1 Quantitative Risk Assessment of Infectious Diseases caused by Waterborne E.coli during Flood in Cities of Developing Countries The 2nd International Symposium on Vietnam wAter Cooperation Initiative for livable cities Monday, 16/12/2013, Hanoi, Vietnam Tran Thi Viet NGA, Hanoi University of Civil Engineering, Vietnam Mai HOANG THI, Toru WATANABE, Kensuke FUKUSHI, University of Tokyo, Japan

2 2 Pathogens Infection Low level sanitation Low education level – lack of knowledge on infection Low accessibility to medication Low level sanitation Low education level – lack of knowledge on infection Low accessibility to medication Climate change: Increasing extreme event Rapid urbanization increases inundation Removal of excess water need power (pump) Many Asian cities are developed in low land at coastal area Climate change: Increasing extreme event Rapid urbanization increases inundation Removal of excess water need power (pump) Many Asian cities are developed in low land at coastal area Septic tank Background

3 Background Waterborne diseases are defined as diseases infecting people through water contaminated by pathogens. Waterborne disease epidemics: a massive outbreak of watery diarrhea causing by cryptosporidium oocysts occurred among 403,000 residents in Milwaukee, USA in 1993 ( Mackenzie,1994). Diarrhea after weather-related or flood-related naturals disasters tend to increase (Kunii et al.,2002,Ivers et al., 2006, WHO, 2006 ). Diarrhea outbreak after natural disasters in developing countries is more severe than that in developed countries (Bissell, 1983, Ivers et al., 2006). In the historical flood in the Middle of Vietnam (1999), it is said that there were about 10,000 people reporting diarrhea(CNDC,1999). E.Coli concentration in flood water ： 4.6×10 5 ～ 2.2×10 7 MPN/100mL Health risk assessment of infectious diseases related to flood is important.

4 Urban inundation 4 Flood in Hue, Sep Photo provided by Quan Direct effects Personal property damage Life loss Infrastructure damage

5 Walking Cooking Cleaning Moving furniture Washing Pathogen Infection

6 Health risk assessment How many pathogenic organisms get into the human body?  Pathogen concentration of environment (drinking water, showering water, flood water, water on/in food etc.)  Level of exposure (time of showering, amount of drinking water etc.) Dose-response relationship 6

7 Research Objective and Area In infection risk assessment, infrastructure of a certain area, people behavior patterns, culture or people susceptibility to pathogens… are needed To develop risk assessment model to evaluate infection through water in urban areas and apply it during flooding periods Research Objective Research area selection The Old Town of Hue city, Vietnam  Area: 5.21km 2, population:65,270 people (2006)  Precipitation in Oct. and Nov. 1,200 ~ 1600 mm (30% annual precipitation)  Developing country  Low improvement of infrastructure  Prevalence of target disease ( diarrhea)

8 Research area Sewage pipes system Area without sewage pipes system Drain outlet Cultivation in lakes Sewage pipes system in Old Town Solid waste disposed to the lake Slow flood withdrawal, long inundation time No sewage works Sewage pipes system Drainage is discharged directly to rivers, ponds, soil. Improvement of sewage pipes system is under construction Lakes system 43 lakes built around 200 years ago for climatic consistency and flood drainage off, but now are not dredged regularly

9 Household effluent treatment system Landfill Human-waste pond Fish farming pond House Septic tank Human waste collecting truck 3 chambers - septic tank conformation 2 chambers -septic tank conformation

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11 Model structure Pollution source Water Concentration of pathogen in water environment （ CFU/mL) Drinking, eating （ drinking water, raw vegetable…) Domestic water Moving furniture,cooking, playing in the flood… Feces Feces vomit Direct contact Flood time Normal time Eating, drinking （ drinking water, drinking ice, raw vegetable… ） Washing, bathing, swimming, fishing… Pathogens containing flow from septic tank Flood time

12 Infection risk calculation method Amount of raw vegetable eating at home (g) Amount of ice drinking at home (mL) Flood water intake amount by moving furniture (mL) Flood water intake amount by cooking in the flood (mL) Concentration of E.coli in raw vegetable (CFU/ ｇ ) Concentration of E.coli in ice (CFU/ ｇ ) Concentration of E.coli in flood water （ CFU/mL ） Concentration of E.Coli in flood water （ CFU/mL ） Intake number of E.Coli by eating raw vegetable at home Intake number of E.coli by drinking ice at home Intake number of E.Coli by moving furniture in the flood Intake number of E.coli by cooking In the flood Infection risk by moving furniture in the flood Infection risk by drinking ice at home Infection risk by eating raw vegetable at home Infection risk by cooking in the flood Intake number of E.coli in flooding time Total infection risk in flooding time ….....

13 People behavior survey  Number of interviewees: 1021 people (number of cases using for analysis ： 989 people ）  House without second floor or loft ： 54.8%  Average number of inundation times in 2007 ： 3.5 times, average inundation days: 6.5 days  Household water ： tap water (98%), well water, rain water, lake water (3%)  Drinking water Normal time: tap water (85.1%), filtered water or bottle water (15.9%)  Flood time: tap water (69.1%), filtered water or bottle water (25.3%), rain water (4.7%) Well water Filtered water

14 Concentration of fecal indicator organisms in water environments Results above are geometric mean values NM: not measured Sample No of samples E..coliTotal ColiformsFecal Coliforms UnitCFU/100mL(×10 2 ) Tap water3< NM Filtered water1<0.011NM Drinking ice Made at home1< NM Bought at drinking shop NM Bought at ice shop 40.03>30NM Well water20.09>30.3 Swimming pool water1< <0.01 River water Moat water310>30076 Lake water Irrigation water NM Inundation water3290>

15 Pathogen’s dose-response model Beta Poison model for E.Coli （ Haas et al., 1999 ） d: dose α = N50 = 8.6×10 7 Single infection risk Annual infection risk n: number of exposure times per year

16 Infection risk calculation result using survey data Action in normal time Infection risk per 24 hours （ ×10 -4 ) Annual infection risk （ ×10 -4 ） Eating raw vegetable at home Eating raw vegetable at shop0.259 Fishing Swimming0.029 Drinking ice at shop Drinking ice at home Total risk in normal time Action in flood time Infection risk per 24 hours （ ×10 -4 )Annual infection risk （ ×10 -4 ） Moving furniture in flood Cooking in flood Playing in flood Going shopping in flood Going to work in flood50322 Eating raw vegetable in flood26165 Swimming in the flood213 Drinking ice in the flood Total risk in flood time

17 Infection risk simulation result Action in normal timeSimulation resultCalculation result Eating raw vegetable at home Eating raw vegetable at shop Swimming189 Fishing1215 Drinking ice at shop10.4 Drinking ice at home10.2 Total infection risk in normal time Actions in flood timeSimulation resultCalculation result Moving furniture in flood Playing in flood Going shopping in flood Cooking in flood Going to work in flood Eating raw vegetable at home Swimming in flood19113 Drinking ice at home Total risk in flood time （ Annual infection risk×10 -4 ）

18 Flood induced diseases among the urban poor Case of Metro Manila, Philippines

19 Flood Prone Areas in Metro Manila West Mangahan Area Pasig-Marikina Basin KAMANAVA Area

20 Metro Manila - Inundation map A1F1 inundation mapStatus-quo Inundation map

21 Metro Manila - Inundation map Not covered in our model as it is not overflow from Pasig-Marikina River Flood due to insufficient drainage Not covered in our model due to lack of lateral profile data Area not covered in our analysis

22 Estimated daily risk and number of infected people in Metro Manila

23 Conclusions  Results from the survey of water behavior of people living in the Citadel, Hue city elucidate the transmission routes of infectious diseases.  In the normal time, the highest level of risk is due to eating raw vegetables at home or restaurants. Annual risk of eating raw vegetables is 0.018, accounted for 69% of total risk in no-flooding time.  Annual average risk during flood is 0.45, which is 17 times higher than the risk in no-flooding time. The highest risk moving in flood time is moving furniture or cleaning in flood water.  As with any risk assessment, there are many sources of uncertainty in this analysis. An important assumption was applied here is that all E. coli in the water are pathogenic. Among E. coli, there are many non-pathogenic bacteria, so the results of risk studies may significant larger than that in the fact.

24 Thank you for your listening ! Measurable health effects associated with water related- activities in flood waters (Cabelli, 1982)