DYNAMICAL ANALYSIS FOR THE EVOLUTION MODEL OF INSECTICIDE RESISTANCE IN ANOPHELES MOSQUITOES

• Main Author: Suandi, Dani
• Format: Dissertations
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/59565
• Institution: Institut Teknologi Bandung

Anopheles is a mosquito that transmits malaria to humans through plasmodium sporozoites in their saliva which enter the human body during the blood collection process. Malaria control efforts recommended by WHO to date include three activities, namely artemisinin based combination therapy (ACT), insecticide-treated bed nets (ITNs), and indoor residual spraying (IRS). In these efforts, two of them are closely related to insecticides. The success of using this insecticide can be seen in the reduction in the global mortality rate caused by malaria by up to 69 percent. Unfortunately, the continued use of insecticides can cause resistance problems in mosquito populations. This is evidenced by the many reports from various insecticide-using countries which note that several Anopheles species have been declared resistant. In the 2020 WHO report, globally cases of pyrethroid resistance occurred in 69% of reported sites, 63.4% of sites reported organochlorine resistance, 31.7% carbamate resistance and 24.9% reported organophosphate resistance. In Indonesia, symptoms of Anopheles mosquito resistance to the insecticide dieldrin have been detected in 10 endemic areas, namely Aceh, North Sumatra, Bangka Belitung, Lampung, Central Java, West Nusa Tenggara, East Nusa Tenggara, West Sulawesi, Maluku and North Maluku. This prompted the development of resistance mitigation aimed at slowing resistance. A series of efforts to reduce the risk of insecticide resistance in mosquito populations continues to be developed. Among the mitigation of resistance is through insecticide rotation techniques. For example, when pyrethroids are used for giving insecticide-treated mosquito nets, spraying the walls of the house with residual insecticides uses other insecticides besides pyrethroids. This rotation technique can be successful in slowing the rate of resistance if the level of understanding in the problem of resistance is good enough. But unfortunately so far the rotation of the use of insecticides is only based on different active ingredients. Whereas the correct rotation technique is based on how the insecticide works (Mode of Action). This is because different active ingredients do not guarantee to have different ways of working. As a result, the potential for resistance in the mosquito population is still high. In fact, there is not only resistance to one type of insecticide, but also multiple resistance problems arise. In an effort to mitigate insecticide resistance in Anopheles mosquitoes, a genetic-based mathematical model was constructed to study the evolution of resistance. The mathematical model construction process is carried out in three stages which are presented in different chapters. In the first stage the model of one locus of two alleles is constructed at the haploid level. The genetic aspects involved in modeling at this stage include the fitness level which represents the selection in random crosses. The model constructed is a system of second order differential equations that can be reduced to first order. At this level, the model reduction technique still adopts exponential growth. In this model, the insecticidal factor is involved through a linear reduction in the fitness level of individuals who are phenotypically susceptible. Analysis of the existence and stability of the equilibrium is carried out in detail. Numerical simulations of several scenarios were carried out to review the role of insecticides on the evolution of resistance. At this stage, the authors can capture situations where the use of insecticides can accelerate the rate of resistance. The involvement of insecticidal factors that directly affect the fitness level in randomized crosses is considered a weakness in the first stage model. In fact, insecticidal factors directly affect individual mosquitoes. This is the motivation in the construction of the model in the second stage. Unlike before, at this stage, the model constructed is based on individual mosquitoes while maintaining their genetic process. Consequently, logistical factors and demographic influences such as intrinsic births and natural deaths need to be involved. The involvement of logistic factors by simultaneously maintaining the genetic process is a new breakthrough in genetic modeling. Meanwhile, the insecticide factor involved does not affect the fitness level but affects the individual directly. The set of invariants of the model, which is relevant to the first rule of thumb in biological modelling, is checked to ensure that the solution is not negative over time. The existence of an implicit polymorphic equilibrium is given more attention in this model. This was done to study the potential for genetic diversity in environmental situations exposed to insecticides. The explicit monomorphic equilibrium stability is analyzed by looking at the sign of the eigenvalues of the Jacobi matrix which is evaluated at the equilibrium point. The case of non-hyperbolic equilibrium stability is analyzed in detail using the center manifold theory. In addition, the situation where the insecticidal factor is neglected supports the model to be reduced to two dimensions. This situation is taken into consideration to investigate the biological justification that can be seen from the qualitative behavior. This stage gives us insight that only a very minimal use of insecticides can prevent the genetic change of mosquitoes from becoming resistant. In addition, the results of the polymorphic equilibrium stability analysis indicate that genetic diversity can occur when the fitness level of the heterozygous sub-population is higher than the other fitness levels. This is one of the biological justifications that support the relevance of the model constructed in describing the evolution of resistance. The problem of double resistance was studied in the third stage by constructing a two-locus model that correlated with insecticide targets. The model is a non-linear system of differential equations built by involving genetic factors such as recombination. The simultaneous involvement of recombination factors, selection factors, logistic factors and demographic factors is a new thing in genetic modeling. Random mating which took into account the resistance status at two loci was the main reason for the involvement of recombination factors in the model. At this stage, the insecticidal factor was not directly involved in the model because the domain under consideration was not at the individual level but at the haploid stage. However, the fitness data used in the simulation is field data that represents the condition of areas exposed to and not exposed to insecticides. Special cases based on the distribution of fitness levels, such as the allele model and the epistasis model, are presented and evaluated as material for qualitative studies. A detailed analysis was carried out to show the stability of the resistant monomorphic which biologically describes the level of dominance of the resistant genotype in the long term. Meanwhile, from the epistasis model, genetic diversity in the gene pool was investigated through the existence and stability of polymorphic equilibrium numerically using the Monte Carlo method. Through this stage, we gain a view that recombination factors are involved in influencing the existence of multiple resistances. However, the most sensitive factor to changes in system behavior is the logistic factor, which is then followed by the fitness level. The fitness level of the susceptible sub population receives special attention because of its inversely proportional effect on the evolution of the resistant sub population. This means that the abundance of susceptible sub-populations can be used as a control for the evolution of resistance. The results of this study provide an idea in mitigating insecticide resistance, among others by delaying the use of insecticides until a situation where the population has returned to being vulnerable. This is also supported by a finding that the abundance of susceptible subpopulations can reduce the rate of resistance growth. On a practical level, periodic monitoring is needed to determine the resistance status in the area where the insecticide will be used. The results of the analysis also lead to a conclusion that the management of larvae sources, such as environmental manipulation and larvicides, is still relevant in an effort to reduce the risk of insecticide resistance.