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Effects of Permethrin and Permethrin Resistance on Zika Transmission

Author: Helen Chen


The Zika virus is a mosquito-borne virus that has affected 65 countries since 2015. There are currently no cures for the virus, so at-risk populations rely solely on preventative methods such as nets and insecticide sprays, with limited effectiveness. COBWEB, a computer simulation software, was used to determine the effectiveness of an introduction of the insecticide “permethrin” in the Brazilian city of Olaria, where the Zika virus is particularly virulent. Due to its flexibility and wide array of parameters, COBWEB was also able to model the effects of permethrin resistance on Zika transmission. The result was that permethrin significantly reduced fatality rates from the Zika virus and permethrin with resistance was still comparatively better than the control. This information is helpful, as it can be applied to other areas where the Zika virus is especially virulent, and serve as a potential solution to this emerging disease.


Vector-borne infectious diseases are human illnesses caused by vectors, which are carriers of diseases or medicine. Vectors do not cause disease, but they spread infection when they’re passed from one organism to another. These vector-borne diseases are typically transmitted by animal hosts, and they make up a significant portion of the world’s disease burden. Indeed, nearly half of the global population is infected with at least one type of vector-borne disease pathogen.[1] Mosquitoes are the most common disease vector, and they spread pathogens by ingesting them from an infected host during a blood meal and then injecting them into a new host.[2] The Zika virus has spread rapidly across Eastern Brazil and Mexico by mosquitoes since its first identification in Africa in 1947. As of 22 June 2016, 61 countries and territories have reported continuing Zika transmission.[2]

In Brazil alone, there have been an estimated 440 000 – 1 300 000 cases of Zika[3]. Propagated by the vector Aedes aegypti, or the yellow fever mosquito, the Zika virus can result in a number of symptoms, ranging from rashes and joint pain to total body paralysis.[4] When pregnant women are infected with Zika, their fetuses often display birth defects such as microcephaly, a rare neurological condition resulting in abnormal head sizes; in Paraiba, a province in Northeastern Brazil, the health ministry released statistics revealing that 114 babies per every 10,000 live births were born with suspected microcephaly – more than 1% of all newborns.[5] Since there are no solutions to the Zika virus as of now, preventative measures such as nets are used to prevent undue exposure to the disease vector.

Permethrin is a synthetic form of the naturally occurring insecticide, pyrethrum, which comes from Chrysanthemums. It is an insecticide to mosquitoes, ticks and other insects.[6] Its usage is highly effective, and it was shown through a study by the Institute of Medicine Forum on Microbial Threats that when lightweight uniforms from the military are treated until moist (approximately 4.5 oz) of permethrin (concentration 0.5%), it gives them 97.7% protection from mosquito bites.[1]

Using the large-scale biological simulation software “COBWEB”, the effectiveness of the insecticide “permethrin” in reducing the spread of Zika was modelled. This simulation focuses on the city of Olaria, Brazil, where the Zika virus is especially virulent. Furthermore, the study examines the growing trend of permethrin resistance in the Aedes aegypti vector, which affects the efficacy of insecticides in preventing further spread of Zika. Three different simulations were created for comparison purposes; one was the control, one had the application of permethrin, and one had permethrin with the added factor of insecticide resistance. In comparing the three simulations, the research team was able to determine the best way of dealing with the emerging disease.

Methods and Materials

COBWEB, which stands for Complexity and Organized Behaviour Within Environmental Bounds, is an agent-based, Java coded software, used to study interconnected and interdependent components of complex systems in numerous fields of study. COBWEB explores how components, such as mosquito and human populations, change and adapt as different variables are manipulated. It is used to create virtual laboratories and facilitate the study of how different populations of agents are influenced by various environmental changes. This permits the assessment of growth, decline, or sustainability of the populations within their environment over time. Additionally, abiotic factors such as permethrin can be included to study the effects of its introduction in the virtual laboratory. The effect of human migration on Zika transmission rate can be simulated using COBWEB by translating population and treatment circumstances into agents and environmental factors.

Based on data by the World Health Organization, the Zika virus has been prevalent in Brazil. However, not all areas of Brazil have reported evidence of the Zika virus. The city of Olaria was selected as the environment because it has the highest concentration of the virus; in the 0.79 area covering the average flight range of Aedes mosquitoes, it was found that there were 3,505 and 4,828 female mosquitoes in the MosquiTrap and aspirator, respectively, totalling 8,333.[7] By applying Olaria’s population to transmission rates, the population variance upon addition of the pesticide was observed.

A. Setting up COBWEB: Assumptions and/or arbitrary figures

Some arbitrary numbers and assumptions were made for a few parameters in COBWEB. In the environment tab in COBWEB, three Agent types were chosen (Figure 1). Agent 1 was assigned to represent the human population of Olaria, while Agent 2 was set to represent mosquitos; Agent 3 represents the permethrin, which is a control factor in the experiment. Additionally, it was decided that the environment would be 180 x 180 in dimensions. By increasing the environment and space for the various components to interact, more reliable data is produced. All other parameters were kept in their default state.

Figure 1: This is where the desired simulation is configured. This can exemplify a number of systems such as a section of a forest, ocean, city or body part that the user wants to study. The environment is represented on a 2D grid. This represents the city of Olaria.

This experiment had three simulations. The first (the control) was a simulation featuring just humans and the vector. The second (Simulation 2) featured humans, the vector, and the insecticide permethrin. The last (Simulation 3) featured humans, the insecticide-resistant vector, and permethrin. For ease of explanation, this paper will first explain the Control, Simulation 2, followed by Simulation 3 (Figures 2 – 7).

Several tabs on the COBWEB software were used. The “resources”, “agents”, “food web”, and “diseases” tabs were the main factors that were manipulated for the purposes of this study. The “resources” tab was used to sustain the various populations (in this case, mosquitos, humans, and permethrin) and ensure that they had the ‘resources’ to function in the experiment. The “agents” tab was used to model the various populations and their respective roles within the environment. In order to ensure accuracy, the population of Olaria and the mosquito count were determined and inputted as the agent counts. The “food web” function was used to control the interactions and interrelationships between the agents. Finally, the “disease” function was used to study the effects of Zika on the fatality rates in Olaria and the transmission of microcephaly. The contact transmission rate, child transmission rate, and use of permethrin as a “vaccine” with a specific effectiveness were used to study the effects that permethrin has on the simulation and the effects that Zika has on future populations.

The tick number at the top of the screen represents the time period in which a simulation runs. This number, which was kept constant in all three models, is relative and is representative of a sample time period. The numerical time is not the most important, as the trend over a constant period of time provides the most conclusive and useful results. However, for the purposes of this simulation, the tick number was chosen to represent days, so each tick represented one day of the year.

CONTROL: Vector and Humans, with no Permethrin

Figure 2A: In the “Resources” tab of COBWEB, certain resource amounts were allotted to the different agents to ensure they have enough ‘food’ to function and progress through the experiment. “Agent 1” corresponds to the human population and “Agent 2” corresponds to the mosquito population.

Figure 2B: In the “Agents” tab of COBWEB, the counts of the different agents, which were determined from research, were inputted to ensure reliability of the results. The other factors were determined upon experimentation and done in ratios to depict the patterns of the agents.

Figure 2C: In the “Food Web” tab of COBWEB, the interconnectedness between the two agents was depicted. For instance, “Agent 2” has a checkmark for “Agent 1” because the mosquito population affects the human population.

Figure 2D: In the “Disease” tab of COBWEB, the infected fraction and the child transmission rate (as of 2015) were inputted. Since the Zika virus leads to microcephaly, a birth defect, the percentage of children who have parents infected with the virus and that acquire the condition was inputted.

SIMULATION 2: Vector with Permethrin

Figure 3: To reiterate, the food web function was employed to depict the interactions between the agents and the three varieties of food. Agent 3 (permethrin) “consumes” mosquitoes to signify that it kills them. Food 1 represents food both mosquitoes and humans need to survive; this mostly signifies water since it is the resource that both agents need to the greatest extent to survive. Food 2 represents food just meant for mosquitoes. “Food 3” is there to simply keep permethrin levels relatively consistent throughout the simulation’s progress; it can be seen as a source of permethrin.

Figure 4: The next step was setting the agent parameters. Agent 1 represents the human population of 1893 in Olaria, Brazil[8], Agent 2 is the mosquito population of 2500, or the average of the female population size[9], and Agent 3 is the control group, or in this case the insecticide. The breed energy is higher for Agent 1 to signify that ‘more energy’ is required to reproduce, concluding that birth rates of humans are lower than that of mosquitos.

Figure 5: The initially infected fraction was approximately 7%[9], the contact transmission rate was set as the default, and the child transmission rate was 15%, as not all babies exposed to the Zika virus would be infected; the average was taken of the predicted 10-20% chance of infection.[5] For agent 2, the factors were all kept constant. This was also seen for agent 3 but with exception to the effectiveness rate of 97.7%, as the pesticide gives 97.7% protection from mosquito bites. As seen in Figure 3, agent 3 ‘eats’ agent 2, so the contact transmission rate is translated in that respect.

SIMULATION 3: Insecticide-Resistant Vector with Permethrin

Figure 6: All the factors are identical to that of the second simulation, except in the third where the vector’s resistance to permethrin is modelled in under the “vaccine effectiveness” tab. According to various experiments that have studied insecticide resistance of Aedes aegypti vector, the vector can show resistance ranging from 90% to 95% of its interactions with permethrin.[10] The vaccine effectiveness was averaged as 93% to represent the mean and most common resistance statistic.


To effectively compare the three simulations, the tick count (i.e. the time step in the model) was consistently kept at 800. Since this number is quite large, it ensures an observable trend; there could be significant changes in an ecosystem over short periods of time which may skew the findings and thus, the results.


In the control simulation, it was seen that there was a rapid growth in the mosquito population and a rapid decrease in the human population. This can be attributed to the fact that without interference, there is exponential growth in the number of mosquitos, and thus an exponential growth in the interactions between mosquitoes and humans.

Figure 7A: The control simulation examined the effects of the Zika virus in Olaria without permethrin. In this simulation, the tick count number was 800, which was representative of 800 days. The graph above depicts the population of humans over time, which is steadily decreasing.

Figure 7B: The graph above depicts the population of mosquitoes over time, which increases and then decreases after the population reaches 7000.


The second simulation also had a tick count of 800 for consistency. In this simulation, the human population experienced an initial decrease in population, followed by a steady increase by about 4 times the initial population (Figure 5). The mosquito population rapidly declined and then levelled out at zero after around 411 days (Figure 6). Once the mosquito population hit zero, there was an observable spike in the human population, as expected. The supply of permethrin was made to be steady and constant over time to maximize its influence on the population.

Figure 8: This graph shows the increase in human population over a time span of 800 days.

Figure 9. This graph shows the decrease in the Aedes aegypti population over a span of 800 days.


The third simulation, which also had a tick count of 800 days, displayed the resistance development of the Aedes species mosquito with insecticide. Because mosquitos “learn” and eventually develop resistance to certain treatments, it is important to study their effects over time and to what degree the resistance impinges on the effectiveness of permethrin.

Figure 10: The population of Aedes aegypti vector spikes before declining to zero unlike the second simulation (Figure 5), where the mosquito population declines without spiking; this is attributable to the implementation of insecticide resistance.

Figure 11: The graph above depicts the trend of the human population in Olaria when the city is under the subjection of permethrin-resistant mosquitoes. The human population rises steadily, but at a slower rate than the second simulation (Figure 5).


From the study, it is evidenced that Zika is best controlled with permethrin. Although the consideration and inclusion of permethrin resistance created a deviance from the results with sole permethrin application, the results were still comparatively better than the control; the human population increased and the mosquito population decreased to a greater extent when permethrin was applied. Additionally, in comparing the graphs depicting simulation 3 and the control, it can be seen that simulation 3 yields a smaller fatality rate for humans and a greater fatality rate for mosquitos.

However, in consideration of insecticide resistance, the results and trends were not as significant as those without. For instance, the population of the Aedes aegypti spiked before declining to zero. This was likely due to insecticide resistance, which allowed the initial population of mosquitoes to further propagate before declining, as expected. Also, the human population in the third simulation rose steadily but at a slower rate than in the simulation without insecticide resistance. This was likely due to insecticide resistance, which could’ve made it easier for mosquitoes to infect humans and thus, slowed population growth.

Although care was taken to ensure error was minimized, there are a few inevitable errors that could have affected the data from this study. Firstly, the model does not account for certain environmental factors such as differences in temperature, humidity, and elevation, all of which could influence the reproductive and survival rates of the Aedes aegypti vector. More specifically, the rise of global temperatures as a result of climate change would undoubtedly significantly affect the vector populations. As the crisis is affecting the viability of populations, it can affect the results of the survivability and reproduction of mosquitoes. Extreme weather can also drastically influence the populations of humans and mosquitoes, making the data less reliable. These factors could be included in the next iteration of this work.

Besides environmental factors being neglected from the study, numerous socio-economic factors were not factored into the study. The methods assumed that women and men were equally susceptible to Zika, because biologically speaking, their chances of contracting the virus were equal. However, in many rural areas such as Olaria, the city in question, a significant number of women work in the fields and in adverse conditions, thereby increasing their chances of exposure to the vector; about 70% of rural people in Brazil engage in agricultural employment, and female-headed households, which are becoming increasingly common, make up 27% of the poor rural population.

Thus, had these environmental and socio-economic elements been factored into the simulation, the simulation results could have been different. Another potential source of error was with the averaging of the effectiveness of permethrin, which created a sample rate as opposed to a range. However, the average was the most prevalent among the tests, so it was used to find the main and most prevalent occurrence in the range of possibilities.

Furthermore, in the real world, a small population of mosquitoes may survive because of insecticide resistance, and pass on the disease; in this way, the number of mosquitoes who can transmit the Zika virus could grow exponentially. However, the COBWEB software isn’t able to factor that circumstance into the simulation, so the model features all mosquitoes dying out. This additional factor would have affected the mosquito populations over time, but the model does give a guideline and approximate trend for the reduction of mosquitoes.

Although there may have been discrepancies with the data, COBWEB still had the ability to produce results similar to the data provided by the WHO. In the future, factors such as the impact of climate change and the migration of people who have the disease could be studied, and comparisons can be made between Permethrin and other solutions for mitigating the spread of the Zika virus.


There is potential for more studies to be performed using the current Zika models in COBWEB. The insecticide permethrin has shown promising ability to decrease the population affected by Zika in Olaria. This can be applied to all of Brazil, and extend to other countries where Zika is virulent. This research explicates permethrin as an effective barrier to the initial interaction of humans and mosquitoes. This study suggests an alternative solution to the existing options of nets and human reproduction practices. The next step is to explore how climate change can affect these agents, and how encouraging governments to mitigate the effects of the changing climate can impact the population\’s health and well-being. Another area of future study deals with the effectiveness of a Zika vaccine, as a study has shown 17 out of 18 tests on monkeys to be effective. Overall, permethrin was shown to be effective in reducing the interactions between humans and mosquitoes, and concurrently reducing the cases of Zika. This research can be applied to other countries, such as Ecuador, where the Zika virus is also very virulent.


A great thank you to Dr. Brad Bass, a status professor at the University of Toronto and Nobel Peace Prize Co-recipient, for developing the COBWEB software and for mentoring the team along the way.


  1. Threats, Institute. 2008. \”Summary And Assessment\”. Ncbi.Nlm.Nih.Gov.

  2. \”Zika Situation Report\”. 2016. World Health Organization.

  3. Bogoch, Isaac, Oliver Brady, Moritz Kraemer, Matthew German, Marisa Creatore, and Manisha Kulkarni. 2016. \”Anticipating The International Spread Of Zika Virus From Brazil\”. Europe PMC.

  4. \”What We Know About Zika\”. 2018. CDC.

  5. \”More Brazilian Babies Born With Defects\”. 2018. BBC News.

  6. Bloomington, Indiana, Indiana Bloomington, IU Bloomington, and Indiana University. 2018. \”Insect Precautions – Permethrin, Deet, And Picaridin: IU Health Center\”. Healthcenter.Indiana.Edu.

  7. Massad, Eduardo, Marcos Amaku, Francisco Countinho, Claudio Struchiner, Luis Lopez, Annelies Wilder-Smith, and Marcelo Burattini. 2018. Estimating The Size Of Aedes Aegypti Populations From Dengue Incidence Data: Implications For The Risk Of Yellow Fever Outbreaks. Ebook. Accessed November 18.

  8. \”Olaria (Municipality, Brazil) – Population Statistics, Charts, Map And Location\”. 2018. Citypopulation.De.

  9. Maciel-de-Freitas, Rafael, Alvaro Eiras, and Ricardo Lourenco-de-Oliveira. 2008. \”Calculating The Survival Rate And Estimated Population Density Of Gravid Aedes Aegypti (Diptera, Culicidae) In Rio De Janeiro, Brazil\”.

  10. Rodriguez, Maria, Juan Bisset, and Ditter Fernandez. 2007. \”Home\”. Bioone.Org.

About the Author

Helen Chen is a student who is extremely passionate about global health, law, chemistry, and development. She is also an SDGs advocate who was a Canadian Youth Representative at the Commission on the Status of Women Youth Forum and PGA High Level Event on Education; she focuses mainly on climate action, gender equality, and education. She enjoys using her knowledge and skills to help others. She is now studying statistics at Duke University.

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