Mapping of secondary forest age in China using stacked generalization and Landsat time series.

A national distribution of secondary forest age (SFA) is essential for understanding the forest ecosystem and carbon stock in China. While past studies have mainly used various change detection algorithms to detect forest disturbance, which cannot adequately characterize the entire forest landscape. This study developed a data-driven approach for improving performances of the Vegetation Change Tracker (VCT) and Continuous Change Detection and Classification (CCDC) algorithms for detecting the establishment of forest stands. An ensemble method for mapping national-scale SFA by determining the establishment time of secondary forest stands using change detection algorithms and dense Landsat time series was proposed. A dataset of national secondary forest age for China (SFAC) for 1 to 34 and with a 30-m spatial resolution was produced from the optimal ensemble model. This dataset provides national, continuous spatial SFA information and can improve understanding of secondary forests and the estimation of forest carbon storage in China.

Assessing work resumption in hospitals during the COVID-19 epidemic in China using multiscale geographically weighted regression.

The resumption of work and production is one of the key issues during the novel coronavirus (COVID-19) post-epidemic phase. We used location-based service data of mobile devices to assess the work resumption of 22,098 hospitals in mainland China. The multiscale influences of the determinants on work resumption in hospitals, including medical-service capacity, human movement, and epidemic severity, were examined using the multiscale geographically weighted regression technique. This study provides a novel insight into the assessment of work resumption in hospitals and its determinants, and is flexible to be extended to evaluate the work resumption of other industries. The findings can introduce helpful information for other countries to implement the strategies of work recovery during the post-epidemic phase.

Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China.

OBJECTIVES: The novel coronavirus (COVID-19) epidemic is reaching its final phase in China. The epidemic data are available for a complete assessment of epidemiological parameters in all regions and time periods. METHODS: This study aims to present a spatiotemporal epidemic model based on spatially stratified heterogeneity (SSH) to simulate the epidemic spread. A susceptible-exposed/latent-infected-removed (SEIR) model was constructed for each SSH-identified stratum (each administrative city) to estimate the spatiotemporal epidemiological parameters of the outbreak. RESULTS: We estimated that the mean latent and removed periods were 5.40 and 2.13 days, respectively. There was an average of 1.72 latent or infected persons per 10,000 Wuhan travelers to other locations until January 20th, 2020. The space-time basic reproduction number (R0) estimates indicate an initial value between 2 and 3.5 in most cities on this date. The mean period for R0 estimates to decrease to 80%, and 50% of initial values in cities were an average of 14.73 and 19.62 days, respectively. CONCLUSIONS: Our model estimates the complete spatiotemporal epidemiological characteristics of the outbreak in a space-time domain. These findings will help enhance a comprehensive understanding of the outbreak and inform the strategies of prevention and control in other countries worldwide.

First, second and potential third generation spreads of the COVID-19 epidemic in mainland China: an early exploratory study incorporating location-based service data of mobile devices.

OBJECTIVES: The outbreak of atypical pneumonia caused by the novel coronavirus (COVID-19) has currently become a global concern. The generations of the epidemic spread are not well known, yet these are critical parameters to facilitate an understanding of the epidemic. A seafood wholesale market and Wuhan city, China, were recognized as the primary and secondary epidemic sources. Human movements nationwide from the two epidemic sources revealed the characteristics of the first-generation and second-generation spreads of the COVID-19 epidemic, as well as the potential third-generation spread. METHODS: We used spatiotemporal data of COVID-19 cases in mainland China and two categories of location-based service (LBS) data of mobile devices from the primary and secondary epidemic sources to calculate Pearson correlation coefficient,r, and spatial stratified heterogeneity, q, statistics. RESULTS: Two categories of device trajectories had generally significant correlations and determinant powers of the epidemic spread. Bothr and q statistics decreased with distance from the epidemic sources and their associations changed with time. At the beginning of the epidemic, the mixed first-generation and second-generation spreads appeared in most cities with confirmed cases. They strongly interacted to enhance the epidemic in Hubei province and the trend was also significant in the provinces adjacent to Hubei. The third-generation spread started in Wuhan from January 17-20, 2020, and in Hubei from January 23-24. No obvious third-generation spread was detected outside Hubei. CONCLUSIONS: The findings provide important foundations to quantify the effect of human movement on epidemic spread and inform ongoing control strategies. The spatiotemporal association between the epidemic spread and human movements from the primary and secondary epidemic sources indicates a transfer from second to third generations of the infection. Urgent control measures include preventing the potential third-generation spread in mainland China, eliminating it in Hubei, and reducing the interaction influence of first-generation and second-generation spreads.

Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease.

BACKGROUND: Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspective to explore the spatiotemporal patterns and determinant factors of an HFMD epidemic. METHODS: In this study, we collected weekly HFMD incidence reports for a total of 138 districts in Shandong province, China, from May 2008 to March 2009. A Kalman filter was integrated with geographically weighted regression (GWR) to estimate the HFMD incidence. Spatiotemporal variation characteristics were explored and potential risk regions were identified, along with quantitatively evaluating the influence of meteorological and socioeconomic factors on the HFMD incidence. RESULTS: The results showed that the average error covariance of the estimated HFMD incidence by district was reduced from 0.3841 to 0.1846 compared to the measured incidence, indicating an overall improvement of over 50% in error reduction. Furthermore, three specific categories of potential risk regions of HFMD epidemics in Shandong were identified by the filter processing, with manifest filtering oscillations in the initial, local and long-term periods, respectively. Amongst meteorological and socioeconomic factors, the temperature and number of hospital beds per capita, respectively, were recognized as the dominant determinants that influence HFMD incidence variation. CONCLUSIONS: The estimation accuracy of the HFMD incidence can be significantly improved by integrating a Kalman filter with GWR and the integration is effective for exploring spatiotemporal patterns and determinants of an HFMD epidemic. Our findings could help establish more accurate HFMD prevention and control strategies in Shandong. The present study demonstrates a novel approach to exploring spatiotemporal patterns and determinant factors of HFMD epidemics, and it can be easily extended to other regions and other infectious diseases similar to HFMD.

Spatial-temporal characteristics of epidemic spread in-out flow-Using SARS epidemic in Beijing as a case study.

For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions, the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions. Three typical spatial information parameters including working unit/address, onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed. Furthermore, by the methods of spatial-temporal statistical analysis and network characteristic analysis, spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored, and spatial autocorrelation/heterogeneity, spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed. The results show that (1) The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces, but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong. And the control measurement should focus on the early and interim progress of SARS breakout. (2) The inner output cases had significant positive autocorrelative characteristics in the whole studied region, and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer. (3) The downtown districts were main high-risk hotspots of SARS epidemic in Beijing, the northwest suburban districts/counties were secondary high-risk hotspots, and northeast suburban areas were relatively safe. (4) The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity. The suburban Tongzhou and Changping districts were the underlying high-risk regions, and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow. The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic, and provide a more effective theoretical basis for emergency/control measurements and decision-making.

Exploring the epidemic transmission network of SARS in-out flow in mainland China.

The changing spatiotemporal patterns of the individual susceptible-infected-symptomatic-treated-recovered epidemic process and the interactions of information/material flows between regions, along with the 2002-2003 Severe Acute Respiratory Syndrome (SARS) epidemiological investigation data in mainland China, including three typical locations of individuals (working unit/home address, onset location and reporting unit), are used to define the in-out flow of the SARS epidemic spread. Moreover, the input/output transmission networks of the SARS epidemic are built according to the definition of in-out flow. The spatiotemporal distribution of the SARS in-out flow, spatial distribution and temporal change of node characteristic parameters, and the structural characteristics of the SARS transmission networks are comprehensively and systematically explored. The results show that (1) Beijing and Guangdong had the highest risk of self-spread and output cases, and prevention/control measures directed toward self-spread cases in Beijing should have focused on the later period of the SARS epidemic; (2) the SARS transmission networks in mainland China had significant clustering characteristics, with two clustering areas of output cases centered in Beijing and Guangdong; (3) Guangdong was the original source of the SARS epidemic, and while the infected cases of most other provinces occurred mainly during the early period, there was no significant spread to the surrounding provinces; in contrast, although the input/output interactions between Beijing and the other provinces countrywide began during the mid-late epidemic period, SARS in Beijing showed a significant capacity for spatial spreading; (4) Guangdong had a significant range of spatial spreading throughout the entire epidemic period, while Beijing and its surrounding provinces formed a separate, significant range of high-risk spreading during the mid-late period; especially in late period, the influence range of Beijing's neighboring provinces, such as Hebei, was even slightly larger than that of Beijing; and (5) the input network had a low-intensity spread capacity and middle-level influence range, while the output network had an extensive high-intensity spread capacity and influence range that covered almost the entire country, and this spread and influence indicated that significant clustering characteristics increased gradually. This analysis of the epidemic in-out flow and its corresponding transmission network helps reveal the potential spatiotemporal characteristics and evolvement mechanism of the SARS epidemic and provides more effective theoretical support for prevention and control measures.

[Spatial clustering detecting for schistosomiasis in Nanchang County].

OBJECTIVE: To explore the cluster of schistosomiasis in Nanchang County, Jiangxi Province. METHODS: Based on the data of schistosome infection cases and population, the spatial analysis technology, global autocorrelation and hotspot detection methods in ArcGIS 9.2 software were used to render the spatial autocorrelation coefficient graph. The autocorrelation index change trend of schistosome infection rates in different spatial scales was described. In addition, the hotspot area of schistosome infection rates in study area was detected by using the representative distance for pace parameters combined with actual situation. RESULTS: The spatial distribution of schistosome infection in study area was characteristic of clusters in 2009. The spatial autocorrelation coefficient graph indicated that the peak of wave happened in the distances of 1 900 m, 2 600 m, 3 800 m and 5 000 m, respectively. Comparing with the geographical conditions, the hotspots in 5 000 m spatial scale were more realistic than other distances. In this scale, 2 hotspots and 2 second hotspots were explored. CONCLUSION: The spatial clustering analysis combined with the spatial autocorrelation analysis can explore the schistosomiasis gathering area more precisely.