the best of our knowledge</footnote>
<table><caption>**Table 2** Functions of the model</caption><thead><tr><th>Notation</th><th>Definition</th><th>Expression</th><th>Reference</th></tr></thead><tbody><tr><td>f<sub>E</sub></td><td>Transition function from egg to larva</td><td>T(t) - T<sub>E</sub>/T<sub>DDE</sub></td><td>[22]</td></tr><tr><td>f<sub>L</sub></td><td>Transition function from larva to pupa</td><td>f<sub>L</sub>/T<sub>E</sub></td><td>[22]</td></tr><tr><td>f<sub>P</sub></td><td>Transition function from pupa to emerging adult</td><td>0.021 * e<sup>0.162(T(t) - 10)</sup>/e<sup>0.162*25 - 35 + T(22)</sup></td><td>[22]</td></tr><tr><td>f<sub>Ay</sub></td><td>Transition function from engorged adult to oviposition site-seeking adult</td><td>T(t) - T<sub>Ay</sub>/T<sub>DDA9</sub></td><td>[22]</td></tr><tr><td>m<sub>L</sub></td><td>Mortality function for larva</td><td>e<sup>-T(t)/2</sup> + μ<sub>L</sub></td><td>[31]</td></tr><tr><td>m<sub>P</sub></td><td>Mortality function for pupa</td><td>-T(t)/2 + μ<sub>P</sub></td><td>[31]</td></tr><tr><td>m<sub>A</sub></td><td>Mortality function for adult</td><td>0.1 - 0.00667T(t) + 0.000148T<sup>2</sup>(t)</td><td>[31]</td></tr><tr><td>k<sub>X</sub></td><td>Environment carrying capacity for aquatic stage</td><td>Equation 1</td><td>*</td></tr></tbody></table>

estimated the fixed and variable carrying capacities for each type of larval site as follows:

$$ K_{\text{fixbreeding sites}}(t) = \begin{cases} K * 0.1 & \text{for breeding sites} \\ K * 0.1 & \text{for breeding sites=rice field and t} \\ K & \text{for breeding sites = rice field and t} \end{cases} \in \{\text{river, agricultural crop, water body}, \forall t \notin \{\text{August, September}\} \} \quad (4) $$

$$ K_{\text{varbreeding sites}}(t) = \begin{cases} K * 0.9 & \text{for breeding sites} \\ K * 0.9 & \text{for breeding sites = rice field and t} \\ 0 & \text{for breeding sites = rice field and t} \end{cases} \in \{\text{river, agricultural crop, water body}, \forall t \notin \{\text{August, September}\} \} \quad (5) $$

## Simulation

The simulation of the model was performed with the language “Ocelet”, which is a computer language specialized in the simulation of spatial dynamics, developed by CIRAD [32], for the period from 2013 to 2017. The first year was used as an initialization year, and 10⁷ eggs were initially considered to start the simulation. In the end, the outputs of this model were estimates for each species, at each time step (every week in this study), of the number of individuals in each of the *Anopheles* developmental stages (, , , 1h, 1, 1, 2h, 2, 2) in each commune of the three districts considered.

To assess the impact of the rice-cultivation calendar on the larval carrying capacity ($k_X$), two scenarios were compared over the period 2014–2017: (i) a model without consideration of the agricultural calendar and (ii) a model incorporating the agricultural calendar via the function $K_{var}$ described in Eqs. 1–5. Both scenarios were then simulated to quantify the influence of rice seasonality on the dynamics of *Anopheles* population.

## Validation

The model was validated by comparing the predicted host-seeking density of *Anopheles* (h = 1h + 2h) with entomological data provided by the Medical Entomology Unit of the Institut Pasteur of Madagascar. These include the number of mosquitoes collected every two months between 2014 and 2017 at Vohimasy, between 2014 and 2016 at Mahasoa, and in 2017 at Ambahibe, in the Farafangana district. Initially, Mahasoa had been selected as the second site, alongside Vohimasy, for entomological collections throughout the 2014–2017 period. However, due to security issues, the Mahasoa site was abandoned in 2017, and collections were transferred to Ambahibe. For this study, we focused exclusively on the main *Anopheles* species acting as malaria vectors in the study areas: *An. arabiensis*, *An. coustani*, *An. funestus*, and *An. gambiae* [33–35]. Among these species, we considered the three most aggressive species in each study area. The

<footnote>* To the best of our knowledge</footnote>
Spearman correlation coefficient was computed for each *Anopheles* species to evaluate the congruence between predicted and observed densities.

# Results

## Temporal dynamics

A total of 24 simulation outputs was generated, corresponding to the three modeled *Anopheles* species across the eight study sites. The results revealed temporal dynamics in the predicted abundance of host-seeking anopheles' mosquitoes (Figs. 3, 4 and 5).

In the three study sites of the Farafangana district (Ambahibe, Mahasoa, and Vohimasy), *Anopheles* densities exhibited recurrent annual peaks during the hot and rainy season (Fig. 3). These peaks were particularly pronounced in 2017 at Ambahibe and Mahasoa, whereas previous years showed more moderate densities. At Vohimasy, a sharp increase in mosquito density was observed in 2015. *An. funestus* displayed the highest densities, followed by *An. coustani*; these two species were dominant in Ambahibe and Mahasoa, where their abundance levels were nearly equivalent. In contrast, *An. gambiae* remained relatively scarce, particularly at Vohimasy. In the first two sites, annual peaks occurred each year, with a marked maximum in 2017 at Ambahibe, while earlier years showed lower values. At Vohimasy, densities also varied between years, with a particularly strong increase in 2015. This site is characterized by a high prevalence of rice fields (90% of larval habitats), in contrast to the other two sites, which were primarily dominated by agricultural crops (> 88%), with no rice fields present. Finally, precipitation and temperature data exhibited seasonal variability, with precipitation maxima generally coinciding with peaks in *Anopheles* density.

The sites of Andramy, Anosikely Avaratra, and Morarano (Maevatanana district) exhibited regular seasonal dynamics from 2014 to 2017, marked by density increases during the rainy periods (Fig. 4). No extreme fluctuations were recorded during this timeframe. *An. coustani* was the dominant species across all three sites, followed by *An. gambiae* and then *An. arabiensis*. This ranking remained broadly stable over the years. Larval habitat profiles differed among sites: rice fields predominated in Anosikely Avaratra and Morarano (93% and 52%, respectively), with Morarano also exhibiting a high proportion of riverine areas (38%), whereas agricultural crops were predominant in Andramy (59%). All three habitat types were present at each site, along with small proportions of water bodies in Morarano (1%) and Anosikely Avaratra (2%). Climatic data showed a marked seasonality in precipitation, synchronized with density peaks. Temperatures, on the other hand, exhibited less pronounced variations.

In the Morondava district (Ampasy and Antsakoameloka sites), the observed dynamics followed a similarly regular annual pattern, with consistent density peaks during the hot and humid season from 2014 to 2017 (Fig. 5). No exceptional variations were observed over the period. The mosquito populations were mainly composed of *An. funestus* and *An. coustani*, with comparable densities. *An. arabiensis* was present at lower densities. This species composition remained relatively stable from year to year. In terms of larval habitats, agricultural crops clearly predominated, accounting for 79% in Ampasy and 81% in Antsakoameloka. Riverine areas complemented these habitats, representing 19% and 21%, respectively. Climatic conditions maintained a seasonal pattern: rainfall, although slightly variable from year to year, coincided with periods of high *Anopheles* density. Temperatures remained stable, with moderate fluctuations.

For the Vohimasy site in the Farafangana District, a comparison between simulation outputs and observed entomological field data from 2014 to 2017 showed that the model correctly predicted the dynamics of anophèles mosquito (*An. gambiae*, *An. coustani*, *An. funestus*). The predicted densities of host-seeking *Anopheles* fit well with those observed in the field and were well correlated, with correlation coefficients of 0.70 for *An. gambiae*, 0.76 for *An. coustani*, and 0.73 for *An. funestus*.

In Andramy site, simulated densities of host-seeking *Anopheles* (Ah) were compared between a model without the agricultural calendar and one incorporating it (Fig. 6). For the three species *An. arabiensis*, *An. coustani*, and *An. gambiae*, simulations without the agricultural calendar consistently displayed markedly higher densities than those obtained with the agricultural calendar over the entire 2014–2017 period. The curves without the agricultural calendar reached maximum values between 19,000 Ah·ha⁻¹ and 25,000 Ah·ha⁻¹, whereas simulations with the agricultural calendar showed much lower levels, with a maximum peak of 4,890 Ah·ha⁻¹. This pronounced difference was observed repeatedly throughout the simulated seasonal cycles. Simulations from other study sites and species revealed the same general pattern (Additional file 2).

## Spatial dynamics

Maps showing the spatial distribution of the annual mean density of host-seeking *Anopheles* mosquitoes for the years 2014 to 2017 were obtained, based on the model results. These maps cover the three study districts and reveal substantial spatial heterogeneity (Fig. 7).

In Farafangana, high-density areas in 2014 were mainly concentrated in the north-central region and parts of the eastern zone of the district. In contrast, the northeastern and central areas displayed predominantly low to very
<figure><img src="image_6.png" /><figcaption>**Fig. 3** Population dynamics of host-seeking *Anopheles* at the three sites in Farafangana District. The curves correspond to predicted densities, the dots represent field observations, and the camemberst represent the breeding sites</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4** Population dynamics of host-seeking *Anopheles* at the three sites in Maevatanana District</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5** Population dynamics of host-seeking *Anopheles* at the two sites of Morondava District</figcaption></figure>

low densities, while the southern part exhibited low to moderate densities. In the following year, densities in the north-central zone increased from high to very high. A new high-density area also emerged in the south-central part of the district, contrasting with low to moderate densities observed elsewhere. In 2016, density levels increased across most communes. The year 2017 was characterized by a more balanced spatial distribution, with medium densities becoming more common and an overall attenuation of extreme values (Fig. 7a).

In Maevatanana, the maps also reveal a markedly heterogeneous distribution (Fig. 7b). In 2014, the northern half of the district concentrated most of the high to very high-density zones, while the southern half was

dominated by very low to low densities. This configuration remained largely stable in 2015. In 2016, a one-level increase in density was observed in the southern part of the district, with values rising from very low to low, while little change was noted elsewhere. In 2017, overall density levels remained relatively stable, but spatial heterogeneity persisted.

In Morondava, the spatial distribution remained relatively stable across years, though disparities were still present (Fig. 7c). In 2014, the highest densities were observed in the central area and in some parts of the east and west. In contrast, the south and the remaining western areas showed low to moderate densities. This pattern remained mostly unchanged in 2015, except for
<figure><img src="image_9.png" /><figcaption>**Fig. 6** Comparison of simulations with and without the agricultural calendar in Andramy (Maevatanana). a: *An. arabiensis*, b: *An. coustani*, c: *An. gambiae*</figcaption></figure>

a slight southward extension of low-density areas and the emergence of a more pronounced hotspot in the eastern region. In 2016, low-density zones continued to expand in the southeast, where very low densities shifted to low levels. The rest of the district exhibited a generally stable spatial pattern. In 2017, the distribution of host-seeking *Anopheles* densities was similar to that of the previous year, except in the central area, where densities increased from moderate to high levels.

## Discussion

This study aimed to improve the understanding and prediction of *Anopheles* population dynamics in Madagascar, by adapting a generic life-cycle-based mechanistic model to local ecological and agricultural contexts, shows that the explicit integration of agricultural cultivation

significantly influences the dynamic estimation of larval habitat carrying capacity. To our knowledge, no previous model had explicitly included such an agro-environmental factor to assess the availability of larval habitats.

Comparative simulations (Fig. 6 and Additional file 2) indicate that incorporating the agricultural calendar markedly dampens the dynamics of host-seeking female *Anopheles* populations by reducing the overestimations observed in models based solely on climatic variables, with predicted densities frequently several times lower. This difference, consistently observed across simulated seasonal cycles, suggests that variations in the availability and quality of larval habitats driven by agricultural practices exert a major regulatory effect on adult populations. Thus, beyond climatic fluctuations alone, the integration of agro-environmental variables appears essential for
<figure><img src="image_10.png" /><figcaption>**Fig. 7** Maps of the annual densities of h for 2014, 2015, 2016 and 2017. a: Farafangana district, b: Maevatanana district, c: Morondava district</figcaption></figure>

more realistically reproducing vector dynamics and for improving the temporal relevance of control strategies, particularly in targeting periods with the highest potential effectiveness of interventions.

Although this application focuses on Madagascar, the methodology, based on integrating local agricultural

practices into a mechanistic framework, can be generalized to other regions where agriculture, particularly rice cultivation or other forms of irrigated farming, strongly shapes the dynamics of aquatic habitats, such as parts of West Africa (e.g., the Senegal River Valley) or Southeast Asia (e.g., the Mekong Basin). The explicit integration
of agricultural calendars into mechanistic models thus
offers an innovative, transferable, and relevant meth-
odological pathway for anticipating vector dynamics in
diverse agricultural settings.

The results highlight two essential aspects of malaria
vector dynamics in Madagascar: strong seasonality and
marked spatial heterogeneity. These two characteristics
point to distinct regulatory mechanisms in vector popu-
lation dynamics. The observed seasonality reflects a clear
response of *Anopheles* populations to annual climatic
variations, particularly rainfall and temperature, which
directly influence the availability of larval habitats and
adult mosquito survival. However, this study refines the
understanding of these dynamics by showing that peri-
ods of high abundance are relatively predictable (from
November of year *n* to April of year *n* + 1, correspond-
ing to the warm and wet season), which could ultimately
allow better anticipation of transmission periods. The
spatial contrasts observed, even between nearby locali-
ties, indicate that local-specific factors, beyond climate,
influence vector distribution. This suggests that vector
dynamics are not spatially uniform and that environmen-
tal, ecological, or human-specific conditions play a key
role. This spatial heterogeneity underscores the impor-
tance of using localized approaches to understand and
anticipate variations in transmission risk.

The seasonal and spatial dynamics of *Anopheles* popu-
lations observed in this study are consistent with find-
ings from several previous mechanistic modeling studies,
including Cailly et al. (2012) [22] and Ezanno et al. (2015)
[36], which emphasized the key role of climatic factors in
shaping vector densities. However, our approach stands
out by explicitly incorporating the effects of local agricul-
tural practices, especially the rice-growing calendar, into
the modeling of larval habitats. This choice allowed for a
more accurate representation of environmental carrying
capacity fluctuations in relation to both climatic seasons
and agricultural practices specific to Madagascar. Precip-
itation and agricultural calendars were used to dynami-
cally adjust the availability of larval habitats, resulting in
a more realistic depiction of the environment. By com-
parison, the models by Cailly et al. and Ezanno et al.
used fixed carrying capacities that did not evolve with
habitat changes. Abiodun et al. (2016) [19] introduced
variability in carrying capacity, but without consider-
ing local agricultural features. Other mechanistic mod-
els, such as VECTRI [37], have highlighted the value of
using diverse datasets to simulate malaria transmission
at a regional scale. While our study does not explicitly
model disease transmission, it follows a similar logic by
incorporating local dynamics to better explain the spatial
heterogeneity observed between sites, sometimes even at
sub-district scales. More recent studies, such as Ngowo
et al. (2022) [38] in Tanzania, using Bayesian models to

explore climate change impacts, confirm the growing
importance of spatially explicit and context-sensitive
approaches to malaria vector research. Our results build
on these efforts by offering a mechanistic formalism that
is directly applicable to local contexts. Furthermore,
statistical approaches based on machine learning, such
as those used by Taconet et al. (2021) [39], have proven
effective in linking remote sensing-derived environmen-
tal variables to vector density or biting indices. While
these methods are useful for identifying broad-scale cor-
relations, they generally do not allow for intervention sce-
nario simulations. In this sense, they are complementary
to mechanistic models, which can explicitly test hypoth-
eses regarding control factors. The model presented here
thus contributes to this complementarity by providing a
mechanistic framework informed by local parameters,
with a view toward future applications in targeted vector
risk management.

The model developed in this study offers concrete per-
spectives for the spatiotemporal planning of vector con-
trol interventions. By identifying periods and locations of
high vector density, it enables optimization of interven-
tions such as indoor residual spraying, larviciding, or the
distribution of insecticide-treated nets. Its integration
into entomological surveillance or early warning systems
could help shorten response times and more effectively
target available resources. The model was applied here to
the period 2014–2017 to calibrate and validate its ability
to reproduce observed dynamics based on available ento-
mological data. This validation step is essential before any
operational deployment. In the short term, the model is
designed for prospective use, with the objective of gen-
erating updated maps of vector density to support pub-
lic health stakeholders in managing the risk of malaria
transmission more effectively.

Very little information is available in the study areas on
parameters that affect the life cycle of *Anopheles*. Labo-
ratory experiments on these parameters, and particularly
on the influence of climatic conditions on the survival
and development of *Anopheles*, could help improve the
population dynamics model. Indeed, most of the param-
eters and functions used in this study were drawn from
literature on *Anopheles* species in regions other than
Madagascar. Nepomichene et al. [40] estimated the aver-
age number of eggs laid per female for *An. coustani*,
*An. funestus*, and *An. arabiensis* in Madagascar. Some
parameters were estimated for the target species (β for
*An. gambiae*, σ, γAem, TE, and TDDE for *An. arabiensis*
and *An. funestus*) but in non-Malagasy settings [41–47].
Other parameters, such as γAh, γAo, μE, μL, μP, μem, μr,
TDDE (*An. coustani*, *An. gambiae*), TAg, and TDDAg,
have been estimated for other *Anopheles* species [22, 48].

In Madagascar, only 25 weather stations cover the
entire island. This number is far too low for a territory
of 587,041 km<fnref n="2" />, and the risk of interpolation bias is therefore high. This scarcity of meteorological stations necessitates the use of remotely sensed meteorological estimates. However, most of the available remote sensing products offer relatively low spatial resolution, which limits their suitability for local-scale studies.

## Conclusion

This work constitutes, to our knowledge, the first study demonstrating the impact of formally integrating the agricultural calendar into a mechanistic model of Anopheles population dynamics. In addition to this agro-environmental integration, the model was specifically adapted to four malaria vector species present in Madagascar: *An. arabiensis*, *An. coustani*, *An. funestus*, and *An. gambiae*. This capacity to account for species diversity enhances the model's usefulness in areas where multiple vectors contribute differently to malaria transmission dynamics, making it a valuable and easily transferable tool for anticipating vector dynamics in other malaria-endemic regions where agriculture shapes mosquito ecology.

The approach developed here can be transposed to other agricultural regions where production systems govern the availability of larval habitats. Extending beyond the Malagasy context, it provides a transferable methodological basis for modeling mosquito dynamics.

The results also highlight the importance of environmental and climatic factors in shaping *Anopheles* abundance. The outputs of the model, describing *Anopheles* abundance in space and time, can be directly used to test vector control strategies or as input data for malaria transmission risk models, which could then be validated using malaria incidence data. Such applications would help identify and implement the most effective, context-specific combinations of vector and malaria control measures.

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12942-025-00424-8.

Supplementary Material 1

Supplementary Material 2

## Acknowledgements

The authors would like to acknowledge Luciano Tantely for his valuable advice and expertise in entomology.

## Author contributions

Conceptualization: HAR, AT, FR and SR; Methodology: HAR, FR, GH, FR and TN; Data curation: HAR and TN; Formal analysis: HAR and AT; Writing-original draft: HAR, FR and TN; Writing-review and editing: HAR, TN, HG, RG, SR, FR and AT. All authors read and approved the final manuscript.

## Funding

This study is a part from the Research, Innovation, Surveillance and Evaluation (RISE) project and was funded by the United States Agency for International Development (USAID) under grant No. 72068719CA00001.

## Data availability

No datasets were generated or analysed during the current study.

## Declarations

### Ethics approval and consent to participate

Not applicable

### Consent for publication

Not applicable.

### Competing interests

The authors declare no competing interests.

### Author details

Received: 4 June 2024 / Accepted: 17 October 2025

Published online: 18 November 2025

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