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Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.

Objective: The 2019 coronavirus disease (COVID-19) epidemic has raised international concern.Mental health is becoming an issue that cannot be ignored in our fight against it. This study aimed to explore the prevalence and factors linked to anxiety and depression in hospitalized patients with COVID-19. Methods:A total of 144 patients diagnosed with COVID-19 were included in this study. We assessed depression and anxiety symptoms using the Hospital Anxiety and Depression Scale (HADS), and social support using the Perceived Social Support Scale (PSSS) among patients at admission. Multivariate linear regression analyses were performed to identify factors associated with symptoms of anxiety and depression.Results: Of the 144 participants, 34.72% and 28.47% patients with COVID-19 had symptoms of anxiety or depression, respectively. The bivariate correlations showed that less social support was correlated with more anxious (r=-0.196, p<0.05) and depressive (r=-0.360，p<0.05) symptoms All rights reserved. No reuse allowed without permission. : medRxiv preprint among patients with COVID-19. The multiple linear regression analysis showed that gender (β=1.446, p=0.034), age (β=0.074, p=0.003), oxygen saturation (β =-2.140, p=0.049), and social support (β =-1.545, p=0.017) were associated with anxiety for COVID-19 patients. Moreover, age (β=0.084, p=0.001), family infection with SARS-CoV-2 (β =1.515, p=0.027) and social support (β =-2.236, p＜0.001) were the factors associated with depression. Conclusion:Hospitalized patients with COVID-19 presented features of anxiety and depression.Mental concern and appropriate intervention are essential parts of clinical care for those who are at risk.

SUMMARY Background Avian influenza A(H7N9) virus has caused human infections in China since 2013, and a large epidemic in 2016–17 has prompted concerns of whether the epidemiology has changed to suggest an increasing pandemic threat. Our study aimed to describe the epidemiological characteristics, clinical severity, and time-to-event distributions of A(H7N9) case-patients in the 2016–17 epidemic wave compared with previous waves. Methods We obtained information about all laboratory-confirmed human cases of A(H7N9) virus infection reported in mainland China as of 23 February 2017. We described the epidemiological characteristics across epidemic waves, and estimated the risk for death, mechanical ventilation, and admission to the intensive care unit for patients who required hospitalization for medical reasons. We estimated the incubation periods, and time delays from illness onset to hospital admission, illness onset to initiation of antiviral treatment, and hospital admission to death or discharge. Findings The 2016–17 A(H7N9) epidemic wave began earlier, spread to more counties in affected provinces and had more confirmed cases than previous epidemic waves. There was an increase in the proportion of cases in middle-aged adults and in semi-urban and rural residents. The clinical severity of hospitalized cases in 2016–17 was comparable to the previous epidemic waves. Interpretation Age distribution and case sources changed gradually across epidemic waves, while clinical severity has not changed substantially. Continued vigilance and sustained intensive control efforts are needed to minimize the risk of human infection with A(H7N9) virus. Funding The National Science Fund for Distinguished Young Scholars (grant no. 81525023).