Bayesian modelling of non–gaussian time series of serve acute respiratory illness.

Abstract

Respiratory syncytial virus (RSV), Human metapneumovirus (HMPV) and Influenza are some of the major causes of acute lower respiratory tract infections (ALRTI) in children. Children younger than 1 year are the most susceptible to these infections. RSV and influenza infections occur seasonally in temperate climate regions. We developed statistical models that were assessed and compared to predict the relationship between weather and RSV incidence in chapter 2. Human metapneumovirus (HMPV) have similar symptoms to those caused by respiratory syncytial virus (RSV). Currently, only a few models satisfactorily capture the dynamics of time series data of these two viruses. In chapter 3, we used a negative binomial model to investigate the relationship between RSV and HMPV while adjusting for climatic factors. In chapter 4, we considered multiple viruses incorporating the time varying effects of these components.The occurrence of different diseases in time contributes to multivariate time series data. In this chapter, we describe an approach to analyze multivariate time series of disease counts and model the contemporaneous relationship between pathogens namely, RSV, HMPV and Flu. The use of the models described in this study, could help public health officials predict increases in each pathogen infection incidence among children and help them prepare and respond more swiftly to increasing incidence in low-resource regions or communities. We conclude that, preventing and controlling RSV infection subsequently reduces the incidence of HMPV. 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 generalised linear model (GLM) with a sinusoidal component over time to account for seasonal variation and extended it to a generalised 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. Human metapneumovirus (HMPV) have similar symptoms to those caused by respiratory syncytial virus (RSV). The modes of transmission and dynamics of these epidemics still remain poorly understood. Climatic factors have long been suspected to be implicated in impacting on the number of cases for these epidemics. Currently, only a few models satisfactorily capture the dynamics of time series data of these two viruses. In this study, we used a negative binomial model to investigate the relationship between RSV and HMPV while adjusting for climatic factors. We specifically aimed at establishing the heterogeneity in the autoregressive effect to account for the influence between these viruses. Our findings showed that RSV contributed to the severity of HMPV. This was achieved through comparison of 12 models of various structures, including those with and without interaction between climatic cofactors. Most models do not consider multiple viruses nor incorporate the time varying effects of these components. Common ARIs etiologies identified in developing countries include respiratory syncytial virus (RSV), human metapneumovirus (HMPV), influenza viruses (Flu), parainfluenza viruses (PIV) and rhinoviruses with mixed co-infections in the respiratory tracts which make the etiology of Acute Respiratory Illness (ARI) complex. The occurrence of different diseases in time contributes to multivariate time series data. In this work, the surveillance data are aggregated by month and are not available at an individual level. This may lead to over-dispersion; hence the use of the negative binomial distribution. In this paper, we describe an approach to analyze multivariate time series of disease counts. A previously used model in the literature to address dependence between two different disease pathogens is extended. We model the contemporaneous relationship between pathogens, namely; RSV, HMPV and Flu from surveillance data in a refugee camp (Dadaab) for children under 5 years to investigate for serial correlation. The models evaluate for the presence of heterogeneity in the autoregressive effect for the different pathogens and whether after adjusting for seasonality, an epidemic component could be isolated within or between the pathogens. The model helps in distinguishing between an endemic and epidemic component of the time series that would allow the separation of the regular pattern from irregularities and outbreaks. The use of the models described in this study, can help public health officials predict increases in each pathogen infection incidence among children and help them prepare and respond more swiftly to increasing incidence in low-resource regions or communities. This knowledge helps public health officials to prepare for, and respond more effectively to increasing RSV incidence in low-resource regions or communities. The study has improved our understanding of the dynamics of RSV and HMPV in relation to climatic cofactors; thereby, setting a platform to devise better intervention measures to combat the epidemics. We conclude that, preventing and controlling RSV infection subsequently reduces the incidence of HMPV.