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As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff’s methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff’s statistics for clusters of high population density or large size; otherwise Kulldorff’s statistics are superior. [ABSTRACT FROM AUTHOR]

Several approaches have been proposed for near real-time detection and prediction of the spread of influenza. These include search query data for influenza-related terms, which has been explored as a tool for augmenting traditional surveillance methods. In this paper, we present a method that uses Internet search query data from Baidu to model and monitor influenza activity in China. The objectives of the study are to present a comprehensive technique for: (i) keyword selection, (ii) keyword filtering, (iii) index composition and (iv) modeling and detection of influenza activity in China. Sequential time-series for the selected composite keyword index is significantly correlated with Chinese influenza case data. In addition, one-month ahead prediction of influenza cases for the first eight months of 2012 has a mean absolute percent error less than 11%. To our knowledge, this is the first study on the use of search query data from Baidu in conjunction with this approach for estimation of influenza activity in China. [ABSTRACT FROM AUTHOR]

Background: Acute respiratory infections (ARIs) are the leading cause of children and their leading killer. ARIs are responsible for at least six percent of the world's disability and death. Viruses are one of the most common agents causing ARIs. Few studies on the viral etiology and clinical characteristics of ARIs have been performed in the northwest region of China, including Gansu Province. Methods: Clinical and demographic information and throat swabs were collected from 279 patients from January 1st to December 30st, 2011. Multiplex RT-PCR was performed to detect 16 respiratory viral pathogens. Results: 279 patients were admitted for ARIs. The patients aged from 1 month to 12 years, with the median age of 2 years. Of which, 105 (37.6%) were positive for at least one pathogen. A total of 136 respiratory viral pathogens were identified from the 105 patients. Respiratory syncytial virus (RSV) was the most frequently detected pathogen (26.5%, 36/136), followed by parainfluenza virus (PIV) 1–3 (22.1%, 30/136), human rhinovirus (HRV) (21.3%, 29/136), human coronavirus (CoV) (10.3%, 14/136) and human adenovirus (HAdV) (9.6%, 13/136). Influenza A (Flu A), human metapneumovirus (hMPV) and human bocavirus (BoCA) were found 4.4%, 3.7% and 2.2%, respectively. Influenza B (Flu B) and seasonal influenza A H1N1(sH1N1) were not detected. Single-infections were detected in 30.5% (85/279) of cases. RSV was the most common pathogens in patients under 1 year and showed seasonal variation with peaks during winter and spring. Conclusions: This paper presents data on the epidemiology of viral pathogens associated with ARIs among children in Gansu Province, China. RSV is most frequently detected in our study. The findings could serve as a reference for local CDC in drawing up further plans to prevent and control ARIs. [ABSTRACT FROM AUTHOR]

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model. [ABSTRACT FROM AUTHOR]

Background: Accurate data on coverage of key maternal, newborn, and child health (MNCH) interventions are crucial for monitoring progress toward the Millennium Development Goals 4 and 5. Coverage estimates are primarily obtained from routine population surveys through self-reporting, the validity of which is not well understood. We aimed to examine the validity of the coverage of selected MNCH interventions in Gongcheng County, China. Method and Findings: We conducted a validation study by comparing women’s self-reported coverage of MNCH interventions relating to antenatal and postnatal care, mode of delivery, and child vaccinations in a community survey with their paper- and electronic-based health care records, treating the health care records as the reference standard. Of 936 women recruited, 914 (97.6%) completed the survey. Results show that self-reported coverage of these interventions had moderate to high sensitivity (0.57 [95% confidence interval (CI): 0.50–0.63] to 0.99 [95% CI: 0.98–1.00]) and low to high specificity (0 to 0.83 [95% CI: 0.80–0.86]). Despite varying overall validity, with the area under the receiver operating characteristic curve (AUC) ranging between 0.49 [95% CI: 0.39–0.57] and 0.90 [95% CI: 0.88–0.92], bias in the coverage estimates at the population level was small to moderate, with the test to actual positive (TAP) ratio ranging between 0.8 and 1.5 for 24 of the 28 indicators examined. Our ability to accurately estimate validity was affected by several caveats associated with the reference standard. Caution should be exercised when generalizing the results to other settings. Conclusions: The overall validity of self-reported coverage was moderate across selected MNCH indicators. However, at the population level, self-reported coverage appears to have small to moderate degree of bias. Accuracy of the coverage was particularly high for indicators with high recorded coverage or low recorded coverage but high specificity. The study provides insights into the accuracy of self-reports based on a population survey in low- and middle-income countries. Similar studies applying an improved reference standard are warranted in the future. [ABSTRACT FROM AUTHOR]

Background: syndromic surveillance system has great advantages in promoting the early detection of epidemics and reducing the necessities of disease confirmation, and it is especially effective for surveillance in resource poor settings. However, most current syndromic surveillance systems are established in developed countries, and there are very few reports on the development of an electronic syndromic surveillance system in resource-constrained settings. Objective: this study describes the design and pilot implementation of an electronic surveillance system (ISS) for the early detection of infectious disease epidemics in rural China, complementing the conventional case report surveillance system. Methods: ISS was developed based on an existing platform ‘Crisis Information Sharing Platform’ (CRISP), combining with modern communication and GIS technology. ISS has four interconnected functions: 1) work group and communication group; 2) data source and collection; 3) data visualization; and 4) outbreak detection and alerting. Results: As of Jan. 31st 2012, ISS has been installed and pilot tested for six months in four counties in rural China. 95 health facilities, 14 pharmacies and 24 primary schools participated in the pilot study, entering respectively 74256, 79701, and 2330 daily records into the central database. More than 90% of surveillance units at the study sites are able to send daily information into the system. In the paper, we also presented the pilot data from health facilities in the two counties, which showed the ISS system had the potential to identify the change of disease patterns at the community level. Conclusions: The ISS platform may facilitate the early detection of infectious disease epidemic as it provides near real-time syndromic data collection, interactive visualization, and automated aberration detection. However, several constraints and challenges were encountered during the pilot implementation of ISS in rural China. [ABSTRACT FROM AUTHOR]