Using Partial Least Squares Regression to Fit Small Data of H7N9 Incidence Based on the Baidu Index

Abstract

The internet search data will help the disease control department to estimate the disease in advance. The H7N9 epidemic that occurred in Guangxi Province was used as an example to demonstrate its association with Baidu search data. At first,16 search terms which have high correlation with H7N9 disease were selected by expert determination and calculation. At the same time, the number of disease cases were downloaded from the website of Guangxi CDC. The partial least square regression was choosed to estimate after comparing the regression models for the number of epidemic cases is very less than baidu searches data. To filter independent variables, cross validation and variable importance in projection were applied. The results show that: 1.the proposed method is suitable for fitting the data of H7N9 disease with few samples, and the fitting degree is perfect. 2.it will help to screen out the important searching index which are more relate to H7N9 epidemic by using cross validation and variable import in project. 3.compared with the PCA methods, the proposed method presented great advantages in performance index, especially with the help of cross validation and variable importance in projection. INDEX TERMS Partial least squares regression, H7N9, Baidu index, variable importance in projection.

Spatio-Temporal Changes in Habitat Quality and Linkage with Landscape Characteristics Using InVEST-Habitat Quality Model: A Case Study at Changdang Lake National Wetland, Changzhou, China

“…Furthermore, semi-habitat expansion cannot support the original habitat quality level, leading to the degradation of large areas of native vegetation and the fragmentation of habitat and landscape. Similar situations of habitat quality reduction have been reported in related studies [27,49,50].…”

“…In the wetland, due to Changdang Lake connecting to the Taihu Lake Basin, the water cycle promoted biodiversity and environmental regulation in the watershed. However, the booming population, agricultural expansion and tourism development have threatened habitat quality [49] and led to a decline in habitat quality around the wetland. The areas with low habitat quality gradually aggregated and were mainly distributed in the north and east, where construction land and the river network are dense.…”

The effects of landscape change on habitat quality in arid desert areas based on future scenarios: Tarim River Basin as a case study

“…Furthermore, the VIP, Co and W are important parameters for predicting independent and dependent variables. Generally, VIP values greater than 0.8, greater than 1 and less than 0.5 mean the importance is significant, the most relevant, and weak (meaningless), respectively ( Shawul et al., 2019 ; Gan et al., 2020 ). The squares of the W value that are > 0.2 indicate that the independent variable is more important for the dependent variable ( Shawul et al., 2019 ).…”

Analysis of Collective Response Reveals that COVID-19-Related Activities Start From the End of 2019 in Mainland China

“…Compared to these studies, our work revisits the early stage of the pandemic from the perspective collective response, using search data and mobility data collected from massive mobile users in a participatory fashion. To uncover the latent factors of epidemiological dynamics, (7,(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26) fit parameterized models with the numbers of cases confirmed over time, and interpret the models through the physical and epidemiological meaning of every parameter. Compared to these studies, our work does not make any assumptions on the epidemiological dynamics while providing a model-free analysis on the search and mobility data.…”

“…In addition, mobility data from Baidu Maps also demonstrates the collective response from the perspective of mobility (14,15), where we can compare the massive mobility traces under the pandemic with the regular patterns in past years, so as to sketch how populations move as a response to COVID-19. In summary, please refer to studies (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27) for analyzing the collective response to COVID-19 with digital information.…”