Clusters of spatial, temporal, and space-time distribution of hemorrhagic fever with renal syndrome in Liaoning Province, Northeastern China

<p>Abstract</p> <p>Background</p> <p>Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by Hantavirus, with characteristics of fever, hemorrhage, kidney damage, and hypotension. HFRS is recognized as a notifiable public health problem in China, and Liaoning Province is one of the most seriously affected areas with the most cases in China. It is necessary to investigate the spatial, temporal, and space-time distribution of confirmed cases of HFRS in Liaoning Province, China for future research into risk factors.</p> <p>Methods</p> <p>A cartogram map was constructed; spatial autocorrelation analysis and spatial, temporal, and space-time cluster analysis were conducted in Liaoning Province, China over the period 1988-2001.</p> <p>Results</p> <p>When the number of permutation test was set to 999, Moran's I was 0.3854, and was significant at significance level of 0.001. Spatial cluster analysis identified one most likely cluster and four secondary likely clusters. Temporal cluster analysis identified 1998-2001 as the most likely cluster. Space-time cluster analysis identified one most likely cluster and two secondary likely clusters.</p> <p>Conclusions</p> <p>Spatial, temporal, and space-time scan statistics may be useful in supervising the occurrence of HFRS in Liaoning Province, China. The result of this study can not only assist health departments to develop a better prevention strategy but also potentially increase the public health intervention's effectiveness.</p

Analysis of the geographic distribution of HFRS in Liaoning Province between 2000 and 2005

<p>Abstract</p> <p>Background</p> <p>Hemorrhagic fever with renal syndrome (HFRS) is endemic in Liaoning Province, China, and this province was the most serious area affected by HFRS during 2004 to 2005. In this study, we conducted a spatial analysis of HFRS cases with the objective to determine the distribution of HFRS cases and to identify key areas for future public health planning and resource allocation in Liaoning Province.</p> <p>Methods</p> <p>The annual average incidence at the county level was calculated using HFRS cases reported between 2000 and 2005 in Liaoning Province. GIS-based spatial analyses were conducted to detect spatial distribution and clustering of HFRS incidence at the county level, and the difference of relative humidity and forestation between the cluster areas and non-cluster areas was analyzed.</p> <p>Results</p> <p>Spatial distribution of HFRS cases in Liaoning Province from 2000 to 2005 was mapped at the county level to show crude incidence, excess hazard, and spatial smoothed incidence. Spatial cluster analysis suggested 16 and 41 counties were at increased risk for HFRS (p < 0.01) with the maximum spatial cluster sizes at ≤ 50% and ≤ 30% of the total population, respectively, and the analysis showed relative humidity and forestation in the cluster areas were significantly higher than in other areas.</p> <p>Conclusion</p> <p>Some clustering of HFRS cases in Liaoning Province may be etiologically linked. There was strong evidence some HFRS cases in Liaoning Province formed clusters, but the mechanism underlying it remains unknown. In this study we found the clustering was consistent with the relative humidity and amount of forestation, and showed data indicating there may be some significant relationships.</p

Resonances in J/ψ→ϕπ+π−J/\psi \to \phi \pi ^+\pi ^- and ϕK+K−\phi K^+K^-

A partial wave analysis is presented of $J/\psi \to \phi \pi ^+\pi ^-$ and $\phi K^+K^-$ from a sample of 58M $J/\psi$ events in the BES II detector. The $f_0(980)$ is observed clearly in both sets of data, and parameters of the Flatt\' e formula are determined accurately: $M = 965 \pm 8$ (stat) $\pm 6$ (syst) MeV/c$^2$, $g_1 = 165 \pm 10 \pm 15$ MeV/c$^2$, $g_2/g_1 = 4.21 \pm 0.25 \pm 0.21$. The $\phi \pi \pi$ data also exhibit a strong $\pi \pi$ peak centred at $M = 1335$ MeV/c$^2$. It may be fitted with $f_2(1270)$ and a dominant $0^+$ signal made from $f_0(1370)$ interfering with a smaller $f_0(1500)$ component. There is evidence that the $f_0(1370)$ signal is resonant, from interference with $f_2(1270)$. There is also a state in $\pi \pi$ with $M = 1790 ^{+40}_{-30}$ MeV/c$^2$ and $\Gamma = 270 ^{+60}_{-30}$ MeV/c$^2$; spin 0 is preferred over spin 2. This state, $f_0(1790)$, is distinct from $f_0(1710)$. The $\phi K\bar K$ data contain a strong peak due to $f_2'(1525)$. A shoulder on its upper side may be fitted by interference between $f_0(1500)$ and $f_0(1710)$.Comment: 17 pages, 6 figures, 1 table. Submitted to Phys. Lett.

Measurement of the Branching Fraction of J/psi --> pi+ pi- pi0

Using 58 million J/psi and 14 million psi' decays obtained by the BESII experiment, the branching fraction of J/psi --> pi+ pi- pi0 is determined. The result is (2.10+/-0.12)X10^{-2}, which is significantly higher than previous measurements.Comment: 9 pages, 8 figures, RevTex

Search for K_S K_L in psi'' decays

K_S K_L from psi'' decays is searched for using the psi'' data collected by BESII at BEPC, the upper limit of the branching fraction is determined to be B(psi''--> K_S K_L) < 2.1\times 10^{-4} at 90% C. L. The measurement is compared with the prediction of the S- and D-wave mixing model of the charmonia, based on the measurements of the branching fractions of J/psi-->K_S K_L and psi'-->K_S K_L.Comment: 5 pages, 1 figur

First observation of psi(2S)-->K_S K_L

The decay psi(2S)-->K_S K_L is observed for the first time using psi(2S) data collected with the Beijing Spectrometer (BESII) at the Beijing Electron Positron Collider (BEPC); the branching ratio is determined to be B(psi(2S)-->K_S K_L) = (5.24\pm 0.47 \pm 0.48)\times 10^{-5}. Compared with J/psi-->K_S K_L, the psi(2S) branching ratio is enhanced relative to the prediction of the perturbative QCD ``12%'' rule. The result, together with the branching ratios of psi(2S) decays to other pseudoscalar meson pairs (\pi^+\pi^- and K^+K^-), is used to investigate the relative phase between the three-gluon and the one-photon annihilation amplitudes of psi(2S) decays.Comment: 5 pages, 4 figures, 2 tables, submitted to Phys. Rev. Let

First Measurements of eta_c Decaying into K^+K^-2(pi^+pi^-) and 3(pi^+pi^-)

The decays of eta_c to K^+K^-2(pi^+pi^-) and 3(pi^+pi^-) are observed for the first time using a sample of 5.8X10^7 J/\psi events collected by the BESII detector. The product branching fractions are determined to be B(J/\psi-->gamma eta_c)*B(eta_c-->K^+K^-pi^+pi^-pi^+pi^-)=(1.21+-0.32+- 0.23)X10^{-4}$,B(J/\psi-->gamma eta_c)*B(eta_c-->K^{*0}\bar{K}^{*0}pi^+pi^-)= (1.29+-0.43+-0.32)X10^{-4}$, and (J/\psi-->gamma eta_c)* B(eta_c-->pi^+pi^-pi^+pi^-pi^+pi^-)= (2.59+-0.32+-0.48)X10^{-4}. The upper limit for eta_c-->phi pi^+pi^-pi^+pi^- is also obtained as B(J/\psi-->gamma eta_c)*B(eta_c--> phi pi^+pi^-pi^+pi^-)< 6.03 X10^{-5} at the 90% confidence level.Comment: 11 pages, 4 figure

Study of psi(2S) decays to X J/psi

Using J/psi -> mu^+ mu^- decays from a sample of approximately 4 million psi(2S) events collected with the BESI detector, the branching fractions of psi(2S) -> eta J/psi, pi^0 pi^0 J/psi, and anything J/psi normalized to that of psi(2S) -> pi^+ pi^- J/psi are measured. The results are B(psi(2S) -> eta J/psi)/B(psi(2S) -> pi^+ pi^- J/psi) = 0.098 \pm 0.005 \pm 0.010, B(psi(2S) -> pi^0 pi^0 J/psi)/B(psi(2S) -> pi^+ pi^- J/psi) = 0.570 \pm 0.009 \pm 0.026, and B(psi(2S) -> anything J/psi)/B(psi(2S) -> pi^+ pi^- J/psi) = 1.867 \pm 0.026 \pm 0.055.Comment: 13 pages, 8 figure

Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China

<p>Abstract</p> <p>Background</p> <p>Hemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution, external environmental factors including climate factors may play a significant role in its transmission. The city of Shenyang is one of the most seriously endemic areas for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified climate-related risk factors and their roles in HFRS transmission in Shenyang, China.</p> <p>Methods</p> <p>The annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal component analysis was constructed by using climate data from 2004 to 2009 to extract principal components of climate factors to reduce co-linearity. The extracted principal components and autocorrelation terms of monthly HFRS cases were added into a multiple regression model called principal components regression model (PCR) to quantify the relationship between climate factors, autocorrelation terms and transmission of HFRS. The PCR model was compared to a general multiple regression model conducted only with climate factors as independent variables.</p> <p>Results</p> <p>A distinctly declining temporal trend of annual HFRS incidence was identified. HFRS cases were reported every month, and the two peak periods occurred in spring (March to May) and winter (November to January), during which, nearly 75% of the HFRS cases were reported. Three principal components were extracted with a cumulative contribution rate of 86.06%. Component 1 represented MinRH₀, MT₁, RH₁, and MWV₁; component 2 represented RH₂, MaxT₃, and MAP₃; and component 3 represented MaxT₂, MAP₂, and MWV₂. The PCR model was composed of three principal components and two autocorrelation terms. The association between HFRS epidemics and climate factors was better explained in the PCR model (<it>F </it>= 446.452, <it>P </it>< 0.001, adjusted <it>R</it>²= 0.75) than in the general multiple regression model (<it>F </it>= 223.670, <it>P </it>< 0.000, adjusted <it>R</it>²= 0.51).</p> <p>Conclusion</p> <p>The temporal distribution of HFRS in Shenyang varied in different years with a distinctly declining trend. The monthly trends of HFRS were significantly associated with local temperature, relative humidity, precipitation, air pressure, and wind velocity of the different previous months. The model conducted in this study will make HFRS surveillance simpler and the control of HFRS more targeted in Shenyang.</p