Tracking the distribution and impacts of diseases with biological records and distribution modelling
Tracking the distribution and impacts of diseases with biological records and distribution modelling
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通过生物记录和分布模型跟踪疾病的分布和影响
DOI：
10.1111/bij.12567
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发表时间：
2015
影响因子：
1.9
通讯作者：
Purse B
中科院分区：
文献类型：
--
作者：
Purse B
物种分布模型在流行病学中广泛用于绘制空间格局和疾病和病媒引入的风险，也用于预测由于疾病爆发的高度社会影响和多重环境驱动因素，在未来环境变化的情况下暴露可能如何改变。虽然历史上很少记录病原体和媒介，但监测系统和媒体来源正在产生关于发生的新的在线数据源。此外，越来越多的生态现实主义正被纳入分布建模技术，重点关注影响物种分布的扩散、生物相互作用和进化限制，以及病原体、病媒和野生动物物种共同存在的非生物因素和记录努力的偏见。考虑到病原体和节肢动物媒介系统对植物、动物和人类健康有很大影响，本综述描述了媒介和病原体的生物记录是如何产生的，介绍了分布模型背后的概念，并说明了生态上现实的分布模型的潜力，以深入了解病原体的建立和传播。由于分布建模者的目标是为政策制定者提供规划和评估疾病缓解措施的证据和地图，因此我们强调了目前限制将模型直接转化为政策的因素。如果改进发生数据访问和整合以及与利益攸关方一起迭代开发的联合（相关和机制）建模方法，将来将更好地了解和绘制疾病分布。
Species distribution modelling is widely used in epidemiology for mapping spatial patterns and the risk of introduction of diseases and vectors and also for predicting how exposure may alter given future environmental change, motivated by the high societal impact and the multiple environmental drivers of disease outbreaks. Although pathogens and vectors have historically been sparsely recorded, monitoring systems and media sources are generating novel, online data sources on occurrence. Moreover, increasing ecological realism is being incorporated into distribution modelling techniques, focussing on dispersal, biotic interactions and evolutionary constraints that shape species distributions alongside abiotic factors and biases in recording effort, common to pathogens and vectors and wildlife species. Considering pathogens and arthropod vector systems with high impact on plant, animal and human health, the present review describes how biological records for vectors and pathogens arise, introduces the concepts behind distribution models and illustrates the potential for ecologically realistic distribution models to yield insight into the establishment and spread of pathogens. Because distribution modellers aim to provide policy makers with evidence and maps for planning and evaluation of disease mitigation measures, we highlight factors that currently constrain direct translation of models to policy. Disease distributions will be better understood and mapped in the future given improved occurrence data access and integration and combined (correlative and mechanistic) modelling approaches that are developed iteratively in concert with stakeholders.
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影响因子：
3.7
作者：
Stephens CR;Heau JG;González C;Ibarra-Cerdeña CN;Sánchez-Cordero V;González-Salazar C
通讯作者：
González-Salazar C
影响因子：
1.9
作者：
Purse, B. V.;Falconer, D.;Blackwell, A.
通讯作者：
Blackwell, A.
影响因子：
2.1
作者：
Benedict, Mark Q.;Levine, Rebecca S.;Lounibos, L. Philip
通讯作者：
Lounibos, L. Philip
影响因子：
15.8
作者：
Brownstein JS;Freifeld CC;Reis BY;Mandl KD
通讯作者：
Mandl KD
DOI：
10.1098/rspb.2013.1634
发表时间：
2013-10-07
期刊：
Proceedings. Biological sciences
影响因子：
--
作者：
Godfray HC;Donnelly CA;Kao RR;Macdonald DW;McDonald RA;Petrokofsky G;Wood JL;Woodroffe R;Young DB;McLean AR
通讯作者：
McLean AR