PDF(Pubmed)
Abstract：
Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables.
摘要：
解开天气对传染病传播的影响对健康保护至关重要,准备和预防。因为天气因素是共同的，部分相关的，我们以弯曲杆菌病作为案例研究，利用地理学来分离出各个天气参数对其他季节变量的影响。弯曲杆菌感染遍布全球,是发达国家最常见的细菌性食源性疾病,它们表现出一致但特定国家的季节性。我们开发了一种新的条件发生方法，基于经典的分层，从长远来看，高分辨率,20年来，英格兰和威尔士约有100万例弯曲杆菌病病例与诊断实验室所在地的当地气象数据集相关联。在8°-15°范围内，温度每升高5°（摄氏度），弯曲杆菌病的预测发病率每百万人增加1例。在该范围之外观察到有限的关联。与日长有很强的关联。随着相对湿度在75-80％的区域内，病例趋于增加，而与降雨和风速的关联较弱。该方法能够检查多个因素，并对复杂趋势的出现进行建模，例如，5月至6月英格兰和威尔士弯曲杆菌病的持续急剧增加及其空间变异性。这种透明和直接的方法导致准确的预测，而不依赖于回归模型和/或假设特定的参数。分析的关键输出是对特定当地天气因素条件下的疾病发病率的全面现象学描述。这项研究对于推断弯曲杆菌病的传播机制至关重要；例如，通过模拟假定机制的条件发生率，并将其与现象学模式进行比较作为基准。这些发现挑战了这一假设，通常用统计模型制作，弯曲杆菌病等疾病的转化平均感染率只是环境变量的累加和组合。
