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Spatial Components in Disease Modelling

Kim-hung KWONG and Poh-chin LAIDepartment of Geography

The University of Hong Kong

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Contents

Background Methodology

DataMethods

ResultsMapping of SARS diffusionFactors found from mapsFactors identified from statistics

Implications and future research

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Background (1)

Emerging and Re-emerging of infectious diseases

H1N1 influenza» Killed 20-50m people in 1918 (Spanish Flu)» Caused global alert last year (Swine Flu)

SARS» 1st large epidemic in 21st century» Global attention» Important impacts on HK’s public health policy

AIDS» First case in 1980s» Continuous and serious outbreaks in less developed

countries, such as India, South Africa, etc.

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Background (2)

Conventional approach Investigation on epidemiological features

» E.g. Transmission co-efficiency» E.g. Which organ(s) affected most

Deterministic models for» Illustrating disease spread pattern over TIME» Simple and easy to set up

Problems» Unrealistic assumptions (Small and Tse, 2005)» Ignore socio-economic and demographic factors (Lai

et al., 2004)» No illustration for spread patterns over SPACE

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Background (3)

GIS for disease modelling Well suited for data with spatial dimension (Lai et al., 2004;

Riley et al., 2007) Various spatial models for diseases

» E.g. Model to account for mobility of the population (Sattenspiel and Dietz, 1995)

Environmental, socio-economic and behavioural factors» Important for disease outbreaks» E.g. Population density for SARS (Meng et al., 2005)

offers new insights to disease modelling

But before modelling… 1st step - More understanding on spatial spreading patterns Also important to identify relevant environmental and social-

economic factors

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Methods (1)

Objectives

Objectives To map the spatial diffusion of SARS

» Four phases

To identify location-specific factors contributing to the transmission of SARS

» Percentage of population with tertiary level education

» Percentage of population aged under 15» Percentage of population aged over 65» Non-working population» Median household income» Median personal income» Average number of rooms per household» Net residential density

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Methods (2)

Hypotheses

HypothesesTransport

» H0: There is no relationship between disease spread

and transport infrastructure

» HA: Disease spread follows the pattern of transport

infrastructure

Socio-economic characteristics

» H0: There is no relationship between disease incidence

and various socio-economic characteristics

» HA: Disease incidence correlates with various socio-

economic characteristics

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Methods (3)

Study Area and Period

Study AreaPopulated areas in the territory of Hong KongExcluding

» Country parks and conservation areas» Airports, ports, firing range, etc.

Study PeriodBetween February and June 2003 SARS epidemic of Hong Kong

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Methods (4)

Data

Sources SARS data

» From Hospital Authority» Include patient name, addresses, symptoms, date of

admission… etc.» Patient names and their addresses coded and then

removed to protect privacy

Census data» From Census & Statistics Department» 2001 population census» Down to street block level

Spatial data» From Lands Department» Buildings, roads, hydrology, country parks» Land use data digitized

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Analysis (1)

Mapping SARS

Four phases based on important events1) Early phase (on or before 10 March 2003)

» Patients in room 8A of PWH» Concentration of SARS cases in Sha Tin» Impacts of medical facilities (Meng et al., 2005)

2) Diffusion phase (11 – 17 March 2003)» Linear transmission pattern (North / South of Sha Tin)» Due to major transport network (Fang et al., 2009)

3) SSE phase (18 – 30 March 2003)» Super Spreading Event (SSE) at the Amoy Gardens

( Riley et al., 2003)» SARS spread to other districts

4) Post-SSE phase (31 March – 2 June 2003)» March 31 - Amoy Garden residents segregated» June 2 - end of 2003 epidemic in Hong Kong» SARS affected most districts

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Analysis (2)

Mapping SARS

Transport facilitiesCorrelate with the linear diffusion pattern

in phases 1 and 2

Location of Prince of Wales Hospital

An “epicentre” of 2003 SARS outbreak in HK

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Analysis (3)

Socio-economic Factors

Correlate SARS cases to socio-economic factors

In grids of 150m x 150mCovering whole HK except

» Country parks and conservation areas» Other non-populated areas, e.g. airports

Results2nd null hypothesis partly rejectedDisease incidence correlates with some socio-

economic characteristics» Average no. of rooms per household» Net residential density

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Analysis (4)

Socio-economic Factors

28303.000.390 (**)h) Net residential density

28303.000-.098 (**)g) Average no. of rooms per household

28303.001-.020 (**)c) % of the population over 65 years old

All grids (excluding country parks)

1316.000.204 (**)h) Net residential density

1316.000-.098 (**)g) Average no. of rooms per household

1316.025.062 (*)c) % of the population over 65 years old

Grids with SARS cases only

NSig.CoefficienctsVariables

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Fewer cases in low residential density areas

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Implications and Future Research

Location specific factors affecting SARS transmission Net residential density Medical facilities Major transport infrastructure

Concentrations of SARS in some areas in early phases Sha Tin (Phase 1) Districts North and South of Sha Tin (Phase 2) SARS not distributed evenly or randomly Spatial simulation important to early detection of risky areas Control measures implemented more effectively if spatial modeling

results available

Spatial modeling necessary In short-term forecast of high risk areas To aid decision making on public health matters Facilitated by factors identified from this study

» Especially for factors unique to highly populated cities, such as HK

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Further Information

Please contact

K.H. KwongPhD CandidateDepartment of GeographyThe University of Hong KongEmail: h0110454@hkusua.hku.hkTel: (852) 2859 7028Fax: (852) 2559 8994