Identifying the spatio-temporal risk variability of avian influenza A H7N9 in China

Measurement and Spatial Differentiation of Farmers' Livelihood Resilience Under the COVID-19 Epidemic Outbreak in Rural China

Livelihood resilience is the ability of individuals, families or communities to withstand external shocks based on existing resources. It is an important research paradigm in sustainable development studies. The outbreak of COVID-19 and strict epidemic prevention policies have greatly impacted the production and life of rural farmers in China. The resilience of farmers' livelihoods during the epidemic is crucial to the sustainable development of their livelihoods and regional stability. This study uses classic buffer capacity, self-organization ability, and the capacity for learning a three-dimension livelihood resilience framework using the comprehensive index, OLS, and geographical detector methods based on Hubei province and neighboring Anhui and Chongqing. Rural household survey data investigate the background of epidemic hit the livelihood of farmers resilience and its spatial distribution pattern and identify the key influencing factors. The results show that the livelihood shock faced by farmers was higher than the risk of disease, and the overall level of livelihood resilience was low after the pandemic. Financial capital and social capital can effectively help farmers to eliminate livelihood difficulties. In contrast, natural capital has a limited driving force, and physical and human capital have no obvious impact. The spatial agglomeration differentiation is obvious, indicating that the impact of COVID-19 on livelihoods was closely related to the degree of local socio-economic development and geographical location. The results of this study provide targeted recommendations for the development of epidemic prevention and livelihood resilience policies tailored to local conditions, emphasizing the importance of boosting livelihood recovery at both the government and household levels.

Spatial and temporal analysis of human infection with the avian influenza A (H7N9) virus in China and research on a risk assessment agent-based model

OBJECTIVES

From 2013 to 2017, the avian influenza A (H7N9) virus frequently infected people in China, which seriously affected the public health of society. This study aimed to analyze the spatial characteristics of human infection with the H7N9 virus in China and assess the risk areas of the epidemic.

METHODS

Using kernel density estimation, standard deviation ellipse analysis, spatial and temporal scanning cluster analysis, and Pearson correlation analysis, the spatial characteristics and possible risk factors of the epidemic were studied. Meteorological factors, time (month), and environmental factors were combined to establish an epidemic risk assessment proxy model to assess the risk range of an epidemic.

RESULTS

The epidemic situation was significantly correlated with atmospheric pressure, temperature, and daily precipitation (P < 0.05), and there were six temporal and spatial clusters. The fitting accuracy of the epidemic risk assessment agent-based model for lower-risk, low-risk, medium-risk, and high-risk was 0.795, 0.672, 0.853, 0.825, respectively.

CONCLUSIONS

This H7N9 epidemic was found to have more outbreaks in winter and spring. It gradually spread to the inland areas of China. This model reflects the risk areas of human infection with the H7N9 virus.

Regional infectious risk prediction of COVID-19 based on geo-spatial data

After the first confirmed case of the novel coronavirus disease (COVID-19) was found, it is of considerable significance to divide the risk levels of various provinces or provincial municipalities in Mainland China and predict the spatial distribution characteristics of infectious diseases. In this paper, we predict the epidemic risk of each province based on geographical proximity information, spatial inverse distance information, economic distance and Baidu migration index. A simulation study revealed that the information based on geographical economy matrix and migration index could well predict the spatial spread of the epidemic. The results reveal that the accuracy rate of the prediction is over 87.10% with a rank difference of 3.1. The results based on prior information will guide government agencies and medical and health institutions to implement responses to major public health emergencies when facing the epidemic situation.

Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people's lives and socio-economic development throughout China and across the globe since December 2019. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman's rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspot and cluster/outlier area was observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemic at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks, and had an extremely significant association with social and economic factors. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers.