Achieving the Sustainable Development Goals in the post-pandemic era.

The COVID-19 pandemic continues to pose substantial challenges to achieving the Sustainable Development Goals (SDGs). Exploring systematic SDG strategies is urgently needed to aid recovery from the pandemic and reinvigorate global SDG actions. Based on available data and comprehensive analysis of the literature, this paper highlights ongoing challenges facing the SDGs, identifies the effects of COVID-19 on SDG progress, and proposes a systematic framework for promoting the achievement of SDGs in the post-pandemic era. Progress towards attaining the SDGs was already lagging behind even before the onset of the COVID-19 pandemic. Inequitable distribution of food-energy-water resources and environmental crises clearly threaten SDG implementation. Evidently, there are gaps between the vision for SDG realization and actual capacity that constrain national efforts. The turbulent geopolitical environment, spatial inequities, and trade-offs limit the effectiveness of SDG implementation. The global public health crisis and socio-economic downturn under COVID-19 have further impeded progress toward attaining the SDGs. Not only has the pandemic delayed SDG advancement in general, but it has also amplified spatial imbalances in achieving progress, undermined connectivity, and accentuated anti-globalization sentiment under lockdowns and geopolitical conflicts. Nevertheless, positive developments in technology and improvement in environmental conditions have also occurred. In reflecting on the overall situation globally, it is recommended that post-pandemic SDG actions adopt a "Classification-Coordination-Collaboration" framework. Classification facilitates both identification of the current development status and the urgency of SDG achievement aligned with national conditions. Coordination promotes domestic/international and inter-departmental synergy for short-term recovery as well as long-term development. Cooperation is key to strengthening economic exchanges, promoting technological innovation, and building a global culture of sustainable development that is essential if the endeavor of achieving the SDGs is to be successful. Systematic actions are urgently needed to get the SDG process back on track.

Environmental factors and spatiotemporal distribution characteristics of the global outbreaks of the highly pathogenic avian influenza H5N1.

The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.

Recover the food-energy-water nexus from COVID-19 under Sustainable Development Goals acceleration actions.

The interwoven relationship between food, energy, and water (FEW) is described as the FEW nexus. The COVID-19 pandemic has interrupted the FEW nexus and impeded the progress of FEW-related Sustainable Development Goals (SDGs) (SDG 2: Zero Hunger; 6: Clean Water and Sanitation; 7: Affordable and Clean Energy). We aim to find solutions to recover the FEW nexus from COVID-19. First, we discussed the challenges faced by FEW amid COVID-19. Second, we observed responses of the FEW nexus under COVID-19's interference. Finally, we proposed the solutions that guide the FEW nexus in recovery from the pandemic by mining 164 FEW-related SDG Acceleration Actions. The key solutions include 1) building or upgrading FEW facilities and infrastructure, 2) improving nature's contribution to the FEW nexus, 3) developing digital technologies, 4) innovating the source and production of FEW, and 5) promoting community production and transforming the lifestyle. Our work highlights the importance of feasible and accelerated actions that recover the FEW nexus in the post-pandemic era.

Meteorological factors' effects on COVID-19 show seasonality and spatiality in Brazil.

The meteorological conditions may affect COVID-19 transmission. However, the roles of seasonality and macro-climate are still contentious due to the limited time series for early-stage studies. We studied meteorological factors' effects on COVID-19 transmission in Brazil from February 25 to November 15, 2020. We aimed to explore whether this impact showed seasonal characteristics and spatial variations related to the macro-climate. We applied two-way fixed-effect models to identify the effects of meteorological factors on COVID-19 transmission and used spatial analysis to explore their spatial-temporal characteristics with a relatively long-time span. The results showed that cold, dry and windless conditions aggravated COVID-19 transmission. The daily average temperature, humidity, and wind speed negatively affected the daily new cases. Humidity and temperature played a dominant role in this process. For the time series, the influences of meteorological conditions on COVID-19 had a periodic fluctuation of 3-4 months (in line with the seasons in Brazil). The turning points of this fluctuation occurred at the turn of seasons. Spatially, the negative effects of temperature and humidity on COVID-19 transmission clustered in the northeastern and central parts of Brazil. This is consistent with the range of arid climate types. Overall, the seasonality and similar climate types should be considered to estimate the spatial-temporal COVID-19 patterns. Winter is a critical time to be alert for COVID-19, especially in the northern part of Brazil.

Estimating Economic Losses Caused by COVID-19 under Multiple Control Measure Scenarios with a Coupled Infectious Disease-Economic Model: A Case Study in Wuhan, China.

BACKGROUND: The outbreak of the COVID-19 epidemic has caused an unprecedented public health crisis and drastically impacted the economy. The relationship between different control measures and economic losses becomes a research hotspot. METHODS: In this study, the SEIR infectious disease model was revised and coupled with an economic model to quantify this nonlinear relationship in Wuhan. The control measures were parameterized into two factors: the effective number of daily contacts (people) (r); the average waiting time for quarantined patients (day) (g). RESULTS: The parameter r has a threshold value that if r is less than 5 (people), the number of COVID-19 infected patients is very close to 0. A "central valley" around r = 5~6 can be observed, indicating an optimal control measure to reduce economic losses. A lower value of parameter g is beneficial to stop COVID-19 spread with a lower economic cost. CONCLUSION: The simulation results demonstrate that implementing strict control measures as early as possible can stop the spread of COVID-19 with a minimal economic impact. The quantitative assessment method in this study can be applied in other COVID-19 pandemic areas or countries.

Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects.

Meteorological variables, air pollutants, and socioeconomic factors are associated with COVID-19 transmission. However, it is unclear what impact their interactions have on COVID-19 transmission, whether their impact on COVID-19 transmission is linear or non-linear, and where the inflexion points are. This study examined 1) the spatial and temporal trends in COVID-19 monthly infection rate of new confirmed cases per 100,000 people (Rn) in 188 countries/regions worldwide from March to November 2020; 2) the linear correlation between meteorological variables (temperature (T), rainfall (R), wind speed (WS), relative humidity (RH), air pressure (AP)), air pollutants (nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3)) and socioeconomic aspects (population density (PD), gross domestic product per capita (GDP), domestic general government health expenditure per capita (GHE)) and Rn, and 3) the interaction and non-linear effects of the different variables on Rn, based on GeoDetector and Boosted regression tree. The results showed that the global Rn had was spatially clustered, and the average Rn increased From March to November 2020. Global Rn was negatively correlated with meteorological variables (T, R, WS, AP) and positively correlated with air pollutants (NO2, SO2, O3) and socioeconomic aspects (GDP, GHE). The interaction of SO2 and O3, SO2 and RH, and O3 and T strongly affected Rn. The variables effect on COVID-19 transmission was non-linear, with one or more inflexion points. The findings of this work can provide a basis for developing a global response to COVID-19 for global sustainable development.

Spatiotemporal Distribution of Zika Virus and Its Spatially Heterogeneous Relationship with the Environment.

Infectious diseases have caused some of the most feared plagues and greatly harmed human health. However, despite the qualitative understanding that the occurrence and diffusion of infectious disease is related to the environment, the quantitative relations are unknown for many diseases. Zika virus (ZIKV) is a mosquito-borne virus that poses a fatal threat and has spread explosively throughout the world, impacting human health. From a geographical perspective, this study aims to understand the global hotspots of ZIKV as well as the spatially heterogeneous relationship between ZIKV and environmental factors using exploratory special data analysis (ESDA) model. A geographically weighted regression (GWR) model was used to analyze the influence of the dominant environmental factors on the spread of ZIKV at the continental scale. The results indicated that ZIKV transmission had obvious regional and seasonal heterogeneity. Population density, GDP per capita, and landscape fragmentation were the dominant environmental factors affecting the spread of ZIKV, which indicates that social factors had a greater influence than natural factors on the spread of it. As SARS-CoV-2 is spreading globally, this study can provide methodological reference for fighting against the pandemic.

Integrate ecosystem services in socio-economic development to enhance achievement of sustainable development goals in the post-pandemic era

The COVID-19 pandemic has stalled and rolled back progress on Sustainable Development Goals (SDGs). Ecosystem services (ESs), defined as the contributions of ecosystems to human well-being, underpin the achievement of SDGs. To promote SDG achievement in post-pandemic era, we teased out the links between ESs and SDGs while examining the impact of COVID-19. We found that ESs benefited all SDGs, yet man-made pressures led to degradation of ecosystems and their services. There is broad consensus that the virus lurks in degraded ecosystems and generates spillover due to human interference. The pandemic and global lockdown/restriction disrupted the flow of ESs and altered human ESs demand, threatening the efforts for the SDGs. We suggested: 1) to study the association and traceability of ESs-SDGs under the pandemic;2) to prioritize pressing issues such as health care, livelihood, and resource security and in the long run, we should promote human-nature harmony to achieve the SDGs;and 3) to enhance ESs and to promote the SDGs through local community efforts, ESs accounting, and ecosystem restoration. This paper provides insights into the importance of ESs to the SDGs and helps to promote the SDG achievement by leveraging ESs’ contribution after the pandemic.

Spatial distribution characteristics of the COVID-19 pandemic in Beijing and its relationship with environmental factors.

Investigating the spatial distribution characteristics of the coronavirus disease 2019 (COVID-19) and exploring the influence of environmental factors that drive it is the basis for formulating rational and efficient prevention and control countermeasures. Therefore, this study aims to analyze the spatial distribution characteristics of COVID-19 pandemic in Beijing and its relationship with the environmental factors. Based on the incidences of new local COVID-19 cases in Beijing from June 11 to July 5, the spatial clustering characteristics of the COVID-19 pandemic in Beijing was investigated using spatial autocorrelation analysis. The relation between COVID-19 cases and environmental factors was assessed using the Spearman correlation analysis. Finally, geographically weighted regression (GWR) was applied to explore the influence of environmental factors on the spatial distribution of COVID-19 cases. The results showed that the development of COVID-19 pandemic in Beijing from June 11 to July 5 could be divided into two stages. The first stage was the outward expansion from June 11 to June 21, and the second stage (from June 22 to July 5) was the growth of the transmission in areas with existing previous cases. In addition, there was a ring of low value clusters around the Xinfadi market. This area was the key area for prevention and control. Population density and distance to Xinfadi market were the most critical factors that explained the pandemic development. The findings of this study can provide useful information for the global fighting against COVID-19.