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

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.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.Abstract and extended abstract

clim_rsv_Dadaab_month_up2.txt

Surveillance for viral respiratory illnesses, Respiratory syncytial virus (RSV) was implemented in Dadaab refugee camp in north eastern Kenya in 2007. Paediatric and adult patients who presented at a camp medical unit, and met the case definition for influenza-like illness (ILI) or severe acute respiratory infection (SARI), were enrolled into the laboratory-enhanced respiratory surveillance system and tested for all of the above disease after an informed consent form was completed by adults, older minors, and guardians of all minors <15 years. The monthly counts of all RSV cases among children younger than 5 years were included in the present analysis; the main outcome of interest being monthly RSV incidence rate in this age group. Local weather and climatic data, including: the mean temperature and mean dew point for the day (both in ⁰F); mean sea level pressure for the day in millibars; mean visibility for the day in miles; mean wind speed for the day in knots; minimum and maximum temperature (⁰F) reported during the day; and the total precipitation (in inches) reported during the day were obtained from the World Meteorological Organization’s (WMO’s), World Weather Watch Program, according to WMO Resolution 40 (Cg-XII) (available at http://www7.ncdc.noaa.gov/CDO/cdo)

R code and data

Surveillance for viral respiratory illnesses, Respiratory syncytial virus (RSV) was implemented in Dadaab refugee camp in north eastern Kenya in 2007. Paediatric and adult patients who presented at a camp medical unit, and met the case definition for influenza-like illness (ILI) or severe acute respiratory infection (SARI), were enrolled into the laboratory-enhanced respiratory surveillance system and tested for all of the above disease after an informed consent form was completed by adults, older minors, and guardians of all minors <15 years. The monthly counts of all RSV cases among children younger than 5 years were included in the present analysis; the main outcome of interest being monthly RSV incidence rate in this age group. Local weather and climatic data, including: the mean temperature and mean dew point for the day (both in ⁰F); mean sea level pressure for the day in millibars; mean visibility for the day in miles; mean wind speed for the day in knots; minimum and maximum temperature (⁰F) reported during the day; and the total precipitation (in inches) reported during the day were obtained from the World Meteorological Organization’s (WMO’s), World Weather Watch Program, according to WMO Resolution 40 (Cg-XII) (available at http://www7.ncdc.noaa.gov/CDO/cdo)

Examining strain diversity and phylogeography in relation to an unusual epidemic pattern of respiratory syncytial virus (RSV) in a long-term refugee camp in Kenya

Background: A recent longitudinal study in the Dadaab refugee camp near the Kenya-Somalia border identified unusual biannual respiratory syncytial virus (RSV) epidemics. We characterized the genetic variability of the associated RSV strains to determine if viral diversity contributed to this unusual epidemic pattern. Methods: For 336 RSV positive specimens identified from 2007 through 2011 through facility-based surveillance of respiratory illnesses in the camp, 324 (96.4%) were sub-typed by PCR methods, into 201 (62.0%) group A, 118 (36.4%) group B and 5 (1.5%) group A-B co-infections. Partial sequencing of the G gene (coding for the attachment protein) was completed for 290 (89.5%) specimens. These specimens were phylogenetically analyzed together with 1154 contemporaneous strains from 22 countries. Results: Of the 6 epidemic peaks recorded in the camp over the period, the first and last were predominantly made up of group B strains, while the 4 in between were largely composed of group A strains in a consecutive series of minor followed by major epidemics. The Dadaab group A strains belonged to either genotype GA2 (180, 98.9%) or GA5 (2, < 1%) while all group B strains (108, 100%) belonged to BA genotype. In sequential epidemics, strains within these genotypes appeared to be of two types: those continuing from the preceding epidemics and those newly introduced. Genotype diversity was similar in minor and major epidemics. Conclusion: RSV strain diversity in Dadaab was similar to contemporaneous diversity worldwide, suggested both between-epidemic persistence and new introductions, and was unrelated to the unusual epidemic pattern

data.txt

<p>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 models of various structures, including those with and without interaction between climatic cofactors. 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.</p

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

<div><p>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.</p></div

Correlation-regression analysis.

<p>A: Correlation between RSV incidence and wind speed; B: Correlation between RSV incidence and temperature; C: Correlation between RSV incidence and dew point; and D: Correlation between temperature and wind speed. In these plots, the regression lines of best fit are indicated by bold blue lines.</p

Sanitation Practices and Perceptions in Kakuma Refugee Camp, Kenya: Comparing the Status Quo With a Novel Service-Based Approach

Globally, an estimated 2.5 billion people lack access to improved sanitation. Unimproved sanitation increases the risk of morbidity and mortality, especially in protracted refugee situations where sanitation is based on pit latrine use. Once the pit is full, waste remains in the pit, necessitating the construction of a new latrine, straining available land and funding resources. A viable, sustainable solution is needed. This study used qualitative and quantitative methods to design, implement, and pilot a novel sanitation system in Kakuma refugee camp, Kenya. An initial round of 12 pre-implementation focus group discussions (FGDs) were conducted with Dinka and Somali residents to understand sanitation practices, perceptions, and needs. FGDs and a supplementary pre-implementation survey informed the development of an innovative sanitation management system that incorporated the provision of urine and liquid-diverting toilets, which separate urine and fecal waste, and a service-based sanitation system that included weekly waste collection. The new system was implemented on a pilot scale for 6 weeks. During the implementation, bi-weekly surveys were administered in each study household to monitor user perceptions and challenges. At the end of the pilot, the sanitation system was assessed using a second round of four post-implementation FGDs. Those who piloted the new sanitation system reported high levels of user satisfaction. Reported benefits included odor reduction, insect/pest reduction, the sitting design, the appropriateness for special populations, and waste collection. However, urine and liquid diversion presented a challenge for users who perform anal washing and for women who had experienced female genital mutilation. Refugee populations are often culturally and ethnically diverse. Using residents' input to inform the development of sanitation solutions can increase user acceptability and provide opportunities to improve sanitation system designs based on specific needs

Best model fit to the RSV incidence data (bold lines) with decomposed covariates.

<p>A: Poisson, GLM. B: Poisson, GAM. The standard error bars to the model fit are indicated by the dotted lines (95% confidence bounds). The base year in all these plots was September 2009.</p