Understanding the causality between energy consumption, carbon emissions and economic growth is helpful for policymakers to formulate energy, environmental and economic policies. For the first time, based on nonlinear dynamics, this paper employs multispatial convergent cross mapping (CCM) to revisit the energy-carbon-economy causation for China, India and the G7 countries using both aggregate data and per capita data. The findings indicate that there are significant differences between developing countries and developed countries. A bidirectional nexus between energy consumption, carbon emissions and economic growth is found in China and India, but various causal relationships are identified in the G7 countries, including bidirectional, unidirectional and neutral nexus. The results confirm that the decoupling phenomenon is common in most G7 countries. By leveraging a variety of samples and a new approach, this study provides new evidence for policy authorities to formulate country-specific policies to obtain better environmental quality while achieving sustainable economic growth.

Introduction: At the end of 2019, the COVID-19 broke out, and spread to Guizhou province in January of 2020. Methodology: To acquire the epidemiologic characteristics of COVID-19 in Guizhou province, we collected data from 169 laboratory-confirmed COVID-19 related cases. We described the demographic characteristics of the cases and estimated the incubation period, serial interval and the effective reproduction number. We also presented two representative case studies in Guizhou province: Case Study 1 was an example of the asymptomatic carrier; while Case Study 2 was an example of a large and complex infection chain that involved four different regions, spanning three provinces and eight families. Results: Two peaks in the incidence distribution associated with COVID-19 in Guizhou province were related to the 6.04 days (95% CI: 5.00 – 7.10) of incubation period and 6.14±2.21 days of serial interval. We also discussed the effectiveness of the control measures based on the instantaneous effective reproduction number that was a constantly declining curve. Conclusions: As of February 2, 2020, the estimated effective reproduction number was below 1, and no new cases were reported since February 26. These showed that Guizhou Province had achieved significant progress in preventing the spread of the epidemic. The medical isolation of close contacts was consequential. Meanwhile, the asymptomatic carriers and the super-spreaders must be isolated in time, who would cause a widespread infection.

What is already known on this topic? Brucellosis is a zoonotic infectious disease caused by Brucella spp. The main source of infection in human brucellosis is sick animals, mainly including sheep, goat, and cattle, but sika deer ( Cervus nippon ) can also cause human brucellosis. The first human brucellosis case in Guizhou Province was reported in 2009, and no brucellosis outbreak was reported caused by sika deer ever before. What is added by this report? This is the first reported outbreak of human brucellosis caused by sika deer in Guizhou Province. Inappropriate regulation of animal movement may be the main driver of introducing and spreading brucellosis in southern areas. The ability to diagnose brucellosis in both humans and animals was weak in the county where the outbreak took place. What are the implications for public health practice? It was suggested to prioritize occupational protection and health education for sika deer breeders. The inspection of the movement of animals and the reimbursement policy need to be improved.

Introduction: Coronavirus disease 2019 (COVID-19) has caused an outbreak around the world. Early detection of severe illness is crucial for patients’ survival. We analysed initial clinical characteristics of 146 patients with COVID-19 reported in Guizhou province, China to explore risk factors for transforming mild illness to severe. Methodology: Data of 146 laboratory-confirmed cases were collected and evaluated by the survival analysis of univariate and multivariate Cox proportional hazards model. Results: On initial presentation, patients had fever (51.05%), dry cough (45.45%), headache (16.08%), shortness of breath (7.75%) and gastrointestinal symptoms (13.99%). Among 146 laboratory-confirmed cases, 30 patients (20.55%) had severe illness and needed Intensive Care Unit care for supportive treatment. The remaining patients (116, 79.45%) were non-severe cases. Nineteen (19/146, 13.01%) of 30 patients in the Intensive Care Unit had comorbidities, including hypertension (12, 40.00%), diabetes (5, 16.67%), cardiovascular disease (5, 16.67%) and pulmonary disease (4, 13.33%). For survival analysis, patients who had fever (HR = 3.30, 95% CI = 1.31, 8.29) and comorbidities (HR = 9.76, 95% CI = 4.28, 22.23) at baseline were more likely to be admitted into the Intensive Care Unit. Few variables were not related to the survival time of discharge from baseline to discharge and from Intensive Care Unit care to discharge. Conclusions: Severe patients with COVID-19 should be paid more attention. On initial symptoms, many patients did not have fever, but those with fever were more likely to be admitted to the Intensive Care Unit. Comorbidities were likewise a risk factor of severe COVID-19.