Medical Entomology Unit & WHO Collaborating Centre for Vectors, Institute for Medical Research, Kuala Lumpur, Malaysia.
School of Quantitative Sciences, Universiti Utara Malaysia, Sintok, Kedah, Malaysia.
PLoS One. 2018 Feb 23;13(2):e0193326. doi: 10.1371/journal.pone.0193326. eCollection 2018.
A large scale study was conducted to elucidate the true relationship among entomological, epidemiological and environmental factors that contributed to dengue outbreak in Malaysia. Two large areas (Selayang and Bandar Baru Bangi) were selected in this study based on five consecutive years of high dengue cases. Entomological data were collected using ovitraps where the number of larvae was used to reflect Aedes mosquito population size; followed by RT-PCR screening to detect and serotype dengue virus in mosquitoes. Notified cases, date of disease onset, and number and type of the interventions were used as epidemiological endpoint, while rainfall, temperature, relative humidity and air pollution index (API) were indicators for environmental data. The field study was conducted during 81 weeks of data collection. Correlation and Autoregressive Distributed Lag Model were used to determine the relationship. The study showed that, notified cases were indirectly related with the environmental data, but shifted one week, i.e. last 3 weeks positive PCR; last 4 weeks rainfall; last 3 weeks maximum relative humidity; last 3 weeks minimum and maximum temperature; and last 4 weeks air pollution index (API), respectively. Notified cases were also related with next week intervention, while conventional intervention only happened 4 weeks after larvae were found, indicating ample time for dengue transmission. Based on a significant relationship among the three factors (epidemiological, entomological and environmental), estimated Autoregressive Distributed Lag (ADL) model for both locations produced high accuracy 84.9% for Selayang and 84.1% for Bandar Baru Bangi in predicting the actual notified cases. Hence, such model can be used in forestalling dengue outbreak and acts as an early warning system. The existence of relationships among the entomological, epidemiological and environmental factors can be used to build an early warning system for the prediction of dengue outbreak so that preventive interventions can be taken early to avert the outbreaks.
进行了一项大规模研究，以阐明导致马来西亚登革热疫情的昆虫学、流行病学和环境因素之间的真实关系。本研究基于连续五年高登革热病例，选择了两个大区（塞拉央和班达巴鲁班吉）。使用诱卵器收集昆虫学数据，幼虫数量反映了伊蚊种群规模；随后进行 RT-PCR 筛查，以检测和血清分型蚊子中的登革热病毒。通知病例、发病日期以及干预措施的数量和类型用作流行病学终点，而降雨量、温度、相对湿度和空气污染指数（API）则作为环境数据的指标。实地研究在 81 周的数据收集期间进行。使用相关和自回归分布滞后模型来确定关系。研究表明，通知病例与环境数据间接相关，但滞后一周，即最后 3 周的阳性 PCR；最后 4 周的降雨量；最后 3 周的最大相对湿度；最后 3 周的最高和最低温度；以及最后 4 周的空气污染指数（API）。通知病例还与下周的干预措施有关，而常规干预措施仅在发现幼虫后 4 周才发生，表明有足够的时间进行登革热传播。基于三个因素（流行病学、昆虫学和环境）之间的显著关系，对两个地点进行的估计自回归分布滞后（ADL）模型都产生了很高的准确性，塞拉央为 84.9%，班达巴鲁班吉为 84.1%，可用于预测实际通知病例。因此，该模型可用于预防登革热爆发，并作为预警系统。昆虫学、流行病学和环境因素之间存在关系，可以用于建立登革热爆发预测的预警系统，以便及早采取预防干预措施，避免爆发。
