Zhong Yao

Exploring spatio-temporal patterns of disease incidence can help to identify areas of significantly elevated or decreased risk, providing potential etiologic clues. The study uses the retrospective analysis of space-time scan statistic to detect the clusters of COVID-19 in mainland China with a different maximum clustering radius at the family-level based on case dates of onset. The results show that the detected clusters vary with the clustering radius. Forty-three space-time clusters were detected with a maximum clustering radius of 100 km and 88 clusters with a maximum clustering radius of 10 km from 2 December 2019 to 20 June 2020. Using a smaller clustering radius may identify finer clusters. Hubei has the most clusters regardless of scale. In addition, most of the clusters were generated in February. That indicates China’s COVID-19 epidemic prevention and control strategy is effective, and they have successfully prevented the virus from spreading from Hubei to other provinces over time. Well-developed provinces or cities, which have larger populations and developed transportation networks, are more likely to generate space-time clusters. The analysis based on the data of cases from onset may detect the start times of clusters seven days earlier than similar research based on diagnosis dates. Our analysis of space-time clustering based on the data of cases on the family-level can be reproduced in other countries that are still seriously affected by the epidemic such as the USA, India, and Brazil, thus providing them with more precise signals of clustering.

Currently, wetland stability is under threat due to the joint effects of global climate change and human activity, especially in lakes. Hence, it is necessary to evaluate the health status of wetland ecosystems such as lakes, identify the variables causing the wetland degradation and work to protect the wetlands from the identified variables in the future. Based on fourteen high-resolution autumn remote sensing images from 1989–2013, the classification characteristics and spatial distribution patterns of wetland landscapes in Poyang Lake were studied through quantitative interpretation technology. An established health assessment index system named the EHCI (Ecological Health Comprehensive Index) was used to assess the health status of Poyang Lake. Additionally, the relationship between water regime and health status of wetland landscape distribution of Poyang Lake were investigated by multivariate statistical analysis. The results demonstrated: (1) The total area of three first level (or six second level) types of wetland landscapes showed a stable status, which was long-term maintaining at about 3026 km² from 1989–2013. (2) The water area shows a downward trend, while the areas of vegetation and land-water transition zone show upward trends. (3) The proposed EHCI of the Poyang Lake wetland presented a downward trend. According to the EHCI results from 1989–2013, the health status of Poyang Lake wetland was healthy for two years, unhealthy for four years and sub-healthy for eight years. (4) The water level fluctuation greatly affected the EHCI, and the effect became greater as the water level increased. These results contribute to the understanding of specific effects of hydrological process on the health status of the Poyang Lake wetland. In addition they provide a scientific reference for the maintenance of stable ecosystem functions of the seasonal freshwater lake. These results contribute to the understanding of specific effects of hydrological process on the health status of the Poyang Lake. In addition they provide a scientific reference for the maintenance of stable ecosystem functions of the seasonal freshwater lake.