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

PLOS ONE, Dec 2019

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.

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

June Effect of climate on incidence of respiratory syncytial virus infections in a refugee camp in Kenya: A non-Gaussian time-series analysis Raymond Nyoka 0 1 Jimmy Omony 1 Samuel M. Mwalili 1 Thomas N. O. Achia 0 1 Anthony Gichangi 1 Henry Mwambi 0 1 0 School of Mathematics , Statistics and Computer Science , University of KwaZulu- Natal , Scottsville, South Africa, 2 Molecular Genetics Department , University of Groningen , Groningen , Netherlands , 3 Statistics Department, Jomo Kenyatta University of Agriculture and Technology , Nairobi , Kenya , 4 Jhpiego - an affiliate of John Hopkins University , Westlands, Nairobi , Kenya 1 Editor: Oliver Schildgen, Kliniken der Stadt KoÈln gGmbH , GERMANY 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. Introduction Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infections (ALTRI) in infants and young children [ 1 ][ 2 ]. RSV infections occur seasonally in temperate climate regions [ 3 ]. RSV adversely impacts the health of adults and immunocompromised patients, and is associated with significant mortality and morbidity, particularly in young children and vulnerable infants [ 4 ]. Children younger than 1 year are most susceptible to RSV infection; often 60±70% of children in this age group have been infected at least once, and re-infection can occur throughout their lifetime [ 4 ][ 5 ][ 6 ]. RSV is shed in saliva and nasopharyngeal secretions [ 7 ]. Infected hosts shed higher quantities of viral particles upon exposure to higher-ambient temperatures [ 8 ]. Low humidity during winter enhances RSV viability, and enables its survival for up to 12 hours on nonporous surfaces [ 9 ]. In dry air conditions, large droplets evaporate and remain air-borne for longer periods of time. Some studies have shown that airborne transmission appears to be sensitive to ambient humidity and temperature in temperate regions [ 8 ][ 10 ]. RSV outbreaks show some seasonality that suggests a connection with atmospheric and environmental conditions [ 11 ] [ 12 ]. Most RSV infections in temperate locations occur between November and April [ 13 ]. RSV infection has been associated with winter in these regions because people spend more time indoors, potentially in crowded conditions [ 14 ]. Such climatic regions are different from those of Kenya, which is located on the equator and experiences bimodal seasonal rainfall due to the interaction of the Northern and Southern Hemisphere monsoon systems [ 15 ]. However, variations in climatic factors, such as humidity, temperature, wind speed, rainfall etc., can have a significant impact on disease dynamics. Therefore, it is essential that the RSV incidence be evaluated for equatorial climatic regions to aid accurate predictions of RSV outbreaks. [ 16 ] [ 17 ]. The wide range of statistical methods used to explore the link between RSV outbreaks and climate makes it difficult to elucidate a definitive relationship. Pearson correlation analysis was previously used to explain the associations of RSV-positive cases with meteorological variables [ 11 ]. The univariate analysis of variance (ANOVA), multiple regression analysis, and Spearman's rank correlation were used to assess the association between RSV incidence and meteorological parameters [ 18 ]. A better understanding of the relationship between climate and RSV helps in making reliable predictions of its incidence. Worldwide, as of 2005, 99% of deaths from RSV were reported (...truncated)


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Raymond Nyoka, Jimmy Omony, Samuel M. Mwalili, Thomas N. O. Achia, Anthony Gichangi, Henry Mwambi. Effect of climate on incidence of respiratory syncytial virus infections in a refugee camp in Kenya: A non-Gaussian time-series analysis, PLOS ONE, 2017, Volume 12, Issue 6, DOI: 10.1371/journal.pone.0178323