Applications of spatio temporal analytical methods in surveillance of ross river virus disease

APPLICATIONS OF SPATIO-TEMPORAL ANALYTICAL METHODS IN SURVEILLANCE OF ROSS RIVER VIRUS DISEASE BY WENBIAO HU BMed A thesis submitted for the Degree of Doctor of Philosophy in the Centre for Health Research, Queensland University of Technology MAY 2005 For my wife, Xiaodong and our son Junqian II KEYWORDS Classification and regression trees, cluster analysis, generalised linear model, geographic information system, interpolation, polynomial distributed lag model, principal components analysis, Ross River virus disease, seasonal auto-regressive integrated moving average, socio-ecological factors, time series analysis III SUMMARY The incidence of many arboviral diseases is largely associated with social and environmental conditions Ross River virus (RRV) is the most prevalent arboviral disease in Australia It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods Computerised data files of daily RRV disease cases and daily climatic variables in Brisbane, Queensland during 1985-2001 were obtained from the Queensland Department of Health and the Australian Bureau of Meteorology, respectively Available information on other socio-ecological factors was also collected from relevant government agencies as follows: 1) socio-demographic data from the Australia Bureau of Statistics; 2) information on vegetation (littoral wetlands, ephemeral wetlands, open freshwater, riparian vegetation, melaleuca open forests, wet eucalypt, open forests and other bushland) from Brisbane City Council; 3) tidal activities from the Queensland Department of Transport; and 4) mosquito-density from Brisbane City Council Principal components analysis (PCA) was used as an exploratory technique for discovering spatial and temporal pattern of RRV distribution The PCA results show that the first principal component accounted for approximately 57% of the information, which contained the four seasonal rates and loaded highest and positively for autumn K-means cluster analysis indicates that the seasonality of RRV is IV characterised by three groups with high, medium and low incidence of disease, and it suggests that there are at least three different disease ecologies The variation in spatio-temporal patterns of RRV indicates a complex ecology that is unlikely to be explained by a single dominant transmission route across these three groupings Therefore, there is need to explore socio-economic and environmental determinants of RRV disease at the statistical local area (SLA) level Spatial distribution analysis and multiple negative binomial regression models were employed to identify the socio-economic and environmental determinants of RRV disease at both the city and local (ie, SLA) levels The results show that RRV activity was primarily concentrated in the northeast, northwest and southeast areas in Brisbane The negative binomial regression models reveal that RRV incidence for the whole of the Brisbane area was significantly associated with Southern Oscillation Index (SOI) at a lag of months (Relative Risk (RR): 1.12; 95% confidence interval (CI): 1.06 - 1.17), the proportion of people with lower levels of education (RR: 1.02; 95% CI: 1.01 - 1.03), the proportion of labour workers (RR: 0.97; 95% CI: 0.95 – 1.00) and vegetation density (RR: 1.02; 95% CI: 1.00 – 1.04) However, RRV incidence for high risk areas (ie, SLAs with higher incidence of RRV) was significantly associated with mosquito density (RR: 1.01; 95% CI: 1.00 - 1.01), SOI at a lag of months (RR: 1.48; 95% CI: 1.23 - 1.78), human population density (RR: 3.77; 95% CI: 1.35 - 10.51), the proportion of indigenous population (RR: 0.56; 95% CI: 0.37 - 0.87) and the proportion of overseas visitors (RR: 0.57; 95% CI: 0.35 – 0.92) It is acknowledged that some of these risk factors, while statistically significant, are small in magnitude However, given the high incidence of RRV, they may still be important in practice The results of this study suggest that the spatial pattern of RRV V disease in Brisbane is determined by a combination of ecological, socio-economic and environmental factors The possibility of developing an epidemic forecasting system for RRV disease was explored using the multivariate Seasonal Auto-regressive Integrated Moving Average (SARIMA) technique The results of this study suggest that climatic variability, particularly precipitation, may have played a significant role in the transmission of RRV disease in Brisbane This finding cannot entirely be explained by confounding factors such as other socio-ecological conditions because they have been unlikely to change dramatically on a monthly time scale in this city over the past two decades SARIMA models show that monthly precipitation at a lag months (β=0.004, p=0.031) was statistically significantly associated with RRV disease It suggests that that there may be 50 more cases a year for an increase of 100 mm precipitation on average in Brisbane The predictive values in the model were generally consistent with actual values (root-mean-square error (RMSE): 1.96) Therefore, this model may have applications as a decision support tool in disease control and risk-management planning programs in Brisbane The Polynomial distributed lag (PDL) time series regression models were performed to examine the associations between rainfall, mosquito density and the occurrence of RRV after adjusting for season and auto-correlation The PDL model was used because rainfall and mosquito density can affect not merely RRV occurring in the same month, but in several subsequent months The rationale for the use of the PDL technique is that it increases the precision of the estimates We developed an epidemic forecasting model to predict incidence of RRV disease The results show that 95% and 85% of the variation in the RRV disease was accounted for by the mosquito VI density and rainfall, respectively The predictive values in the model were generally consistent with actual values (RMSE: 1.25) The model diagnosis reveals that the residuals were randomly distributed with no significant auto-correlation The results of this study suggest that PDL models may be better than SARIMA models (R-square increased and RMSE decreased) The findings of this study may facilitate the development of early warning systems for the control and prevention of this widespread disease Further analyses were conducted using classification trees to identify major mosquito species of Ross River virus (RRV) transmission and explore the threshold of mosquito density for RRV disease in Brisbane, Australia The results show that Ochlerotatus vigilax (RR: 1.028; 95% CI: 1.001 – 1.057) and Culex annulirostris (RR: 1.013, 95% CI: 1.003 – 1.023) were significantly associated with RRV disease cycles at a lag of month The presence of RRV was associated with average monthly mosquito density of 72 Ochlerotatus vigilax and 52 Culex annulirostris per light trap These results may also have applications as a decision support tool in disease control and riskmanagement planning programs As RRV has significant impact on population health, industry, and tourism, it is important to develop an epidemic forecast system for this disease The results of this study show the disease surveillance data can be integrated with social, biological and environmental databases These data can provide additional input into the development of epidemic forecasting models These attempts may have significant implications in environmental health decision-making and practices, and may help health authorities determine public health priorities more wisely and use resources more effectively and efficiently VII TABLE OF CONTENTS KEYWORDS .III SUMMARY .IV TABLE OF CONTENTS .1 LIST OF TABLES LIST OF FIGURES .7 DEFINITION OF TERMS 10 ABBREVIATIONS .12 STATEMENT OF ORIGIANL AUTHORSHIP 13 ACKNOWLEDGEMENTS .14 PUBLICATIONS BY THE CANDIDATE (2001 - 2004) 16 CHAPTER 1: INTRODUCTION AND BACKGROUND 20 1.1 INTRODUCTION 20 1.2 AIMS AND HYPOTHESES 24 1.3 SIGNIFICANCE OF THE THESIS 25 1.4 CONTENTS AND STRUCTURE OF THE THESIS 26 CHAPTER 2: APPLICATIONS OF GIS AND SPATIAL ANALYSIS IN MOSQUITO-BORNE DISEASE RESEARCH: A REVIEW OF RELATED LITERATURE 29 2.1 SYSTEMATIC REVIEW 29 2.2 CRITICAL APPRAISAL OF KEY SPATIO-TEMPORAL ANALYTIC METHODS 40 2.3 APPLICATIONS OF GIS AND SPATIO-TEMPORAL ANALYTIC METHODS IN RRV RESEARCH 51 2.4 KNOWLEDGE GAPS IN THIS AREA 56 CHAPTER 3: STUDY DESIGN AND METHOD 58 3.1 STUDY SITE AND STUDY POPULATION .58 3.2 STUDY DESIGN 61 3.3 DATA COLLECTION AND MANAGEMENT 61 3.4 DATA LINKAGES .63 3.5 DATA ANALYSIS 63 3.6 THE LIMITATIONS OF THE STUDY 69 CHAPTER 4: SPATIAL AND TEMPORAL PATTERNS OF ROSS RIVER VIRUS IN BRISBANE, AUSTRALIA 72 ABSTRACT .73 4.1 INTRODUCTION 74 4.2 MATERIAL AND METHODS .76 4.3 RESULTS 78 4.4 DISCUSSION 84 REFERENCES 89 CHAPTER 5: SPATIAL ANALYSIS OF SOCIAL AND ENVIRONMENTAL FACTORS ASSOCIATED WITH ROSS RIVER VIRUS IN BRISBANE, AUSTRALIA 93 ABSTRACT .94 5.1 INTRODUCTION 95 5.2 MATERIALS AND METHODS 96 5.3 RESULTS 99 5.4 DISCUSSION 107 REFERENCES 114 CHAPTER 6: DEVELOPMENT OF A PREDICTIVE MODEL FOR ROSS RIVER VIRUS DISEASE IN BRISBANE, AUSTRALIA 119 ABSTRACT .120 6.1 INTRODUCTION .121 6.2 MATERIALS AND METHODS .123 6.3 RESULTS 128 6.4 DISCUSSION 141 ACKNOWLEDGEMENTS .146 REFERENCES 147 CHAPTER 7: RAINFALL, MOSQUITO DENSITY AND THE TRANSMISSION OF ROSS RIVER VIRUS: AN EPIDEMIC FORECASTING MODEL 153 ABSTRACT .154 7.1 INTRODUCTION .155 7.2 METHODS 157 7.3 RESULTS 159 7.4 DISCUSSION 167 ACKNOWLEDGEMENTS .170 APPENDIX 171 Condon, R., and I Rouse 1995 Acute symptoms and sequelae of Ross River virus infection in South-Western Australia: a follow-up study Clinical and Diagnostic Virology 3: 273-284 Cressie, N 1991 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CRITICAL APPRAISAL OF KEY SPATIO- TEMPORAL ANALYTIC METHODS 40 2.3 APPLICATIONS OF GIS AND SPATIO- TEMPORAL ANALYTIC METHODS IN RRV RESEARCH 51 2.4 KNOWLEDGE GAPS IN THIS AREA