Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug 8;17(8):e0010831.
doi: 10.1371/journal.pntd.0010831. eCollection 2023 Aug.

The biting rate of Aedes aegypti and its variability: A systematic review (1970-2022)

Mondal Hasan Zahid  1   2 , Hannah Van Wyk  1 , Amy C Morrison  3 , Josefina Coloma  4 , Gwenyth O Lee  5 , Varsovia Cevallos  6 , Patricio Ponce  6 , Joseph N S Eisenberg  1
Affiliations

The biting rate of Aedes aegypti and its variability: A systematic review (1970-2022)

Mondal Hasan Zahid et al. PLoS Negl Trop Dis. .

Abstract

Background: Transmission models have a long history in the study of mosquito-borne disease dynamics. The mosquito biting rate (MBR) is an important parameter in these models, however, estimating its value empirically is complex. Modeling studies obtain biting rate values from various types of studies, each of them having its strengths and limitations. Thus, understanding these study designs and the factors that contribute to MBR estimates and their variability is an important step towards standardizing these estimates. We do this for an important arbovirus vector Aedes aegypti.

Methodology/principal findings: We perform a systematic review using search terms such as 'biting rate' and 'biting frequency' combined with 'Aedes aegypti' ('Ae. aegypti' or 'A. aegypti'). We screened 3,201 articles from PubMed and ProQuest databases, of which 21 met our inclusion criteria. Two broader types of studies are identified: human landing catch (HLC) studies and multiple feeding studies. We analyze the biting rate data provided as well as the methodologies used in these studies to characterize the variability of these estimates across temporal, spatial, and environmental factors and to identify the strengths and limitations of existing methodologies. Based on these analyses, we present two approaches to estimate population mean per mosquito biting rate: one that combines studies estimating the number of bites taken per gonotrophic cycle and the gonotrophic cycle duration, and a second that uses data from histological studies. Based on one histological study dataset, we estimate biting rates of Ae. aegypti (0.41 and 0.35 bite/mosquito-day in Thailand and Puerto Rico, respectively).

Conclusions/significance: Our review reinforces the importance of engaging with vector biology when using mosquito biting rate data in transmission modeling studies. For Ae. aegypti, this includes understanding the variation of the gonotrophic cycle duration and the number of bites per gonotrophic cycle, as well as recognizing the potential for spatial and temporal variability. To address these variabilities, we advocate for site-specific data and the development of a standardized approach to estimate the biting rate.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flow chart depicting the process for exclusion and inclusion of articles.
Fig 2
Fig 2
(a) Hourly biting activities across seasons reported by Chompoosri et al. [30], (b) Mean hourly biting reported by Captain-Esoah et al. [21] for 2015 and 2016.
Fig 3
Fig 3. Indoor and outdoor biting activities between 1700–2100 hours in Northern Ghana (Karch et al. [23]). Here, the unit is converted from nightly to an hourly rate.
See this image and copyright information in PMC

References

    1. Centers for Disease Control and Prevention. Fighting the world’s deadliest animal, https://www.cdc.gov/globalhealth/stories/2019/world-deadliest-animal.html. Update date: 08/15/2019. Access date: 05/18/2022.
    1. World Health Organization. Dengue and severe dengue. https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. Update date: 01/10/2022. Access date: 05/18/2022.
    1. Andraud M, Hens N, Marais C, and Beutels P. Dynamic Epidemiological Models for Dengue Transmission: A Systematic Review of Structural Approaches. PloS one. 2012; 7(11): e49085. doi: 10.1371/journal.pone.0049085 - DOI - PMC - PubMed
    1. Ewing D. A., Purse B. V., Cobbold C. A., and White S. M. A Novel Approach for Predicting Risk of Vector-Borne Disease Establishment in Marginal Temperate Environments under Climate Change: West Nile Virus in the UK. Journal of the Royal Society Interface. 2021; 18: 20210049 doi: 10.1098/rsif.2021.0049 - DOI - PMC - PubMed
    1. Gachohi J. M., Njenga M. K., Kitala P., and Bett B. Modelling Vaccination Strategies against Rift Valley Fever in Livestock in Kenya. PLoS Neglected Tropical Diseases. 2016; 10(12): 1–22. e0005049. doi: 10.1371/journal.pntd.0005049 - DOI - PMC - PubMed

Publication types