Villela Daniel A M, Codeço Claudia T, Figueiredo Felipe, Garcia Gabriela A, Maciel-de-Freitas Rafael, Struchiner Claudio J
Fundação Oswaldo Cruz, Programa de Computação Científica-Rio de Janeiro, Brazil.
Fundação Oswaldo Cruz, Departamento de Entomologia, Laboratório de Transmissores de Hematozoários-Rio de Janeiro, Brazil.
PLoS One. 2015 Apr 23;10(4):e0123794. doi: 10.1371/journal.pone.0123794. eCollection 2015.
Strategies to minimize dengue transmission commonly rely on vector control, which aims to maintain Ae. aegypti density below a theoretical threshold. Mosquito abundance is traditionally estimated from mark-release-recapture (MRR) experiments, which lack proper analysis regarding accurate vector spatial distribution and population density. Recently proposed strategies to control vector-borne diseases involve replacing the susceptible wild population by genetically modified individuals' refractory to the infection by the pathogen. Accurate measurements of mosquito abundance in time and space are required to optimize the success of such interventions. In this paper, we present a hierarchical probabilistic model for the estimation of population abundance and spatial distribution from typical mosquito MRR experiments, with direct application to the planning of these new control strategies. We perform a Bayesian analysis using the model and data from two MRR experiments performed in a neighborhood of Rio de Janeiro, Brazil, during both low- and high-dengue transmission seasons. The hierarchical model indicates that mosquito spatial distribution is clustered during the winter (0.99 mosquitoes/premise 95% CI: 0.80-1.23) and more homogeneous during the high abundance period (5.2 mosquitoes/premise 95% CI: 4.3-5.9). The hierarchical model also performed better than the commonly used Fisher-Ford's method, when using simulated data. The proposed model provides a formal treatment of the sources of uncertainty associated with the estimation of mosquito abundance imposed by the sampling design. Our approach is useful in strategies such as population suppression or the displacement of wild vector populations by refractory Wolbachia-infected mosquitoes, since the invasion dynamics have been shown to follow threshold conditions dictated by mosquito abundance. The presence of spatially distributed abundance hotspots is also formally addressed under this modeling framework and its knowledge deemed crucial to predict the fate of transmission control strategies based on the replacement of vector populations.
尽量减少登革热传播的策略通常依赖于病媒控制，其目的是将埃及伊蚊的密度维持在理论阈值以下。传统上，蚊子数量是通过标记释放再捕获（MRR）实验来估计的，但该实验缺乏对病媒准确空间分布和种群密度的适当分析。最近提出的控制病媒传播疾病的策略包括用对病原体感染具有抗性的转基因个体取代易感野生种群。为了优化此类干预措施的成功率，需要准确测量蚊子在时间和空间上的数量。在本文中，我们提出了一个分层概率模型，用于从典型的蚊子MRR实验中估计种群数量和空间分布，并直接应用于这些新控制策略的规划。我们使用该模型和在巴西里约热内卢附近进行的两个MRR实验的数据进行了贝叶斯分析，实验在登革热传播的低季和高季进行。分层模型表明，蚊子的空间分布在冬季呈聚集状态（每处0.99只蚊子，95%置信区间：0.80 - 1.23），在数量高峰期则更为均匀（每处5.2只蚊子，95%置信区间：4.3 - 5.9）。在使用模拟数据时，分层模型的表现也优于常用的费舍尔 - 福特方法。所提出的模型对与抽样设计所带来的蚊子数量估计相关的不确定性来源进行了正式处理。我们的方法在诸如种群抑制或用感染沃尔巴克氏体的抗性蚊子取代野生病媒种群等策略中很有用，因为入侵动态已被证明遵循由蚊子数量决定的阈值条件。在这个建模框架下，还正式考虑了空间分布的数量热点的存在，并且认为了解这些热点对于预测基于病媒种群替代的传播控制策略的结果至关重要。
