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Background: In May 2012, an outbreak of viral hepatitis A was reported to the Guangxi Center for Disease Control and Prevention from a middle school in Liujiang County. An investigation was conducted to identify the cause and mode of transmission and to recommend control and prevention measures.Methods: A case was defined as any person from the middle school with onset of fatigue, anorexia, abdominal pain, diarrhoea or jaundice from 20 February to 20 May 2012. We compared attack rates (AR) between boys and girls, assuming that only boys used well water and girls used pipeline water. We then selected 133 students from three classes in each of the three grades to compare AR by reported water source and drinking history.Results: There were 22 cases, an AR of 3.8% (21/553) for students and 1.5% for teachers (1/65). Those who used well water were 8.7 (95% confidence interval [CI] = 2.1–37.2) times more likely to be ill than those using pipeline water. The cohort study showed that students who reported using well water daily were 5.2 (95% CI = 0.7–41.8) times more likely to be ill than those that reported using the pipeline water daily. Eighteen cases were confirmed as hepatitis A.Conclusion: This hepatitis A outbreak was potentially caused by a contaminated school well. We recommended that the school discontinue using the well and that the students should drink boiled water. As there is a vaccine for hepatitis A, we recommended that several doses of the vaccine be stored for controlling outbreaks and for immunizing susceptible populations in future outbreaks.

Contamination of well with nitrate-N (NO3-N) possess various threats to human health. This problem becomes even more critical when these wells serve as source of drinking water as in the case of many rural parts of USA. This article employs Relevance Vector Machine (RVM) for determination of non-contaminated and contaminated well with nitrate-N (NO3-N) in Polk County, Florida (USA). This research will provide a regional scale integrated GIS-based modeling approach to predict NO3-N contamination of ground water in a cost effective way. This approach also allows for higher true positive results (TPR) with fewer variables when data are imprecise and full of uncertainty which is common with available regional scale data). RVM technique is a Bayesian extension of the Support Vector Machine (SVM). Here, the RVM has been used as a classification tool. Well water quality data (nitrate-N) from 6,917 wells provided by Florida Department of Environmental Protection (USA) has been used to develop the RVM model. An equation has been also presented from the developed RVM model. The developed RVM has been compared with the Artificial Neural Network (ANN) and SVM models. This study shows that the developed RVM produces promising result for prediction of non-contaminated and contaminated well with N. The model is important because its real world applications enable water managers to more effectively manage contaminant levels within specific watersheds. [ABSTRACT FROM AUTHOR]