Mapping insecticide
resistance to
strengthen
malaria control

IR Mapper consolidates reports of insecticide resistance in Anopheles species, Aedes aegypti and Aedes albopictus

Anopheles map Aedes map

The threat of insecticide resistance

Up-to-date, open-access information on insecticide resistance to guide malaria and vector-borne disease-control policy makers, programme managers, and researchers for the deployment of insecticidal tools.

Dynamic visualization

Interactive features to explore both modelled insecticide resistance patterns and resistance intensity data

Standarized results

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

Customizable filters

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

Endemicity

Display endemicity of vector diseases to support informed decision-making for insecticide resistance management strategies

Reliable data sources

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

Data reporting

Download regularly updated data sets that are prepped for analysis, enabling tracking of insecticide resistance trends across time and geography

Data are updated monthly from the latest peer-reviewed publications and national malaria control country-specific reports. Users can upload their own data sets for visualization and comparison with published data.

IR Mapper overview

Launched in 2012, IR Mapper is an open-access tool that collates, aggregates and visualizes data from standardized World Health Organization (WHO) or US Centers for Disease Control & Prevention (CDC) 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 resistance data, comparing it with published results. In 2020, IR Mapper added features to visualize modelled resistance layers and intensity data points, offering a clearer view of resistance spread and severity to support informed vector-control decisions.

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Understanding insecticide resistance levels

Standardized insecticide-susceptibility tests are crucial for assessing the ability of insect populations to resist insecticides. WHO categorizes resistance based on mortality rates and resistance ratios (RR). Confirmed resistance occurs when mortality drops below 90% or RR exceeds 10. Possible resistance is indicated by 90–97% mortality or RR between 5 and 9. Susceptibility is defined as 98–100% mortality or RR less than 5.

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.

Latest updates

The latest IR Mapper update uses the mapping platform MapBox, offering enhanced flexibility and faster data loading times for all available up-to-date insecticide resistance data.

Here are the latest sources informing IR Mapper and its adapted layers:

  • 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