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Modelling strategies to break transmission of lymphatic filariasis - aggregation, adherence and vector competence greatly alter elimination

Identifieur interne : 000E06 ( Pmc/Curation ); précédent : 000E05; suivant : 000E07

Modelling strategies to break transmission of lymphatic filariasis - aggregation, adherence and vector competence greatly alter elimination

Auteurs : M. A. Irvine [Royaume-Uni] ; L. J. Reimer ; S. M. Njenga [Kenya] ; S. Gunawardena [Sri Lanka] ; L. Kelly-Hope ; M. Bockarie ; T. D. Hollingsworth [Royaume-Uni]

Source :

RBID : PMC:4618540

Abstract

Background

With ambitious targets to eliminate lymphatic filariasis over the coming years, there is a need to identify optimal strategies to achieve them in areas with different baseline prevalence and stages of control. Modelling can assist in identifying what data should be collected and what strategies are best for which scenarios.

Methods

We develop a new individual-based, stochastic mathematical model of the transmission of lymphatic filariasis. We validate the model by fitting to a first time point and predicting future timepoints from surveillance data in Kenya and Sri Lanka, which have different vectors and different stages of the control programme. We then simulate different treatment scenarios in low, medium and high transmission settings, comparing once yearly mass drug administration (MDA) with more frequent MDA and higher coverage. We investigate the potential impact that vector control, systematic non-compliance and different levels of aggregation have on the dynamics of transmission and control.

Results

In all settings, increasing coverage from 65 to 80 % has a similar impact on control to treating twice a year at 65 % coverage, for fewer drug treatments being distributed. Vector control has a large impact, even at moderate levels. The extent of aggregation of parasite loads amongst a small portion of the population, which has been estimated to be highly variable in different settings, can undermine the success of a programme, particularly if high risk sub-communities are not accessing interventions.

Conclusion

Even moderate levels of vector control have a large impact both on the reduction in prevalence and the maintenance of gains made during MDA, even when parasite loads are highly aggregated, and use of vector control is at moderate levels. For the same prevalence, differences in aggregation and adherence can result in very different dynamics. The novel analysis of a small amount of surveillance data and resulting simulations highlight the need for more individual level data to be analysed to effectively tailor programmes in the drive for elimination.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-015-1152-3) contains supplementary material, which is available to authorized users.


Url:
DOI: 10.1186/s13071-015-1152-3
PubMed: 26489753
PubMed Central: 4618540

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L. J. Reimer
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L. Kelly-Hope
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M. Bockarie
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<title>Background</title>
<p>With ambitious targets to eliminate lymphatic filariasis over the coming years, there is a need to identify optimal strategies to achieve them in areas with different baseline prevalence and stages of control. Modelling can assist in identifying what data should be collected and what strategies are best for which scenarios.</p>
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<title>Methods</title>
<p>We develop a new individual-based, stochastic mathematical model of the transmission of lymphatic filariasis. We validate the model by fitting to a first time point and predicting future timepoints from surveillance data in Kenya and Sri Lanka, which have different vectors and different stages of the control programme. We then simulate different treatment scenarios in low, medium and high transmission settings, comparing once yearly mass drug administration (MDA) with more frequent MDA and higher coverage. We investigate the potential impact that vector control, systematic non-compliance and different levels of aggregation have on the dynamics of transmission and control.</p>
</sec>
<sec>
<title>Results</title>
<p>In all settings, increasing coverage from 65 to 80 % has a similar impact on control to treating twice a year at 65 % coverage, for fewer drug treatments being distributed. Vector control has a large impact, even at moderate levels. The extent of aggregation of parasite loads amongst a small portion of the population, which has been estimated to be highly variable in different settings, can undermine the success of a programme, particularly if high risk sub-communities are not accessing interventions.</p>
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<sec>
<title>Conclusion</title>
<p>Even moderate levels of vector control have a large impact both on the reduction in prevalence and the maintenance of gains made during MDA, even when parasite loads are highly aggregated, and use of vector control is at moderate levels. For the same prevalence, differences in aggregation and adherence can result in very different dynamics. The novel analysis of a small amount of surveillance data and resulting simulations highlight the need for more individual level data to be analysed to effectively tailor programmes in the drive for elimination.</p>
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<p>The online version of this article (doi:10.1186/s13071-015-1152-3) contains supplementary material, which is available to authorized users.</p>
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<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Parasit Vectors</journal-id>
<journal-id journal-id-type="iso-abbrev">Parasit Vectors</journal-id>
<journal-title-group>
<journal-title>Parasites & Vectors</journal-title>
</journal-title-group>
<issn pub-type="epub">1756-3305</issn>
<publisher>
<publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">26489753</article-id>
<article-id pub-id-type="pmc">4618540</article-id>
<article-id pub-id-type="publisher-id">1152</article-id>
<article-id pub-id-type="doi">10.1186/s13071-015-1152-3</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Modelling strategies to break transmission of lymphatic filariasis - aggregation, adherence and vector competence greatly alter elimination</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Irvine</surname>
<given-names>M. A.</given-names>
</name>
<address>
<email>M.Irvine.1@Warwick.ac.uk</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Reimer</surname>
<given-names>L. J.</given-names>
</name>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Njenga</surname>
<given-names>S. M.</given-names>
</name>
<xref ref-type="aff" rid="Aff5"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Gunawardena</surname>
<given-names>S.</given-names>
</name>
<xref ref-type="aff" rid="Aff4"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Kelly-Hope</surname>
<given-names>L.</given-names>
</name>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Bockarie</surname>
<given-names>M.</given-names>
</name>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Hollingsworth</surname>
<given-names>T. D.</given-names>
</name>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff2"></xref>
</contrib>
<aff id="Aff1">
<label></label>
School of Life Sciences, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, UK</aff>
<aff id="Aff2">
<label></label>
Mathematics Institute, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, UK</aff>
<aff id="Aff3">
<label></label>
Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA UK</aff>
<aff id="Aff4">
<label></label>
Faculty of Medicine, University of Colombo, Colombo, Sri Lanka</aff>
<aff id="Aff5">
<label></label>
Kenya Medical Research Institute (KEMRI), P.O. Box 54840, 00200 Nairobi, Kenya</aff>
</contrib-group>
<pub-date pub-type="epub">
<day>22</day>
<month>10</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="pmc-release">
<day>22</day>
<month>10</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection">
<year>2015</year>
</pub-date>
<volume>8</volume>
<elocation-id>547</elocation-id>
<history>
<date date-type="received">
<day>29</day>
<month>8</month>
<year>2015</year>
</date>
<date date-type="accepted">
<day>6</day>
<month>10</month>
<year>2015</year>
</date>
</history>
<permissions>
<copyright-statement>© Irvine et al. 2015</copyright-statement>
<license license-type="OpenAccess">
<license-p>
<bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1">
<sec>
<title>Background</title>
<p>With ambitious targets to eliminate lymphatic filariasis over the coming years, there is a need to identify optimal strategies to achieve them in areas with different baseline prevalence and stages of control. Modelling can assist in identifying what data should be collected and what strategies are best for which scenarios.</p>
</sec>
<sec>
<title>Methods</title>
<p>We develop a new individual-based, stochastic mathematical model of the transmission of lymphatic filariasis. We validate the model by fitting to a first time point and predicting future timepoints from surveillance data in Kenya and Sri Lanka, which have different vectors and different stages of the control programme. We then simulate different treatment scenarios in low, medium and high transmission settings, comparing once yearly mass drug administration (MDA) with more frequent MDA and higher coverage. We investigate the potential impact that vector control, systematic non-compliance and different levels of aggregation have on the dynamics of transmission and control.</p>
</sec>
<sec>
<title>Results</title>
<p>In all settings, increasing coverage from 65 to 80 % has a similar impact on control to treating twice a year at 65 % coverage, for fewer drug treatments being distributed. Vector control has a large impact, even at moderate levels. The extent of aggregation of parasite loads amongst a small portion of the population, which has been estimated to be highly variable in different settings, can undermine the success of a programme, particularly if high risk sub-communities are not accessing interventions.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>Even moderate levels of vector control have a large impact both on the reduction in prevalence and the maintenance of gains made during MDA, even when parasite loads are highly aggregated, and use of vector control is at moderate levels. For the same prevalence, differences in aggregation and adherence can result in very different dynamics. The novel analysis of a small amount of surveillance data and resulting simulations highlight the need for more individual level data to be analysed to effectively tailor programmes in the drive for elimination.</p>
</sec>
<sec>
<title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s13071-015-1152-3) contains supplementary material, which is available to authorized users.</p>
</sec>
</abstract>
<custom-meta-group>
<custom-meta>
<meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2015</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
</record>

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