PLoS ONE
Volume 12, Issue 6, 2017

Effect of climate on incidence of respiratory syncytial virus infections in a refugee camp in Kenya: A non-Gaussian time-series analysis (Article) (Open Access)

Nyoka R. , Omony J. , Mwalili S.M. , Achia T.N.O. , Gichangi A. , Mwambi H.
  • a School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Scottsville, South Africa
  • b Molecular Genetics Department, University of Groningen, Groningen, Netherlands
  • c Statistics Department, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
  • d School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Scottsville, South Africa
  • e Jhpiego - An affiliate of John Hopkins University, Westlands, Nairobi, Kenya
  • f School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Scottsville, South Africa

Abstract

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.

Author Keywords

[No Keywords available]

Index Keywords

refugee normal distribution human infection rate Refugees controlled study respiratory syncytial virus infection geographic distribution Kenya climate generalized linear model Humans decomposition preschool child Infant Child, Preschool refugee camp Climate change time series analysis Incidence seasonal variation Article high temperature statistical model weather Generalized additive model Child Respiratory Syncytial Virus Infections
Link
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019998213&doi=10.1371%2fjournal.pone.0178323&partnerID=40&md5=3bea48b7487cfa8ba5f13bdfa2d169c7
DOI: 10.1371/journal.pone.0178323
ISSN: 19326203
Cited by: 2
Original Language: English