- Open Access
Incorporating the effects of humidity in a mechanistic model of Anopheles gambiae mosquito population dynamics in the Sahel region of Africa
© Yamana and Eltahir; licensee BioMed Central Ltd. 2013
Received: 9 May 2013
Accepted: 7 August 2013
Published: 9 August 2013
Low levels of relative humidity are known to decrease the lifespan of mosquitoes. However, most current models of malaria transmission do not account for the effects of relative humidity on mosquito survival. In the Sahel, where relative humidity drops to levels <20% for several months of the year, we expect relative humidity to play a significant role in shaping the seasonal profile of mosquito populations. Here, we present a new formulation for Anopheles gambiae sensu lato (s.l.) mosquito survival as a function of temperature and relative humidity and investigate the effect of humidity on simulated mosquito populations.
Using existing observations on relationships between temperature, relative humidity and mosquito longevity, we developed a new equation for mosquito survival as a function of temperature and relative humidity. We collected simultaneous field observations on temperature, wind, relative humidity, and anopheline mosquito populations for two villages from the Sahel region of Africa, which are presented in this paper. We apply this equation to the environmental data and conduct numerical simulations of mosquito populations using the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS).
Relative humidity drops to levels that are uncomfortable for mosquitoes at the end of the rainy season. In one village, Banizoumbou, water pools dried up and interrupted mosquito breeding shortly after the end of the rainy season. In this case, relative humidity had little effect on the mosquito population. However, in the other village, Zindarou, the relatively shallow water table led to water pools that persisted several months beyond the end of the rainy season. In this case, the decrease in mosquito survival due to relative humidity improved the model’s ability to reproduce the seasonal pattern of observed mosquito abundance.
We proposed a new equation to describe Anopheles gambiae s.l. mosquito survival as a function of temperature and relative humidity. We demonstrated that relative humidity can play a significant role in mosquito population and malaria transmission dynamics. Future modeling work should account for these effects of relative humidity.
Effects of humidity on mosquito longevity
Mosquitoes, like all insects, have a limited range of tolerable temperature and humidity . The high surface area to volume ratio of mosquitoes makes them especially sensitive to desiccation at low humidity levels.
Gaaboub et al.  compared the survival of groups of female Anopheles pharoensis mosquitoes at 20°, 26° and 30°C and found little difference in longevity between 50% and 90% relative humidity (RH) conditions at a given temperature. Bayoh and Lindsay  measured the longevity of An. gambiae sensu stricto (s.s.) at 40%, 60%, 80% and 100% RH at 5°C intervals from 5°C to 40°C. Under the assumption that daily probability of survival is independent of mosquito age, there was little difference in survival between 60-100% RH, but survival was slightly reduced at 40% RH.
Molecular biology techniques applied to An. gambiae s.s. held at 42% RH  and 30% RH  found the mosquitoes had undergone physiologic responses to desiccation stress, decreasing their water loss. Mosquitoes held without food or water survived for an average of 15.6 hours at 30% RH compared to 26.2 hours at 70% RH .
Recent studies on mosquito desiccation showed that extremely low levels of RH are fatal to mosquitoes when maintained for periods on the order of hours. These studies placed mosquitoes in vials without access to food or water and added a desiccant to reduce RH levels that are generally kept at <10% RH but not exactly specified. Several such studies found that no An. gambiae s.s. or An. arabiensis females survived for an entire day at <10% RH [9, 10] or <20% RH . In a similar study, a small number of mosquitoes survived up to 30 hours at <10% RH, and acclimation to hot and dry conditions was shown to increase desiccation resistance . However, in another study using the offspring of field captured mosquitoes held at <10% RH, 15% of S form An. gambiae s.s. and 23% of M form An. gambiae s.s. females survived for over 1 day, with 2 out of 30 M form individuals surviving for over 2 days , suggesting that wild mosquitoes in arid regions may have higher desiccation resistance than laboratory colonies.
In summary, An. gambiae longevity does not appear to be substantially affected by relative humidity at ranges greater than 60%, but RH <10% is fatal, usually within hours. There is very little information on mosquito longevity in the range 10-40% RH.
Mosquito survival in malaria models
where T is the daily average air temperature in degrees Celsius. This function gives maximum longevity in the range of 20-25°C, and severe mortality at temperatures below 10°C and above 35°C. This curve was formed based on three data points .
The experiments relating survival to temperature conducted by Bayoh  led to the development of two new formulations of survival probability of Anopheles gambiae to temperature, one by Ermert et al.  and another by Mordecai et al. , shown in Additional file 1. The accuracy of these two formulations and the Martens equation were recently evaluated by Lunde et al. .
Relative humidity has recently been incorporated into several models. Parham et al.  developed a survival curve based on Bayoh’s survival data . Ermert et al.  account for humidity in the Liverpool Malaria Model by subtracting 10% from the daily probability of survival when 10 day accumulated rainfall is below 10 mm. Lunde et al.  use Bayoh’s survival data by fitting a survival curve for each measured value of RH (40%, 60%, 80% and 100%), further adjusted by mosquito size and age. While these formulations for mosquito survival rates are improvements on previous formulations that considered only temperature, they do not reliably capture the effect of very low values of relative humidity (<40% RH) such as those observed during the dry season in the Sahel on mosquito survival. Here, we propose a new equation for mosquito survival incorporating current knowledge on the effects of relative humidity and temperature on survival.
Development of new survival equation
We based our formulation of anopheline survival on an existing relationship between temperature and survival, p(T). In this paper, we use the Martens equation  described above for p(T). However, this equation can be substituted by an alternative formulation such as those evaluated by Lunde et al. , as discussed in Additional file 1. We make the assumption that the survival equation, p(T), accurately describes Anopheles gambiae survival at high and moderate levels of relative humidity.
Observation of mosquito longevity used for development of relative humidity stress factor
Gaaboub et al., 
An. gambiae s.s.
An. gambiae s.s.
Liu et al., 
An. gambiae s.s.
Wang et al., 
An. gambiae s.s.
Gray and Bradley, 
Gray and Bradley, 
An. gambiae s.s.
Gray et al., 
An. gambiae s.s.
Fouet et al., 
An. gambiae s.s.
Lee et al., 
This assumes that temperature and relative humidity act independently on mosquito survival.
The first panel of Figure 4 shows the Martens survival curve (Equation 1), which is a function of temperature only. The second panel shows the RH stress factor calculated above (Equation 2). The third panel of Figure 4 shows the new equation for mosquito survival as a function of temperature and relative humidity (Equation 3).
We do not explicitly consider the possibility of aestivation, by which mosquitoes survive for long periods during the dry season. This mechanism for survival has been observed in several instances in An. gambiae in the Sahel [22, 23], but is still not well understood.
Testing new survival equation
We tested the impact of the new survival equation in HYDREMATS, a mechanistic model of malaria transmission developed to simulate village-scale responses of malaria transmission to interannual climate variability in semi-arid desert fringe environments such as the Sahel, which has been used in a number of recent modeling studies in this region [1, 24–27]. The development of HYDREMATS is described in detail in Bomblies et al. , and key features of the model are included in Additional file 2. The model provides explicit representation of the spatial determinants of malaria transmission. HYDREMATS can be separated into two components: the hydrology component which explicitly represents pooled water available to Anopheles mosquitoes as breeding sites, and the entomology component, which is an agent-based model of disease transmission.
In the hydrology component, rainfall is partitioned between runoff and infiltration, with soil and vegetation properties strongly influencing the partition between these two processes. Uptake of soil water from evapotranspiration is calculated based on climatic variables. Overland flow is modelled using a finite difference solution, and flow velocity is calculated as a function of friction slope, flow depth, and a distributed roughness parameter derived from soil characteristics and vegetation type. The overland flow process is of critical importance for the modelling of water pool formation. The regional unconfined aquifer is represented using a lumped model in which groundwater table fluctuations are simulated. The depth to the water table varies from cell to cell and is a function of topography. The hydrology component of HYDREMATS simulates the spatial distribution of water depths and temperatures for each grid cell, for each timestep. These distributions serve as the inputs for the entomology component of the model . The hydrology component of the model was validated in Banizoumbou and Zindarou, Niger by comparing simulation results to measured soil moisture values (, Figure nine; , Figure four), groundwater level (, Figure five), and observed water pools (, Figures ten and eleven).
The entomology component of HYDREMATS simulates individual mosquito and human agents. Human agents are immobile, and are assigned to village residences, as malaria transmission in this region occurs primarily at night when humans are indoors . Mosquito agents have a probabilistic response to their environment based on a prescribed set of rules governing dispersal and discrete events including development of larval stages, feeding, egg-laying and death . Simulated mosquito numbers compared well with field captures of mosquitoes using CDC light traps (, Figures fourteen and fifteen; , Figure eight).
Simultaneous field observations on temperature, wind, relative humidity, and rainfall were collected for Banizoumbou and Zindarou. In order to assess the effect of relative humidity on mosquito population dynamics, we conducted simulations using observed environmental data for the year 2006. For each village, we conducted one simulation using the original Martens equation for mosquito longevity as a function of temperature only, and 3 simulations using the new equation incorporating temperature and relative humidity using RHS = 42%, 40% and 35%, all at RH C = 5%. We also conducted a simulation for each village with RHS = 42% and RH C = 0%.
The incorporation of relative humidity into simulations of mosquito populations substantially decreases mosquito longevity. In cases such as Zindarou where breeding sites are available beyond the end of the wet season, the drop in relative humidity could explain, at least in part, the rapid decline of the mosquito population in field observations. However, the timing of the decline of mosquitoes in the simulation (late October) occurred approximately four weeks after the decrease in captured mosquitoes (late September/early October), indicating that other factors likely played a role in limiting mosquito numbers.
The adverse effects of low humidity on mosquito longevity have been known for decades . Here, we have taken a commonly used equation for mosquito survival as a function of temperature and added the effects of relative humidity. While other researchers have incorporated humidity into their models [16, 20, 21], our equation is unique in that it reflects the fatal effect of the extremely low values of relative humidity that are observed during the dry season in the Sahel. Using evidence from mosquito survival studies, we assumed that relative humidity does not affect survival rates at high and moderate values of RH, but at a value RH S (~42% RH), survival decreases until a critical value RH C (~5% RH) where it is assumed that no individual can survive for longer than 24 hours. In the two villages of the Sahel described here, daily averages of relative humidity remained below 30% for the majority of the dry season.
The primary mode of variability in mosquito populations in these villages features two distinct seasons; a wet season with a high population of mosquitoes and relatively high malaria transmission (July-November) and a dry season with a low population of mosquitoes and low malaria transmission (December – June). When we simulate mosquito populations using HYDREMATS parameterized with the Martens survival equation, it reproduces this mode of variability in Banizoumbou, where mosquito breeding sites were not available beyond the wet season, but it fails to reproduce the same mode in Zindarou, where breeding sites persist into the dry season. However, when we incorporate the constraints on survival due to humidity developed here into HYDREMATS, the model reproduces this observed mode of variability in both villages.
While the equation for mosquito survival developed here improved the model’s ability to simulate the observed seasonal pattern of mosquitoes in Zindarou, the timing of the decline of captured mosquitoes preceded the drop in relative humidity by approximately 4 weeks, indicating that other factors must be playing a role in the mosquito decline. Other potential factors involved in this decline in mosquitoes could include a lack of nutrient availability for larvae and establishment of predator populations in the long-lasting water pools, which can be represented in HYDREMATS but were not included in the simulations for this study. Bomblies et al.  noted that the decline in mosquito population corresponded with the harvest of millet crops and hypothesized that aquatic stage mosquitoes may have depended on the availability of millet pollen. While anopheline larvae were found in the persistent water pools, it is possible that these pools become less attractive as breeding sites as the rainy season progresses, perhaps due to increased vegetation, turbidity or predator activity. Another possible explanation for the decline in mosquito captures could be the triggering of aestivation, where mosquitoes retreat to sheltered locations and cease regular activities, leading to a decrease in captured mosquitoes despite the continued presence of water pools.
In addition to the dramatic reduction in the mosquito population simulated in Zindarou as a result of low RH, mosquito longevity in individual mosquitoes plays an important role in malaria transmission dynamics. In order to transmit the parasite, a mosquito must survive long enough to bite an infected person, surpass the extrinsic incubation period of the parasite, roughly 6–10 days in warm climates , and then bite a second (uninfected) person. This amplifies the effect of shortened lifespan, such that even a small decrease in lifespan can have a very significant effect on malaria transmission .
Humidity may also play a role in determining future environmental suitability for malaria transmission under climate change. Climate change impact studies often focus on rainfall and temperature (eg. [32, 33]), but relative humidity may change as well. Observations over the last few decades indicate that while global mean specific humidity is increasing, the accompanying increase in temperatures means there has been little change in relative humidity [34, 35]. However, statistically significant changes in relative humidity have been observed on regional scales between 1975 and 2005 . Current general circulation models indicate that increasing levels of greenhouse gas emissions could lead to substantial disruption of the West African Monsoon , which could alter spatial and temporal patterns of relative humidity in the Sahel.
We proposed a new equation to describe mosquito survival as a function of temperature and relative humidity. We demonstrated that relative humidity can play a significant role in mosquito survival and malaria transmission dynamics. In the Sahel, where dry season RH regularly drops to levels known to significantly decrease mosquito longevity, relative humidity can be as important as temperature and rainfall in determining the environmental suitability for mosquitoes and malaria transmission. The primary mode of variability in mosquito populations in these villages features two distinct seasons; a wet season with a high population of mosquitoes and relatively high malaria transmission and a dry season with a low population of mosquitoes and low malaria transmission. We showed that when we simulate mosquito populations using HYDREMATS parameterized with the Martens survival equation, it fails to reproduce the same mode in Zindarou, where breeding sites persist into the dry season. However, when we incorporate the constraints on survival due to humidity developed here into HYDREMATS, the model reproduces this observed mode of variability in both villages. Future modeling work should therefore account for these effects of relative humidity.
This was work was funded by U.S. National Science Foundation grant EAR- 0946280. Meteorological data for Banizoumbou village were provided by the Institut de Recherche pour le Développement through the African Monsoon Multidisciplinary Analyses (AMMA) programme. Based on a French initiative, AMMA was built by an international scientific group and is currently funded by a large number of agencies, especially from France, UK, US and Africa. It has been the beneficiary of a major financial contribution from the European Community’s Sixth Framework Research Programme. Mosquito data were collected in collaboration with Ibrahim Arzika of Centre de Recherche Médicale et Sanitaire.
- Bomblies A, Duchemin JB, Eltahir EAB: A mechanistic approach for accurate simulation of village-scale malaria transmission. Malar J. 2009, 8 (1): 223-10.1186/1475-2875-8-223.PubMed CentralView ArticlePubMedGoogle Scholar
- Bomblies A, Duchemin JB, Eltahir EAB: Hydrology of malaria: Model development and application to a Sahelian village. Water Resour Res. 2008, 44 (12):Google Scholar
- Sultan B, Janicot S: The West African monsoon dynamics. Part II: The “preonset” and “onset” of the summer monsoon. J Clim. 2003, 16 (21): 3407-3427. 10.1175/1520-0442(2003)016<3407:TWAMDP>2.0.CO;2.View ArticleGoogle Scholar
- Wigglesworth V: The Principles of Insect Physiology. 1939, London: Methuen & Co. Ltd.Google Scholar
- Gaaboub I, El-Sawaf S, El-Latif M: Effect of different relative humidities and temperatures on egg-production and longevity of adults of Anopheles (Myzomyia) pharoensis Theob. Zeitschrift Für Angewandte Entomologie. 1971, 67 (1–4): 88-94.Google Scholar
- Bayoh MN: Studies on the development and survival of Anopheles gambiae sensu stricto at various temperatures and relative humidities. 2001, Durham: PhD thesis. University of DurhamGoogle Scholar
- Liu K, Tsujimoto H, Cha SJ, Agre P, Rasgon JL: Aquaporin water channel AgAQP1 in the malaria vector mosquito Anopheles gambiae during blood feeding and humidity adaptation. Proc Natl Acad Sci U S A. 2011, 108 (15): 6062-10.1073/pnas.1102629108.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang MH, Marinotti O, Vardo-Zalik A, Boparai R, Yan G: Genome-wide transcriptional analysis of genes associated with acute desiccation stress in Anopheles gambiae. PLoS One. 2011, 6 (10): e26011-10.1371/journal.pone.0026011.PubMed CentralView ArticlePubMedGoogle Scholar
- Gray EM, Bradley TJ: Physiology of desiccation resistance in Anopheles gambiae and Anopheles arabiensis. Am J Trop Med Hyg. 2005, 73 (3): 553-PubMedGoogle Scholar
- Fouet C, Gray E, Besansky NJ, Costantini C: Adaptation to aridity in the malaria mosquito Anopheles gambiae: Chromosomal inversion polymorphism and body size influence resistance to desiccation. PLoS One. 2012, 7 (4): e34841-10.1371/journal.pone.0034841.PubMed CentralView ArticlePubMedGoogle Scholar
- Gray EM, Rocca K, Costantini C, Besansky NJ: Inversion 2La is associated with enhanced desiccation resistance in Anopheles gambiae. Malar J. 2009, 8: 215-10.1186/1475-2875-8-215.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee Y, Meneses CR, Fofana A, Lanzaro GC: Desiccation resistance among subpopulations of Anopheles gambiae ss from Selinkenyi. Mali. J Med Entomol. 2009, 46 (2): 316-320. 10.1603/033.046.0216.View ArticlePubMedGoogle Scholar
- Martens W, Niessen L, Rotmans J, Jetten T, McMichael A: Potential impact of global climate change on malaria risk. Environ Health Perspect. 1995, 103 (5): 458-10.1289/ehp.95103458.PubMed CentralView ArticlePubMedGoogle Scholar
- Craig MH, Snow RW, le Sueur D: A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today. 1999, 15 (3): 105-111. 10.1016/S0169-4758(99)01396-4.View ArticlePubMedGoogle Scholar
- Parham PE, Michael E: Modelling the Effects of Weather and Climate Change on Malaria Transmission. Environ Health Perspect. 2009, 118 (5): 620-626. 10.1289/ehp.0901256.PubMed CentralView ArticleGoogle Scholar
- Ermert V, Fink A, Jones A, Morse A: Development of a new version of the Liverpool Malaria Model. I. Refining the parameter settings and mathematical formulation of basic processes based on a literature review. Malar J. 2011, 10 (1): 35-10.1186/1475-2875-10-35.PubMed CentralView ArticlePubMedGoogle Scholar
- Martens W: Health impacts of climate change and ozone depletion: An eco-epidemiological modelling approach. 1997, Maastricht: PhD thesis. University of Maastricht, Dept. of MathematicsGoogle Scholar
- Mordecai EA, Paaijmans KP, Johnson LR, Balzer C, Ben‒Horin T, Moor E, McNally A, Pawar S, Ryan SJ, Smith TC: Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol Lett. 2013, 16 (1): 22-30. 10.1111/ele.12015.View ArticlePubMedGoogle Scholar
- Lunde TM, Bayoh MN, Lindtjørn B: How malaria models relate temperature to malaria transmission. Parasit Vectors. 2013, 6 (1): 1-10. 10.1186/1756-3305-6-1.View ArticleGoogle Scholar
- Parham PE, Pople D, Christiansen-Jucht C, Lindsay S, Hinsley W, Michael E: Modeling the role of environmental variables on the population dynamics of the malaria vector Anopheles gambiae sensu stricto. Malar J. 2012, 11 (1): 1-13. 10.1186/1475-2875-11-1.View ArticleGoogle Scholar
- Lunde TM, Korecha D, Loha E, Sorteberg A, Lindtjørn B: A dynamic model of some malaria-transmitting anopheline mosquitoes of the Afrotropical region. I. Model description and sensitivity analysis. Malar J. 2013, 12 (1): 28-10.1186/1475-2875-12-28.PubMed CentralView ArticlePubMedGoogle Scholar
- Omer SM, Cloudsley-Thompson J: Survival of female Anopheles gambiae Giles through a 9-month dry season in Sudan. Bull World Health Organ. 1970, 42 (2): 319-PubMed CentralPubMedGoogle Scholar
- Lehmann T, Dao A, Adamou A, Kassogue Y, Diallo M, Sékou T, Coscaron-Arias C: Aestivation of the African malaria mosquito, Anopheles gambiae in the Sahel. Am J Trop Med Hyg. 2010, 83 (3): 601-606. 10.4269/ajtmh.2010.09-0779.PubMed CentralView ArticlePubMedGoogle Scholar
- Gianotti RL, Bomblies A, Eltahir EAB: Hydrologic modeling to screen potential environmental management methods for malaria vector control in Niger. Water Resour Res. 2009, 45 (8): W08438Google Scholar
- Yamana TK, Eltahir EA: Early warnings of the potential for malaria transmission in rural Africa using the hydrology, entomology and malaria transmission simulator (HYDREMATS). Malar J. 2010, 9: 323-10.1186/1475-2875-9-323.PubMed CentralView ArticlePubMedGoogle Scholar
- Bomblies A, Eltahir EAB: Assessment of the impact of climate shifts on malaria transmission in the Sahel. EcoHealth. 2010, 6 (3): 426-437.View ArticleGoogle Scholar
- Yamana TK, Eltahir EAB: On the use of satellite-based estimates of rainfall temporal distribution to simulate the potential for malaria transmission in rural Africa. Water Resour Res. 2011, 47 (2): W02540Google Scholar
- Service M: Mosquito ecology: field sampling methods: 2nd ed. 1993, London, U.K.: Elsevier Applied ScienceView ArticleGoogle Scholar
- Mayne B: A study of the influence of relative humidity on the life and infectibility of the mosquito. Indian J Med Res. 1930, 17 (4): 1119-1137.Google Scholar
- Detinova TS: Age-grouping methods in Diptera of medical importance with special reference to some vectors of malaria. Monogr Ser World Health Organ. 1962, 47: 13-191.PubMedGoogle Scholar
- Macdonald G: Epidemiological basis of malaria control. Bull World Health Organ. 1956, 15: 613-626.PubMed CentralPubMedGoogle Scholar
- Tanser FC, Sharp B, le Sueur D: Potential effect of climate change on malaria transmission in Africa. The Lancet. 2003, 362 (9398): 1792-1798. 10.1016/S0140-6736(03)14898-2.View ArticleGoogle Scholar
- Ermert V, Fink AH, Morse AP, Paeth H: The impact of regional climate change on malaria risk due to greenhouse forcing and land-use changes in tropical Africa. Environ Health Perspect. 2012, 120 (1): 77-84.PubMed CentralView ArticlePubMedGoogle Scholar
- Jones P, Trenberth K, Ambenje P, Bojariu R, Easterling D, Klein T, Parker D, Renwick J, Rusticucci M, Soden B: Observations: surface and atmospheric climate change. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL. 2007, Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 235-336.Google Scholar
- Willett KM, Gillett NP, Jones PD, Thorne PW: Attribution of observed surface humidity changes to human influence. Nature. 2007, 449 (7163): 710-712. 10.1038/nature06207.View ArticlePubMedGoogle Scholar
- Dai A: Recent climatology, variability, and trends in global surface humidity. J Clim. 2006, 19 (15): 3589-3606. 10.1175/JCLI3816.1.View ArticleGoogle Scholar
- Cook KH, Vizy EK: Coupled model simulations of the West African monsoon system: Twentieth-and twenty-first-century simulations. J Clim. 2006, 19 (15): 3681-3703. 10.1175/JCLI3814.1.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.