Population Biology of Vector-Borne Diseases
Population Biology of Vector-Borne Diseases
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媒介传播疾病的群体生物学
DOI：
10.1093/oso/9780198853244.003.0010
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
English S
中科院分区：
文献类型：
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作者：
English S
病媒传播疾病的流行病学模型是评估疾病风险和预测各种疾病控制方案效力的重要量化工具。例如，模型可以告诉我们病媒动态的某些变化的速度和程度，例如由于诱捕或气候变化导致的死亡率增加，将在各种情况下影响病媒丰度和疾病发病率（帕勒姆等人，2015年进行了综述;参见Rock等人，2017年的示例）。用于此类目的的模型的可靠性取决于对载体、寄生虫和宿主生物学的深入了解，但相关信息通常很少，尤其是来自实地的信息（Cator等人，2019）。所有这些模型都对病媒生物学和疾病传播方面作出了假设。在许多情况下，如疟疾-蚊子系统的Ross-MacDonald模型的各种公式（Smith等人，2012年，本卷第2章），假设关键参数有一个固定值，通常来自实验室实验中测量的平均值。然而，现实总是更加复杂，涉及的参数值在个体、菌株、物种和情况之内和之间存在很大差异。虽然这些关于宿主（人类或其他动物）生物学的详细数据（例如免疫力的变化）经常被纳入模型中，但关于媒介和寄生虫生物学的类似细节却很少受到关注。例如，病媒的死亡率通常被假定为终生不变，但越来越明显的是，个体在非常年幼或非常年老时死亡率可能会增加（Hargrove等人，2011年;哈灵顿等人，2008年）。将病媒生物学的这种复杂性纳入流行病学模型可能会对疾病传播动力学的结果产生影响（Bellan 2010; Rock等人，2015）。在本章中，我们首先描述了传统的媒介传播疾病建模方法。我们给出了几个例子，说明如何在这种方法的基础上，对病媒和寄生虫特征进行更复杂的假设，可以显着改变预测的疾病动态。我们讨论了在自然病媒种群参数化这些模型的详细研究的重要性。我们主要关注采采蝇（舌蝇属），原生动物锥虫寄生虫的载体（Leak 1999）。动物非洲锥虫（AAT）在整个非洲引起牲畜的广泛发病和死亡，导致牲畜和作物生产的经济损失估计至少为1.3
Epidemiological models of vector-borne diseases are important quantitative tools for assessing disease risk and predicting the efficacy of various options for disease control. For example, models can tell us the speed and extent to which certain changes in vector dynamics, such as an enhanced death rate due to trapping or climate change, will affect vector abundance and disease incidence in various situations (reviewed in Parham et al. 2015; see example of Rock et al. 2017). The reliability of models for such purposes depends on a solid understanding of the biology of the vectors, parasites and hosts, but pertinent information is often sparse, especially from the field (Cator et al. 2019). All such models make assumptions about aspects of vector biology and disease transmission. In many cases, as in the various formulations of the Ross-MacDonald model for the malaria-mosquito system (Smith et al. 2012, Chapter 2 in current volume), it is assumed that crucial parameters have a fixed value, often derived from mean values measured in laboratory experiments. Reality is invariably more complex, however, involving parameter values that differ substantially within and among individuals, strains, species and situations. While such detailed data on host (humans or other animals) biology—for example in terms of variation in immunity—is often incorporated into models, similar detail on vector and parasite biology has received less attention. For example, the death rate of vectors is often assumed to be constant throughout life, yet it is increasingly apparent that individuals may suffer enhanced mortality when very young or very old (Hargrove et al. 2011; Harrington et al. 2008). Incorporating such complexity on vector biology into epidemiological models can have implications for the outcomes of disease transmission dynamics (Bellan 2010; Rock et al. 2015). In this chapter, we first describe the classic approach to modeling vector-borne disease. We give several examples of how building on this approach with more complicated assumptions about vector and parasite traits can significantly change the predicted disease dynamics. We discuss the importance of detailed studies in natural vector populations to parameterize these models. We focus largely on tsetse (Glossina spp.), vectors of protozoan trypanosome parasites (Leak 1999). Animal African trypanosomiasis (AAT) causes extensive morbidity and mortality in livestock across Africa, resulting in an estimated economic loss in livestock and crop production of at least 1.3