Infection risk TOTAL RISK

Infection Risk Assessment with Exposure to
Pathogens in the Flood Water
- The case of City of Manila, Philippines
Tran Thi Viet Nga1 and Kensuke Fukushi2
1Institute
for Environmental Science and Engineering, National University of Civil Engineering, Vietnam
2Integrated Research System for Sustainability Science (IR3S), the University of Tokyo, Japan
Presented by Psyche Fontanos , IR3S, UT
International Conference on Sustainability Science in Asia 2012
Bali, Indonesia, January 12, 2012
JICA-WB-ADB Joint Study
Climate Change Impact and Adaptation in Asian Coastal Mega-Cities
Overall Framework
JICA
(Manila)
–
ADB
–
(HoChiMinh City)
World Bank alliance
(Bangkok, Kolkata)
Case study of Metro Manila
Methodology
City Case Studies
Solutions to
Operations
JICA – IR3S alliance
E.g. JICA: Metro Manila
Coastal Engineering & Storm surge: University of Ibaraki
River hydro: CTI International
Transport: ALMEC
Urban poor: Ateneo de Manila University
Firms: National Statistics Office
Health: University of Tokyo
- Urban planners, local governments
- Concerted donor efforts
(e.g. World Bank, ADB, bilateral donors)
2
2
• Metro Manila (Metropolitan
Manila; National Capital
Region, NCR)
• political, economic, social,
cultural, and educational
center of the Philippines
• 1 municipality, 16 cities
(regional center is City of
Manila)
• 11.5 M population based on
2007 census (13% of
national pop.)
3
Flood Prone Areas in
Metro Manila
KAMAN
AVA Area
Pasig-Marikina Basin
West Mangahan Area
4
Inundation in Metro Manila
Infection
Pathogens
Wastewater/
Septic wastes
6
Research Objective
Quantify the human health
risks associated with
exposures to pathogens in
flood water
Study area: City of Manila
(Pop. – 1.6M as of 2000)
7
8
Exposure to
pathogens
….
8
Calculation of Risk
Distribution of Population (City of Manila,
2000)
POPULATION
<4
years-old
5-14
years-old
15-59
years-old
> 60
years-old
6 11
64
19
0-4 years-old
5-14 years-old
15-59 years-old
>60 years-old
Outdoor timeContact time with flood water*
Intake rate  Ingestion volume*
Dose
*Daily activities and behaviors of each
age groups were based on literature.
**Ingestion volumes were derived from
US-EPA Risk Assessment Guidance
Infection risk
TOTAL RISK
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Inundation scenarios
Level
Inundation depth
Description
I
0-50 cm
Most houses will stay dry and it is still possible to walk
through the water
II
50-100 cm
There will be at least 50cm of water on the ground floor
III
100-200 cm
The ground floor of the houses will be flooded
IV
>200 cm
Both the first floor and often also the roof will be covered
by water
*Classification was based on the Flood Fighting Act, Japan, 2001
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Assumptions
Parameter
Concentration of E. coli
Water Ingestion Rate (indirect
transfer during walking)
Age <4
Age 5-14
Age 15-59
Age >60
Water Ingestion Rate during
swimming
Age 5-14
Age 15-59
Time spent outdoor
Age <4
Age 5-14
Age 15-59
Age >60
Fraction of outdoor time spent in
water
Dose-response model
(Hass equation)
N50
α
Symbol
CE
Unit
Notes
MPN/100ml A mean value of 30,000 MPN/100mL
(18,000-50000) was taken for E.Coli
concentration in flood water (Nga,
Master thesis, 1999)
ml/hour
50 ml/h
10 ml/h
10 ml/h
10 ml/h
ml/hour
T
hours/day
F
%
100 ml/h
50 ml/h
Assumed 2 hours
Assumed 4 hours
Assumed 4 hours
Assumed 1 hour
Assumed, varies according to
inundation levels (50-100%)
8.6x10^7
0.1778
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Pathogen’s dose-response model
Beta Poisson model for E. coli（Haas et al., 1999）
For single infection risk:

d 1/α 
P(d )  1  1 
2 1 
 N 50



α
where d
= dose
α
= slope parameter = 0.1778 (derived)
N50 = medium infectious dose = 8.6×107 (derived)
For annual infection risk:
Pannual  1  1  P(d )
n
where n
= number of exposure times per year
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Single risk and annual risk associated with pathogen
exposure during flooding period
Age Group
0-4 years-old
5-14 years-old
15-59 yearsold
>60 years-old
Total
Risk
Inundation depth (cm)
< 50
50-100
100-200
>200
Daily risk
0.001491
0.002968
0.005879
0.005879
Total risk
0.029407
0.057715
0.111231
0.111231
Daily risk
0.000598
0.001194
0.005879
0.011536
Total risk
0.011898
0.023615
0.111231
0.207095
Daily risk
0.000598
0.001194
0.005879
0.011536
Total risk
0.011898
0.023615
0.111231
0.207095
Daily risk
0.000150
0.000299
0.001491
0.001491
Total risk
0.002992
0.005972
0.029407
0.029407
Daily risk
0.000674
0.001345
0.005631
0.010328
Total risk
0.013398
0.026556
0.106796
0.187491
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Generation of Maps
ArcGIS 9.2 by the US Environment
Research Institute (US ESRI)
 Using the following data provided
by the Metropolitan Manila
Development Authority (MMDA)
to create maps of population
density, inundation and risk
assessments.

barangay boundaries of Metro
Manila as of 2000 with the
barangays in Manila City grouped
together by District;
 statistics of population based on
the census conducted by the
National Statistics Office in 2000;
 and the GRID data of inundation
scenarios
Map
people
Mapof
ofinfected
infectious
people
Map
Mapof
ofdaily
dailyrisk
risk

Inundation
Inundationmap
map
Map
density
Mapof
ofpopulation
population
density
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Map of Population Density
District 1
District 6
District 12
District 14
15
Inundation map
16
Map of Daily Risk and Infected People
17
Map of Daily Risk and Infected People, MM
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Summary

Contact with flood water poses significant human health risks for residents in the floodprone region like Manila, and particularly for poor children and youth.

The risk of contracting gastrointestinal illness due to E. coli from accidental ingestion of
flood water in Manila over the course of a year varies according to inundation levels and
age.

To verify the results, evidence of group behavior during floods, inundation water quality
and natural, social and economic data pertaining to the study area need to be collected.

This study hoped to make a contribution to the quantification of climate change-related
risks, and to simulate further discussion and reflection on methodologies for
undertaking quantitative assessments.

Quantifying such risks can assist in future health planning (i.e., allocating clinic and
health centers to more vulnerable areas) and community-based natural disaster risk
management (i.e., prioritizing areas to respond to during a disaster, locating where to
intensify flood awareness programs, etc)
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References







Donovan E., Unice K., Robert J.D., Harris M., and Finley B. Risk of Gastrointestinal
Disease Associates with Exposure to Pathogens in the Water of the Lower Passaic
River. Applied and Environmental Microbiology, Feb. 2008, p. 994-1003
Dufour A.P, Evans O., Behymer T. D., and Cantu R. Water ingestion during swimming
activities in a pool: a pilot study. J. Water Health 4:425-430
Charles N. Haas, Joan B. Rose, Charles P. Gerba. Quantitative microbiological risk
assessment. John Wiley and Sons, NY, 1999
JICA, 2001. Metro Manila Flood Control Project
Nga T.T.V. Master thesis. Asian Institute of Technology, 1999.
US-EPA. Risk Assessment guidance for Superfund. Vol.1. Human health evaluation
manual (Part A). EPA/540/1-89/002. US-EPA, Washington DC.
Zoleta-Nantes, D. 2002. Differential Impacts of Flood Hazards among the Street
children, the Urban Poor and Residents of Wealthy Neighborhood in Metro Manila,
Philippines. Journal of Mitigation and Adaptation Strategies for Global Change. 7(3):
239-266. The Netherlands: Kluwer Publishing.
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Thank you for your attention!
Terima kasih!