spatial and temporal analysis of barmah forest virus disease...
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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
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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:
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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.
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To my husband, Daniel,
our daughter, Sunita
and my parents.
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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
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SLA level. Most likely spatial and space-time clusters are detected at the same
locations across coastal Queensland (p
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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.
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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
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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
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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 Data collection 72
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
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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
FOREST VIRUS DISEASE IN QUEENSLAND,
AUSTRALIA 102
Abstract 103
5.1 Introduction 104
5.2 Methods 106
5.2.1 Study area 106
5.2.2 Data collection 108
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
FOREST VIRUS DISEASE IN QUEENSLAND,
AUSTRALIA 126
Abstract 127
6.1 Introduction 128
6.2 Methods 130
6.2.1 Study area 130
6.2.2 Data collection 130
6.2.2.1 BFV disease cases 130
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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 Statistical analysis 135
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
7.1 Introduction 151
7.2 Methods 153
7.2.1 Study area 153
7.2.2 Data collection 154
7.2.2.1 BFV data 154
7.2.2.2 Explanatory variables 154
7.2.3 Statistical analysis 155
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
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CHAPTER 8 FORECASTING THE FUTURE RISK OF BARMAH
FOREST VIRUS DISEASE UNDER CLIMATE CHANGE
SCENARIOS IN QUEENSLAND, AUSTRALIA 178
Abstract 179
8.1 Introduction 180
8.2 Methods 182
8.2.1 Study area 182
8.2.2 Data collection 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
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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
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APPENDIX C ETHICS APPROVAL AND DATA PERMISSION 249
C.1 Ethics Approval 249
C.2 Data 251
REFERENCES 252
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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
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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
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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
Figure 8.2: (a) Geographical distribution of BFV disease under current climate for
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
Figure 8.3: (a) Geographical distribution of BFV disease under current climate for
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
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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
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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).
.
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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
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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.
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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
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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).
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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.
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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.
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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” .
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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.
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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
CHAPTER 6 Manuscript 5
Spatial and temporal clustersCHAPTER 5 Manuscript 4
Spatial and temporal distribution
CHAPTER 4 Manuscript 3
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
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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/
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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]/
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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
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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).
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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).
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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.
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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
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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
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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