Aims: To determine the association between rainfall rate and occurrence of enterovirus infection related to contamination of drinking water.
Methods and Results: One fatality case and three cases of severe illness were observed during the enterovirus epidemic in a village in southern Taiwan from 16 September to 3 October 1998. Groundwater samples were collected from the public well in the village after heavy rainfall to test for enterovirus using the reverse transcription‐polymerase chain reaction (RT‐PCR) assay. The RT‐PCR assay detected the enterovirus in the groundwater sample collected on 26 September 1998. The logistic regression model also revealed a statistically significant association between the rainfall rate and the observation of cases of enterovirus infection.
Conclusions: According to the fitted logistic regression model, the probability of detecting cases of enterovirus infection was greater than 50% at rainfall rates >31 mm h−1. The higher the rainfall rate, the higher the probability of enterovirus epidemic.
Significance and Impact of the Study: Contamination of drinking water by the enterovirus may lead to epidemics that cause deaths and severe illness, and such contamination may be caused by heavy rainfall. The major finding in this study is that the enterovirus could be flushed to groundwater in an unconfined aquifer after a heavy rainfall. This work allows for a warning level so that an action can be taken to minimize future outbreaks and so protect public health.
Entomological indices have been used to quantitatively express vector density, but the threshold of larval indices of Aedes albopictus in dengue epidemics is still undefined. We conducted a case-control study to identify the thresholds of Aedes albopictus larval indices in dengue epidemics. Two unit levels of analysis were used: district and street. The discriminative power of the indices was assessed by receiver operating characteristic (ROC) curves. The association between the entomologic indices and dengue transmission was further explored by a logistic regression model. At the district level, there was no significant difference in the Breteau index (BI) between districts that reported cases and those did not (t=0.164, p>0.05), but the Container index (CI) did show a significant difference (t=2.028, p<0.01). The AUC (Area Under the Curve) of BI, CI, and prediction value were 0.540, 0.630, and 0.533, respectively. Predicting at the street level, the AUC of BI, CI, and prediction values were 0.684, 0.660, and 0.685, respectively, and 0.861, 0.827, and 0.867 for outbreaks. BI=5.1, CI=5.4, or prediction value =0.491were suggested to control the epidemic efficiently with the fewest resources, where BI=4.0, CI=5.1, or PRE =0.483 were suggested to achieve effectiveness. Journal of Vector Ecology 40 (2): 240-246. 2015.
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