Study on the spatiotemporal trend of Japanese encephalitis in Guangxi,based on geographic information system and space-time permutation scan statistic / 中华流行病学杂志

Objective To study the spatiotemporal trend of Japanese encephalitis in Guangxi Zhuang Autonomous Region between 1989 and 2006.Methods Retrospective space-time permutation scan statistic and inverse distance weighted (IDW) interpolation were employed to detect the spatiotemporal trend of Japanese encephalitis in Guangxi,from the year 1989 to 2006.Results The spatiotemporal pattern of Japanese encephalitis was divided into four phases by IDW interpolation maps,from 1989 to 2006.The first phase was spatiotemporal cluster located in southeast region,from 1989 to 1996.The second phase showed discrete distribution from 1997 to 1998.The third phase of spatiotemporal cluster located in Lingshan county,Pubei county and Bobai county,in 1999.And the last phase was spatiotemporal cluster located in northwest region from 2000 to 2006.Three statistically significant spatiotemporal clusters were detected by retrospective space-time permutation scan statistic.The primary cluster appeared in 1999 (LLR=253.25,P=0.001,RR=4.62),with 109°54′ E,22°28′ N (located in Pubei county) as its center and radiated 45.24 km.From 2000 to 2006,the secondary cluster showed in northwest (LLR=75.91,P=0.001,RR = 1.88),with center located at 105°23′ E,24°68′ N (Longlin county),and radiated 199.85 kn.From 1989 to 1996,the other secondary cluster appeared in the southeast area(LLR=46.29,P=0.001,RR= 1.16),with center located at 110°94′ E,24°03′N(Zhaoping county) and radiated 229.12 km.Conclusion Space-time permutation scan statistic and geographical information system could be applied to quantitatively detect the potentially spatiotemporal trend of the disease.The spatiotemporal cluster shifted from southeast to northwest,from 1989 to 2006.

Comparison between early outbreak detection models and simulated Outbreaks of measles in Beijing / 中华流行病学杂志

Objective Using simulated outbreaks to choose the optimal model and its related parameters on measles so as to provide technical support for developing an Auto Warning System(AWS).Methods AEGiS-Cluster Creation Tool was applied to simulate a range oftmique outbreak signals.Then these simulations were added to the aetnal daily counts of measles from the National Disease Surveillance System,between 2005 and 2007.Exponential weighted moving average(EWMA),C1-MILD(C1),C2.MEDIUM(C2).C3-ULTRA(C3)and space.time permutation scar statistic model were comprehensively applied to detect these simulations.Tools for evaluation as Youden's index and detection time were calculated to optimize parameters before an optimal model was finally chosen.Results EWMA(λ=0.6,κ=1.0),C1(κ=0.1,H=3σ),C2(k=0.1,H=30),C3(κ=1.0,H=4σ)and space-time permutation scan statistic(maximum temporal cluster size=7 d,maximum spatial cluster size=5 km)appeared to be the optimal parameters among these models.Youden's index of EWMA was 90.8%and detection time being 0.121 d.Youden's index of C1 was 88.7%and detection time being 0.142 d.Youden's index of C2 was 92.9%and detection time being 0.121 d.Youden's index of C3 was 87.9%and detection time being 0.058 d.Youden's index of space-time permutation scan statistic was 94.3%and detection time being 0.176 d.Conelusion Among these five early warning detection models.space-time permutation scan statistic model had the highest efficacy.