Insecticide
resistance data for supporting
vector-borne disease control

Providing data-driven insights into insecticide resistance trends for Anopheles species, Aedes aegypti and Aedes albopictus to strengthen evidence-based decision-making

Insecticide resistance insights

IR Mapper provides up-to-date open-access information on insecticide resistance to inform malaria and vector-borne disease control policy makers, programme manager and researchers of geographical and temporal trends in insecticide resistance.

Dynamic visualization

Interactive maps to explore patterns in resistance using data captured by entomological surveillance and using modelled resistance risk surfaces

Standarized results

Showcase results based on WHO or CDC- standardised insecticide resistance testing protocols

Customizable filters

Filter data by country, vector species, insecticide class, and other relevant criteria

Reliable data sources

Data are sourced from peer-reviewed publications and reports by national malaria control programmes

Analyses-ready data

Download regularly updated datasets that are pre-formatted for analysis

Upload data

Users are able to upload and instantly visualise their own data on a map alongside the published data

IR Mapper overview

Launched in 2012, IR Mapper is an open-access tool that collates, aggregates and visualizes data from standardised tests on mosquito susceptibility to insecticides.

Use of data included in this tool is welcomed, with credit to IR Mapper.

Originally focused on Anopheles mosquitoes, the primary malaria vector, it also includes resistance data for Aedes aegypti and Aedes albopictus, which transmit diseases including dengue, chikungunya, and Zika. Users can explore and upload their own resistance data (provided it has been georeferenced, and coordinates are available) and add it to the map alongside the published data. Since 2020, IR Mapper has also included additional layers derived from statistical models which generate estimates of resistance and intensity across sub-Saharan Africa, providing insights into resistance trends in areas where observed data are unavailable. These have been supplemented with additional estimates of vector occurrence and malaria endemicity obtained from VectorAtlas and Malaria Atlas Project respectively.

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The importance of insecticide resistance data

Vector control plays a pivotal role in reducing the risk of vector-borne diseases such as malaria. The effectiveness of insecticide-based vector control interventions is being jeopardised by the emergence of insecticide resistance and therefore poses a major threat to vector-borne disease control. Contemporary information on geographical trends in resistance is therefore essential to mitigate its impact on public health and allow decision-makers to make evidence-based decisions when planning and implementing their interventions. The goal of IR Mapper is to make data-driven insights into historical and emerging trends in resistance accessible to those who require it.

Data sources

Data are extracted monthly from peer-reviewed scientific publications and reports, including the US President's Malaria Initiative (PMI) country insecticide susceptibility reports (for Anopheles map) and VectorBase. The Aedes map incorporates data provided by Professor Hilary Ranson in 2016.

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A new menu on the Anopheles map allows users to view modelled resistance layers developed by Professor Catherine Moyes' Geospatial Modelling of Insect Vectors (GMIV) group. These layers fill data gaps in insecticide-resistance surveillance across sub-Saharan Africa and are generated using a published geostatistical ensemble model. They estimate the mean mortality rates of An. gambiae s.l. to insecticides like alpha-cypermethrin, lambda-cyhalothrin, deltamethrin, permethrin, and DDT from 2005 to 2022. Additionally, probability layers created through collaboration between GMIV, Liverpool School of Tropical Medicine (LSTM), the Clinton Health Access Initiative (CHAI), and IR Mapper indicate the likelihood that pyrethroid resistance in An. gambiae s.l. (at the district level) exceeds WHO thresholds for susceptibility, confirmed resistance, or the 10–80% mortality range for deploying piperonyl butoxide-treated nets, which mitigate metabolic resistance to pyrethroids.

Though An. funestus s.l. is another key malaria vector in Africa, there is insufficient data to model pyrethroid resistance for this species. In areas where An. funestus s.l. is present, users should consider An. gambiae s.l. resistance data along with local data on An. funestus s.l. The Anopheles map also allows users to download analysis-ready geospatial datasets on insecticide resistance in malaria vectors, which can be used to track trends in resistance over time and across regions.

References

These references relate to the additional modelled layers which can be viewed in IR Mapper.

For information on the sources of the observed data points that are displayed in IR Mapper, please download the dataset on the relevant map page.

  • Moyes CL, Athinya DK, Seethaler T, et al. Evaluating insecticide resistance across African districts to aid malaria control decisions. Proc Natl Acad Sci. August 2020:202006781.

  • Hancock, P. A., Hendriks, C., Tangena, J. A., Gibson, H., Hemingway, J., Coleman, M., Gething, P. W., Cameron, E., Bhatt, S., & Moyes, C. L. (2020). Mapping trends in insecticide resistance phenotypes in African malaria vectors. PLoS Biology, 18(6), e3000633.

  • Weiss, D. J., Lucas, T. C., Nguyen, M., Nandi, A. K., Bisanzio, D., Battle, K. E., ... & Gibson, H. S. (2019). Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum,2000-17: a spatial and temporal modelling study. The Lancet, 394(10195), 322-331.

  • Battle, K. E., Lucas, T. C., Nguyen, M., Howes, R. E., Nandi, A. K., Twohig, K. A., ... & Gibson, H. S. (2019). Mapping the global endemicity and clinical burden of Plasmodium vivax, 2000-17: a spatial and temporal modelling study. The Lancet, 394(10195), 332-343.

  • Messina, J. P., Kraemer, M. U., Brady, O. J., Pigott, D. M., Shearer, F. M., Weiss, D. J., ... & Brownstein, J. S. (2016). Mapping global environmental suitability for Zika virus. Elife, 5, e15272.

  • Bhatt, S., Gething, P. W., Brady, O. J., Messina, J. P., Farlow, A. W., Moyes, C. L., ... & Myers, M. F. (2013). The global distribution and burden of dengue. Nature, 496(7446), 504-507.

  • Nsoesie, E. O., Kraemer, M. U., Golding, N., Pigott, D. M., Brady, O. J., Moyes, C. L., ... & Hay, S. I. (2016). Global distribution and environmental suitability for chikungunya virus, 1952to 2015. Eurosurveillance, 21(20), 30234.

IR Mapper process

IR Mapper is a collaborative effort between Vestergaard and KEMRI-CGHR.

Step 1

Vestergaard extracts data monthly

Step 2

KEMRI-CGHR verifies data accuracy

Step 3

Verified data is uploaded to platform

Get in touch

Leave us a message or email us at: info@irmapper.com