Cargando…
The human-snail transmission environment shapes long term schistosomiasis control outcomes: Implications for improving the accuracy of predictive modeling
INTRODUCTION: Schistosomiasis is a chronic parasitic trematode disease that affects over 240 million people worldwide. The Schistosoma lifecycle is complex, involving transmission via specific intermediate-host freshwater snails. Predictive mathematical models of Schistosoma transmission have often...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983867/ https://www.ncbi.nlm.nih.gov/pubmed/29782500 http://dx.doi.org/10.1371/journal.pntd.0006514 |
_version_ | 1783328518350831616 |
---|---|
author | Gurarie, David Lo, Nathan C. Ndeffo-Mbah, Martial L. Durham, David P. King, Charles H. |
author_facet | Gurarie, David Lo, Nathan C. Ndeffo-Mbah, Martial L. Durham, David P. King, Charles H. |
author_sort | Gurarie, David |
collection | PubMed |
description | INTRODUCTION: Schistosomiasis is a chronic parasitic trematode disease that affects over 240 million people worldwide. The Schistosoma lifecycle is complex, involving transmission via specific intermediate-host freshwater snails. Predictive mathematical models of Schistosoma transmission have often chosen to simplify or ignore the details of environmental human-snail interaction in their analyses. Schistosome transmission models now aim to provide better precision for policy planning of elimination of transmission. This heightens the importance of including the environmental complexity of vector-pathogen interaction in order to make more accurate projections. METHODOLOGY AND PRINCIPAL FINDINGS: We propose a nonlinear snail force of infection (FOI) that takes into account an intermediate larval stage (miracidium) and snail biology. We focused, in particular, on the effects of snail force of infection (FOI) on the impact of mass drug administration (MDA) in human communities. The proposed (modified) model was compared to a conventional model in terms of their predictions. A longitudinal dataset generated in Kenya field studies was used for model calibration and validation. For each sample community, we calibrated modified and conventional model systems, then used them to model outcomes for a range of MDA regimens. In most cases, the modified model predicted more vigorous post-MDA rebound, with faster relapse to baseline levels of infection. The effect was pronounced in higher risk communities. When compared to observed data, only the modified system was able to successfully predict persistent rebound of Schistosoma infection. CONCLUSION AND SIGNIFICANCE: The observed impact of varying location-specific snail inputs sheds light on the diverse MDA response patterns noted in operational research on schistosomiasis control, such as the recent SCORE project. Efficiency of human-to-snail transmission is likely to be much higher than predicted by standard models, which, in practice, will make local elimination by implementation of MDA alone highly unlikely, even over a multi-decade period. |
format | Online Article Text |
id | pubmed-5983867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59838672018-06-17 The human-snail transmission environment shapes long term schistosomiasis control outcomes: Implications for improving the accuracy of predictive modeling Gurarie, David Lo, Nathan C. Ndeffo-Mbah, Martial L. Durham, David P. King, Charles H. PLoS Negl Trop Dis Research Article INTRODUCTION: Schistosomiasis is a chronic parasitic trematode disease that affects over 240 million people worldwide. The Schistosoma lifecycle is complex, involving transmission via specific intermediate-host freshwater snails. Predictive mathematical models of Schistosoma transmission have often chosen to simplify or ignore the details of environmental human-snail interaction in their analyses. Schistosome transmission models now aim to provide better precision for policy planning of elimination of transmission. This heightens the importance of including the environmental complexity of vector-pathogen interaction in order to make more accurate projections. METHODOLOGY AND PRINCIPAL FINDINGS: We propose a nonlinear snail force of infection (FOI) that takes into account an intermediate larval stage (miracidium) and snail biology. We focused, in particular, on the effects of snail force of infection (FOI) on the impact of mass drug administration (MDA) in human communities. The proposed (modified) model was compared to a conventional model in terms of their predictions. A longitudinal dataset generated in Kenya field studies was used for model calibration and validation. For each sample community, we calibrated modified and conventional model systems, then used them to model outcomes for a range of MDA regimens. In most cases, the modified model predicted more vigorous post-MDA rebound, with faster relapse to baseline levels of infection. The effect was pronounced in higher risk communities. When compared to observed data, only the modified system was able to successfully predict persistent rebound of Schistosoma infection. CONCLUSION AND SIGNIFICANCE: The observed impact of varying location-specific snail inputs sheds light on the diverse MDA response patterns noted in operational research on schistosomiasis control, such as the recent SCORE project. Efficiency of human-to-snail transmission is likely to be much higher than predicted by standard models, which, in practice, will make local elimination by implementation of MDA alone highly unlikely, even over a multi-decade period. Public Library of Science 2018-05-21 /pmc/articles/PMC5983867/ /pubmed/29782500 http://dx.doi.org/10.1371/journal.pntd.0006514 Text en © 2018 Gurarie et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gurarie, David Lo, Nathan C. Ndeffo-Mbah, Martial L. Durham, David P. King, Charles H. The human-snail transmission environment shapes long term schistosomiasis control outcomes: Implications for improving the accuracy of predictive modeling |
title | The human-snail transmission environment shapes long term schistosomiasis control outcomes: Implications for improving the accuracy of predictive modeling |
title_full | The human-snail transmission environment shapes long term schistosomiasis control outcomes: Implications for improving the accuracy of predictive modeling |
title_fullStr | The human-snail transmission environment shapes long term schistosomiasis control outcomes: Implications for improving the accuracy of predictive modeling |
title_full_unstemmed | The human-snail transmission environment shapes long term schistosomiasis control outcomes: Implications for improving the accuracy of predictive modeling |
title_short | The human-snail transmission environment shapes long term schistosomiasis control outcomes: Implications for improving the accuracy of predictive modeling |
title_sort | human-snail transmission environment shapes long term schistosomiasis control outcomes: implications for improving the accuracy of predictive modeling |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983867/ https://www.ncbi.nlm.nih.gov/pubmed/29782500 http://dx.doi.org/10.1371/journal.pntd.0006514 |
work_keys_str_mv | AT gurariedavid thehumansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling AT lonathanc thehumansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling AT ndeffombahmartiall thehumansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling AT durhamdavidp thehumansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling AT kingcharlesh thehumansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling AT gurariedavid humansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling AT lonathanc humansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling AT ndeffombahmartiall humansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling AT durhamdavidp humansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling AT kingcharlesh humansnailtransmissionenvironmentshapeslongtermschistosomiasiscontroloutcomesimplicationsforimprovingtheaccuracyofpredictivemodeling |