2019-nCoV: new challenges from coronavirus / 中华预防医学杂志

The outbreak of pneumonia caused by the novel coronavirus 2019-nCoV in Wuhan, Hubei province of China, at the end of 2019 shaped tremendous challenges to China's public health and clinical treatment. The virus belongs to the β genus Coronavirus in the family Corornaviridae, and is closely related to SARS-CoV and MERS-CoV, causing severe symptoms of pneumonia. The virus is transmitted through droplets, close contact, and other means, and patients in the incubation period could potentially transmit the virus to other persons. According to current observations, 2019-nCoV is weaker than SARS in pathogenesis, but has stronger transmission competence; it's mechanism of cross-species spread might be related with angiotensin-converting enzyme Ⅱ (ACE2), which is consistent with the receptor SARS-CoV. After the outbreak of this disease, Chinese scientists invested a lot of energy to carry out research by developing rapid diagnostic reagents, identifying the characters of the pathogen, screening out clinical drugs that may inhibit the virus, and are rapidly developing vaccines. The emergence of 2019-nCoV reminds us once again of the importance of establishing a systematic coronavirus surveillance network. It also poses new challenges to prevention and control of the emerging epidemic and rapidly responses on scientific research.

Study on the influence of landscape elements regarding on the transmission of hemorrhagic fever with renal syndrome in Changsha / 中华预防医学杂志

<p><b>OBJECTIVE</b>To explore the influence of landscape elements on the transmission of hemorrhagic fever with renal syndrome (HFRS) in Changsha.</p><p><b>METHODS</b>A total of 327 cases of HFRS diagnosed between year 2005 - 2009 were recruited in the study. Based on the demographic data, meteorological data and the data of second national land survey during the same period, a GIS landscape elements database of HFRS at the township scale of Changsha was established. Spatial-temporal cluster analysis methods were adopted to explore the influence of landscape elements on the spatial-temporal distribution of HFRS in Changsha during the year of 2005 - 2009.</p><p><b>RESULTS</b>The annual incidences of HFRS in Changsha between year 2005 - 2009 were 1.16/100 000 (70 cases), 0.95/100 000 (58 cases), 1.40/100 000(87 cases), 0.75/100 000(47 cases) and 1.02/100 000(65 cases) respectively. The results of poisson regression model analysis of principal component showed that the incidence of HFRS was positively correlated with farmland area (M = 29.00 km2) and urban and rural area (M = 6.12 km2; incidence rate ratios (IRR) = 1.34, 95% CI: 1.27 - 1.41); but negatively correlated with forestland area (M = 39.00 km2; IRR = 0.67, 95% CI: 0.55 - 0.81) and garden plot area (M = 0.99 km2; IRR = 0.74, 95% CI: 0.63 - 0.86). A significant cluster of the spatial-temporal distribution of HFRS cases was found in the study. The primary cluster (28.9 N, 113.37 E, radius at 22.22 km, RR = 5.23, log likelihood ratio (LLR) = 51.61, P <0.01, 67 cases of HFRS and incidence at 4.4/100 000) was found between year 2006 and 2007; and the secondary cluster (28.2 N, 113.6 E, RR = 10.77, LLR = 16.01, P < 0.01, 11 cases of HFRS and the incidence at 10.6/100 000) was found between year 2008 and 2009.</p><p><b>CONCLUSION</b>The landscape elements were found to be closely related to the prevalence and transmission of HFRS.</p>

Study on sensitivity of climatic factors on influenza A (H1N1) based on classification and regression tree and wavelet analysis / 中华预防医学杂志

<p><b>OBJECTIVE</b>To analyze the periodicity of pandemic influenza A (H1N1) in Changsha in year 2009 and its correlation with sensitive climatic factors.</p><p><b>METHODS</b>The information of 5439 cases of influenza A (H1N1) and synchronous meteorological data during the period between May 22th and December 31st in year 2009 (223 days in total) in Changsha city were collected. The classification and regression tree (CART) was employed to screen the sensitive climatic factors on influenza A (H1N1); meanwhile, cross wavelet transform and wavelet coherence analysis were applied to assess and compare the periodicity of the pandemic disease and its association with the time-lag phase features of the sensitive climatic factors.</p><p><b>RESULTS</b>The results of CART indicated that the daily minimum temperature and daily absolute humidity were the sensitive climatic factors for the popularity of influenza A (H1N1) in Changsha. The peak of the incidence of influenza A (H1N1) was in the period between October and December (Median (M) = 44.00 cases per day), simultaneously the daily minimum temperature (M = 13°C) and daily absolute humidity (M = 6.69 g/m(3)) were relatively low. The results of wavelet analysis demonstrated that a period of 16 days was found in the epidemic threshold in Changsha, while the daily minimum temperature and daily absolute humidity were the relatively sensitive climatic factors. The number of daily reported patients was statistically relevant to the daily minimum temperature and daily absolute humidity. The frequency domain was mostly in the period of (16 ± 2) days. In the initial stage of the disease (from August 9th and September 8th), a 6-day lag was found between the incidence and the daily minimum temperature. In the peak period of the disease, the daily minimum temperature and daily absolute humidity were negatively relevant to the incidence of the disease.</p><p><b>CONCLUSION</b>In the pandemic period, the incidence of influenza A (H1N1) showed periodic features; and the sensitive climatic factors did have a "driving effect" on the incidence of influenza A (H1N1).</p>

The warning model and influence of climatic changes on hemorrhagic fever with renal syndrome in Changsha city / 中华预防医学杂志

<p><b>OBJECTIVE</b>To realize the influence of climatic changes on the transmission of hemorrhagic fever with renal syndrome (HFRS), and to explore the adoption of climatic factors in warning HFRS.</p><p><b>METHODS</b>A total of 2171 cases of HFRS and the synchronous climatic data in Changsha from 2000 to 2009 were collected to a climate-based forecasting model for HFRS transmission. The Cochran-Armitage trend test was employed to explore the variation trend of the annual incidence of HFRS. Cross-correlations analysis was then adopted to assess the time-lag period between the climatic factors, including monthly average temperature, relative humidity, rainfall and Multivariate Elño-Southern Oscillation Index (MEI) and the monthly HFRS cases. Finally the time-series Poisson regression model was constructed to analyze the influence of different climatic factors on the HFRS transmission.</p><p><b>RESULTS</b>The annual incidence of HFRS in Changsha between 2000 - 2009 was 13.09/100 000 (755 cases), 9.92/100 000 (578 cases), 5.02/100 000 (294 cases), 2.55/100 000 (150 cases), 1.13/100 000 (67 cases), 1.16/100 000 (70 cases), 0.95/100 000 (58 cases), 1.40/100 000 (87 cases), 0.75/100 000 (47 cases) and 1.02/100 000 (65 cases), respectively. The incidence showed a decline during these years (Z = -5.78, P < 0.01). The results of Poisson regression model indicated that the monthly average temperature (18.00°C, r = 0.26, P < 0.01, 1-month lag period; IRR = 1.02, 95%CI: 1.00 - 1.03, P < 0.01), relative humidity (75.50%, r = 0.62, P < 0.01, 3-month lag period; IRR = 1.03, 95%CI: 1.02 - 1.04, P < 0.01), rainfall (112.40 mm, r = 0.25, P < 0.01, 6-month lag period; IRR = 1.01, 95CI: 1.01 - 1.02, P = 0.02), and MEI (r = 0.31, P < 0.01, 3-month lag period; IRR = 0.77, 95CI: 0.67 - 0.88, P < 0.01) were closely associated with monthly HFRS cases (18.10 cases).</p><p><b>CONCLUSION</b>Climate factors significantly influence the incidence of HFRS. If the influence of variable-autocorrelation, seasonality, and long-term trend were controlled, the accuracy of forecasting by the time-series Poisson regression model in Changsha would be comparatively high, and we could forecast the incidence of HFRS in advance.</p>

Spatio-temporal process and the influencing factors on influenza A (H1N1) pandemic in Changsha / 中华流行病学杂志

Objective To analyze the spatio-temporal process on 2009 influenza A (HlNl) pandemic in Changsha and the influencing factors during the diffusion process. Methods Data were from the following 5 sources, influenza A (HlNl) pandemic gathered in 2009, Geographic Information System (GIS) of Changsha, the broad range of theorems and techniques of hot spot analysis, spatio-temporal process analysis and Spearman correlation analysis. Results Hot spot areas appeared to be more in the economically developed areas, such as cities and townships. The cluster of spatial-temporal distribution of influenza A (HlNl) pandemic was most likely appearing in Liuyang city (RR=22.70,P<0.01). The secondary cluster would include districts as Yuelu (RR=6A9,P< 0.01) , Yuhua (RR=81.63, P<0.01). Xingsha township appeared as the center in the Changsha county (RR=2.90, P<0.01) while townships as Yutangping (RR=19.31, P<0.01) , Chengjiao (RR=73.14,P<0.01) and Longtian appeared as the center in the west of Ningxiang county (RR= 14.43,P<0.01) and Wushan as the center in the Wangcheng county (RR= 13.84,P<0.01). As time went on, the epidemic moved towards the eastern and more developed regions. Regarding factor analysis, population, the amount of students, geographic relationship and business activities etc. appeared to be the key elements influencing the transmission of influenza A (H1N1) pandemic. At the beginning of the epidemic, population density served as the main factor (r=0.477, P<0.05) but during the initial and fast growing stages, it was replaced by the size of students to serve as the important indicator (r=0.831, P<0.01; r=0.518, P<0.01). However, during the peak of the epidemics, the business activities played an important role (r=-0.676, P<0.01). Conclusion Groups under high risk and districts with high incidence rates were shifting, along with the temporal process of influenza A(H1N1) pandemic, suggesting that the protection measures need to be adjusted, according to the significance of influencing factors at different stages.

Study on the spread of influenza A (H1N1) under community based simulation model / 中华流行病学杂志

A simulation experiment was carried out by applying the simulation model to spread of influenza A (H1N1) in communities with different population density. Population at the community-level was divided into susceptible, infected and recovered ones, according to the susceptive-infective-removal (SIR) model, and the age structure of the population was set on the basis of data from the Fifth Population Census. Contact and moving of the individuals were based on the Network Random Contact Model and the mortality and infection mode were established in line with the influenza A (H 1N 1) medical description. The results of an example analysis showed that the infection rate was closely related to the density of the community-based population while the rate on early infection grew rapidly. Influenza A (H1N1) seemed more likely to break out in the community with population density of over 50/hm2. Comparative tests showed that vaccination could effectively restrain the spread of influenza A (H1N1) at the community level. Conclusion Population density,and the coverage of influenza vaccination were risk factors for influenza A (H1N1) epidemics.Results of the experiment showed of value, for prevention and vaccination on this topic.