By Jemal Mohammed-Awel
Malaria is a deadly disease that is transmitted to humans via the bite of an infected adult female Anopheles mosquito, which requires blood meals to develop its eggs. Susceptible humans can acquire a malaria infection when they are bit by an infectious mosquito, and susceptible mosquitoes can acquire malaria if they bite an infectious human. This disease causes severe public health and socioeconomic burdens in geographies that encompass more than half of the world’s population [5]. According to the World Health Organization (WHO), malaria accounted for 249 million cases and 608,000 deaths across the world in 2022.
Since 2000, numerous coordinated global efforts have sought to reduce the burden of malaria. Such efforts are primarily based on the widescale, universal use of chemical insecticides like long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), and larvicide, though other malaria control methods—including artemisinin-based combination therapy to treat malaria symptoms, intermittent preventative drug therapy for high-risk groups, and the recently approved malaria vaccine (which confers only modest protection)—have proven beneficial as well. Collectively, these approaches have resulted in a dramatic global decline of the disease. Ongoing programs such as the Global Technical Strategy for Malaria 2016-2030 and ZERO by 40 are working to respectively eradicate malaria by 2030 or 2040.
Maintaining this forward progress is difficult, in part due to the emergence of mosquitoes that are resistant to all available insecticides for vector control [1-7]. Mosquitoes are considered to be insecticide resistant if the insecticide’s ability to kill them upon contact is greatly reduced or eliminated. Insecticide resistance occurs due to a genetic change in mosquitoes — specifically a gene that confers such resistance. WHO estimates that insecticide resistance, if left unchecked, could lead to a substantial resurgence of malaria incidence and mortality.
Resistance traits often seem to incur a fitness cost in mosquitoes that could undermine malaria transmission, particularly if the cost decreases adult mosquito survival rates. Resistance may also affect male mating success and the life cycles of adult males and females [1-7]. The balance of fitness costs and the benefits of resistance may therefore vary appreciably between the sexes, with implications for both the spread of resistance alleles and malaria epidemiology [1, 3-7]. And even for highly resistant mosquitoes, insecticide exposure may result in behavioral changes (e.g., reduced host seeking) or delayed mortality that could undermine their vectorial capacities [4].
Intuitively, insecticide resistance may diminish the effectiveness of LLINs, IRS, and other insecticide-based mosquito controls. The precise impact of insecticide resistance on malaria epidemiology is not yet known, and different studies have reached varying conclusions [1, 3-7]. A recent investigation across five countries found that while LLIN users exhibited lower rates of malaria infection and disease, no relationship exists between laboratory-assessed insecticide resistance and malaria epidemiology [3, 6]. Another study suggested that insecticide resistance can undermine the control of malaria, and an unrelated paper indicated that such resistance caused the disease to rebound in South Africa [3, 6]. However, the fitness costs that are associated with insecticide resistance could also undermine transmission [3, 4, 6]. Despite these numerous uncertainties, researchers are still greatly interested in determining how to best manage malaria-carrying parasites and mosquito insecticide resistance — especially given the potential consequences of resistance and the absence of viable alternative insecticides.
To better understand the link between insecticide resistance and malaria transmission, scientists must develop a robust mathematical and computational modeling framework that considers key aspects of this relationship. Our recent research focuses on this major global public health problem. Specifically, we seek to answer the most important question in the malaria ecology/epidemiology community: Does insecticide resistance affect malaria transmission?
Realistically modeling the impact of insecticide resistance necessitates the use of a genetic epidemiology framework that captures the mosquito population genetics (by genotype) and the nuances of human disease, subject to the use of insecticide-based mosquito control methods. Although numerous genetic mathematical models in the literature examine the evolution and spread of insecticide resistance in vector populations, very few models incorporate both the population genetics of malaria mosquitoes and malaria epidemiology [1-6]. Our deterministic model—which stratifies the total mosquito population in terms of type (wild or resistant to the chemical insecticides of mosquito control) and feeding preferences (indoor or outdoor)—accounts for both of these factors [2]. We later developed a genetic epidemiology malaria model that couples the epidemiology of malaria with vector population genetics [3]. The model stratifies the adult female mosquito population into three genotype groups: homozygous sensitive, heterozygous, and homozygous resistant.
Figure 1. Schematic diagram of the genetic epidemiology malaria transmission dynamics model. Figure courtesy of [4].
We further extended this model by explicitly accounting for the dynamics of mosquitoes in the aquatic stage of the vector life cycle and adult mosquito sex structure [6]. We also generated a genetic ecology model to assess the combined impacts of insecticide resistance, local temperature variability, and insecticide-based interventions on population abundance and the control of malaria mosquitoes by genotype [1]. Recently, we expanded our original model [3] by explicitly incorporating mosquito biting behavior (indoor and outdoor bites) and LLINs’ genotype-specific mosquito repellence property [4]. This updated model derives a nonlinear function for the genotype-specific mosquito-human contact rate that incorporates the possibility of multiple mosquito bite attempts to obtain a successful blood meal from human hosts. These model structures collectively allow for the simultaneous investigation of malaria transmission dynamics in both humans and mosquitoes and the evolution of insecticide resistance in mosquitoes. Some of our models’ key novelties include the detailed aspects of the genetic processes that cause the evolution of insecticide resistance, as well as several fitness costs of insecticide resistance in the vector. Figure 1 is a schematic diagram of our genetic epidemiology model.
In order to effectively offer practical insights about the relationship between insecticide resistance and malaria epidemiology, we must realistically estimate the fitness cost parameters that are associated with insecticide resistance. However, the existing literature offers no available data that pertains to these fitness costs for mosquitoes. By continuing to collaborate with other mathematical modelers, molecular biologists, and entomologists, we hope to ultimately estimate the unknown values of these parameters, which we can then incorporate into various models.
In the future, we intend to develop extended novel mathematical models that incorporate other pertinent aspects of malaria in the presence of insecticide-based interventions. For example, the human age structure, resistance to artemisinin-based therapy, the effects of climate change, land-use changes, human mobility, and the recently approved malaria vaccine are all worthy of consideration. Scientists could even adapt techniques and tools from our genetic epidemiology framework to study the transmission dynamics and control of other mosquito-borne diseases, such as chikungunya, dengue fever, West Nile virus, and Zika virus.
Jemal Mohammed-Awel delivered a minisymposium presentation on this research at the 2023 SIAM Conference on Control and Its Applications, which took place in Philadelphia, Pa., last year.
Acknowledgments: This project is a joint research endeavor with collaborators from the University of Maryland, College Park and Arizona State University. It is supported by the National Science Foundation under DMS-2052355 (transferred to DMS-2221794).
References
[1] Brozak, S.J., Mohammed-Awel, J., & Gumel, A.B. (2022). Mathematics of a single-locus model for assessing the impacts of pyrethroid resistance and temperature on population abundance of malaria mosquitoes. Infect. Dis. Model., 7(3), 277-316.
[2] Mohammed-Awel, J., Agusto, F., Mickens, R.E., & Gumel, A.B. (2018). Mathematical assessment of the role of vector insecticide resistance and feeding/resting behavior on malaria transmission dynamics: Optimal control analysis. Infect. Dis. Model., 3, 301-321.
[3] Mohammed-Awel, J., & Gumel, A.B. (2019). Mathematics of an epidemiology-genetics model for assessing the role of insecticides resistance on malaria transmission dynamics. Math. BioSci., 312, 33-49.
[4] Mohammed-Awel, J., & Gumel, A.B. (2023). Can insecticide resistance increase malaria transmission? A genetics-epidemiology mathematical modeling approach. J. Math. Biol., 87(2), 28.
[5] Mohammed-Awel, J., & Gumel, A.B. (2024). A genetic-epidemiology modeling framework for malaria mosquitoes and disease. In A.B. Gumel (Ed.), Mathematical and computational modeling of phenomena arising in population biology and nonlinear oscillations. Contemporary mathematics (Vol. 793). Providence, RI: American Mathematical Society.
[6] Mohammed-Awel, J., Iboi, E.A., & Gumel, A.B. (2020). Insecticide resistance and malaria control: An epidemiology-genetics modeling approach. Math. BioSci., 325, 108368.
[7] Mohammed-Awel, J., Numfor, E., Zhao, R., & Lenhart, S. (2021). A new mathematical model studying imperfect vaccination: Optimal control approach. J. Math. Anal. Appl., 500(2), 125132.
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Jemal Mohammed-Awel is an associate professor of mathematics at Morgan State University. He holds a Ph.D. in mathematics from the State University of New York at Buffalo. Mohammed-Awel uses modeling, rigorous mathematical analysis, data analytics, and computation to gain deep qualitative insights into the dynamical behavior of real-life systems that arise in the natural and engineering sciences, with emphasis on the spread and control of infectious diseases.
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