The economic impacts of COVID-19 and city lockdown: Early evidence from China

Spatiotemporal Dynamics of COVID-19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

The global COVID-19 outbreak severely hampered the growth of the global economy, prompting the implementation of the strictest prevention policies in China. Establishing a significant relationship between changes in nighttime light and COVID-19 lockdowns from a geospatial perspective is essential. In light of nighttime light remote sensing, we evaluated the spatiotemporal dynamic effects of COVID-19 city lockdowns on human activity intensity in the Zhengzhou region. Prior to the COVID-19 outbreak, nighttime light in the Zhengzhou region maintained a significant growth trend, even under regular control measures. However, following the October 2022 COVID-19 lockdown, nighttime light experienced a substantial decrease. In the central area of Zhengzhou, nighttime light decreased by at least 18% compared to pre-lockdown levels, while in the sub-center, the decrease was around 14%. The areas where nighttime light decreased the most in the central region were primarily within a 15 km radius, while in the sub-center, the decrease was concentrated within a 5 km radius. These changes in both statistical data and nighttime light underscored the significant impact of the COVID-19 lockdown on economic activities in the Zhengzhou region.

The impact of COVID-19 pandemic on the world's major economies: based on a multi-country and multi-sector CGE model

Objective

To quantitatively assess the impact of COVID-19 pandemic on public health, as well as its economic and social consequences in major economies, which is an international public health concern. The objective is to provide a scientific basis for policy interventions.

Subject and methods

This study utilizes a multi-country, multi-sector CGE-COVID-19 model to analyze the repercussions of the pandemic in 2022. The re-search focuses on quantifying the effects of COVID-19 on the macroeconomy and various industry sectors within six economies: the United States, China, the EU, the United Kingdom, Japan, and South Korea.

Results

The COVID-19 pandemic shock had the most significant impact on China and the EU, followed by notable effects observed in the United States and the United Kingdom. In contrast, South Korea and Japan experienced relatively minimal effects. The reduction in output caused by the pandemic has affected major economies in multiple sectors, including real industries such as forestry and fisheries, and the services such as hotels and restaurants.

Conclusion

The overall negative macroeconomic impact of the epidemic on major economies has been significant. Strategic interventions encompassing initiatives like augmenting capital supply, diminishing corporate taxes and fees, offering individual subsidies, and nurturing international cooperation held the potential to mitigate the detrimental economic consequences and enhance the global-economic amid the pan-demic. Consequently, this study contributes to the advancement of global anti-epidemic policies targeting economic recovery. Moreover, using the CGE-COVID-19 model has enriched the exploration of general equilibrium models in PHEIC events.

The optimisation of public health emergency governance: a simulation study based on COVID-19 pandemic control policy

BACKGROUND

The outbreak of the COVID-19 pandemic sparked numerous studies on policy options for managing public health emergencies, especially regarding how to choose the intensity of prevention and control to maintain a balance between economic development and disease prevention.

METHODS

We constructed a cost-benefit model of COVID-19 pandemic prevention and control policies based on an epidemic transmission model. On this basis, numerical simulations were performed for different economies to analyse the dynamic evolution of prevention and control policies. These economies include areas with high control costs, as seen in high-income economies, and areas with relatively low control costs, exhibited in upper-middle-income economies.

RESULTS

The simulation results indicate that, at the outset of the COVID-19 pandemic, both high-and low-cost economies tended to enforce intensive interventions. However, as the virus evolved, particularly in circumstances with relatively rates of reproduction, short incubation periods, short spans of infection and low mortality rates, high-cost economies became inclined to ease restrictions, while low-cost economies took the opposite approach. However, the consideration of additional costs incurred by the non-infected population means that a low-cost economy is likely to lift restrictions as well.

CONCLUSIONS

This study concludes that variations in prevention and control policies among nations with varying income levels stem from variances in virus transmission characteristics, economic development, and control costs. This study can help researchers and policymakers better understand the differences in policy choice among various economies as well as the changing trends of dynamic policy choices, thus providing a certain reference value for the policy direction of global public health emergencies.

Compound impact of COVID-19, economy and climate on the spatial distribution of global agriculture and food security

As the COVID-19 pandemic continues to unfold around the world, the per unit area yield of the world's three major crops (i.e. maize, rice and wheat) decreased simultaneously for the first time in 20 years, and nearly 2.37 billion people faced food insecurity in 2020. Around 119-124 million people were pushed back into extreme poverty. Drought is one of the natural hazards that mostly affect agricultural production, and 2020 is one of the three warmest years on record. When the pandemic, economic recession and extreme climate change occur simultaneously, food crisis will often be exacerbated. Due to the limited research on the geographic modelling of crops and food security at the country level, we investigated the effects of the COVID-19 pandemic (COVID-19 incidence and mortality rate), economic (GDP and per capita GDP), climate (temperature change and drought), and their compound effects on three crop yields and food security in the world. On the basis of verifying the spatial autocorrelation, we used the global ordinary least squares model to select the explanatory variables. Then, geographically weighted regression (GWR) and multi-scale GWR (MGWR), were utilised to explore spatial non-stationary relationships. Results indicated that the MGWR was more efficient than the traditional GWR. On the whole, per capita GDP was the most important explanatory variable for most countries. However, the direct threats of COVID-19, temperature change and drought on crops and food security were small and localised. This study is the first to utilise advanced spatial methods to analyse the impacts of natural and human disasters on agriculture and food security in various countries, which can serve as a geographical guide for the World Food Organization, other relief agencies and policymakers to conduct food aid, health and medical assistance, financial support, climate change policy formulation, and anti-epidemic policy formulation.