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Updated: Jul 17, 2026
Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
Published on: March 16, 2019
Integrating routinely collected mosquito vector and human behavioral data for malaria prevention: A case example in
Kaci D McCoy1, Allison Hendershot2, Abdoulaye Bangoura3
1Breakthrough ACTION Project, Johns Hopkins Center for Communication Programs, Baltimore, MD, USA. kacidmccoy@gmail.com.
Background:
Integrating human behavioral data with data on malaria vector biting rates can help to quantify human exposure to malaria vectors and identify gaps in protection; however, to date, the availability of necessary human location and behavior data have been more limited as compared to that of corresponding entomological data. To expand the data available for use in vector-human data integrations, this case example explored the process and interpretations of integrating human behavior estimates from the Malaria Behavior Survey (MBS), a large-scale cross-sectional survey measuring behaviors and their determinants, with routinely collected VectorLink entomological surveillance data in Central Malawi.
Methods:
This study followed established guidance on vector-human data integration to guide extraction of necessary data inputs from existing, routinely collected data. Hourly nighttime human behavior and location estimates, as well as population insecticide treated bednet (ITN) use rate, were derived from questions in the Malawi MBS, conducted in Malawi in May-July 2021. Hourly nighttime human biting rates were derived from human landing catches (HLCs) carried out by VectorLink in Malawi in June 2021. Human and vector data were then integrated to provide weighted estimates of exposure to vector bites, accounting for proportions of people indoors or outdoors, and protected by ITNs, each hour of the night.
Results:
When accounting for mosquito and human location, most exposure to vector bites was estimated to occur indoors (96%), at times when people were asleep (87%). Although most mosquito bites occurred indoors during sleeping hours-when ITNs could offer protection-only 35% of all exposure to bites was estimated to be prevented, considering that the population-level usage rate of ITNs was only 40%. ITN access, which was only 39%, was identified as an important factor contributing to the protection gap.
Conclusion:
This case example suggests that integrating routinely collected entomological and human behavioral data can provide programmatically relevant insights. Future studies or programs may consider data integration of similar sources to better inform vector control or social and behavior change activities.
