Evaluation of diarrhea disease risk attributed to inundation water use on a local scale in Cambodia using hydrological model simulations
Amano, Ayako; Sakuma, Taisuke; Kazama, So; Gunawardhana, Luminda
Diarrhea disease in Cambodia is caused mainly by regular seasonal flooding in the lower Mekong River basin. This study simulated diarrhea infectious disease risk attributed to inundation water use for drinking purposes on a local scale in Cambodia and compared the results with those obtained from regional-scale simulations. A distributed flood simulation model, which consisted of a dynamic wave model and a non-uniform flow model, was employed, along with a coliform bacteria diffusion model and a dose-response model. The model results were independently verified with field-observed coliform bacteria concentrations during the dry and flood seasons. According to our results, the daily average risk for local-scale analysis is approximately two-to-three times greater for different local settlements than the estimated risk on a regional scale. On a local scale, diarrhea disease risk depends on specific human behaviors (i.e., the load of fecal coliform input to the inundation area) and geographical arrangements in different residential settings (i.e., facility for fast-receding flood water). As such, the time difference to decrease the maximum risk by 50 % is as much as twice as high for different residential settings. A significant positive correlation (R2 = 0.83) was obtained between the model-simulated annual incidence risk of diarrhea and outpatients' diarrhea cases estimated from public health center records. It was also found that the regional model could explain approximately 34 % of the diarrhea cases estimated by outpatient records but that local-scale risk estimations could closely match outpatient records. Our study suggests that regional risk assessments of waterborne diseases are important for understanding relative risk distributions in a large inundation area but that detailed local-scale analysis are needed to predict the most likely consequences and plan countermeasures.