Understanding the role of genetic methods in the control of mosquito borne disease under increased insecticide resistance and continued climate warming scenarios

Author: Bryan P. White, MS Biology, MPH; bpcwhite@gmail.com

Publication Date: 11/20/2019

Global clinical significance of mosquito borne disease

Arthropod borne disease (ABDs), diseases transmitted to humans via arthropod vectors, are of major global health concern because of their high mortality rates and potential to undergo range expansions due to climate change (Mayer et al., 2017). Of the ABDs, mosquito borne diseases (MBD) account for the highest mortality rates with Dengue fever and malaria contributing to 486 thousand global deaths annually, or 79% of vector and parasitic disease (WHO 2000-2016 disease burden estimates). While malaria causes more annual deaths, Dengue virus (DENV) causes more apparent infections at 390 million infections (Bhatt et al., 2013, Suwanmanee et al. 2016) compared to malaria’s 200 million (WHO, https://www.who.int/features/factfiles/malaria/en/). With a suspected underestimated dengue infection rate by as much as 3x due to a combination of misdiagnoses, systematic under-reporting, and hospital system avoidance (Bhatt et al., 2013), the global health burden of these two MBDs is significant and has warranted major efforts for the development of control and containment methods by public health organizations including WHO (https://www.who.int/features/factfiles/malaria/en/), the Global Fund (https://www.theglobalfund.org/en/malaria/), the Bill & Melinda Gates Foundation (https://www.gatesfoundation.org/what-we-do/global-health/malaria), and the Gavi, the Vaccine Alliance (Gavi) (https://www.gavi.org/about/mission/). Currently there is not a widely available, effective, vaccine or treatment for DENV (Corbel et al., 2016), and vaccination against one DENV serotype can cause enhanced disease against other DENV serotypes (Halstead, 2017), making vaccination development problematic and their safety uncertain. In total, the global economic burden of Dengue fever is estimated to be $9 billion (Roiz et al, 2018). All these factors combine make Ae. aegypti and Ae. albopictus critical targets for research into mosquito control strategies, which includes an understanding of their population biology, ecology, viral transmission, and disease physiology.

Because of their high infection rate and potential for range expansions, the Dengue carrying mosquitoes Aedes (Ae) aegypti and Ae. albopictus have been the targets of extensive vector tracking, mapping, and control efforts (Brady et al., 2012). Additionally, both species are competent vectors for at least five widespread arboviruses (chikungunya virus (CHIKV), dengue virus (DENV), Rift Valley fever, yellow fever, and Zika virus (ZIKV)), with Ae. aegypti generally considered to be the more competent vector of DENV than Ae. albopictus (Moyes, 2017). Ae. aegypti alone (Dengue and Chikunguya) puts 2.5 billion at risk of infection (Faucon, 2015). Recent concerns about the rise in microcephaly cases in Brazil associated with ZIKV (Corbel et al. 2016) and resurgences as far north as California (Gloria-Soria et al., 2014) have driven efforts to understand current and future ecological ranges of DENV vector mosquitoes. The most likely source of breeding populations of Ae. aegypti found in California during 2013 was found through genetic methods to be the southeastern (Houston/New Orleans) United States with commerce identified as a likely transmission source (Gloria-Soria et al., 2014) suggesting the potential for expansions into suitable habitats is high. More recent projections suggest that suitable habitat for both Ae. aegypti and albopictus might reach as far north as Washington in the Western United States and New Jersey in the Eastern (Johnson et al., 2017).

DENV and ZIKV are both closely related flavaviruses, however, they exhibit markedly different clinical presentations and the pathogenesis of the virus is not well understood in either (Suwanmanee et al., 2016). Transmissibility and pathogenicity of DENV is a complex interaction between both host and vector genetics and physiology (Sim et al., 2016). DENV strains experience genetic diversification after outbreak occurs potentially increasing local transmissibility due to rapidly occurring selection processes (Allicock et al., 2012). In Ae. aegypti, mosquito gut microbiota confers a protective effect against DENV and ultimately impacts transmissibility (Sim et al., 2016). Due to the complexity and variability of the viral lysogenic cycles, DENV and ZIKV might be considered highly evolvable and able to rapidly adapt to new host populations (Finol and Ooi, 2019). The rapid adaptive rate of flavaviruses makes the development of effective vaccines or control methods designed to reduce transmissibility challenging, leaving mosquito population control that incorporate the use of insecticides the only viable options for controlling the burden of MBD globally (Hemingway et al., 2006).

Insecticide resistance as an emerging threat to mosquito control strategies

Unfortunately, the usefulness of insecticides as mosquito population control methods is in jeopardy due to the development of insecticide resistance. Insecticide resistance is already negatively impacting control efforts (Vontas et al., 2012) and has been identified as a major challenge for mosquito control efforts, along with predicting outbreaks and communicating vector control strategies with land stakeholders (Dale et al., 2017). Insecticide resistance could increase annual deaths by 120,000/year due to the spread of resistance genes (Sawadogo et al., 2017). Supported by the WHO, the Worldwide Insecticide Resistance Network (WIN) was established to coordinate a global effort to detect and manage insecticide resistance in mosquito vectors of arboviruses and develop alternative strategies for vector control (Corbel et al., 2016). Current WIN efforts include the publication of reviews geared towards outlining standard frameworks for the management of Aedes-specific disease, creating systematic reviews of arboviruses to assist in WHO management decisions, and identify current challenges in vector control related to insecticide resistance (Roiz et al., 2018). Similar to WIN, the Innovative Vector Control Consortium (IVCC) works to develop new, non-pyrethroid insecticides, longer lasting insecticides, develop informatics tools for tracking, monitoring, and decision management surrounding insecticide resistance (Hemingway et al., 2006).

One strategy to mitigate the development of insecticide resistance is to implement mixture (multiple different classes of insecticides at the same time) or rotation (rotating between different classes) strategies to prevent the development of resistance to one class. Resistance to all four classes of insecticide (carbamates, organochlorines, organophosphates and pyrethroids) has developed in Aedes aegypti (Ranson et al., 2010). However, the number of approved insecticides by the WHO is limited which makes pyrethroid resistance a high risk to current control efforts (Kelly-Hope et al., 2008) and the development of new, WHO approved methods typically costs over $180 million USD (https://www.extension.purdue.edu/extmedia/PPP/PPP-71.pdf). Limitations of available insecticides are exacerbated when even under successful insecticide-based control methods the ecological niche in which the mosquitoes inhabit remains intact, meaning that vector mosquitoes easily repopulate areas that have been cleared in following seasons due to migration (Wang et al., 2013).

While efforts to understand the effects of insecticide resistance on mosquito control strategies are in their fledgling stages, the processes through which resistance genes themselves are gained and transmitted between populations are not well understood. Successfully mitigating insecticide resistance becomes confounded as resistance genes can be transmitted between populations through gene flow, making any efforts to eliminate resistance genes from wild populations problematic or impossible (Weeraratne et al., 2018). Additional problems include cross resistance with agricultural pesticides, where both agriculture and urban centers can harbor populations with increased insecticide resistance (Reid et al., 2016); cross resistance between molecular pathways where Aedes mosquitoes have been identified as having resistance to both pyrethroids and organophosphates on the same allele (Ranson et al., 2011), but the full scope and specific molecular mechanisms of those resistance pathways are currently unknown (Moyes et al., 2017); and the recent discovery that some types of resistance pathways originate from copy number variants (CNVs) rather than protein coding variants (Faucon et al., 2017). Furthermore, there are four classes of insecticide resistance mechanisms (metabolic resistance, cuticular resistance, cross-resistance, and target-site resistance (Ranson et al., 2011), which allows for multiple avenues in which the evolution and development of insecticide resistance can occur.

One of the most common delivery mechanisms for insecticides are insecticide-treated bed nets (ITNs), which are used globally for both malaria, dengue, and other MBD control (Read et al., 2009). Another method, indoor residual spraying (IRS), was used globally and was initially responsible for large reductions in mosquito populations (Read et al., 2009) prior to the development of insecticide-resistance genes. Most ITN/IRS kill extremely rapidly which contributes to resistance evolution due to the extremely high fitness cost of lacking resistance genes, which suggests the use of late-life-acting (LLA) insecticides (insecticides that kill after mosquitoes have already reproduced) could inhibit the development of resistance genes (Read et al., 2009). While even LLAs are extremely effective, since pyrethroids are the only class of insecticide approved for their use, they still might contribute to the evolution of resistance in the form of partial resistance. Partial resistance (increased morbidity/reproductive rate but not mortality) can eventually leading to full resistance (insecticides have negligible effect on reproduction) (Viana et al., 2016). Ultimately, any use of insecticides might contribute in some way towards the development and proliferation of insecticide resistance genes in mosquito populations.

Given that insecticide resistance is so widespread, the implementation of a standardized, structured framework for both monitoring, managing, and reporting developments in insecticide resistance has been put forward by Sternberg et al. (2017) as a part of the World Health Organization Global Plan for Insecticide Resistance Management (WHO GPIRM, https://www.who.int/malaria/publications/atoz/gpirm/en/). The framework set out by Sternberg et al. (2017) includes what are called six pillars, or six essential categories to be included in a mosquito control program. The first pillar consists of insecticide resistance strategies including reducing insecticide use and increasing insecticide diversity; The second suggests that insecticide resistance monitoring should be an integral part of any program; The third, is that new vector controls tools are needed, which includes the development of new insecticides as well as new delivery methods (e.g., long-lasting insecticide nets (LLINs) and rotation/mosaic strategies); The forth pillar suggests that filling knowledge games (epidemiological or otherwise) is a critical step towards successful management programs. What are the epidemiological consequences of resistance? How can we evaluate resistance management strategies? These are important areas of research that must be undertaken and receive buy-in from stakeholders. The fifth and final pillar deals with the cost-benefit of long-term strategies that might cost more vs. short term strategies that cost less. If long-term strategies are implemented that result in creating insect populations that are more susceptible to insecticides (less resistant), the long-term benefits might outweigh the short-term.

Mosquito range expansions as a result of climate change

Today, Aedes mosquitoes are globally distributed in at least 128 countries making the upper bounds of humans at risk of MBD close to 3.9 billion (Brady et al., 2012). Historically, Ae. aegypti most likely originated as a zoophilic, forest-dwelling, species native to sub-Saharan Africa that diverged into two, distinct non-African domesticated metapopulation and another hybrid tree-dwelling/domesticated metapopulation remaining in Africa (Brown et al., 2011). Genetic data support the hypothesis that Ae. aegypti has been domesticated by humans and originated as an African species through a single sub-speciation event and was later brought to the New World, the Pacific and Southeast Asia via human trade routes (Brown et al., 2013). Interestingly, Ae. aegypti and Ae. albopictus genomic data recently revealed that these two sister species diverged ~71 million years ago (Chen et al., 2015), making them a relatively ancient split in terms of typical divergence times within animal genera.

Figure 1. Current known range of the primary dengue and zika virus vectors Ae. aegypti (the Asian Tiger Mosquito) Ae. albopictus according to CDC 2017 predictive models adapted from Johnson et al., 2017.

Figure 2. Future predicted range of Aedes albopictus and Aedes aegypti under a simulated climate warming scenario (Ryan et al., 2019).

Figure 3. Future predicted transmission range of Dengue fever (Butterworth et al., 2017).

The potential for climate change to influence the distribution of arthropod disease vectors was suggested as early as 1995 (Martens et al.). More recently, Hales et al. (2002) estimated that 5-6 billion people will be at risk of dengue infection by the year 2085 if climate change (warming) occurs, 3-5 billion if not. However, some authors have argued that human activity, rather than climate change, will be the primary driver of MBD movement (Reiter et al., 2001). In some cases, population at risk of dengue might experience a net decrease due to increased GDP in spite of increased range expansions of the dengue mosquito, Ae. aegypti (Astrom et al., 2012; Ryan et al., 2019). Currently about 33% (18.5 million) of the 55 million people living in the North-Eastern USA are within the range of Ae. albopictus, a group expected to expand to 60% (33 million) by the end of the century due to climate change (Rochlin et al., 2013). Regardless of the cause, Ae. albopictus is both at its highest recorded endemic range and is rapidly expanding globally (Kraemer et al., 2015).

Climate change is likely to produce distributional shifts in both Ae. aegypti and Ae. albopictus that will have significant health impacts globally (Campbell et al., 2015). However, predictions of future mosquito ranges and disease transmissibility vary depending on the parameters incorporated into climate projection models and the severity of climate change that occurs (e.g., theoretical maximum warming – no human intervention vs. theoretical minimum warming – human intervention). Under some climate scenarios, milder warming might be worse than extreme warming since those scenarios will fail to produce damaging thermal conditions that reduce arthropod viability (Williams et al., 2016). Socioeconomic status, specifically, poverty, is linked to habitat availability for Ae. aegypti, making high poverty areas in the southeastern United States particularly vulnerable to arbovirus outbreaks (Monaghan et al., 2016). Recently, Monaghan et al. (2018) have argued that both human activity (socioeconomic, population growth, urbanization, etc.) as well as climate change will be important factors in determining future mosquito ranges. Range expansions coupled with the proliferation of insecticide resistance creates a two-pronged problem for those implementing mosquito control efforts and lead to scenarios where either mosquito borne disease acts as an emerging disease  (Ryan et al., 2019), or already bad health outcomes become even worse (Sutherst, 2004; McMichael et al., 2006; Watts et al., 2015).

Gene drives as a mosquito control strategy
In order to counteract the two-pronged challenge brought by range expansions due to climate change and the proliferation of insecticide resistance, novel vector control methods such as gene drives have become an active area of research. Gene drives, genetic constructs that can increase the frequency of an introduced allele in a population at a rate faster than traditional Mendelian inheritance, appear like promising tools to add to the current array of mosquito control systems (Adelman and Tu, 2016). This increased inheritance rate is achieved by the creation of genetic elements that can convert heterozygous alleles into homozygous alleles ensuring the transmission of the inserted elements into the next generation. The accumulation of the elements in a population assert a mosquito control effect by causing either a population crash or reduction of disease transmission (Esvelt et al., 2014; Komor et al., 2017). Use of gene drives in the field as mosquito control techniques might be highly effective but still controversial due to the risks (extinctions, species jumping, off-target effects, etc.) associated with potentially permanently altering wild populations (Champer et al., 2016). Any development of gene drives for intended use in wild populations should incorporate some of the safeguards outlined by Esvelt et al. (2014) and [others].

Limited testing of gene drives in either lab or field populations have recently been explored. In an application of the sterile insect technique using a gene drive, manipulation of the mosquito male determining gene (“M factor”) using a CRISPR-Cas9 gene drive may be more effective than standard techniques but might also drive populations to crash before establishing the allele (Adelman et al., 2016). An HEG model that is restricted to only gemetogenesis forcing the production of heterozygotes would allow the targeting female fertility genes that only fail under homozygous conditions (recessive inheritance) could be used, but ultimately cannot be maintained in the population due to its high effective fitness cost (Hammond et al., 2016). The use of gene drives to reduce the transmissibility of a pathogen has also been tested in An. stephensi through the creation of a gene drive system using CRISPR-Cas9 to insert a 17kb DNA construct containing genes that limited the ability of mosquitoes to propagate the malaria Plasmodium falciparum parasite (anti-P. falciparum effector genes) (Gantz et al., 2015).

Although gene drives as a use in vector control method have shown some promise during laboratory testing in Drosophila (Gantz?) and Anopheles (Gantz et al., 2015), the development of nuclease-resistant variants in target populations might block gene drives before they can spread to sufficient levels within the target population (Kyrou  et al., 2018). Developing gene drive systems that experience reduced resistance potential in the wild is an active area of research (Unckless et al., 2017). Alternative methods utilizing gene drives might offer more directed control strategies but are difficult to implement for many reasons including the Anopheles mosquito, a primary vector for malarial disease, is itself resistant to genetic manipulation (Wang et al., 2013).

Developing and Implementing Gene Drives

Due to the cost prohibitive nature and current lack of ethical “fail-safe” controls (e.g., reversing introduced gene drives) in transgenic and gene drive experimental models, the use of stochastic simulation models to identify optimal genetic targets for implementation of new mosquito control technologies can help policy makers make informed decisions regarding the budget and direction of mosquito control research programmes. One model, the Skeeter Buster model was developed as a modification of the Container Inhabiting Mosquitoes Simulation Model (CIMSiM) in order to include both spatial and genetic components as part of a stochastic population dynamic model of Ae. aegypti. (Magori et al., 2009). Efforts have been made to quantify the optimal number of mosquitoes to be released under Medea and Killer-Rescue transgenic constructs using the Skeeter Buster model, however no effort was made to estimate cost of those releases (Legros et al., 2013).

Using stochastic modeling techniques allows researchers to test hypotheses across a wide variety of landscapes and climates without expending significant resources. For example, Legros et al. (2016) compared the Skeeter Buster and AedesBA mosquito population models under two different climate regimes: Buenos Aires, Argentina (strongly seasonal, temperate) and Iquitos, Peru (weekly seasonal, tropical). They found that the strongly seasonal variation (Buenos Aires, Argentina) produced more disagreement between the two types of models. Additionally, there is some uncertainty as to whether gene drives are necessary for achieving satisfactory mosquito results in the case of the slow release of a non-lethal antipathogenic gene (Okamoto et al., 2014). The uncertainty and costs surrounding transgenic and gene drive technologies highlights the need for the continued use of simulation models. One area of caution is the possibility that even with extreme non-Mendelian patterns driving populations close to extinction, even small, remnant mosquito populations might still be effective at maintaining transmission rates high enough to continue propagating viral transmission (Deredec et al., 2011). This possibility further highlights the need for the development of biologically accurate population models.


Climate warming and insecticide resistance are two major contributing factors to increasing mosquito ranges globally. Depending on local climate variability, some regions will experience no change in viral transmission, in some regions viral transmission will be emergent, and in others transmission might decrease. The net effects of climate change will most likely result in increased overall transmission, the human impact of which will likely be in the hundreds of millions newly at risk to exposure of mosquito borne disease (Ryan et al., 2019). Developing methods for establishing a surveillance network is a critical research goal for mosquito control actors (Kelly-Hope et al., 2008; Girod et al., 2016; Williams et al., 2016; Bardosh et al., 2017), as is developing predictive models that can be used as early-warning systems against disease outbreaks (Morin et al., 2018; Nance et al., 2018).

            As mosquito population ranges expand, the development and protection of insecticide resistance genes in so-called mosquito metapopulations will make management and control increasingly challenging. In densely populated urban sprawls, insecticide-based management techniques might be impractical or impossible to use. As such, new methods will be needed to limit mosquito populations. In this paper, we have highlighted gene drives and other genetic methods as alternatives to insecticide-based management techniques, but this is in no way an exhaustive list. However, we recommend genetic methods because they do not cause harm to humans in the form of toxicity (insecticides) or land use modification (reduced economic value of land), and can be “turned off” to allow natural mosquito populations to recover in areas where viral transmission has been eliminated. Furthermore, we propose that genetic control methods be optimized using stochastic population simulations, and that simulations be integrated with biological, climate, and control method factors in order to produce predictive models of viral transmission that can be used by public health officials to make management decisions.

Call to Action

The risk of exposure to Dengue virus and other flaviviruses will rise in the majority of affected regions and emerge in previously unaffected regions. As mosquito populations expand and environmental conditions become more conducive to viral transmission, the public health burden of mosquito borne disease will drastically increase. With increasing population ranges, gene flow between mosquito populations will both protect the existence of and facilitate the transfer of insecticide resistance genes. So-called insecticide resistance metapopulations of viral-laden mosquitoes will pose increasingly difficult management challenges to public health departments globally. Notwithstanding these challenges will be the public health departments of the Southeastern United States, and less immediately, public health departments spanning from the West Coast, throughout the Midwest, and East Coast of the United States. No American will go without feeling the effects of climate change one way or another. And so we urge public health departments in these regions to begin development of mosquito control strategies that do not rely solely on the use of insecticides and to begin public information campaigns to educate and inform the public of the risks posed to them of mosquito borne disease.

Supplementary Information

Other notes:

  • Highlights the need for comprehensive genetic databases for invasive species in place before invasions occur extenuated by increased migrations due to climate change or the purposeful introduction of invasive species through bioterror activities (Gloria-Soria et al., 2014).
  • Accurate disease models are needed to avoid unnecessary funding expenditures towards surveillance and vector control (Williams et al., 2016).
  • Malaria is one of the few climate sensitive health outcomes that has been modeled by different groups (Caminade et al., 2014).
  • Unfair distribution of current control resources (e.g. gender bias, socioeconomic status, etc.)
  • Blood meals increase total bacterial content, but reduce diversity. Altering mosquito gut microbiome content might be a target for transmissibility reduction. (Wang et al., 2011)
  • Mosquito bacterial communities offer a protective role against P. falciparum infection. Inhibiting that protective element might decrease transmission by increasing morbidity and mortality associated with parasite infection. (Boissiere et al., 2012)
  • Insects lack an adaptive immune system meaning that they are susceptible to plasmodium infections, but have mechanisms for limiting infection severity. SEE: Oliveira Conclusion 1 (Simoes et al., 2017)
  • Understanding mosquito incubation period, survival, biting rate, emergence rates, and availability of blood meals are critical components to developing biologically accurate models that can be used to determine cost effectiveness of control methods. (Brady et al., 2016)

Model parameters

Papers identifying model parameters for stochastic simulations

Brady et al., 2013Ae. albopictus has a higher overall survival rate but Ae. aegypti has a broader temperature range tolerance which suggests survival rates are site and species specific.
Eisen et al., 2013Competition of Ae. aegypti between other container-inhabiting mosquitoes is also a critical factor towards population modeling, in addition to socioeconomic status of the region and local climate. This suggests population modeling scenarios have a multitude of axes that must be evaluated to understand the range potential of this species.
Semenza et al., 2014During the months of August, September, and October incidence rate ratio of imported Dengue fever can increase by up to 1.7 per 10,000 travellers.
Honorio et al., 2002Female Aedes mosquitoes can fly up to 800 m in 6 days, rapidly spreading the virus if infected
Williams et al., 2016Increased temperatures favor mosquito reproduction, but decreased humidity decreases egg survivorship, meaning not all future warming scenarios result in increased Dengue burden in Australia.
Otero et al., 2009Probability of a dengue outbreak is dependent on seasonal variation in temperature and breeding site availability
Gardner et al., 2012Travel volumes and predictive species distribution models can be used to determine cities most at risk of dengue transmission and to identify optimal regions for increased surveillance.
Brady et al., 2014Under similar human population sizes and blood feeding patterns, the longer lifespan of Ae. albopictus might make it a more competent vector of Dengue than Ae. aegypti.
Hales et al., 2002Vapor pressure is a critical component to modeling dengue transmission
Nance et a., 2018 
Butterworth et al., 2017 

Papers identifying insecticide resistance genes

Faucon et al., 2017Ae. aegypti populations demonstrate varying degrees of resistance from susceptible (no marked resistance) to highly resistant (up to 750 fold resistant compared to susceptible populations)
Ishak et al., 2017Ae. aegypti populations in Malaysia exhibit varying degrees of resistance to multiple types of insecticide. The up regulation of P450 genes in these populations suggests monooxegenases are involved in a general detoxification pathway that confers resistance to multiple classes of insecticide (pyrethroids and organophosphate in this case)
Goindin et al., 2017Ae. agypti exhibits high degrees of resistance (up to 33 fold in temephos) to imultiple insecticides
Weill et al., 2004An identical mutation (G119S) found in the gene for acetylcholinesterase (AChE) has been identified in four different mosquito species across two different subfamilies (Anopheles and Culex) suggesting similar selective pressures are being exerted on mosquitoes that can result in convergent evolution
Faucon et al., 2015Analyzed 760 candidate resistance genes in Ae. aegypti and identified copy number variants (CNVs) associated with deltamethrin (a pyrethroid) resistance in 41 genes, most of which were P450s
David et al., 2013Cytochrome P450 genes have been broadly identified as contributors to insecticide resistance, but only a few have been functionally validated in four mosquito species (Ae. aegypti, An. gambiae, An. funestus, and C. quinquifasciatus)
Osta et al., 2012Determined that ace1 resistance mutation frequencies were drastically reduced following a switch to pyrethroids from other OPs suggesting an insecticide rotation strategy would be effective at minimizing the accumulation of resistance alleles
Kioulos et al., 2014High frequency (up to 63.0% for kdr) of pyrethroid resistance mutations (kdr and ace1) in Greek popuplations Culex pipiens suggests historical insecticide use has maintained the presence of insecticide-resistance alleles
Ranson et al.,2008Ideally an insecticide control program would involve the rotation of different insecticide classes at a frequency that prevented populations from developing and maintaining resistance mutations, but that frequency and rotation strategy are not currently understood
Alout et al., 2007Identified a new mutation in AChE (F290V) that has a less costly fitness impact than the G119S mutation but confers resistance primarily to dichlorvos (an OP) rather than to general CX and OPs
Sayono et al., 2016In Java, Ae. aegypti were found to exhibit resistance to pyrethroids via mutations in a Voltage-Gated Sodium Channel (AaNaV) gene
Wang et al., 2012Insecticide resistance mutations in kdr are specific to the compound (eg DDT vs pyrethroids) and allele frequencies vary between populations meaning that control strategies could be custom tailored to the regime
Kioulos et al., 2014Suggests the idea of insecticide rotation on a yearly basis – (What is the optimal insecticide use interval?)
George et al., 2015Temephos (organophosphate) use was associated with a reduction in entemological indices but not a reduction in dengue transmission
Ranson et al., 2008Localized resistance

Literature Cited

Adelman, Z.N. and Tu, Z., 2016. Control of mosquito-borne infectious diseases: sex and gene drive. Trends in parasitology, 32(3), pp.219-229.

Allicock, O.M., Lemey, P., Tatem, A.J., Pybus, O.G., Bennett, S.N., Mueller, B.A., Suchard, M.A., Foster, J.E., Rambaut, A. and Carrington, C.V., 2012. Phylogeography and population dynamics of dengue viruses in the Americas. Molecular biology and evolution, 29(6), pp.1533-1543.

Alout, H., Berthomieu, A., Hadjivassilis, A. and Weill, M., 2007. A new amino-acid substitution in acetylcholinesterase 1 confers insecticide resistance to Culex pipiens mosquitoes from Cyprus. Insect biochemistry and molecular biology, 37(1), pp.41-47.

Åström, C., Rocklöv, J., Hales, S., Béguin, A., Louis, V. and Sauerborn, R., 2012. Potential distribution of dengue fever under scenarios of climate change and economic development. Ecohealth, 9(4), pp.448-454.

Baldacchino, F., Caputo, B., Chandre, F., Drago, A., della Torre, A., Montarsi, F. and Rizzoli, A., 2015. Control methods against invasive Aedes mosquitoes in Europe: a review. Pest management science, 71(11), pp.1471-1485.

Bardosh, K.L., Ryan, S.J., Ebi, K., Welburn, S. and Singer, B., 2017. Addressing vulnerability, building resilience: community-based adaptation to vector-borne diseases in the context of global change. Infectious diseases of poverty, 6(1), p.166.

Bhatt, S. et al. The global distribution and burden of dengue. Nature 496, 504–507 (2013).

Boissière, A., Tchioffo, M.T., Bachar, D., Abate, L., Marie, A., Nsango, S.E., Shahbazkia, H.R., Awono-Ambene, P.H., Levashina, E.A., Christen, R. and Morlais, I., 2012. Midgut microbiota of the malaria mosquito vector Anopheles gambiae and interactions with Plasmodium falciparum infection. PLoS pathogens, 8(5), p.e1002742.

Brady, O.J., Gething, P.W., Bhatt, S., Messina, J.P., Brownstein, J.S., Hoen, A.G., Moyes, C.L., Farlow, A.W., Scott, T.W. and Hay, S.I., 2012. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS neglected tropical diseases, 6(8), p.e1760.

Brady, O.J., Johansson, M.A., Guerra, C.A., Bhatt, S., Golding, N., Pigott, D.M., Delatte, H., Grech, M.G., Leisnham, P.T., Maciel-de-Freitas, R. and Styer, L.M., 2013. Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings. Parasites & vectors, 6(1), p.351.

Brady, O.J., Golding, N., Pigott, D.M., Kraemer, M.U., Messina, J.P., Reiner Jr, R.C., Scott, T.W., Smith, D.L., Gething, P.W. and Hay, S.I., 2014. Global temperature constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. Parasites & vectors, 7(1), p.338.

Brady, O.J., Godfray, H.C.J., Tatem, A.J., Gething, P.W., Cohen, J.M., McKenzie, F.E., Perkins, T.A., Reiner, R.C., Tusting, L.S., Sinka, M.E. and Moyes, C.L., 2016. Vectorial capacity and vector control: reconsidering sensitivity to parameters for malaria elimination. Transactions of the Royal Society of Tropical Medicine and Hygiene, 110(2), pp.107-117.

Brown, J.E., Evans, B.R., Zheng, W., Obas, V., Barrera‐Martinez, L., Egizi, A., Zhao, H., Caccone, A. and Powell, J.R., 2014. Human impacts have shaped historical and recent evolution in Aedes aegypti, the dengue and yellow fever mosquito. Evolution, 68(2), pp.514-525.

Brown, J.E., McBride, C.S., Johnson, P., Ritchie, S., Paupy, C., Bossin, H., Lutomiah, J., Fernandez-Salas, I., Ponlawat, A., Cornel, A.J. and Black, W.C., 2011. Worldwide patterns of genetic differentiation imply multiple ‘domestications’ of Aedes aegypti, a major vector of human diseases. Proceedings of the Royal Society of London B: Biological Sciences, p.rspb20102469.

Caminade, C., Kovats, S., Rocklov, J., Tompkins, A.M., Morse, A.P., Colón-González, F.J., Stenlund, H., Martens, P. and Lloyd, S.J., 2014. Impact of climate change on global malaria distribution. Proceedings of the National Academy of Sciences, 111(9), pp.3286-3291.

Campbell, L.P., Luther, C., Moo-Llanes, D., Ramsey, J.M., Danis-Lozano, R. and Peterson, A.T., 2015. Climate change influences on global distributions of dengue and chikungunya virus vectors. Phil. Trans. R. Soc. B, 370(1665), p.20140135.

Champer, J., Buchman, A. and Akbari, O.S., 2016. Cheating evolution: engineering gene drives to manipulate the fate of wild populations. Nature Reviews Genetics, 17(3), p.146.

Chen, X.G., Jiang, X., Gu, J., Xu, M., Wu, Y., Deng, Y., Zhang, C., Bonizzoni, M., Dermauw, W., Vontas, J. and Armbruster, P., 2015. Genome sequence of the Asian Tiger mosquito, Aedes albopictus, reveals insights into its biology, genetics, and evolution. Proceedings of the National Academy of Sciences, 112(44), pp.E5907-E5915.

Ciota, A.T., Chin, P.A. and Kramer, L.D., 2013. The effect of hybridization of Culex pipiens complex mosquitoes on transmission of West Nile virus. Parasites & vectors, 6(1), p.305.

Corbel, V. et al. Tracking Insecticide Resistance in Mosquito Vectors of Arboviruses: The Worldwide Insecticide resistance Network (WIN). PLoS Negl. Trop. Dis. 10, e0005054 (2016).

Dale, P. & Knight, J. Mosquito Control: Perspectives on Current Issues and Challenges. Annals of Community Medicine and Practice

Dale, P. E. R. & Knight, J. M. Managing mosquitoes without destroying wetlands: an eastern Australian approach. Wetlands Ecol. Manage. 20, 233–242 (2012).

David, J.P., Ismail, H.M., Chandor-Proust, A. and Paine, M.J.I., 2013. Role of cytochrome P450s in insecticide resistance: impact on the control of mosquito-borne diseases and use of insecticides on Earth. Philosophical Transactions of the Royal Society B: Biological Sciences, 368(1612), p.20120429.

Deredec, A., Godfray, H.C.J. and Burt, A., 2011. Requirements for effective malaria control with homing endonuclease genes. Proceedings of the National Academy of Sciences, 108(43), pp.E874-E880.

Eisen, L. and Moore, C.G., 2013. Aedes (Stegomyia) aegypti in the continental United States: a vector at the cool margin of its geographic range. Journal of medical entomology, 50(3), pp.467-478.

Esvelt, K.M., Smidler, A.L., Catteruccia, F. and Church, G.M., 2014. Concerning RNA-guided gene drives for the alteration of wild populations. eLife, Published online July 17, 2014.

Faucon, F., Dusfour, I., Gaude, T., Navratil, V., Boyer, F., Chandre, F., Sirisopa, P., Thanispong, K., Juntarajumnong, W., Poupardin, R. and Chareonviriyaphap, T., 2015. Unravelling genomic changes associated with insecticide resistance in the dengue mosquito Aedes aegypti by deep targeted sequencing. Genome research.

Faucon, F., Gaude, T., Dusfour, I., Navratil, V., Corbel, V., Juntarajumnong, W., Girod, R., Poupardin, R., Boyer, F., Reynaud, S. and David, J.P., 2017. In the hunt for genomic markers of metabolic resistance to pyrethroids in the mosquito Aedes aegypti: An integrated next-generation sequencing approach. PLoS neglected tropical diseases, 11(4), p.e0005526.

Finol, E. and Ooi, E.E., 2019. Evolution of subgenomic RNA shapes dengue virus adaptation and epidemiological fitness. iScience, 16, pp.94-105.

Gantz, V.M., Jasinskiene, N., Tatarenkova, O., Fazekas, A., Macias, V.M., Bier, E. and James, A.A., 2015. Highly efficient Cas9-mediated gene drive for population modification of the malaria vector mosquito Anopheles stephensi. Proceedings of the National Academy of Sciences, 112(49), pp.E6736-E6743.

Gardner, L.M., Fajardo, D., Waller, S.T., Wang, O. and Sarkar, S., 2012. A predictive spatial model to quantify the risk of air-travel-associated dengue importation into the United States and Europe. Journal of tropical medicine, 2012.

George, L., Lenhart, A., Toledo, J., Lazaro, A., Han, W.W., Velayudhan, R., Ranzinger, S.R. and Horstick, O., 2015. Community-effectiveness of temephos for dengue vector control: a systematic literature review. PLoS neglected tropical diseases, 9(9), p.e0004006.

Girod, R. et al. Detection of Chikungunya Virus Circulation Using Sugar-Baited Traps during a Major Outbreak in French Guiana. PLoS Negl. Trop. Dis. 10, e0004876 (2016).

Gloria-Soria, A., Brown, J.E., Kramer, V., Yoshimizu, M.H. and Powell, J.R., 2014. Origin of the dengue fever mosquito, Aedes aegypti, in California. PLoS neglected tropical diseases, 8(7), p.e3029.

Goindin, D. et al. Levels of insecticide resistance to deltamethrin, malathion, and temephos, and associated mechanisms in Aedes aegypti mosquitoes from the Guadeloupe and Saint Martin islands (French West Indies). Infect Dis Poverty 6, 38 (2017).

Hales, S., De Wet, N., Maindonald, J. and Woodward, A., 2002. Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. The Lancet, 360(9336), pp.830-834.

Halstead, S.B., 2017. Dengvaxia sensitizes seronegatives to vaccine enhanced disease regardless of age. Vaccine, 35(47), pp.6355-6358.

Hammond, A., Galizi, R., Kyrou, K., Simoni, A., Siniscalchi, C., Katsanos, D., Gribble, M., Baker, D., Marois, E., Russell, S. and Burt, A., 2016. A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae. Nature biotechnology, 34(1), p.78.

Hemingway, J., Beaty, B.J., Rowland, M., Scott, T.W. and Sharp, B.L., 2006. The Innovative Vector Control Consortium: improved control of mosquito-borne diseases. Trends in parasitology, 22(7), pp.308-312.

Honório, N.A., Silva, W.D.C., Leite, P.J., Gonçalves, J.M., Lounibos, L.P. and Lourenço-de-Oliveira, R., 2003. Dispersal of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in an urban endemic dengue area in the State of Rio de Janeiro, Brazil. Memórias do Instituto Oswaldo Cruz, 98(2), pp.191-198.

Ishak, I.H., Kamgang, B., Ibrahim, S.S., Riveron, J.M., Irving, H. and Wondji, C.S., 2017. Pyrethroid resistance in Malaysian populations of dengue vector Aedes aegypti is mediated by CYP9 family of cytochrome P450 genes. PLoS neglected tropical diseases, 11(1), p.e0005302.

Johnson, T.L., Haque, U., Monaghan, A.J., Eisen, L., Hahn, M.B., Hayden, M.H., Savage, H.M., McAllister, J., Mutebi, J.P. and Eisen, R.J., 2017. Modeling the environmental suitability for Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the contiguous United States. Journal of medical entomology, 54(6), pp.1605-1614.

Kelly-Hope, L., Ranson, H. and Hemingway, J., 2008. Lessons from the past: managing insecticide resistance in malaria control and eradication programmes. The Lancet infectious diseases, 8(6), pp.387-389.

Kioulos, I., Kampouraki, A., Morou, E., Skavdis, G. and Vontas, J., 2014. Insecticide resistance status in the major West Nile virus vector Culex pipiens from Greece. Pest management science, 70(4), pp.623-627.

Komor, A.C., Badran, A.H. and Liu, D.R., 2017. CRISPR-based technologies for the manipulation of eukaryotic genomes. Cell, 168(1-2), pp.20-36.

Kraemer, M. U. G. et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4, e08347 (2015).

Kyrou, K., Hammond, A.M., Galizi, R., Kranjc, N., Burt, A., Beaghton, A.K., Nolan, T. and Crisanti, A., 2018. A CRISPR–Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes. Nature biotechnology, 36(11), p.1062.

Lambrechts, L. et al. Assessing the epidemiological effect of wolbachia for dengue control. Lancet Infect. Dis. 15, 862–866 (2015).

Legros, M., Otero, M., Romeo Aznar, V., Solari, H., Gould, F. and Lloyd, A.L., 2016. Comparison of two detailed models of Aedes aegypti population dynamics. Ecosphere, 7(10).

Legros, M., Xu, C., Morrison, A., Scott, T.W., Lloyd, A.L. and Gould, F., 2013. Modeling the dynamics of a non-limited and a self-limited gene drive system in structured Aedes aegypti populations. PLoS One, 8(12), p.e83354.

Magori, K., Legros, M., Puente, M.E., Focks, D.A., Scott, T.W., Lloyd, A.L. and Gould, F., 2009. Skeeter Buster: a stochastic, spatially explicit modeling tool for studying Aedes aegypti population replacement and population suppression strategies. PLoS neglected tropical diseases, 3(9), p.e508.

Martens, W.J.M., Jetten, T.H., Rotmans, J. and Niessen, L.W., 1995. Climate change and vector-borne diseases: a global modelling perspective. Global environmental change, 5(3), pp.195-209.

Mayer, S.V., Tesh, R.B. and Vasilakis, N., 2017. The emergence of arthropod-borne viral diseases: A global prospective on dengue, chikungunya and zika fevers. Acta tropica, 166, pp.155-163.

McMichael, A.J., Woodruff, R.E. and Hales, S., 2006. Climate change and human health: present and future risks. The Lancet, 367(9513), pp.859-869.

Monaghan, A. J. et al. On the Seasonal Occurrence and Abundance of the Zika Virus Vector Mosquito Aedes Aegypti in the Contiguous United States. PLoS Curr. 8, (2016).

Monaghan, A.J., Sampson, K.M., Steinhoff, D.F., Ernst, K.C., Ebi, K.L., Jones, B. and Hayden, M.H., 2018. The potential impacts of 21st century climatic and population changes on human exposure to the virus vector mosquito Aedes aegypti. Climatic change, 146(3-4), pp.487-500.

Moyes, C.L., Vontas, J., Martins, A.J., Ng, L.C., Koou, S.Y., Dusfour, I., Raghavendra, K., Pinto, J., Corbel, V., David, J.P. and Weetman, D., 2017. Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans. PLoS neglected tropical diseases, 11(7), p.e0005625.

Okamoto, K.W., Robert, M.A., Gould, F. and Lloyd, A.L., 2014. Feasible introgression of an anti-pathogen transgene into an urban mosquito population without using gene-drive. PLoS neglected tropical diseases, 8(7), p.e2827.

Osta, M.A., Rizk, Z.J., Labbé, P., Weill, M. and Knio, K., 2012. Insecticide resistance to organophosphates in Culex pipiens complex from Lebanon. Parasites & vectors, 5(1), p.132.

Ranson, H., Burhani, J., Lumjuan, N. and Black IV, W.C., 2010. Insecticide resistance in dengue vectors. TropIKA. net [online], 1(1).

Ranson, H., N’guessan, R., Lines, J., Moiroux, N., Nkuni, Z. and Corbel, V., 2011. Pyrethroid resistance in African anopheline mosquitoes: what are the implications for malaria control? Trends in parasitology, 27(2), pp.91-98.

Read, A.F., Lynch, P.A. and Thomas, M.B., 2009. How to make evolution-proof insecticides for malaria control. PLoS biology, 7(4), p.e1000058.

Reid, M.C. and McKenzie, F.E., 2016. The contribution of agricultural insecticide use to increasing insecticide resistance in African malaria vectors. Malaria journal, 15(1), p.107.

Reiter, P., 2001. Climate change and mosquito-borne disease. Environmental health perspectives, 109(Suppl 1), p.141.

Rochlin, I., Ninivaggi, D. V., Hutchinson, M. L. & Farajollahi, A. Climate change and range expansion of the Asian tiger mosquito (Aedes albopictus) in Northeastern USA: implications for public health practitioners. PLoS One 8, e60874 (2013).

Sawadogo, S.P., Niang, A., Bilgo, E., Millogo, A., Maïga, H., Dabire, R.K., Tripet, F. and Diabaté, A., 2017. Targeting male mosquito swarms to control malaria vector density. PloS one, 12(3), p.e0173273.

Sayono, S., Hidayati, A.P.N., Fahri, S., Sumanto, D., Dharmana, E., Hadisaputro, S., Asih, P.B.S. and Syafruddin, D., 2016. Distribution of voltage-gated sodium channel (Nav) alleles among the Aedes aegypti populations in central Java Province and its association with resistance to pyrethroid insecticides. PLoS One, 11(3), p.e0150577.

Semenza, J.C., Sudre, B., Miniota, J., Rossi, M., Hu, W., Kossowsky, D., Suk, J.E., Van Bortel, W. and Khan, K., 2014. International dispersal of dengue through air travel: importation risk for Europe. PLoS neglected tropical diseases, 8(12), p.e3278.

Sim, S. and Hibberd, M.L., 2016. Genomic approaches for understanding dengue: insights from the virus, vector, and host. Genome biology, 17(1), p.38.

Simões, M.L., Mlambo, G., Tripathi, A., Dong, Y. and Dimopoulos, G., 2017. Immune regulation of Plasmodium is Anopheles species specific and infection intensity dependent. MBio, 8(5), pp.e01631-17.

Sutherst, R.W., 2004. Global change and human vulnerability to vector-borne diseases. Clinical microbiology reviews, 17(1), pp.136-173.

Suwanmanee, S. & Luplertlop, N. Dengue and Zika viruses: lessons learned from the similarities between these Aedes mosquito-vectored arboviruses. J. Microbiol. 55, 81–89 (2017).

Unckless, R.L., Clark, A.G. and Messer, P.W., 2017. Evolution of resistance against CRISPR/Cas9 gene drive. Genetics, 205(2), pp.827-841.

Viana, M., Hughes, A., Matthiopoulos, J., Ranson, H. and Ferguson, H.M., 2016. Delayed mortality effects cut the malaria transmission potential of insecticide-resistant mosquitoes. Proceedings of the National Academy of Sciences, 113(32), pp.8975-8980.

Vontas, J., Kioulos, E., Pavlidi, N., Morou, E., Della Torre, A. and Ranson, H., 2012. Insecticide resistance in the major dengue vectors Aedes albopictus and Aedes aegypti. Pesticide Biochemistry and Physiology, 104(2), pp.126-131.

Wang, S. and Jacobs-Lorena, M., 2013. Genetic approaches to interfere with malaria transmission by vector mosquitoes. Trends in biotechnology, 31(3), pp.185-193.

Wang, Y., Gilbreath III, T.M., Kukutla, P., Yan, G. and Xu, J., 2011. Dynamic gut microbiome across life history of the malaria mosquito Anopheles gambiae in Kenya. PloS one, 6(9), p.e24767.

WANG, Z.M., LI, C.X., Xing, D., YU, Y.H., Liu, N., XUE, R.D., DONG, Y.D. and ZHAO, T.Y., 2012. Detection and widespread distribution of sodium channel alleles characteristic of insecticide resistance in Culex pipiens complex mosquitoes in China. Medical and veterinary entomology, 26(2), pp.228-232.

Watts, N., Adger, W.N., Agnolucci, P., Blackstock, J., Byass, P., Cai, W., Chaytor, S., Colbourn, T., Collins, M., Cooper, A. and Cox, P.M., 2015. Health and climate change: policy responses to protect public health. The Lancet, 386(10006), pp.1861-1914.

Weeraratne, T.C., Surendran, S.N., Walton, C. and Karunaratne, S.P., 2018. Genetic diversity and population structure of malaria vector mosquitoes Anopheles subpictus, Anopheles peditaeniatus, and Anopheles vagus in five districts of Sri Lanka. Malaria journal, 17(1), p.271.

Weill, M., Malcolm, C., Chandre, F., Mogensen, K., Berthomieu, A., Marquine, M. and Raymond, M., 2004. The unique mutation in ace‐1 giving high insecticide resistance is easily detectable in mosquito vectors. Insect molecular biology, 13(1), pp.1-7.

Werren, J. H., Baldo, L. & Clark, M. E. Wolbachia: master manipulators of invertebrate biology. Nat. Rev. Microbiol. 6, 741–751 (2008).

Williams, C. R. et al. Projections of increased and decreased dengue incidence under climate change. Epidemiol. Infect. 144, 3091–3100 (2016).

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: