Nyoka Raymond, Omony Jimmy, Mwalili Samuel M, Achia Thomas N O, Gichangi Anthony, Mwambi Henry
School of Mathematics, Statistics and Computer Science, University of KwaZulu- Natal, Scottsville, South Africa.
Molecular Genetics Department, University of Groningen, Groningen, Netherlands.
PLoS One. 2017 Jun 1;12(6):e0178323. doi: 10.1371/journal.pone.0178323. eCollection 2017.
Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infections (ALRTI) in children. Children younger than 1 year are the most susceptible to RSV infection. RSV infections occur seasonally in temperate climate regions. Based on RSV surveillance and climatic data, we developed statistical models that were assessed and compared to predict the relationship between weather and RSV incidence among refugee children younger than 5 years in Dadaab refugee camp in Kenya. Most time-series analyses rely on the assumption of Gaussian-distributed data. However, surveillance data often do not have a Gaussian distribution. We used a generalized linear model (GLM) with a sinusoidal component over time to account for seasonal variation and extended it to a generalized additive model (GAM) with smoothing cubic splines. Climatic factors were included as covariates in the models before and after timescale decompositions, and the results were compared. Models with decomposed covariates fit RSV incidence data better than those without. The Poisson GAM with decomposed covariates of climatic factors fit the data well and had a higher explanatory and predictive power than GLM. The best model predicted the relationship between atmospheric conditions and RSV infection incidence among children younger than 5 years. This knowledge helps public health officials to prepare for, and respond more effectively to increasing RSV incidence in low-resource regions or communities.
呼吸道合胞病毒（RSV）是儿童急性下呼吸道感染（ALRTI）的主要病因之一。1岁以下儿童最易感染RSV。在温带气候地区，RSV感染呈季节性发生。基于RSV监测和气候数据，我们开发了统计模型，并对其进行评估和比较，以预测肯尼亚达达布难民营5岁以下难民儿童的天气与RSV发病率之间的关系。大多数时间序列分析依赖于数据呈高斯分布的假设。然而，监测数据往往不具有高斯分布。我们使用了一个随时间具有正弦成分的广义线性模型（GLM）来考虑季节变化，并将其扩展为具有平滑立方样条的广义相加模型（GAM）。在时间尺度分解前后，气候因素作为协变量纳入模型，并对结果进行比较。协变量分解后的模型比未分解的模型更能拟合RSV发病率数据。气候因素协变量分解后的泊松GAM能很好地拟合数据，并且比GLM具有更高的解释力和预测力。最佳模型预测了5岁以下儿童的大气条件与RSV感染发病率之间的关系。这些知识有助于公共卫生官员为资源匮乏地区或社区RSV发病率的上升做好准备，并更有效地做出应对。
