Study on the interaction between different pathogens of Hand, foot and mouth disease in five regions of China.

Objectives: This study aims to explore the interaction of different pathogens in Hand, foot and mouth disease (HFMD) by using a mathematical epidemiological model and the reported data in five regions of China. Methods: A cross-regional dataset of reported HFMD cases was built from four provinces (Fujian Province, Jiangsu province, Hunan Province, and Jilin Province) and one municipality (Chongqing Municipality) in China. The subtypes of the pathogens of HFMD, including Coxsackievirus A16 (CV-A16), enteroviruses A71 (EV-A71), and other enteroviruses (Others), were included in the data. A mathematical model was developed to fit the data. The effective reproduction number (R eff ) was calculated to quantify the transmissibility of the pathogens. Results: In total, 3,336,482 HFMD cases were collected in the five regions. In Fujian Province, the R eff between CV-A16 and EV-A71&CV-A16, and between CV-A16 and CV-A16&Others showed statistically significant differences (P < 0.05). In Jiangsu Province, there was a significant difference in R eff (P < 0.05) between the CV-A16 and Total. In Hunan Province, the R eff between CV-A16 and EV-A71&CV-A16, between CV-A16 and Total were significant (P < 0.05). In Chongqing Municipality, we found significant differences of the R eff (P < 0.05) between CV-A16 and CV-A16&Others, and between Others and CV-A16&Others. In Jilin Province, significant differences of the R eff (P < 0.05) were found between EV-A71 and Total, and between Others and Total. Conclusion: The major pathogens of HFMD have changed annually, and the incidence of HFMD caused by others and CV-A16 has surpassed that of EV-A71 in recent years. Cross-regional differences were observed in the interactions between the pathogens.

Transmissibility of hand, foot, and mouth disease in 97 counties of China.

Hand, foot, and mouth disease (HFMD) is a serious disease burden in the Asia-Pacific region, including China. This study calculated the transmissibility of HFMD at county levels in Jiangsu Province, China, analyzed the differences of transmissibility and explored the possible influencing factors of its transmissibility. We built a mathematical model for seasonal characteristics of HFMD, estimated the effective reproduction number (Reff), and compared the incidence rate and transmissibility in different counties using non-parametric tests, rapid cluster analysis and rank-sum ratio in 97 counties in Jiangsu Province from 2015 to 2020. The average daily incidence rate was between 0 and 4 per 100,000 people in Jiangsu Province from 2015-2020. The Quartile of Reff in Jiangsu Province from 2015 to 2020 was 1.54 (0.49, 2.50). Rugao District and Jianhu District had the highest transmissibility according to the rank-sum ratio. Reff generally decreased in 2017 and increased in 2018 in most counties, and the median level of Reff was the lowest in 2017 (P < 0.05). The transmissibility was different in 97 counties in Jiangsu Province. The reasons for the differences may be related to the climate, demographic characteristics, virus subtypes, vaccination, hygiene and other infectious diseases.

Tensor Maps for Synchronizing Heterogeneous Shape Collections.

Establishing high-quality correspondence maps between geometric shapes has been shown to be the fundamental problem in managing geometric shape collections. Prior work has focused on computing efficient maps between pairs of shapes, and has shown a quantifiable benefit of joint map synchronization, where a collection of shapes are used to improve (denoise) the pairwise maps for consistency and correctness. However, these existing map synchronization techniques place very strong assumptions on the input shapes collection such as all the input shapes fall into the same category and/or the majority of the input pairwise maps are correct. In this paper, we present a multiple map synchronization approach that takes a heterogeneous shape collection as input and simultaneously outputs consistent dense pairwise shape maps. We achieve our goal by using a novel tensor-based representation for map synchronization, which is efficient and robust than all prior matrix-based representations. We demonstrate the usefulness of this approach across a wide range of geometric shape datasets and the applications in shape clustering and shape co-segmentation.