Understanding heterogeneities in mosquito-bite exposure and infection distributions for the elimination of lymphatic filariasis

It is well known that individuals in the same community can be exposed to a highly variable number of mosquito bites. This heterogeneity in bite exposure has consequences for the control of vector-borne diseases because a few people may be contributing significantly to transmission. However, very fe...

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Published inProceedings of the Royal Society. B, Biological sciences Vol. 285; no. 1871; p. 20172253
Main Authors Irvine, Michael A., Kazura, James W., Hollingsworth, T. Deirdre, Reimer, Lisa J.
Format Journal Article
LanguageEnglish
Published England The Royal Society 31.01.2018
The Royal Society Publishing
EditionRoyal Society (Great Britain)
Subjects
Animals
Anopheles - physiology
Aquatic insects
Bite Heterogeneity
Biting
Decision making
Disease
Disease control
Disease transmission
Ecology
Elephantiasis, Filarial - prevention & control
Environment
Exposure
Female
Filariasis
Geospatial Model
Heterogeneity
Humans
Infections
Insect bites
Insect Bites and Stings - epidemiology
Insect Bites and Stings - etiology
Insecticide-Treated Bednets - statistics & numerical data
Lymphatic Filariasis
Male
Models, Theoretical
Mosquito Control - methods
Mosquito Vectors - physiology
Mosquitoes
Nets
Papua New Guinea - epidemiology
Parasitic diseases
Prevalence
Spatial Analysis
Spatial distribution
Spatial Heterogeneity
Vector Control
Vector-borne diseases
Papua New Guinea
lymphatic filariasis
bite heterogeneity
spatial heterogeneity
geospatial model
vector control
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Abstract It is well known that individuals in the same community can be exposed to a highly variable number of mosquito bites. This heterogeneity in bite exposure has consequences for the control of vector-borne diseases because a few people may be contributing significantly to transmission. However, very few studies measure sources of heterogeneity in a way which is relevant to decision-making. We investigate the relationship between two classic measures of heterogeneity, spatial and individual, within the context of lymphatic filariasis, a parasitic mosquito-borne disease. Using infection and mosquito-bite data for five villages in Papua New Guinea, we measure biting characteristics to model what impact bed-nets have had on control of the disease. We combine this analysis with geospatial modelling to understand the spatial relationship between disease indicators and nightly mosquito bites. We found a weak association between biting and infection heterogeneity within villages. The introduction of bed-nets increased biting heterogeneity, but the reduction in mean biting more than compensated for this, by reducing prevalence closer to elimination thresholds. Nightly biting was explained by a spatial heterogeneity model, while parasite load was better explained by an individual heterogeneity model. Spatial and individual heterogeneity are qualitatively different with profoundly different policy implications.
AbstractList It is well known that individuals in the same community can be exposed to a highly variable number of mosquito bites. This heterogeneity in bite exposure has consequences for the control of vector-borne diseases because a few people may be contributing significantly to transmission. However, very few studies measure sources of heterogeneity in a way which is relevant to decision-making. We investigate the relationship between two classic measures of heterogeneity, spatial and individual, within the context of lymphatic filariasis, a parasitic mosquito-borne disease. Using infection and mosquito-bite data for five villages in Papua New Guinea, we measure biting characteristics to model what impact bed-nets have had on control of the disease. We combine this analysis with geospatial modelling to understand the spatial relationship between disease indicators and nightly mosquito bites. We found a weak association between biting and infection heterogeneity within villages. The introduction of bed-nets increased biting heterogeneity, but the reduction in mean biting more than compensated for this, by reducing prevalence closer to elimination thresholds. Nightly biting was explained by a spatial heterogeneity model, while parasite load was better explained by an individual heterogeneity model. Spatial and individual heterogeneity are qualitatively different with profoundly different policy implications.
Author Reimer, Lisa J.
Hollingsworth, T. Deirdre
Kazura, James W.
Irvine, Michael A.
AuthorAffiliation 1 School of Life Sciences , University of Warwick , Warwick , UK
2 Institute of Applied Mathematics , University of British Columbia , Vancouver , Canada
3 Center for Global Health and Disease , Case Western Reserve University , Cleveland , OH , USA
4 Big Data Institute , Li Ka Shing Centre for Health Information and Discovery , University of Oxford , Oxford , UK
5 Department of Vector Biology , Liverpool School of Tropical Medicine , Liverpool , UK
AuthorAffiliation_xml – name: 3 Center for Global Health and Disease , Case Western Reserve University , Cleveland , OH , USA
– name: 5 Department of Vector Biology , Liverpool School of Tropical Medicine , Liverpool , UK
– name: 1 School of Life Sciences , University of Warwick , Warwick , UK
– name: 4 Big Data Institute , Li Ka Shing Centre for Health Information and Discovery , University of Oxford , Oxford , UK
– name: 2 Institute of Applied Mathematics , University of British Columbia , Vancouver , Canada
Author_xml – sequence: 1
  givenname: Michael A.
  orcidid: 0000-0003-4785-8998
  surname: Irvine
  fullname: Irvine, Michael A.
  email: m.irvine@math.ubc.ca
  organization: School of Life Sciences, University of Warwick, Warwick, UK; Institute of Applied Mathematics, University of British Columbia, Vancouver, Canada
– sequence: 2
  givenname: James W.
  surname: Kazura
  fullname: Kazura, James W.
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  givenname: T. Deirdre
  orcidid: 0000-0001-5962-4238
  surname: Hollingsworth
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  organization: School of Life Sciences, University of Warwick, Warwick, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
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  givenname: Lisa J.
  surname: Reimer
  fullname: Reimer, Lisa J.
  organization: Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK
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Issue 1871
Keywords lymphatic filariasis
bite heterogeneity
spatial heterogeneity
geospatial model
vector control
Language English
License open-access: http://creativecommons.org/licenses/by/4.0/: Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
2018 The Author(s).
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Snippet It is well known that individuals in the same community can be exposed to a highly variable number of mosquito bites. This heterogeneity in bite exposure has...
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StartPage 20172253
SubjectTerms Animals
Anopheles - physiology
Aquatic insects
Bite Heterogeneity
Biting
Decision making
Disease
Disease control
Disease transmission
Ecology
Elephantiasis, Filarial - prevention & control
Environment
Exposure
Female
Filariasis
Geospatial Model
Heterogeneity
Humans
Infections
Insect bites
Insect Bites and Stings - epidemiology
Insect Bites and Stings - etiology
Insecticide-Treated Bednets - statistics & numerical data
Lymphatic Filariasis
Male
Models, Theoretical
Mosquito Control - methods
Mosquito Vectors - physiology
Mosquitoes
Nets
Papua New Guinea - epidemiology
Parasitic diseases
Prevalence
Spatial Analysis
Spatial distribution
Spatial Heterogeneity
Vector Control
Vector-borne diseases
Title Understanding heterogeneities in mosquito-bite exposure and infection distributions for the elimination of lymphatic filariasis
URI https://royalsocietypublishing.org/doi/full/10.1098/rspb.2017.2253
https://www.ncbi.nlm.nih.gov/pubmed/29386362
https://www.proquest.com/docview/2006912979/abstract/
https://pubmed.ncbi.nlm.nih.gov/PMC5805933
Volume 285
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