Policy responses to COVID-19 pandemic waves: Cross-region and cross-sector economic impact

Self-Reported Long COVID and Its Impact on COVID-19-Related Worries and Behaviors After Lifting the COVID-19 Restrictions in China

OBJECTIVE

Since the lifting of the COVID-19 restrictions in China in November 2022, there has been a notable surge in the COVID-19 infection rate. Little is known about the prevalence of long COVID among the general adult population and its impact on COVID-19-related worries and behaviors after the policy change.

METHODS

This cross-sectional study recruited 1530 adults with prior COVID-19 infection in Guangzhou from February to March 2023. Logistic regression analyses and trend analyses were performed to investigate the associations between long COVID- and COVID-19-related worries and preventive behaviors.

RESULTS

The estimated prevalence of long COVID among adults in China was 18.0% (95% confidence interval: 16.1% to 20.0%). Common long COVID symptoms included cough (60.7%), fatigue (47.6%), dyspnea (34.5%), palpitation (26.2%), and insomnia (25.1%). Adjusted for background variables, individuals with long COVID exhibited higher level of COVID-19-related worries compared to those who had fully recovered from the infection (reference: without long COVID; adjusted odds ratios ranged from 1.87 to 2.55, all p values < 0.001). Participants primarily expressed worries regarding the potential for COVID-19 reinfection, the impact of the pandemic on daily life, the increasing number of COVID-19 cases and deaths, and the capacity of the healthcare system. While long COVID did not statistically significantly affect their preventive behaviors.

CONCLUSIONS

Long COVID was prevalent among the general adult population in China after lifting the COVID-19 restrictions, and it had a significant impact on COVID-19-related worries. This study highlights the importance of monitoring the mental health of individuals with long COVID and developing targeted intervention strategies to improve their adherence to preventive measures.

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.

Do We Learn From Errors? The Economic Impact of Differentiated Policy Restrictions in Italy

This paper investigates the economic impact of the three tiers risk framework implemented in Italy against the COVID-19 pandemic during the Autumn of 2020. Exploiting a large-scale dataset encompassing daily credit card transactions mediated by a large Italian bank, we estimate a set of panel event study models to disentangle the impact of restrictions with low, medium and high stringency levels in terms of consumption reduction. We show that space-time differentiated policies tend to produce stronger welfare losses for progressively more stringent restrictions in specific sectors targeted by these policies such as Retail and Restaurants. However, when we compare provinces implementing the same level of policy stringency, we show that territories with higher income per capita and larger concentration of manufacturing and service activities experience both significantly worse economic and epidemiological performances. Overall, our results suggest that policy makers should properly account for local socio-economic characteristics when designing tailored restrictions entailing an equal and homogeneous impact across territories.

Containment measures during the COVID pandemic: The role of non-pharmaceutical health policies

Many countries have imposed a set of non-pharmaceutical health policy interventions in an effort to slow the spread of the COVID-19 pandemic. The objective of this paper is to examine the effects of the interventions, drawing on evidence from the OECD countries. A special feature here is the mechanism that underlies the impact of the containment policies. To this end, a causal mediation analysis decomposing the total effect into a direct and an indirect effect is conducted. The key finding is a dual cause-effect channel. On the one hand, there is a direct effect of the non-pharmaceutical interventions on the various health variables. Beyond this, a quantitatively dominant indirect impact of non-pharmaceutical interventions operating via voluntary changes in social distancing is shown.

The impact of the COVID-19 pandemic on China's economic structure: An input-output approach

Recognizing the impact of COVID-19 on economic structure is an urgently required task for the post-pandemic era. However, studies have been hampered in undertaking this task by a lack of current data and the use of inappropriate methods. This paper fills the gap in the literature by applying a network analysis method using the newly released input-output tables of China and evaluating the structural impacts on the economy, including the changes in the sectoral closeness, betweenness, risk condition, and network backbone. The modelling results demonstrate that the pandemic has accelerated the structural transformation process of the Chinese economy: the traditional growth engines, such as the petroleum and finance industries, have lagged, whereas new growth engine sectors, including the digital services and scientific research industries, have expanded rapidly. Accordingly, we propose that the government formulate policies to stabilize old growth engine industries and foster new drivers to promote a sustainable economic recovery in China.