Spatial and temporal analysis of Barmah Forest virus disease ...eprints.qut.edu.au/55047/1/Suchithra_Naish_Thesis.pdfSpatial and temporal analysis of Barmah Forest virus disease in

Spatial and temporal analysis of Barmah Forest virus

disease in Queensland, Australia

By

Suchithra Naish MSc; PG Dip.Sci; MPhil

A thesis submitted for the Degree of Doctor of Philosophy at

School of Public Health,

Queensland University of Technology,

Australia

August, 2012

I

STATEMENT OF ORIGINAL AUTHORSHIP

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature:

Name: Suchithra Naish

Date:

II

ACKNOWLEDGMENTS

I am thankful to my supervisory team, Prof. Shilu Tong, Prof. Kerrie Mengersen

and Dr. Wenbiao Hu, for their critical and thoughtful comments, and guidance

and support through the course of my PhD study. At all times throughout my

candidature they have maintained diligence and interest for my research. I would

like to specially thank my principal supervisor Prof. Shilu Tong for his

professional guidance, suggestions and comments on my study. I would like to

thank Prof. Kerrie Mengersen for her statistical advice and helpfu l comments on

my research. I am grateful to Dr Wenbiao Hu for his suggestions.

I am grateful to Queensland Health, Australian Bureau of Meteorology,

Australian Bureau of Statistics, Department of Transport and Main Roads,

Council of Scientific Industrial Research Organisation and Department of

Environment and Resource Management for providing the data.

I also acknowledge all my colleagues and research office staff at School of

Public Health, Faculty of Health for their friendship during this journey.

I am immensely thankful to my beloved husband for his constant love, emotional

support, sacrifice and endless encouragement which made me to achieve my

academic quest. I am especially thankful to my daughter for her sincere love and

understanding of my time. I am indebted to my mother and family for their

continuous love and support.

III

To my husband, Daniel,

our daughter, Sunita

and my parents.

IV

ABSTRACT

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne

diseases in Australia. The number of outbreaks and the incidence rate of BFV in

Australia have attracted growing concerns about the spatio-temporal complexity

and underlying risk factors of BFV disease. A large number of notifications has

been recorded continuously in Queensland since 1992. Yet, little is known about

the spatial and temporal characteristics of the disease. I aim to use notification

data to better understand the effects of climatic, demographic, socio -economic

and ecological risk factors on the spatial epidemiology of BFV disease

transmission, develop predictive risk models and forecast future disease risks

under climate change scenarios.

Computerised data files of daily notifications of BFV disease and climatic

variables in Queensland during 1992-2008 were obtained from Queensland

Health and Australian Bureau of Meteorology, respectively. Projections on

climate data for years 2025, 2050 and 2100 were obtained from Council of

Scientific Industrial Research Organisation . Data on socio-economic,

demographic and ecological factors were also obtained from relevant government

departments as follows: 1) socio-economic and demographic data from Australian

Bureau of Statistics; 2) wetlands data from Department of Environment and

Resource Management and 3) tidal readings from Queensland Department of

Transport and Main roads.

Disease notifications were geocoded and spatial and temporal patterns of disease

were investigated using geostatistics. Visualisation of BFV disease incidence

rates through mapping reveals the presence of substantial spatio -temporal

variation at statistical local areas (SLA) over time. Results reveal high incidence

rates of BFV disease along coastal areas compared to the whole area of

Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a

statistically significant relationship between BFV disease incidence rates and age

groups (2 = 7587, p

V

SLA level. Most likely spatial and space-time clusters are detected at the same

locations across coastal Queensland (p

VI

Important contributions arising from this research are that: (i) it is innovative to

identify high-risk coastal areas by creating buffers based on grid-centroid and the

use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression

method was used to account for spatial dependence and heterogeneity of data in

the study area; (iii) it determined a range of potential spatial risk factors for BFV

disease; and (iv) it predicted the future risk of BFV disease outbreaks under

climate change scenarios in Queensland, Australia.

In conclusion, the thesis demonstrates that the distribution of BFV disease

exhibits a distinct spatial and temporal variation. Such variation is influenced by

a range of spatial risk factors including climatic, demographic, socio-economic,

ecological and tidal variables. The thesis demonstrates that spatial regression

method can be applied to better understand the transmission dynamics of BFV

disease and its risk factors. The research findings show that disease notification

data can be integrated with multi-factorial risk factor data to develop build-up

models and forecast future potential disease risks under climate change

scenarios. This thesis may have implications in BFV disease control and

prevention programs in Queensland.

Key Words

Barmah Forest virus; climate change scenarios; forecast; geographical

information systems; modelling; mosquito-borne diseases; projections; spatial

and temporal analysis.

VII

TABLE OF CONTENTS

STATEMENT OF ORIGINAL AUTHORSHIP I

ACKNOWLEDGMENTS II

ABSTRACT IV

TABLE OF CONTENTS VII

LIST OF TABLES XV

LIST OF FIGURES XVI

GLOSSARY OF KEY TERMS XVIII

ABREVIATIONS XX

PUBLICATIONS BY THE CANDIDATE XXI

CHAPTER 1 INTRODUCTION 1

1.1 Aims and hypotheses 4

1.2 Significance and innovation 5

1.3 Structure of the thesis 5

1.4 References 8

CHAPTER 2 APPLICATIONS OF GIS AND SPATIAL ANALYSIS IN

BARMAH FOREST VIRUS RESEARCH: A REVIEW

OF RELATED LITERATURE 12

2.1 Introduction 12

2.1.1 Systematic review of the literature 12

2.2 GIS and spatio-temporal approaches used in mosquito-borne disease research 13

2.2.1 GIS and geographic data 13

2.2.2 Geostatistics /Spatial analysis 14

2.2.2.1 Visualisation /mapping 14

2.2.2.2 Exploratory data analysis 15

2.2.2.3 Exploring spatial and temporal patterns 15

2.2.2.4 Spatial dependence and spatial autocorrelation 16

2.2.2.5 Spatial interpolation/smoothing 17

2.2.2.6 Variogram /Semivariogram modelling 17

2.2.2.7 Spatial clustering 18

2.2.2.8 Cluster /hot spot detection 18

VIII

2.2.2.9 Spatial modelling 19

2.2.2.10 Discriminant function analysis 19

2.2.2.11 Spatial regression 19

2.2.2.12 Predictive modelling 20

2.3 Critical review of the major findings in BFV research 21

2.3.1 Distribution and outbreaks of BFV disease 21

2.3.2 Role of climatic factors 23

2.3.2.1 Seasonality 25

2.3.2.2 Lag effect 25

2.3.3 Role of non-climatic factors 27

2.3.4 Applications of GIS and spatial analysis in BFV disease research 29

2.4 Discussion 30

2.5 Research gaps 32

2.6 Research questions 33

2.7 Link between knowledge gaps and research questions 33

2.8 References 35

CHAPTER 3 STUDY DESIGN AND METHODS 51

3.1 Study area and population 51

3.2 Study Design 51

3.3 Data collection and management 52

3.3.1 Data collection 52

3.3.1.1 BFV disease data 52

3.3.1.2 Climate data 52

3.3.1.3 Climate zone data 53

3.3.1.4 Tidal data 53

3.3.1.5 Population and socio-economic data 54

3.3.1.6 Mesh block data 55

3.3.1.7 Buffering 55

3.3.1.8 Wetlands data 56

3.3.2 Projections data 57

3.3.2.1 Climate data 57

3.3.3 Data management 58

3.4 Data linkages 58

IX

3.5 Statistical analysis 58

3.5.1 Visualising /mapping the spatial and temporal patterns 59

3.5.2 Spatial and temporal cluster/hot spot analysis 60

3.5.3 Spatial modelling 60

3.5.3.1 Univariate analysis 60

3.5.3.2 Bivariate analysis 60

3.5.3.3 Multivariable analysis 60

3.5.4 Prediction and forecasting analysis 62

3.6 References 63

CHAPTER 4 SPATIO-TEMPORAL PATTERNS OF BARMAH

FOREST VIRUS DISEASE IN QUEENSLAND,

AUSTRALIA 66

Abstract 67

4.1 Introduction 69

4.2 Methods 71

4.2.1 Study area 71


4.2.2.1 Ethics statement 72

4.2.2.2 BFV disease data 72

4.2.2.3 Population data 72

4.2.2.4 Geocoding 73

4.2.3 Data analysis 73

4.2.3.1 Spatial and temporal analyses 73

4.2.3.2 Spatial analysis 75

4.2.3.3 Spatial autocorrelations 75

4.2.3.4 Semi-variogram analysis 75

4.2.3.5 Kriging interpolation 76

4.2.3.6 Inverse distance weighing (IDW) interpolation 76

4.2.3.7 Temporal analysis 77

4.3 Results 77

4.3.1 Descriptive analysis 77

4.3.2 Spatial and temporal analyses of BFV disease among SLAs 79

4.3.2.1 Incidence rates 79

X

4.3.2.2 Spatial autocorrelation 81

4.3.2.3 Standardised incidence rates 82

4.3.2.4 Semi-variogram analysis and kriging 85

4.4 Discussion 85

Appendix 4.1 91

4.5 References 94

CHAPTER 5 SPATIAL AND TEMPORAL CLUSTERS OF BARMAH


AUSTRALIA 102

Abstract 103

5.1 Introduction 104

5.2 Methods 106

5.2.1 Study area 106


5.2.3 Data management and geocoding 108

5.2.4 Statistical analysis 109

5.3 Results 110

5.3.1 Epidemic curves and outbreaks 110

5.3.2 Purely spatial analysis 111

5.3.3 Space-Time analysis 112

5.4 Discussion 117

5.5 Conclusions 119

5.6 References 120

CHAPTER 6 WETLANDS, CLIMATE ZONES AND BARMAH


AUSTRALIA 126

Abstract 127


6.2 Methods 130



6.2.2.1 BFV disease cases 130

XI

6.2.2.2 Population 131

6.2.2.3 Koeppen climatic zone classification 131

6.2.2.4 Wetlands in Queensland 132

6.2.2.5 Buffer zones 133


6.2.3.1 Descriptive statistics 135

6.2.3.2 Discriminant analysis 135

6.3 Results 136

6.3.1 Descriptive statistics 136

6.3.2 Discriminant analysis models 137

6.3.2.1 Predictive ability within the climate zones 138

6.4 Discussion 140

6.5 References 145

CHAPTER 7 SPATIAL REGRESSION ANALYSIS OF RISK

FACTORS FOR BARMAH FOREST VIRUS DISEASE

TRANSMISSION IN QUEENSLAND, AUSTRALIA 148

Abstract 149


7.2 Methods 153



7.2.2.1 BFV data 154

7.2.2.2 Explanatory variables 154


7.2.3.1 Data structure 155

7.2.3.2 Spatial regression modelling and analysis 156

7.3 Results 158

7.3.1 Study characteristics 158

7.3.2 Regression models 159

7.4 Discussion 165

7.5 Conclusions 168

7.6 References 170

XII

CHAPTER 8 FORECASTING THE FUTURE RISK OF BARMAH

FOREST VIRUS DISEASE UNDER CLIMATE CHANGE

SCENARIOS IN QUEENSLAND, AUSTRALIA 178

Abstract 179


8.2 Methods 182



8.2.3 Statistical analyses 183

8.2.3.1 Model building 183

8.2.3.2 Projection of future risks 184

8.3 Results 185

8.3.1 Regression model 185

8.3.2 Future regions at risk 185

8.4 Discussion 190

8.5 Conclusions 194

Appendix 8.1 195

8.6 References 196

CHAPTER 9 GENERAL DISCUSSION 204

9.1 Overview 204

9.2 Substantive discussion 205

9.3 Significance and innovation of the study 208

9.4 Implications of the study 208

9.5 Strengths of the study 210

9.6 Limitations of the study 210

9.6.1 Information bias 211

9.6.2 Confounding factors 212

9.7 Recommendations 212

9.7.1 Disease and data management 212

9.7.2 Additional data collection 213

9.7.3 Public health interventions 213

9.7.4 Community health education 214

9.7.5 Future research directions 214

XIII

9.8 References 216

APPENDIX A PRELIMINARY LITERATURE REVIEW 218

Abstract 219

A.1 INTRODUCTION 220

A.1.1 Characteristics and Ecology of Barmah Forest Virus Disease 220

A.2 LITERATURE REVIEW 222

A.2.1 Literature search strategies 222

A.2.2 Key predictors of BFV transmission 223

A.2.3 Other risk factors 226

A.2.4 Forecasting 226

A.2.5 Mapping the BFV outbreaks 227

A.2.6 Future Prospects 227

A.3 CONCLUSIONS 228

A.4 References 229

APPENDIX B METHODOLOGY 233

Abstract 234

B.1 INTRODUCTION 235

B.2 Methodology 236

B.2.1 Study area 236

B.2.2 Spatial data collection 237

B.2.2.1 BFV Cases 237

B.2.2.2 Wetlands 238

B.2.2.3 Meteorology 238

B.2.2.4 Tides 239

B.2.2.5 Climate zones and Bioregions 239

B.2.2.6 Socio-economic and demographic data 241

B.2.2.7 Census 242

B.2.3 Spatial data sampling design 243

B.2.4 Software 245

B.3 Final Database and results 245

B.4 Conclusions 245

B.5 References 246

XIV

APPENDIX C ETHICS APPROVAL AND DATA PERMISSION 249

C.1 Ethics Approval 249

C.2 Data 251

REFERENCES 252

XV

LIST OF TABLES

Table 4.1: Descriptive statistics of incidence rates of BFV disease in Queensland,

Australia, (n=6,683) ............................................................................................ 78

Table 4.2: Characteristics of BFV disease transmission and population growth

during 1993 and 2008 (n= 478 SLAs) ................................................................ 80

Table 4.3: Spatial autocorrelation analysis for BFV disease in Queensland, 1993-

2008 ..................................................................................................................... 82

Table 4.4: SLAs with significant difference between observed and expected

values of BFV cases ............................................................................................ 84

Table 5.1: Purely spatial BFV disease clusters in Queensland, Australia, using a

spatial cluster size of 10% of the population at risk and 200km circle

radius. ................................................................................................................ 113

Table 5.2: Space-time BFV disease clusters in Queensland, Australia, using a

maximum spatial cluster size of 10% of the population at risk,

200km circle radius and at different temporal windows ................................... 115

Table 6.1: Descriptive statistics for BFV incidence by climate zone in Queensland ............ 136

Table 6.2: Correlations between BFV incidence rates and wetland classes in

Queensland, 1992-2008 .................................................................................... 137

Table 6.3: Significant predictor variables in order of decreasing standardised

canonical discriminant function coefficient in the models ............................... 138

Table 6.4: Significant predictor variables in order of decreasing standardised

canonical discriminant function coefficient to determine potential

habitats for BFV vectors at various buffer zones using discriminant

analysis .............................................................................................................. 140

Table 7.1: Characteristics of study variables in Queensland, 2000-2008 .............................. 158

Table 7.2: Spearman correlation coefficients between incidence rates of BFV

disease and explanatory variables ..................................................................... 159

Table 7.3: Risk factors of BFV disease transmission for whole area in Queensland ............ 162

Table 7.4: Risk factors of BFV disease transmission for coastal areas in

Queensland ........................................................................................................ 163

Table 8.1: Regression coefficients of climate, socio-economic and tidal variables

on the BFV disease in the entire coastal region in Queensland ........................ 185

Table A.1: Studies retrieved from literature search on climate variability, social

and environmental factors and the BFV disease in Australia ........................... 223

XVI

LIST OF FIGURES

Figure 1.1: Flowchart of the thesis chapters. ............................................................................. 7

Figure 3.1: Tidal stations across Queensland (n=24) ............................................................... 54

Figure 3.2: Demonstration of average of 2 high tides and 2 low tides .................................... 54

Figure 3.3: Spatial regression modelling flow chart ................................................................ 61

Figure 4.1: Temporal distribution of BFV disease in Queensland, 1993 to 2008 .................... 78

Figure 4.2: Incidence rates of BFV disease by age and gender in Queensland,

1993-2008 ........................................................................................................... 79

Figure 4.3: Maps showing the incidence rates of BFV disease by SLA over

different periods (A:1993-1996, B:1997-2000, C:2001-2004 and

D:2005-2008) ...................................................................................................... 81

Figure 4.4: Maps showing the inverse distance weighting interpolated incidence

rates of BFV disease over different periods (A:1993-1996, B:1997-

2000, C:2001-2004 and D:2005-2008) ............................................................... 83

Figure 4.5: Map of the standardised incidence rates (1/100,000 people) of BFV

disease by SLA in Queensland, 1993-2008 ........................................................ 84

Figure 4.6: Panel A showing a smoothed map of standardised incidence rates of

BFV disease using kriging and panel B showing a semi-variogram

model ................................................................................................................... 85

Figure 5.1: Spatial distribution of BFV disease cases during 1992-2008, the

statistical local area (SLA) boundaries, the major cities and the SLAs

with highest and lowest incidence rates in Queensland .................................... 107

Figure 5.2: The epidemic pattern of BFV disease monthly cases and annual

incidence rates in Queensland, 1992-2008 ....................................................... 111

Figure 5.3: Purely spatial significant clusters of BFV disease identified in

Queensland, 1992-2008. (Each cluster provides cluster number,

cluster radius and SLAs (n) included). ............................................................. 112

Figure 5.4: Space-time significant clusters of BFV disease identified in

Queensland, 1992-2008 at a temporal window of a) 1month, b)

3months, c) 6 months, d) 9 months and e) 12 months. (Each cluster

provides cluster number, cluster radius, SLAs (n) included and time

frame (m/yy)). ................................................................................................... 114

Figure 6.1: Workflow of the study ......................................................................................... 130

Figure 6.2: Koeppen climate classification as illustrated by the Australian Bureau

of Meteorology .................................................................................................. 132

Figure 6.3: Sample area of Queensland showing spatial distribution of BFV cases

and (A) wetlands (locations only) and streets (indicating urban

areas); (B) wetlands (locations and classes); (C) wetlands (locations

and salinity modifiers or tidal influence) .......................................................... 134

Figure 7.1: Map showing the study area, Queensland in Australia ........................................ 153

XVII

Figure 7.2: Moran’s-I scatter plot for OLS and SLM residuals for Whole areas in

Queensland (SEM figure is available from authors) ......................................... 161

Figure 7.3: Moran’s-I scatter plot for OLS and SLM residuals for coastal areas in

Queensland (SEM figure is available from authors) ........................................ 164

Figure 8.1: (a) Geographical distribution of BFV disease under current climate for

Queensland entire coastal regions, (b) forecast of rainfall effect on

potential probabilities of risk of BFV (minimum temperature

constant) for 2025, (c) 2050 and (d) 2100 ........................................................ 187


Queensland entire coastal regions, (b) forecast of minimum

temperature effect on potential probabilities of risk of BFV disease

(rainfall constant) for 2025, (c) 2050 and (d) 2100........................................... 188


Queensland entire coastal regions, (b) forecast of rainfall and

minimum temperature effect on potential probabilities of risk of

BFV disease for 2025, (c) 2050 and (d) 2100 ................................................... 189

Figure 9.1: Framework of research results in this thesis ........................................................ 205

Figure 9.2: Recommendations flow chart .............................................................................. 212

Figure A.1: GIS based distribution of notified BFV cases in Queensland,

Australia, 1992-2001 (Numbers in parentheses indicate the number

of localities). ..................................................................................................... 228

Figure B.1: Study area, Queensland, Australia. ..................................................................... 237

Figure B.2: Example of geocoded BFV cases near Mackay, Queensland ............................. 238

Figure B.3: Example of wetlands near Mackay, Queensland ................................................ 238

Figure B.4: Meteorology data for Queensland, December 2008 (a) maximum

temperature and (b) rainfall............................................................................... 239

Figure B.5: Tidal monitoring stations, Queensland ............................................................... 239

Figure B.6: Koeppen classifications, Queensland .................................................................. 240

Figure B.7: Bioregions, Queensland ...................................................................................... 241

Figure B.8: Statistical local areas, Queensland ...................................................................... 242

Figure B.9: Example of SLA and mesh blocks in Mackay .................................................... 242

Figure B.10: Example of quadrats with their unique identifier codes, 10 km radius

circle and centroid points. Estuarine wetlands are shown as an

example for Mackay.......................................................................................... 243

Figure B.11: Coastline grids, Queensland .............................................................................. 245

XVIII

GLOSSARY OF KEY TERMS

Glossary Description

Akaike’s Information Criteria It is a measure of the relative goodness of fit of a statistical

model.

Buffer It is a zone around a map feature measured in units of distance or

time. It is useful for proximity analysis.

Cadastral Information on land ownership

Centroid It is the center of gravity of a geographic unit

Cluster analysis It is a method of classification that places objects in groups based

on the characteristics they possess.

Eigenvalues Measure the amount of the variation explained by each principal

component (PC) and will be largest for first PC and smaller for

subsequent PCs. An eigenvalue 1 indicates that PC accounts for

more variance than accounted by one of the original variables

Geographical Information

System

A system of hardware, software and procedures (tools) designed

to capture, manage and analyse and display geo-referenced data

for solving complex planning and management problems

Mesh blocks It is the micro-level geographic unit includes data on number of

dwellings and the overall population for the latest census year

Moran’s-I It is a test statistic for spatial autocorrelation. It can be positive or

negative.

Multicollinearity In a multiple regression with more than one X variable, two or

more X variables are collinear if they show strong linear

relationships. This makes estimation of regression coefficients

impossible. It can also produce large estimated standard errors

for the coefficients of the X variables involved.

Projections These are used to convert the spherical surface of the earth to a

map's flat surface.

Spatial autocorrelation It refers to the correlation of a variable with itself in space. It can

be positive and negative.

Semivariogram It is one of the significant functions to indicate spatial correlation

in observations measured at sample locations. It is commonly

represented as a graph that shows the variance in measure with

distance between all pairs of sampled locations.

Spatial data analysis It differs from non-spatial data analysis in that the location of an

observation impacts the result.

Spatial dependence It exists when the value associated with one location is dependent

on those of other locations

Spatial heterogeneity It exists when structural changes related to location exist in a dataset

Spatial lag It is a variable that essentially averages the neighbouring values

of a location (the value of each neighbouring location is

multiplied by the spatial weight and then the products are

summed). It can be used to compare the neighbouring values

with those of the location itself.

Spatial regression models These are statistical models that account for the presence of

XIX

Glossary Description

spatial effects, i.e., spatial autocorrelation (or more generally

spatial dependence) and/or spatial heterogeneity.

Statistical Local Area

It is a general purpose spatial unit. It is the base spatial unit used

to collect and disseminate statistics other than those collected

from the Population Censuses.

Residuals

These reflect the overall badness-of-fit of the model. They are

the differences between the observed values o the outcome

variable and the corresponding fitted values predicted by the

regression line (the vertical distance between the observed values

and the fitted line).

.

XX

ABREVIATIONS

Abbreviation Description

ABS Australian Bureau of Statistics

AIC Akaike’s Information criterion

AUC Area Under Curve

BFV Barmah Forest Virus

BOM Bureau of Meteorology

CDB Communicable Diseases Branch

CI Confidence Interval

CSIRO Council of Scientific Industrial Research Organisation

CSV Comma Separated Values

DERM Department of Environment and Resource Management

IBRA Interim Biogeographically Regionalisation for Australia

IDW Inverse Distance Weighting

IR Incidence Rates

IPCC Intergovernmental Panel on Climate Change

EWS Early Warning Systems

GIS Geographical Information Systems

LGA Local Government Area

LLR Log Likelihood Ratio

NNDSS National Notifiable Diseases Surveillance System

OLS Ordinary Least Squares

QLD Queensland

ROC Receiver Operating Characteristic

SARIMA Seasonal Auto Regression Integrated Moving Average

SATSCAN Spatial, temporal or Space-Time Scan statistics

SCDFC Standardised Canonical Discriminant Function Coefficients

SEIFA Socio Economic Information For Families in Australia

SEM Spatial Error Model

SIR Standardised Incidence Rates

SLM Spatial Lag Model

SOI Southern Oscillation Index

SPSS Statistical Package for Social Sciences

XXI

PUBLICATIONS BY THE CANDIDATE

1. Naish S., Tong S. Socio-environmental variability and Barmah forest virus

disease Transmission: a review of Epidemiological evidence and future

Prospects. International Journal of Geoinformatics 2009, 7 (1): 37-42

2. Naish S., Hu, W., Mengersen K, Tong S. Emerging methods in using GIS to

analyse Barmah Forest virus disease in Queensland, Australia. Proceedings in

International Conference on Health GIS 2011: 94-99

3. Naish S., Hu W., Mengersen K., Tong, S. Spatio-temporal patterns of Barmah

Forest virus disease in Queensland, Australia. PLoS ONE 2011, 6 (10): e25688

4. Naish S., Hu W., Mengersen K., Tong S. Spatial and temporal clusters of

Barmah Forest virus disease in Queensland, Australia. Tropical Medicine and

International Health 2011, 16 (7): 884-893

5. Naish S., Mengersen K., Hu W., Tong S. Wetlands, climate zones and Barmah

Forest virus disease in Queensland, Australia - Transactions of the Royal Society

of Tropical Medicine and Hygiene - in print.

6. Naish S., Tong S., Hu W., Mengersen K. Spatial regression analysis of risk

factors for Barmah Forest virus disease in Queensland, Australia - Submitted.

7. Naish S., Tong S., Hu W., Mengersen K. Forecasting the future risk of Barmah

Forest virus disease under climate change scenarios in Queensland, Australia -

To be submitted.

1

CHAPTER 1 INTRODUCTION

Arboviruses are an increasing threat to population health globally

(Benitez 2009; Fitzsimmons et al. 2009; García-Sastre et al. 2009; Olano

et al. 2009; Smith et al. 2011). Global climate change is expected to

increase the activity of arboviruses and their vectors by raising the

temperature and sea levels, and changing rainfall patterns (I.P.C.C.

2011). Australia is not immune to the impact of climate change and

mosquito-borne diseases have become a significant health concern for

Australians (CSIRO 2011; Russell 2009; Smith et al. 2011). The

Australian Department of Health and Ageing indicates that as the climate

warms, the tropical weather zone in Australia will spread south, bringing

with it disease vectors prevalent in tropical weather zones (2007).

More than 75 arboviruses have been documented in Australia, but only 12

are related to human disease and all are transmitted by mosquitoes

(Russell 1993). Of the arboviruses important in human infection, Barmah

Forest virus (BFV) is an emerging and wide spread arbovirus, causing

BFV disease in Australia. BFV belongs to the Alphavirus genus and

Togaviridae family (Russell 1995). Arbovirus activity is dependent on

numerous factors such as the availability of water (especially rainfall and

tides), temperature, mosquito vectors, reservoir hosts, geography and

population demographics and human activity (McMichael et al. 2008;

Gould and Higgs 2009; Lafferty 2009; Lindsay and Mackenzie 1998;

Mackenzie et al. 1994; Russell 1995; Russell 2009).

BFV disease is a notifiable disease and the notifications have been

documented in every state and territory in Australia. For example, during

the period 2005-2010, 10,192 BFV notifications were recorded in

Australia. Of these, the majority of notifications was from Queensland

state (n=5,399), followed by New South Wales (n=2,819). BFV appears to

be of interesting public health importance in Queensland, which is

experiencing rapid population growth. Serological surveys indicate that

BFV disease may cause widespread human infection. A confirmed case is

laboratory evidenced by isolation of BFV or detection of BF virus by

2

nucleic acid testing or significant increase in antibody level o r detection

of BFV specific IgM (Australian Department of Health and Ageing 2009) .

All cases of laboratory confirmed BFV disease should be reported to the

Queensland Health, by law (Queensland Health 2009). However,

notification data does have some limitations. There is no distinction

between presumptive cases (single positive IgM serology) and confirmed

cases (fourfold greater increase in antibody titre between acute and

convalescent sera), while the patient location is recorded as the

residential address (post code) for each case, which may not be where the

infection occurred. An assumption could be made that in certain large

areas work, recreation and transmission can occur within the same area or

region. However, it is likely that a small number of cases can be

misclassified. BFV has been associated with human disease since 1988

and the reported incidence has been increasing as diagnostic reagents

have become available and clinicians and the general public have become

aware of it. However, referrals to serological tests for BFV have

remained stable in Queensland medical laboratories over the last decade.

For epidemiological studies and understanding the trends and distribution

of BFV disease, notification data have been considered (Bi et al. 2000;

Quinn et al. 2005; Tong et al. 2005).

Recent BFV outbreaks mean that levels of antibodies are high in

individuals, which confers some protection to both natural hosts and

humans. Conversely, little activity means immunity is low and the

population is highly susceptible (New South Wales Health Department

2007). Recently, arbovirus activity has been increased due to changes in

human land use (e.g., irrigation and wetland development) and resulted in

massive mosquito breeding (Dale and Knight 2008; New South Wales

Health Department 2007). On the coastal areas, the rainfall is more

consistent and mosquito activity is more regular. In saltmarshes, tidal

inundation also promotes breeding of mosquitoes. A combination of high

tides and heavy rainfall has been associated with outbreaks (Dhileepan

1996; Doggett et al. 1999; Merianos et al. 1992; Miller et al. 2005; WHO

2006).

3

Predictive models based on climatic and socio-economic factors using

disease incidence are useful tools in providing early warning systems for

predicting outbreaks of mosquito-borne diseases (WHO 2004; WHO

2006). Only two non-spatial models have been developed for predicting

BFV disease using climatic, socio-environmental and tidal variables

(Naish et al. 2009; Naish et al. 2006; WHO 2004; WHO 2006). It is

likely that the exacerbation of current greenhouse conditions will lead to

longer periods of high mosquito activity in the tropical regions where

BFV disease is already widespread. This is because mosquito activity is

linked with temperature and therefore more cases of BFV disease can

occur in the warmer north of the state with its longer mosquito season. In

addition, the widespread locations may expand further within temperate

regions, and outbreaks may become more frequent in those areas (Jacups

et al. 2008).

No studies have examined the spatial relationships between climatic,

socio-economic and ecological factors, and BFV disease at a state -level

with long-term data. Understanding the spatial distribution and dynamics

of BFV disease outbreaks is central to the design of prevention and

control strategies. I examine the dynamics of BFV disease outbreaks

across Queensland and investigate the possible effects of projected

climate change on the transmission of BFV disease .

Prediction of outbreaks of BFV disease requires an analysis of climatic,

ecological, demographic, social and economic factors and disease data. It

is generally agreed that geographic information systems (GIS) are useful

tools to enable the improved identification of the spatially intensified

incidence or infection zones and high-risk areas or clusters or hot spots

and to provide information on climatic, socio-economic, demographic and

ecological determinants of BFV disease transmission. I use descriptive

statistics, GIS tools, spatial and temporal analyses, geostatistics and

spatial regression modelling analyses to understand the spatial and

temporal characteristics and transmission dynamics of BFV disease in

Queensland.

4

I anticipated that empirical modelling of BFV disease outbreaks and

existing climatic, ecological and socio-economic conditions will give an

improved understanding of the influence of these conditions on BFV

disease outbreak patterns. Also, it may be possible to apply future

climate projections to predict the probability of future risk of BFV

disease outbreaks in different geographical regions across Queensland.

1.1 AIMS AND HYPOTHESES

The overall aim of this study is to assess the relationship between BFV

disease and potential risk factors and to predict the probability of future

risk of BFV disease transmission under climate change scenarios in

Queensland, Australia.

The specific objectives of this research are to:

1. Explore the spatial and temporal patterns of BFV disease ,

2. Identify the spatial and temporal clusters of high-risk areas of BFV

disease,

3. Assess the relationships between the spatial distribution of climatic,

socio-economic, ecological and tidal factors and the incidence of BFV

disease and

4. Develop a spatial predictive model that can assist in predicting the

future potential risk of BFV disease in Queensland under climate

change scenarios.

The hypotheses of this research are:

1. A spatio-temporal method can identify the hot spots/high-risk areas of

BFV transmission in Queensland,

2. BFV disease risk in Queensland can be predicted from the model

based on climatic, socio-economic, ecological and tidal variables and

3. The scenario-based risk assessment model can be used to predict the

effects of climate change on BFV disease in Queensland.

5

1.2 SIGNIFICANCE AND INNOVATION

This research is significant because its findings may be used to assist the

development of public health policy and practice on BFV disease

surveillance and control. It may not only be allied in the assessment of

the impact of climatic and socio-ecological changes on BFV disease, but

also for other tropical and sub-tropical mosquito-borne diseases in

Queensland. This research is innovative because the relationships

between climatic, socio-economic, ecological and tidal factors and BFV

disease have not been investigated at the spatial resolution intended (i.e.,

at a statistical local area) across a wide regional area such as Queensland.

It is also innovative by producing disease forecasting model under

climate change scenarios recommended by the Intergovernmental Panel

on Climate Change (IPCC) for Australia and mapping future BFV disease

risk which have not been conducted in previous research.

1.3 STRUCTURE OF THE THESIS

This thesis is presented in the publication style . It consisted of seven

scientific manuscripts, some of which have been published in peer -

reviewed Journals and others under review/submission. Each manuscript

was designed to stand on its own and was written in a conventional

publication style for a particular journal (Figure 1.1). The structure and

contents used in submitting the manuscripts have largely been retained.

Therefore, overlaps and repetitions may occur between individual

chapters. The structure of the thesis is as follows:

Chapter 2 reviews literature on application of GIS-based spatial and

temporal approaches used in mosquito-borne disease research. It briefly

reviews the research on BFV disease risk factors in Australia, which was

published in “International Journal of Geoinformatics”.

Chapter 3 describes the study design and methods in general, as each

paper has its own methods section. It also describes a detailed

methodology for selecting coastal areas only. This was published in

“Conference proceedings on Health GIS Managing Health Geospatially” .

6

Chapter 4 focuses on visualising and analysing the spatial and temporal

patterns of BFV disease in SLAs in Queensland using GIS tools and

geostatistics, which was published in “PLoS ONE”.

Chapter 5 identifies the spatial and temporal clusters/hot spots of BFV

disease at a SLA level in Queensland using SaTScan method, which was

published in “Tropical Medicine and International Health”.

Chapter 6 assesses the relationship between wetlands, climate zones and

BFV disease in Queensland using a discriminant analysis and was

submitted to “Transactions of Royal Society of Tropical Medicine and

Hygiene” (in print).

Chapter 7 determines the spatial climatic, socio-economic, ecological and

tidal risk factors for BFV transmission in Queensland using spatial

regression analysis and was submitted to Environment International .

Chapter 8 develops an epidemic predictive model using existing climatic,

ecological, socio-economic and tidal data to forecast future risk of BFV

disease outbreaks in Queensland under climate change scenarios and will

be submitted.

Chapter 9 discusses the main findings across the five chapters and makes

conclusions in relation to the overall aims of the study. This chapter

further discusses the study limitations, future research direction and

public health implications of the research.

Tables and figures are presented in the text to facilitate reading and

understanding. The references are presented at the end of each chapter.

A complete list of bibliography (including references cited in the

individual manuscripts) is provided at the end of the thesis.

7

Figure 1.1: Flowchart of the thesis chapters.

DiscussionCHAPTER 9

Predictive Models – Forecasting

Manuscript 7CHAPTER 8

Spatial modelling and risk factor determinants

CHAPTER 7 Manuscript 6

Wetlands and climate zone relationships


Spatial and temporal clustersCHAPTER 5 Manuscript 4

Spatial and temporal distribution


MethodsCHAPTER 3Part of Methods in

Manuscript 2(Refer to Appendix B)

Literature ReviewCHAPTER 2Part of Literature

Review in Manuscript 1(Refer to Appendix A)

IntroductionCHAPTER 1

8

1.4 REFERENCES

Australian Department of Health and Ageing (2007) Temperature

Control: Warm House in Winter, Cool House in Summer. [Online],

http://www.cana.net.au/socialimpacts/australia/health.html

Accessed on: June 7, 2009.

Australian Department of Health and Ageing (2009) National Notifiable

Diseases Surveillance System, Communicable Diseases

Surveillance - Highlights. [Online],

www.health.gov.au/internet/wcms/publishing.nsf Accessed on:

October 15, 2010.

Benitez, M.A. (2009) Climate change could affect mosquito -borne

diseases in Asia. Lancet 373, 1070.

Bi, P., Tong, S., Donald, K., et al. (2000) Southern Oscillation Index and

transmission of the Barmah Forest virus infection in Queensland,

Australia. Journal of Epidemiology and Community Health 54, 69-

70.

CSIRO (2011) OzClim Climate change scenario. CSIRO [Online],

www.csiro.au Accessed on: December 7, 2011.

Dale, P.E.R. & Knight, J.M. (2008) Wetlands and mosquiotes: a review.

Wetlands Ecology and Management

Dhileepan, K. (1996) Mosquito seasonality and arboviral disease

incidence in Murray Valley, southeast Australia. Medical Journal

of Veterianry Entomology 10, 375-384.

Doggett, S.L., Russell, R.C., Clancy, J., et al. (1999) Barmah Forest virus

epidemic on the south coast of New South Wales, Australia, 1994 -

1995: viruses, vectors, human cases, and environmental factors.

Journal Of Medical Entomology 36, 861-868.

Fitzsimmons, G., Wright, P., Johansen, C., et al. (2009) Arboviral

diseases and malaria in Australia, 2007/08: annual report of the

National Arbovirus and Malaria Advisory Committee.

Communicable Diseases Intelligence 33, 155-169.

http://www.cana.net.au/socialimpacts/australia/health.htmlhttp://www.health.gov.au/internet/wcms/publishing.nsfhttp://www.csiro.au/

9

García-Sastre, A., Endy, T.P. & Moselio, S. (2009) Arboviruses.

Encyclopedia of Microbiology. Oxford: Academic Press.

Gould, E.A. & Higgs, S. (2009) Impact of climate change and other

factors on emerging arbovirus diseases. Transactions of the Royal

Society of Tropical Medicine and Hygiene 103, 109-121.

I.P.C.C. (2011) Intergovernmental Panel on Climate Change -

Publications and Data. IPCC [Online], [email protected]

Accessed on: December 7, 2011.

Jacups, S.P., Whelan, P.I. & Currie, B.J. (2008) Ross River virus and

Barmah Forest virus infections: A review of history, ecology, and

predictive models, with implications for tropical northern

Australia. Vector-Borne and Zoonotic Diseases 8, 283-297.

Lafferty , K.D. (2009) The ecology of climate change and infectious

diseases. Ecology 90, 888-900.

Lindsay, M. & Mackenzie, J. (1998) Vector-borne diseases and climate

change in Australasian region:major concerns and the public health

response. In: Curson, P., Guest, C., Jackson, E. (ed.) Climate

change and human health in Asia-Pacific region. Canberra:

Greenpeace.

Mackenzie, J.S., Lindsay, M.D., Coelen, R.J., et al. (1994) Arboviruses

causing human disease in the Australasian zoogeographic region.

Archives in Virology 136, 447-467.

McMichael, A.J., Woodruff, R.E., Kenneth, H.M., et al. (2008) Climate

change and infectious diseases. The Social Ecology of Infectious

Diseases. San Diego: Academic Press.

Merianos, A., Farland, A.M., Patel, M., et al. (1992) A concurr ent

outbreak of Barmah Forest and Ross River disease in Nhulunbuy,

Northern territory. Communicable Diseases Intelligence (Australia)

16, 110-111.

Miller, M., Roche, P., Yohannes, K., et al. (2005) Australia's notifiable

diseases status, 2003. Annual report of the National Notifiable

http://[email protected]/

10

Diseases Surveillance System. Communicable Diseases

Intelligence (Australia) 29, 45-46.

Naish, S., Hu, W., Nicholls, N., et al. (2009) Socio -environmental

predictors of Barmah forest virus transmission in coastal areas,

Queensland, Australia. Tropical Medicine and International

Health 14, 247-256.

Naish, S., Hu, W., Nicholls, N., et al. (2006) Weather variability, tides,

and Barmah Forest virus disease in the Gladstone region, Australia.

Environmental Health Perspectives 114, 678-683.

New South Wales Health Department, N. (2007) Control guidelines for

infectious diseases. [Online],

www.health.nsw.gov.au/infect/control.html Accessed on: February

20, 2009.

Olano, J.P., Walker, D.H., Alan, D.T.B., et al. (2009) Agents of

Emerging Infectious Diseases. Vaccines for Biodefense and

Emerging and Neglected Diseases. London: Academic Press.

Queensland Health (2009) Communicable Diseaes Australia. Public

Health Act, National Notifiable Diseases Surveillance System,

Austtralian Department of Health and Ageing [Online],

http://www.health.gov.au/internet/main/Publishing.nsf/Content/cda

-cdna-index.htm Accessed on: July 20, 2009.

Quinn, H.E., Gatton, M.L., Hall, G., et al. (2005) Analysis of Barmah

forest virus disease activity in Queensland, Australia, 1993 -2003:

Identification of a large, isolated outbreak of disease. Journal Of

Medical Entomology 42, 882-890.

Russell, R.C. (1993) Mosquitoes and mosquito-borne disease in

southeastern Australia. Sydney: Department of Entomology.

Russell, R.C. (1995) Arboviruses and their vectors in Australia: an update

on the ecology and epidemiology of some mosquito -borne

arboviruses. Review of Medical and Veterinary Entomology 83,

141-158.

http://www.health.nsw.gov.au/infect/control.htmlhttp://www.health.gov.au/internet/main/Publishing.nsf/Content/cda-cdna-index.htmhttp://www.health.gov.au/internet/main/Publishing.nsf/Content/cda-cdna-index.htm

11

Russell, R.C. (2009) Mosquito-borne disease and climate change in

Australia: time for a reality check. Australian Journal of

Entomology 48, 1 - 7.

Smith, D.W., Speers, D.J. & Mackenzie, J.S. (2011) The viruses o f

Australia and the risk to tourists. Travel Medicine And Infectious

Disease 9, 113-125.

Tong, S., Hayes, J.F. & Dale, P. (2005) Spatiotemporal variation of

notified Barmah Forest virus infections in Queensland, Australia,

1993-2001. International Journal of Environmental Health

Research 15, 89-98.

WHO (2004) Using Climate to Predict Infectious Disease Outbreaks: A

Review. Geneva: WHO.

WHO (2006) Global Early Warning System for Major Animal Diseases,

including Zoonoses (GLEWS).

12

CHAPTER 2 APPLICATIONS OF GIS AND SPATIAL

ANALYSIS IN BARMAH FOREST VIRUS RESEARCH: A

REVIEW OF RELATED LITERATURE

2.1 INTRODUCTION

During the past decade, BFV disease has become widespread and caused

epidemics in several parts of Australia and the emergence may be

attributable to many factors including socio -environmental variables,

increased human movements, urbanisation, deforestation, land use and

population growth. However, changes in the local climatic pattern may

also have affected the BFV transmission. Therefore, a systematic

literature review was conducted to examine the potential effects of risk

factors on the distribution of BFV disease.

2.1.1 Systematic review of the literature

The search strategies and selection criteria are outlined in this section.

Literature searches were conducted using multiple electronic databases

including Medline (EBSCO host), Biological abstracts, PubMed, Scopus

and ISI Web of Knowledge. Additional relevant publications were

identified through perusal of publications and their reference lists. Most

relevant articles were retrieved using the combination of key words and

MeSH headings in the online literature searches: arbovirus OR vector -

borne OR mosquito-borne disease (Malaria OR Dengue OR Ross River

virus OR Barmah Forest virus) AND risk factor OR risk determinant OR

climatic OR social OR tide OR environmental OR ecological AND

geographical information system OR GIS OR spatial analysis OR space -

time as Boolean/Phrase. Medline was considered as the largest and most

reliable medical information database that includes subjects such as

environmental sciences, medicine, geography, biological and social

sciences. It includes over 4,600 current biomedical journal s. The

literature searches were carried out for January 1992 to January 2012 as

the first BFV disease clusters were diagnosed in 1992 in Western

Australia. Original research articles with direct relevance were

13

identified, retrieved and included in the li terature review analysis. In

addition, research reports from international and local government

organisations were also included in this analysis.

2.2 GIS AND SPATIO-TEMPORAL APPROACHES USED IN

MOSQUITO-BORNE DISEASE RESEARCH

2.2.1 GIS and geographic data

GIS is defined as “an organised collection of computer hardware,

software, geographical data, and personnel designed to efficiently

capture, store, update, manipulate, analyse and display all forms of

geographically referenced data” (ESRI 1990). One of the powerful

features of a GIS is the ability to overlay several map layers. When

multiple geographic data are stored in a common coordinate system,

many map layers can be viewed simultaneously and allows the user to

look through the set of maps in order to unders tand better the spatial

relationships among different layers (Martin 2009).

GIS can be used to develop or sustain hypotheses regarding disease

outbreaks through conducting quick and less expensive ecological studies

using existing databases and easily computerised data (Marilyn et al.

1997). It can also be used to simplify certain steps crucial to conduct

environmental epidemiologic research. For example, to visualise or map

data, most systems provide a wide range of mapping options such as

colours, symbols, annotation, legends, scales and other cartographic

features as well as the ability to produce maps, graphs and tables.

GIS techniques allow the health researcher to go beyond the simple

mapping of the disease incidence rates within predetermined

administrative boundaries (e.g., state, region) (Marilyn et al. 1997).

Other specialised functions include automated address matching, distance

operators, buffer analysis, spatial database query and polygon overlay

analysis. However, most GISs have inadequate statistical functions. GIS

output can be used as an input into other software for statistical analyses.

Once data are statistically modelled, they can be input back into GIS for

14

mapping (Waller 1996).

In the last few years, the use of GIS has given important practical

contributions to the investigation on the spatial component of arboviral

diseases such as malaria, ross river virus (RRV) infection and dengue

(Chansang and Kittayapong 2007; Cheah et al. 2006; Lian et al. 2007;

Vanwambeke et al. 2006; Woodruff et al. 2006; Wu et al. 2009).

In the broad sense, GIS applications in spatial epidemiology can be as

simple as a means of visualising and analysing geographic distribution of

diseases through time, thus revealing spatial and temporal trends and

patterns that would be more difficult to understand in tabular or other

formats. Analytical functions in GIS can also help answer specific

questions by performing spatial statistical tasks, such as overlaying or

combining different layers of information to determine dependencies and

relationships between outbreaks and risk factors.

2.2.2 Geostatistics /Spatial analysis

Geostatistics represents a set of tools for the analysis of spatial data and

deals with spatial continuity and weak stationarity (Cressie 1993).

Spatial analysis can be described as the ability to manipulate spatial data

into different forms and extract additional meaning as a result (Bailey

and Gatrell 1995). Recently, geostatistics has been defined as the

collection of statistical methods in which data location plays an important

role in the study design or data analysis (Saxena et al. 2009 ). It has been

used widely to characterise spatial variation in rela tively small datasets

and to predict unobserved values using kriging informed by the modelled

semi-variogram (Fotheringham and Rogerson 2009) .

In a spatial epidemiology context, three types of geostatistical/spatial

analysis tasks are involved: visualisation/mapping, exploratory data

analysis and modelling (Goodchild 2000).

2.2.2.1 Visualisation /mapping

Visualisation is the graphical presentation of geospatial data in order to

utilise the graphs to unravel spatial problems (for example, spatial

15

dependency) (MacEachren et al. 1999). Visual data exploration of spatial

data has several advantages: it is inherent and does not involve

understanding of complex mathematical and computational methods. It is

also effective when little is known about the data and when the data are

noisy or heterogeneous (Keim 2002). In recent years, mapping in the

medical context has developed so rapidly (Cliff 1995) that the

presentation of maps is established as a basic tool in the analysis of

public health data (Lawson et al. 2000).

Recently, there has been a keen interest in mapping mosquito -borne

diseases such as malaria, RRV infection and dengue (Dale et al. 1998;

Dale 1986; Hay et al. 2004; Huang et al. 2011; Tong et al. 2001) using

GIS. Such maps would make it possible to plan control measures in high -

risk areas and significantly increase the cost efficiency of the control

programs. For example, in analysing the temporal correlations between

malaria incidence and climate variables, Huang et al (2011) has used GIS

mapping tools to examine the spatial patterns of malaria cases. Risk

maps have been used in tracking mosquito distribution in several parts of

the world (Baker 2010; Smith 1995; U.S. Geological Survey 2011) .

2.2.2.2 Exploratory data analysis

Exploratory data analysis refers to describing patte rns in the distribution

of a disease using location data and helps in the formulation of new

hypotheses about the processes that gave rise to the data. It includes

simplified statistical tests to explore potential predictors of the disease,

smoothing/interpolation techniques to highlight spatial patterns and

empirical variogram estimation to explore spatial autocorrelation (Lloyd

2010).

2.2.2.3 Exploring spatial and temporal patterns

Spatial aggregation of objects generates a variety of distinct spatial

patterns that can be characterised by the size and shape of the

aggregations, and can be measured according to the extent of similarity

between the objects in their attributes or quantitative values (Fortin et al.

16

1989) . These properties of spatial patterns are indicative of underlying

processes and factors that generate and modify them over time.

2.2.2.4 Spatial dependence and spatial autocorrelation

A key point to explore in spatial analysis is to examine spatial patterning

in the variable or variables (Cliff 1973). Spatial dependence refers to the

dependence of neighbouring values on one another (Haining 2003). The

fundamental characteristic of spatial dependence is that the observations

or values close together in space tend to be more similar than those that

are far because the values that are located together in space tend to

influence each other and often share similar characteristics (based on

‘The first law of Geography’ (Tobler 1979)) and thus violate the

assumption of independence in statistical analyses. Spatial

autocorrelation is the measure of spatial dependence. A positive spatial

autocorrelation means the neighbouring values are similar and a negative

autocorrelation means the neighbouring values are dissimilar.

The best known test statistic against spatial autocorrelation is the

application of Moran’s-I statistic (Moran 1948) to regression residuals

(Moran 1950), popularised in the work of Cliff and Ord (1981). Moran’s-

I measures the correlation among spatial observations and permits in

finding the characteristics of the spatial pattern (clustered, disperse d,

random) among areas. A detailed review on spatial autocorrelation was

conducted by Goodchild (1985). Moran scatter plot is the useful tool for

the exploration of spatial autocorrelation. The plot relates individual

values to weighted averages of neighbouring values and the slope of a

regression line fitted to the points in the scatter plot gives Moran’s -I. In

general, correlation decreases with distance until it reaches or approaches

zero (Bailey and Gatrell 1995).

In recent years, Moran’s-I test has been frequently applied to a variety of

epidemiological problems to test spatial patterns in mosquito-borne

disease research (Cheah et al. 2006; Gatton et al. 2004; Haque et al.

2009; Liu et al. 2008; Onozuka and Hagihara 2008; Wen et al. 2010).

For example, Moran’s-I was used to test the spatial autocorrelation

17

among residuals in a Kenyan study (Li 2008) and in a Taiwanese study

(Wu et al. 2009).

2.2.2.5 Spatial interpolation/smoothing

Estimation of exposures within a geographic region using GIS can be

achieved in two ways: 1) through spatial interpolation of measured data

points and 2) through modelling techniques. Mapping spatial distribution

of disease cases (for example BFV disease) and potential risk areas

requires converting points into surfaces (Fotheringham and Rogerson

2009). In general, spatially interpolated/smoothed data are more

appropriate for disease mapping than raw rates. Some spatial techniques

such as inverse distance weighting (IDW) and kriging are extensively

used to interpolate/smooth new data points or filter signals from noise

(Lloyd 2010). A review on spatial interpolation was well conducted by

Burrough and McDonnell (1998).

Inverse distance weighting is the simplest and most commonly used

approach to interpolation in GIS programs for producing surfaces using

interpolation of weighted average of the scatter points (Cliff and Ord

1981). The technique is based on the assumption that the interpolating

surface should be influenced mostly by nearby points and less by the

more distant points (Fishcher 2005). It is rapid and easy to implement

(Lloyd 2005) and has commonly been applied to climate data and di sease

counts (Hickey et al. 2011; Huang et al. 2011; Wen et al. 2010; Woodruff

et al. 2006; Wu et al. 2009). For example, Tachiri et al (2006) used IDW

to interpolate daily temperature. An evaluation of the residuals

determined that IDW performed better than kriging for the purpose of

their study.

2.2.2.6 Variogram /Semivariogram modelling

A variogram/semivariogram modelling technique is used to describe the

spatial dependence between the observed measurements as a function of

the distance between them. It is a plot of semivariance against lag

distance (i.e., the distance between a pair of observations). ‘Lag’ is used

18

to describe the distance and direction by which observations are separated

(Lloyd 2010). Semivariance refers to half the squared difference between

data values. If the observations are spatially correlated, there will be an

increase in semivariance as the distance between observations i ncrease

(Cressie 1985). A mathematical model is commonly fitted to the

empirical semi-variogram plot for use in geostatistics. Various methods

may be adopted in the fitting and several important considerations need

to be considered during fitting (Lloyd 2010). Geostatistics has been

widely used to characterise spatial variation in relatively small datasets

and to predict unobserved values using kriging informed by the

semivariogram model (Oliver 1990). Kriging has also been widely used

in the study of spatial and temporal analysis of several mosquito -borne

diseases (Bogojevic 2007; Li 2008; Tachiri 2006) .

2.2.2.7 Spatial clustering

Spatial clustering is a process of grouping a set of spatial objects into

clusters so that objects within a cluster have high similarity in

comparison to one another, but dissimilar to objects on other clusters.

This method is used to identify clusters/hot spots in disease tracking.

2.2.2.8 Cluster /hot spot detection

A ‘hot spot’ is an area of high response or an elevated cluster for an

event or ‘a condition indicating some form of clustering in a spatial

distribution’ (Osei and Duker 2008). Several statistical techniques have

been developed to assess clustering of events jointly in space and time.

Cluster location techniques are also based on hypothesis-testing methods,

whereby the study region is literally scanned for clusters by

superimposing a number of circular (or elliptical) windows to identify the

group of contiguous areas with the most significant excess risk (Besag

1991; Kulldorff 1997). Spatial scan statistic (SaTScan) is a cluster

detection technique that allows detection of both global clustering and the

identification of the location of specific clusters, and clustering in space,

time, and space and time and test the significance of clusters (Kulldorff

2002). It has been used in several mosquito-borne disease studies. For

19

example, Coleman et al (2009) have used SaTScan method to find out the

clusters associated with malaria prevalence to design control programs.

Another study by Lian et al (2007) has applied a similar method to scan

spatial and temporal clusters of West Nile virus disease in Texas.

2.2.2.9 Spatial modelling

Modelling involves techniques for estimating pathogen transmission

factors in space. Spatial statistical models are aimed 1) to assess

statistical significance between predictors and spatially correlated disease

outcome data, 2) to establish a mathematical relation between the disease

and its predictor/s, and 3) to obtain a model-based prediction of the

disease outcome at non-sampled locations (kriging/IDW) when the

disease data are available at fixed locations (geostatistical data). Several

studies have examined the association between climate, climate

variability and vector-borne diseases using spatial modelling techniques

(Abeku et al. 2004; Gibbs et al. 2006; Hu et al. 2007; O'Connell 2005;

Woodruff et al. 2006; Wu et al. 2009; Zou et al. 2007).

2.2.2.10 Discriminant function analysis

Discriminant analysis is a statistical technique used to discriminate

between two or more mutually exclusive groups of objects with respect to

several variables simultaneously (Klecka 1980). The most well-known

technique is ‘Fisher’s linear discriminant function analysis’. It has been a

common practice to use discriminant function analysis in exploratory data

analysis. The technique has been applied in various studies, for example,

to develop a climate-based model of malaria transmission in Kenya

(Snow et al. 1998) to determine the effect of landscape structure on

mosquito densities in Northern Thailand (Overgaard 2003) and to predict

the mosquito densities on heterogeneous land cover in Western Thailand

(Charoenpanyanet and Chen 2008) .

2.2.2.11 Spatial regression

Spatial regression deals with the specification, estimation, and diagnostic

checking of regression models with incorporated spatial effects. Spatial

20

effects can be broadly classified into two types: spatial dependence and

spatial heterogeneity (Anselin 1995). Spatial regression analysis consists

of three components: 1) the specification of spatial dependence in a

regression model, 2) the detection of presence of spatial autocorrelations

and 3) the review of the estimation methods (such as maximum likelihood

etc.).

In the absence of clear etiologic knowledge, spatial auto -regression

analysis is more powerful than classical regression analysis to measure

quantitative relationships between disease and environmental factors,

because the latter ignores spatial dependence of spatial patterns while the

former fully explains spatial autocorrelation and spatial stability (Anselin

1995).

GeoDa software of Anselin et al (2006) allows for spatial autoregressive

modelling. A spatial lag model is an earlier representation of the

equilibrium outcome of processes of spatial and social interaction. In the

spatial error models, the spatial autocorrelation does not enter as an

additional variable in the model, but instead affects the covariance

structure of the random disturbance terms. In other words, the spatial

error identifies spatial autocorrelation in the error structure of the

specified regression model. In contrast, the spatial lag model identifies

spatial autocorrelation in the covariance structure of the dependent

variable (Anselin 2006).

Spatial regression modelling has been applied to predict dengue incidence

in Thailand (Thammapalo et al. 2008) and malaria in Kenya (Li 2008).

For example, Wu et al (Wu et al. 2009) examined temperature and other

environmental factors on dengue transmission in subtropical Taiwan

using spatial regression analysis. Regression analysis was applied to

detect the causes of haemorrhagic fever in China (Feng 2011).

2.2.2.12 Predictive modelling

GIS has long been performed to assess and identify, at the regional or

country level, potential determinants of mosquito-borne diseases

including demographic, socio-economic, environmental and climatic

21

variables, to better appreciate the underlying characteristics of predicted

areas at risk using a logistic regression model (Jacups et al. 2011;

Woodruff et al. 2002; Woodruff et al. 2006). These studies have

combined both GIS and modelling techniques to explore the determinants

of mosquito-borne disease transmission and provided useful information

for deciding where and when public health inte rventions are most needed.

2.3 CRITICAL REVIEW OF THE MAJOR FINDINGS IN BFV

RESEARCH

2.3.1 Distribution and outbreaks of BFV disease

Barmah Forest virus was named after it was first isolated in 1974 from

Culex annulirostris (Skuse) mosquitoes collected in Barmah Forest near

the Murray River in Northern Victoria, Australia (Marshall et al. 1982)

and simultaneously from mosquitoes collected in southwest Queensland

(Doherty et al. 1979). BFV is commonly transmitted by mosquito species,

mainly in the genera Culex and Aedes in inland areas and by saltmarsh

mosquitoes, such as Aedes in the coastal areas. Aedes vigilax (Skuse) is

found in the estuaries and mangroves of coastal Queensland and northern

New South Wales, and is abundant in summer months (December, January

and February) (Russell 1998). Aedes camptorhynchus (Thomson) is

mostly distributed in southern (Victoria) coastal regions. The major

breeding sites of the saltmarsh mosquito (breed only in saltmarsh waters),

Ae. vigilax include temporary brackish pools and marshes filled as a

result of tidal inundation. As many of these breeding sites are in

environmentally sensitive locations and occur over extensive

geographical areas, reduction of source is often not practical or

acceptable, and vector control measures are usually limited. This

species’ extensive flight range, which often exceeds 5 kilometres, also

makes vector control difficult (Cashman et al. 2008). However, little

information is available on local and urban mosquito abundance and

current, updated and detailed distributions of competent BFV vectors in

Queensland remain unknown.

BFV is typically a zoonotic disease in humans, caused by pathogens

22

transmitted from other animals (Russell 1998). BF virus has been

associated with native mammals (macropods, e.g. kangaroos and

wallabies) (Poidinger et al. 1997) but the involvement of birds as well

has not been ruled out (Mackenzie 1998). However, possums, cats and

dogs are unlikely to be important hosts for BF virus (Boyd et al. 2001;

Boyd and Kay 2002). BFV disease in humans is non-fatal and infections

can be either asymptomatic or symptomatic (Phillips et al. 1990). It

causes a prolonged and debilitating disease, known as epidemic

polyarthritis (Aaskov et al. 1981). The symptoms include fever, skin rash,

arthralgia and myalgia (Boughton et al. 1984; Dalgarno et al. 1984;

Boughton 1994; Flexman et al. 1998). The incubation period is between 7

and 9 days (Fraser and Cunningham 1980). The disease affects people of

all ages irrespective of gender. Currently, there is no specific treatment

once infection is contracted and no vaccine to prevent, therefore disease

management is primarily aimed at alleviation of symptoms.

There is a trend of increasing BFV disease notifications in Australia over

recent years (Fitzsimmons et al. 2009). In 1992, Merianos et al first

reported a BFV disease outbreak in Nhulunbuy in the Northern Territory

(1992), followed by Passmore et al from Victoria (2002). In 1993-1994,

Lindsay et al reported outbreaks from south-western Australia (Lindsay et

al. 1995). In 1995, Doggett et al documented BFV cases from New South

Wales south coast (1999).

In Queensland, routine serological screening for BFV antibody began in

1991 (Hills and Sheridan 1997). However, there was serological evidence

of extensive BFV disease outbreaks throughout Queensland before this

time. Screening of serum collected from residents in 1989 indicated that

approximately 0.23% of the population was infected per year (Phillips et

al. 1990). A review of BFV disease notifications between 1992 and 1995

indicated that clinical disease associated with BFV infection was

widespread throughout Queensland, with the highest crude notification

rates (44 cases/100,000 people) in central and south -western areas of the

state (Hills and Sheridan 1997). However, the BFV disease notification

rates were underestimated because general health practitioners requesting

23

BFV disease serology were relatively low, with only 36-48% of epidemic

polyarthritis cases tested for antibodies to BFV (Quinn et al. 2005).

Geographic variation in testing patterns for BFV throughout Queensland

was noticed with testing rates lower in northern Queensland compared

with southern testing centres (Kelly-Hope et al. 2002). Since the

commencement of national reporting in 1995 (except for the Northern

Territory, which commenced reporting in 1997), there has been an

average of 830 cases each year, of which 43-69% have been reported

from Queensland (Liu et al. 2008). Although BFV is considered to be

endemic in Queensland, there has been considerable variation in the

number of notifications reported each year (between 310 and 1242 cases

each year) (Fitzsimmons et al. 2009).

2.3.2 Role of climatic factors

The role of climate as a driving force for BFV outbreaks in Australia has

been given a considerable discussion recent ly (Lyth et al. 2005;

McMichael et al. 2008; Olano et al. 2009; Russell 2009; Tolle 2009;

Lafferty 2009). However, the transmission mechanism of disease remains

to be elucidated as it has a complex ecology (Mackenzie et al. 1994;

Gould and Higgs 2009). Previous studies indicate that, for the

transmission of BFV, the virus and its reservoir, the vector, the human

population, and climatic conditions are essential factors (Hills and

Sheridan 1997; Lindsay et al. 1995; Mackenzie et al. 1998; Mackenzie et

al. 1994; Mackenzie and Smith 1996; Russell and Kay 2004).

Temperature can affect vector abundance (Reeves et al. 1994) and extend

distribution, vector development, reproductive and biting rates, pathogen

incubation period (Kramer et al. 1983; Turell 1993), and adult longevity.

Temperature varies on multiple time scales such as daily, monthl y,

seasonally and annually and to adjust to such environmental changes,

mosquitoes may adopt certain behavioural thermal changes (Bradshaw et

al. 2004). As temperature sets boundaries on the distribution of mosquito

species, global warming might alter the range (in altitude and latitude) of

suitable habitat for a particular mosquito species (Lafferty 2009).

24

Usually warmer temperatures mean shorter times between blood meals,

quicker extrinsic incubation times for viruses, and a shorter life

expectancy for adults (Harley et al. 2001; Russell 1998). Temperature

affects the capacity for individual mosquitoes to survive for virus

incubation and transmission to a host (Epstein 2002; Weinstein 1997) .

Russell stated that an increase in temperature without rainfall

compensation could lead to desiccation of adult mosquitoes which require

humid micro-environments for resting (2001).

Rainfall plays an important role in BFV epidemiology. Mosquitoes need

water for breeding (egg laying and larval development) and adult

survival. Many mosquitoes breed in pools or marshes and, therefore,

mosquito abundance is affected by rainfall and the availability of surface

water. Several studies have found positive associations between heavy

rainfall and subsequent outbreaks of mosquito-borne diseases (Hu et al.

2004; Hu et al. 2007; Lafferty 2009; Tong et al. 2002; Tong et al. 2005).

Rainfall also affects relative humidity and the longevity of the adult

mosquito. Some studies have demonstrated that humidity can also

influence the transmission of the disease. High humidity increases

mosquito population and the occurrence of disease outbreak (Russell

2009; Jacups et al. 2008). However, temperature, rainfall and humidity

may have synergistic effects on BFV disease transmission.

Few studies have investigated the relationships between climate variables

and BFV disease, and few investigated the risk factors associated with

BFV disease. In 1999, Doggett et al compared the distribution of BFV

infection with mosquito abundance and virus isolations (Doggett et al.

1999). They obtained data on climate variables such as temperature and

rainfall and tides, BFV cases and mosquito data from 1994 to 1995

(short-term) in New South Wales. They found that mosquito populations

had increased due to the increased levels of rainfall and flooding and

several high tides which then had caused increased BFV cases.

Therefore, their study suggested that weather patterns may have played a

major role in the outbreaks.

25

2.3.2.1 Seasonality

The geographic distribution of mosquito species and their seasonal

activity is mostly determined by temperature and rainfall (Weinstein

1997). Seasonal patterns have been observed in mosquito -borne diseases

by some authors (Gatton et al. 2005; Gatton et al. 2004; Hu et al. 2004;

Kelly-Hope et al. 2002; Tong et al. 2008; Tong et al. 2004) suggesting

that seasonality is related to climate. BFV disease is mostly seasonal

with peak occurrence in February (summer) and March (autumn) an d

climate sensitive. Temperature and rainfall change with the season, and

most mosquito-borne diseases have apparent seasonality. Climate change

is most likely to affect mosquito-borne diseases with seasonal patterns.

2.3.2.2 Lag effect

Time lags between climate and mosquito abundances are statistically

challenging for investigating seasonal effects on mosquito -borne

diseases. Favourable conditions that provide suitable habitat for

mosquito larvae do not immediately lead to disease transmission because

mosquito and pathogen development take time. Therefore, it is important

to consider a variety of lags to determine the best fit to the data.

Therefore, lag time has been included in climate models for several

arboviral diseases (Tong and Hu 2001; Woodruff et al. 2006).

Passmore and his team (2002) compared the rainfall data for two years

(2001 and 2002) which was collected from several mosquito trapping

sites and weekly mosquito counts data in Victoria. They found a positive

relationship between mosquito counts and rainfall. Additionally, they

found that dry summer in inland areas and wetter than average summer in

coastal areas were favourable conditions for the inland mosquito Cx.

annulirostris and salt marsh mosquito Ae. camptorhynchus respectively.

Few studies have explored the potential risk factors for the transmission

of BFV in Australia (Appendix A). For example, Naish et al have

explored the relationship between BFV disease and climate variables such

as maximum and minimum temperature, rainfall and relative humidity

(Naish et al. 2006). They performed an ecologic time-series analysis to

26

examine the association between climate variability and the transmission

of BFV for the years 1992 to 2001 (10 years) in Gladstone region in

Queensland. Time series plots indicated that the data showed seasonality

and seasonal auto-regressive integrated moving average (SARIMA) was a

good fit for diseases exhibiting seasonality. Therefore, SARIMA model

was used to examine the relation between the climate variability, tides,

and the monthly incidence of notified BFV disease. They also included

lag effects as lags are important in mosquito distribution. They found that

the best match between climate and BFV disease occurred at a lag of 5

months for monthly minimum temperature and current month for high

tide and these variables were considered as the important risk factors in

the transmission of BFV disease in Gladstone. However, in this study,

other factors such as socio-economic variables were not included as the

study was conducted in one geographic region and socio -economic status

in the modelling analyses was not feasible (Naish et al. 2006).

Recently, Naish et al conducted another study to determine the climatic

and socio-economic risk factors for BFV disease incidence along coastal

regions of Queensland (Naish et al. 2009). They included incidence of

BFV disease cases from six coastal cities as the dependent variable and

climatic (maximum and minimum temperature, rainfall, relative

humidity), tidal (low and high tide) and socio-economic variables (SEIFA

index) as predictors. Generalised linear models using the negative

binomial distribution with log link function were performed to assess the

impact of climate variability on BFV transmission. They found that

maximum temperature, high tide and SEIFA index were key risk factors

in the transmission of BFV. They also explored how the fit between BFV

outbreaks and climate varied with the lags chosen and found that the lag

with the best fit was different for different climate and tidal variables.

Therefore, lag fitting is necessary to reveal the various patterns of BFV

transmission. The study was also validated for 1, 2, and 3 years of data.

This study results could be applicable to other mosquito -borne diseases

(e.g., RRV) from other tropical and subtropical coastal areas as most of

the related factors were included in the analyses. The study suggests that

27

climate is a key determinant in the transmission of BFV disease (Naish et

al. 2009).

2.3.3 Role of non-climatic factors

Mosquitoes can occupy a range of habitats and can survive extreme

environmental conditions (Tennessen 1993). In Australia, all mosquitoes

(with one exception, Aedes Aegypti) have a close relationship with

wetlands (Dale and Knight 2008) and each species of mosquito has

preferential breeding habitats. The nature and location of a wetland can

influence the species present at a particular site (Russell 1999).

However, mosquitoes have a very short life cycle (from four days to a

month), and their eggs could remain dormant for more than

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