ABSTRACT
The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade. The COVID-19 pandemic shows diverse impacts on different economies that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of international trade network vulnerabilities in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world, regional, and country scales for the pre- and post- COVID-19 outbreak period. The spatio-temporal patterns indicate that countries and regions with an effective COVID-19 containment such as East Asia show the strongest resilience, especially Mainland China, followed by high-income countries with fast vaccine roll-out (e.g., U.S.), whereas low-income countries (e.g., Africa) show high vulnerability. Our results encourage a comprehensive strategy to enhance international trade resilience when facing future pandemic threats including effective non-pharmaceutical measures, timely development and rollout of vaccines, strong governance capacity, robust healthcare systems, and equality via international cooperation. The overall findings elicit the hidden global trading disruption, recovery, and growth due to the adverse impact of the COVID-19 pandemic.
ABSTRACT
BACKGROUND: Mobility restriction was one of the primary measures used to restrain the spread of COVID-19 globally. Governments implemented and relaxed various mobility restriction measures in the absence of evidence for almost 3 years, which caused severe adverse outcomes in terms of health, society, and economy. OBJECTIVE: This study aimed to quantify the impact of mobility reduction on COVID-19 transmission according to mobility distance, location, and demographic factors in order to identify hotspots of transmission and guide public health policies. METHODS: Large volumes of anonymized aggregated mobile phone position data between January 1 and February 24, 2020, were collected for 9 megacities in the Greater Bay Area, China. A generalized linear model (GLM) was established to test the association between mobility volume (number of trips) and COVID-19 transmission. Subgroup analysis was also performed for sex, age, travel location, and travel distance. Statistical interaction terms were included in a variety of models that express different relations between involved variables. RESULTS: The GLM analysis demonstrated a significant association between the COVID-19 growth rate ratio (GR) and mobility volume. A stratification analysis revealed a higher effect of mobility volume on the COVID-19 GR among people aged 50-59 years (GR decrease of 13.17% per 10% reduction in mobility volume; P<.001) than among other age groups (GR decreases of 7.80%, 10.43%, 7.48%, 8.01%, and 10.43% for those aged ≤18, 19-29, 30-39, 40-49, and ≥60 years, respectively; P=.02 for the interaction). The impact of mobility reduction on COVID-19 transmission was higher for transit stations and shopping areas (instantaneous reproduction number [Rt] decreases of 0.67 and 0.53 per 10% reduction in mobility volume, respectively) than for workplaces, schools, recreation areas, and other locations (Rt decreases of 0.30, 0.37, 0.44, and 0.32, respectively; P=.02 for the interaction). The association between mobility volume reduction and COVID-19 transmission was lower with decreasing mobility distance as there was a significant interaction between mobility volume and mobility distance with regard to Rt (P<.001 for the interaction). Specifically, the percentage decreases in Rt per 10% reduction in mobility volume were 11.97% when mobility distance increased by 10% (Spring Festival), 6.74% when mobility distance remained unchanged, and 1.52% when mobility distance declined by 10%. CONCLUSIONS: The association between mobility reduction and COVID-19 transmission significantly varied according to mobility distance, location, and age. The substantially higher impact of mobility volume on COVID-19 transmission for longer travel distance, certain age groups, and specific travel locations highlights the potential to optimize the effectiveness of mobility restriction strategies. The results from our study demonstrate the power of having a mobility network using mobile phone data for surveillance that can monitor movement at a detailed level to measure the potential impacts of future pandemics.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Travel , Pandemics/prevention & control , China/epidemiology , DemographyABSTRACT
The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade. The COVID-19 pandemic shows diverse impacts on different economies that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of international trade network vulnerabilities in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world, regional, and country scales for the pre- and post- COVID-19 outbreak period. The spatio-temporal patterns indicate that countries and regions with an effective COVID-19 containment such as East Asia show the strongest resilience, especially Mainland China, followed by high-income countries with fast vaccine roll-out (e.g., U.S.), whereas low-income countries (e.g., Africa) show high vulnerability. Our results encourage a comprehensive strategy to enhance international trade resilience when facing future pandemic threats including effective non-pharmaceutical measures, timely development and rollout of vaccines, strong governance capacity, robust healthcare systems, and equality via international cooperation. The overall findings elicit the hidden global trading disruption, recovery, and growth due to the adverse impact of the COVID-19 pandemic.
ABSTRACT
In response to the coronavirus disease 2019 (COVID-19) pandemic, various countries have sought to control COVID-19 transmission by introducing non-pharmaceutical interventions. Restricting population mobility, by introducing social distancing, is one of the most widely used non-pharmaceutical interventions. Although similar population mobility restriction interventions were introduced, their impacts on COVID-19 transmission are often inconsistent across different regions and different time periods. These differences may provide critical information for tailoring COVID-19 control strategies. In this paper, anonymized high spatiotemporal resolution mobile-phone location data were employed to empirically analyze and quantify the impact of lockdowns on population mobility. Both the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China and the San Francisco Bay Area (SBA) in the United States were studied. In response to the lockdowns, a general reduction in population mobility was observed, but the structural changes in mobility are very different between the two bays: 1) GBA mobility decreased by approximately 74.0-80.1% while the decrease of SBA was about 25.0-42.1%; 2) compared to SBA, the GBA had smoother volatility in daily volume during the lockdown. The volatility change indexes for GBA and SBA were 2.55% and 7.52%, respectively; 3) the effect of lockdown on short- to long-distance mobility was similar in GBA while the medium- and long-distance impact was more pronounced in SBA.
ABSTRACT
Background: Restricting human mobility is an effective strategy used to control disease spread. However, whether mobility restriction is a proportional response to control the ongoing COVID-19 pandemic is unclear. We aimed to develop a model that can quantify the potential effects of various intracity mobility restrictions on the spread of COVID-19. Methods: In this modelling study, we used anonymous and aggregated mobile phone sightings data to build a susceptible-exposed-infectious-recovered transmission model for COVID-19 based on the city of Shenzhen, China. We simulated how disease spread changed when we varied the type and magnitude of mobility restrictions in different transmission scenarios, with variables such as the basic reproductive number (R 0), length of infectious period, and the number of initial cases. Findings: 331 COVID-19 cases distributed across the ten regions of Shenzhen were reported on Feb 7, 2020. In our basic scenario (R 0 of 2·68), mobility reduction of 20-60% within the city had a notable effect on controlling COVID-19 spread: a flattening of the peak number of cases by 33% (95% UI 21-42) and delay to the peak number by 2 weeks with a 20% restriction, 66% (48-75) reduction and 4 week delay with a 40% restriction, and 91% (79-95) reduction and 14 week delay with a 60% restriction. The effects of mobility restriction were increased when combined with reductions of 25% or 50% in transmissibility of the virus. In specific analyses of mobility restrictions for individuals with symptomatic infections and for high-risk regions, these measures also had substantial effects on reducing the spread of COVID-19. For example, the peak of the epidemic was delayed by 2 weeks if the proportion of individuals with symptomatic infections who could move freely was maintained at 20%, and by 4 weeks if two high-risk regions were locked down. The simulation results were also affected by various transmission parameters. Interpretation: Our model shows the effects of various types and magnitudes of mobility restrictions on controlling COVID-19 outbreaks at the city level in Shenzhen, China. The model could help policy makers to establish the optimal combinations of mobility restrictions during the COVID-19 pandemic, especially to assess the potential positive effects of mobility restriction on public health in view of the potential negative economic and societal effects. Funding: Guangdong Medical Science Fund, and National Natural Science Foundation of China.