Spatial Components in Disease Modelling  Kim-hung KWONG and Poh-chin LAI Department of Geography The University of Hong Kong
Contents <ul><li>Background </li></ul><ul><li>Methodology </li></ul><ul><ul><ul><li>Data </li></ul></ul></ul><ul><ul><ul><...
Background  (1) <ul><li>Emerging and Re-emerging of infectious diseases </li></ul><ul><ul><ul><li>H1N1 influenza </li></ul...
Background  ( 2 ) <ul><li>Conventional approach </li></ul><ul><ul><ul><li>Investigation on epidemiological features </li><...
Background  ( 3 ) <ul><li>GIS for disease modelling </li></ul><ul><ul><ul><li>Well suited for data with spatial dimension ...
Methods  (1) Objectives <ul><li>Objectives </li></ul><ul><ul><ul><li>To map the spatial diffusion of SARS </li></ul></ul><...
Methods  (2) Hypotheses <ul><li>Hypotheses </li></ul><ul><ul><ul><li>Transport </li></ul></ul></ul><ul><ul><ul><ul><ul><li...
Methods  (3) Study Area and Period <ul><li>Study Area </li></ul><ul><ul><ul><li>Populated areas in the territory of Hong K...
Methods  (4) Data <ul><li>Sources </li></ul><ul><ul><ul><li>SARS data </li></ul></ul></ul><ul><ul><ul><ul><ul><li>From Hos...
Analysis  (1) Mapping SARS <ul><li>Four phases based on important events </li></ul><ul><ul><ul><li>1)  Early phase   ( on ...
 
Analysis  (2) Mapping SARS <ul><li>Transport facilities </li></ul><ul><ul><ul><li>Correlate with the linear diffusion patt...
Analysis  (3) Socio-economic Factors <ul><li>Correlate SARS cases to socio-economic factors </li></ul><ul><ul><ul><li>In g...
Analysis  (4) Socio-economic Factors 28303 .000 .390 (**) h) Net residential density 28303 .000 -.098 (**) g) Average no. ...
Fewer cases in low residential density areas
Implications and Future Research <ul><li>Location specific factors affecting SARS transmission </li></ul><ul><ul><ul><li>N...
Further Information <ul><li>Please contact </li></ul><ul><ul><li>K.H. Kwong </li></ul></ul><ul><ul><li>PhD Candidate </li>...
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Spatial Components in Disease Modelling - Kim-hung KWONG and Poh-chin LAI

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Spatial Components in Disease Modelling - Kim-hung KWONG and Poh-chin LAI - Department of Geography - The University of Hong Kong

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Spatial Components in Disease Modelling - Kim-hung KWONG and Poh-chin LAI

  1. 1. Spatial Components in Disease Modelling Kim-hung KWONG and Poh-chin LAI Department of Geography The University of Hong Kong
  2. 2. Contents <ul><li>Background </li></ul><ul><li>Methodology </li></ul><ul><ul><ul><li>Data </li></ul></ul></ul><ul><ul><ul><li>Methods </li></ul></ul></ul><ul><li>Results </li></ul><ul><ul><ul><li>Mapping of SARS diffusion </li></ul></ul></ul><ul><ul><ul><li>Factors found from maps </li></ul></ul></ul><ul><ul><ul><li>Factors identified from statistics </li></ul></ul></ul><ul><li>Implications and f uture research </li></ul>
  3. 3. Background (1) <ul><li>Emerging and Re-emerging of infectious diseases </li></ul><ul><ul><ul><li>H1N1 influenza </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Killed 20-50m people in 1918 (Spanish Flu) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Caused global alert last year (Swine Flu) </li></ul></ul></ul></ul></ul><ul><ul><ul><li>SARS </li></ul></ul></ul><ul><ul><ul><ul><ul><li>1 st large epidemic in 21 st century </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Global attention </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Important impacts on HK’s public health policy </li></ul></ul></ul></ul></ul><ul><ul><ul><li>AIDS </li></ul></ul></ul><ul><ul><ul><ul><ul><li>First case in 1980s </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Continuous and serious outbreaks in less developed countries, such as India, South Africa, etc. </li></ul></ul></ul></ul></ul>
  4. 4. Background ( 2 ) <ul><li>Conventional approach </li></ul><ul><ul><ul><li>Investigation on epidemiological features </li></ul></ul></ul><ul><ul><ul><ul><ul><li>E.g. Transmission co-efficiency </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>E.g. Which organ(s) affected most </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Deterministic models for </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Illustrating disease spread pattern over TIME </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Simple and easy to set up </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Problems </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Unrealistic assumptions ( Small and Tse, 2005) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Ignore socio-economic and demographic factors (Lai et al., 2004) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>No illustration for spread patterns over SPACE </li></ul></ul></ul></ul></ul>
  5. 5. Background ( 3 ) <ul><li>GIS for disease modelling </li></ul><ul><ul><ul><li>Well suited for data with spatial dimension (Lai et al., 2004; Riley et al., 2007) </li></ul></ul></ul><ul><ul><ul><li>Various spatial models for diseases </li></ul></ul></ul><ul><ul><ul><ul><ul><li>E.g. Model to account for mobility of the population (Sattenspiel and Dietz, 1995) </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Environmental, socio-economic and behavioural factors </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Important for disease outbreaks </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>E.g. Population density for SARS (Meng et al., 2005) </li></ul></ul></ul></ul></ul><ul><ul><ul><li>offers new insights to disease modelling </li></ul></ul></ul><ul><li>But before modelling … </li></ul><ul><ul><ul><li>1 st step - More understanding on spatial spreading patterns </li></ul></ul></ul><ul><ul><ul><li>Also important to identify relevant environmental and social-economic factors </li></ul></ul></ul>
  6. 6. Methods (1) Objectives <ul><li>Objectives </li></ul><ul><ul><ul><li>To map the spatial diffusion of SARS </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Four phases </li></ul></ul></ul></ul></ul><ul><ul><ul><li>To identify location-specific factors contributing to the transmission of SARS </li></ul></ul></ul><ul><ul><ul><ul><ul><li>P ercentage of population with tertiary level education </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>P ercentage of population aged under 15 </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>P ercentage of population aged over 65 </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>N on-working population </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>M edian household income </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>M edian personal income </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>A verage number of rooms per household </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>N et residential density </li></ul></ul></ul></ul></ul>
  7. 7. Methods (2) Hypotheses <ul><li>Hypotheses </li></ul><ul><ul><ul><li>Transport </li></ul></ul></ul><ul><ul><ul><ul><ul><li>H 0 : There is no relationship between disease spread and transport infrastructure </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>H A : Disease spread follows the pattern of transport infrastructure </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Socio-economic characteristics </li></ul></ul></ul><ul><ul><ul><ul><ul><li>H 0 : There is no relationship between disease incidence and various socio-economic characteristics </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>H A : Disease incidence correlates with various socio-economic characteristics </li></ul></ul></ul></ul></ul>
  8. 8. Methods (3) Study Area and Period <ul><li>Study Area </li></ul><ul><ul><ul><li>Populated areas in the territory of Hong Kong </li></ul></ul></ul><ul><ul><ul><li>Excluding </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Country parks and conservation areas </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Airports, ports, firing range, etc. </li></ul></ul></ul></ul></ul><ul><li>Study Period </li></ul><ul><ul><ul><li>Between February and June 2003 </li></ul></ul></ul><ul><ul><ul><li>SARS epidemic of Hong Kong </li></ul></ul></ul>
  9. 9. Methods (4) Data <ul><li>Sources </li></ul><ul><ul><ul><li>SARS data </li></ul></ul></ul><ul><ul><ul><ul><ul><li>From Hospital Authority </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Include patient name, addresses, symptoms, date of admission… etc. </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Patient names and their addresses coded and then removed to protect privacy </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Census data </li></ul></ul></ul><ul><ul><ul><ul><ul><li>From Census & Statistics Department </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>2001 population census </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Down to street block level </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Spatial data </li></ul></ul></ul><ul><ul><ul><ul><ul><li>From Lands Department </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Buildings, roads, hydrology, country parks </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Land use data digitized </li></ul></ul></ul></ul></ul>
  10. 10. Analysis (1) Mapping SARS <ul><li>Four phases based on important events </li></ul><ul><ul><ul><li>1) Early phase ( on or before 10 March 2003 ) </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Patients in room 8A of PWH </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Concentration of SARS cases in Sha Tin </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Impacts of medical facilities (Meng et al., 2005) </li></ul></ul></ul></ul></ul><ul><ul><ul><li>2) Diffusion phase ( 11 – 17 March 2003 ) </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Linear transmission pattern (North / South of Sha Tin) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Due to major transport network ( Fang et al., 2009) </li></ul></ul></ul></ul></ul><ul><ul><ul><li>3) SSE phase ( 18 – 30 March 2003 ) </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Super Spreading Event ( SSE ) at the Amoy Garden s ( Riley et al. , 2003) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>SARS spread to other districts </li></ul></ul></ul></ul></ul><ul><ul><ul><li>4) Post-SSE phase ( 31 March – 2 June 2003 ) </li></ul></ul></ul><ul><ul><ul><ul><ul><li>March 31 - Amoy Garden residents segregated </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>June 2 - end of 2003 epidemic in Hong Kong </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>SARS affected most districts </li></ul></ul></ul></ul></ul>
  11. 12. Analysis (2) Mapping SARS <ul><li>Transport facilities </li></ul><ul><ul><ul><li>Correlate with the linear diffusion pattern in phases 1 and 2 </li></ul></ul></ul><ul><li>Location of Prince of Wales Hospital </li></ul><ul><ul><ul><li>An “epicentre” of 2003 SARS outbreak in HK </li></ul></ul></ul>
  12. 13. Analysis (3) Socio-economic Factors <ul><li>Correlate SARS cases to socio-economic factors </li></ul><ul><ul><ul><li>In grids of 150m x 150m </li></ul></ul></ul><ul><ul><ul><li>Covering whole HK except </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Country parks and conservation areas </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Other non-populated areas, e.g. airports </li></ul></ul></ul></ul></ul><ul><li>Results </li></ul><ul><ul><ul><li>2 nd null hypothesis partly rejected </li></ul></ul></ul><ul><ul><ul><li>Disease incidence correlates with some socio-economic characteristics </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Average no. of rooms per household </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Net residential density </li></ul></ul></ul></ul></ul>
  13. 14. 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 N Sig. Coefficiencts Variables
  14. 15. Fewer cases in low residential density areas
  15. 16. Implications and Future Research <ul><li>Location specific factors affecting SARS transmission </li></ul><ul><ul><ul><li>Net residential density </li></ul></ul></ul><ul><ul><ul><li>Medical facilities </li></ul></ul></ul><ul><ul><ul><li>Major transport infrastructure </li></ul></ul></ul><ul><li>Concentrations of SARS in some areas in early phases </li></ul><ul><ul><ul><li>Sha Tin (Phase 1) </li></ul></ul></ul><ul><ul><ul><li>Districts North and South of Sha Tin (Phase 2) </li></ul></ul></ul><ul><ul><ul><li>SARS not distributed evenly or randomly </li></ul></ul></ul><ul><ul><ul><li>Spatial simulation important to early detection of risky areas </li></ul></ul></ul><ul><ul><ul><li>Control measures implemented more effectively if spatial modeling results available </li></ul></ul></ul><ul><li>Spatial modeling necessary </li></ul><ul><ul><ul><li>In short-term forecast of high risk areas </li></ul></ul></ul><ul><ul><ul><li>To aid decision making on public health matters </li></ul></ul></ul><ul><ul><ul><li>Facilitated by factors identified from this study </li></ul></ul></ul><ul><ul><ul><ul><ul><li>Especially for factors unique to highly populated cities, such as HK </li></ul></ul></ul></ul></ul>
  16. 17. Further Information <ul><li>Please contact </li></ul><ul><ul><li>K.H. Kwong </li></ul></ul><ul><ul><li>PhD Candidate </li></ul></ul><ul><ul><li>Department of Geography </li></ul></ul><ul><ul><li>The University of Hong Kong </li></ul></ul><ul><ul><li>Email: [email_address] </li></ul></ul><ul><ul><li>Tel: (852) 2859 7028 </li></ul></ul><ul><ul><li>Fax: (852) 2559 8994 </li></ul></ul>

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