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血吸虫交配、聚集及传播断点的种群生物学:对高危社区最终阶段更可靠的模型分析

Population biology of Schistosoma mating, aggregation, and transmission breakpoints: more reliable model analysis for the end-game in communities at risk.

作者信息

Gurarie David, King Charles H

机构信息

Department of Mathematics, Case Western Reserve University, Cleveland, Ohio, United States of America; Center for Global Health and Diseases, Case Western Reserve University, Cleveland, Ohio, United States of America.

Center for Global Health and Diseases, Case Western Reserve University, Cleveland, Ohio, United States of America.

出版信息

PLoS One. 2014 Dec 30;9(12):e115875. doi: 10.1371/journal.pone.0115875. eCollection 2014.

DOI:10.1371/journal.pone.0115875
PMID:25549362
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4280120/
Abstract

Mathematical modeling is widely used for predictive analysis of control options for infectious agents. Challenging problems arise for modeling host-parasite systems having complex life-cycles and transmission environments. Macroparasites, like Schistosoma, inhabit highly fragmented habitats that shape their reproductive success and distribution. Overdispersion and mating success are important factors to consider in modeling control options for such systems. Simpler models based on mean worm burden (MWB) formulations do not take these into account and overestimate transmission. Proposed MWB revisions have employed prescribed distributions and mating factor corrections to derive modified MWB models that have qualitatively different equilibria, including 'breakpoints' below which the parasite goes to extinction, suggesting the possibility of elimination via long-term mass-treatment control. Despite common use, no one has attempted to validate the scope and hypotheses underlying such MWB approaches. We conducted a systematic analysis of both the classical MWB and more recent "stratified worm burden" (SWB) modeling that accounts for mating and reproductive hurdles (Allee effect). Our analysis reveals some similarities, including breakpoints, between MWB and SWB, but also significant differences between the two types of model. We show the classic MWB has inherent inconsistencies, and propose SWB as a reliable alternative for projection of long-term control outcomes.

摘要

数学建模广泛应用于对传染病控制方案的预测分析。对于具有复杂生命周期和传播环境的宿主 - 寄生虫系统进行建模会出现具有挑战性的问题。大型寄生虫,如血吸虫,栖息在高度碎片化的栖息地中,这些栖息地影响着它们的繁殖成功率和分布。过度分散和交配成功率是对此类系统控制方案建模时需要考虑的重要因素。基于平均虫负荷(MWB)公式的更简单模型没有考虑这些因素,从而高估了传播。提议的MWB修订采用了规定的分布和交配因子校正来推导修改后的MWB模型,这些模型具有定性不同的平衡点,包括寄生虫灭绝的“断点”,这表明通过长期大规模治疗控制实现消除的可能性。尽管MWB方法被广泛使用,但没有人试图验证其背后的范围和假设。我们对经典的MWB和最近的“分层虫负荷”(SWB)建模进行了系统分析,后者考虑了交配和繁殖障碍(阿利效应)。我们的分析揭示了MWB和SWB之间的一些相似之处,包括断点,但也显示了这两种模型之间的显著差异。我们表明经典的MWB存在内在不一致性,并提议将SWB作为预测长期控制结果的可靠替代方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/f0265e7fb419/pone.0115875.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/a167df828452/pone.0115875.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/05125e67221b/pone.0115875.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/3eeb18218ae6/pone.0115875.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/39c615969b96/pone.0115875.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/69679e071215/pone.0115875.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/48d36f029603/pone.0115875.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/9d9125244e8e/pone.0115875.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/b32d6cf56a18/pone.0115875.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/b0817a090a6e/pone.0115875.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/f0265e7fb419/pone.0115875.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/a167df828452/pone.0115875.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/05125e67221b/pone.0115875.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/3eeb18218ae6/pone.0115875.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/39c615969b96/pone.0115875.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/69679e071215/pone.0115875.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/48d36f029603/pone.0115875.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/9d9125244e8e/pone.0115875.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/b32d6cf56a18/pone.0115875.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/b0817a090a6e/pone.0115875.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f37/4280120/f0265e7fb419/pone.0115875.g010.jpg

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