Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission Among Air Passengers in China.

BACKGROUND: Modern transportation plays a key role in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and new variants. However, little is known about the exact transmission risk of the virus on airplanes. METHODS: Using the itinerary and epidemiological data of coronavirus disease 2019 (COVID-19) cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on 23 January 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travelers. RESULTS: In total, 175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60% (34/5622, 95% confidence interval [CI] .43-.84%) and 0.33% (18/5400, 95% CI .21-.53%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases, respectively. The seats immediately adjacent to the index cases had an AR of 9.2% (95% CI 5.7-14.4%), with a relative risk 27.8 (95% CI 14.4-53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7%, 95% CI .4%-1.2%). The upper-bound AR increased from 0.7% (95% CI 0.5%-1.0%) to 1.2% (95% CI .4-3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours. CONCLUSIONS: The ARs among travelers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic.

Who and which regions are at high risk of returning to poverty during the COVID-19 pandemic?

Pandemics such as COVID-19 and their induced lockdowns/travel restrictions have a significant impact on people’s lives, especially for lower-income groups who lack savings and rely heavily on mobility to fulfill their daily needs. Taking the COVID-19 pandemic as an example, this study analysed the risk of returning to poverty for low-income households in Hubei Province in China as a result of the COVID-19 lockdown. Employing a dataset including information on 78,931 government-identified poor households, three scenarios were analysed in an attempt to identify who is at high risk of returning to poverty, where they are located, and how the various risk factors influence their potential return to poverty. The results showed that the percentage of households at high risk of returning to poverty (falling below the poverty line) increased from 5.6% to 22% due to a 3-month lockdown. This vulnerable group tended to have a single source of income, shorter working hours, and more family members. Towns at high risk (more than 2% of households returning to poverty) doubled (from 27.3% to 46.9%) and were mainly located near railway stations;an average decrease of 10–50 km in the distance to the nearest railway station increased the risk from 1.8% to 9%. These findings, which were supported by the representativeness of the sample and a variety of robustness tests, provide new information for policymakers tasked with protecting vulnerable groups at high risk of returning to poverty and alleviating the significant socio-economic consequences of future pandemics.

Assessing work resumption in hospitals during the COVID-19 epidemic in China using multiscale geographically weighted regression.

The resumption of work and production is one of the key issues during the novel coronavirus (COVID-19) post-epidemic phase. We used location-based service data of mobile devices to assess the work resumption of 22,098 hospitals in mainland China. The multiscale influences of the determinants on work resumption in hospitals, including medical-service capacity, human movement, and epidemic severity, were examined using the multiscale geographically weighted regression technique. This study provides a novel insight into the assessment of work resumption in hospitals and its determinants, and is flexible to be extended to evaluate the work resumption of other industries. The findings can introduce helpful information for other countries to implement the strategies of work recovery during the post-epidemic phase.

Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space.

Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.

Governing public health emergencies during the coronavirus disease outbreak: Lessons from four Chinese cities in the first wave.

The ongoing coronavirus disease (COVID-19) pandemic has had a far-reaching impact on urban living, prompting emergency preparedness and response from public health governance at multiple levels. The Chinese government has adopted a series of policy measures to control infectious disease, for which cities are the key spatial units. This research traces and reports analyses of those policy measures and their evolution in four Chinese cities: Zhengzhou, Hangzhou, Shanghai and Chengdu. The theoretical framework stems from conceptualisations of urban governance and its role in public health emergencies, wherein crisis management and emergency response are highlighted. In all four cities, the trend curves of cumulative diagnosed cases, critical policies launched in key time nodes and local governance approaches in the first wave were identified and compared. The findings suggest that capable local leadership is indispensable for controlling the coronavirus epidemic, yet local governments' approaches are varied, contributing to dissimilar local epidemic control policy pathways and positive outcomes in the fight against COVID-19. The effectiveness of disease control is determined by how local governments' measures have adapted to geospatial and socioeconomic heterogeneity. The coordinated actions from central to local governments also reveal an efficient, top-down command transmission and execution system for coping with the pandemic. This article argues that effective control of pandemics requires both a holistic package of governance strategies and locally adaptive governance measures/processes, and concludes with proposals for both a more effective response at the local level and identification of barriers to achieving these responses within diverse subnational institutional contexts.

Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China.

OBJECTIVES: The novel coronavirus (COVID-19) epidemic is reaching its final phase in China. The epidemic data are available for a complete assessment of epidemiological parameters in all regions and time periods. METHODS: This study aims to present a spatiotemporal epidemic model based on spatially stratified heterogeneity (SSH) to simulate the epidemic spread. A susceptible-exposed/latent-infected-removed (SEIR) model was constructed for each SSH-identified stratum (each administrative city) to estimate the spatiotemporal epidemiological parameters of the outbreak. RESULTS: We estimated that the mean latent and removed periods were 5.40 and 2.13 days, respectively. There was an average of 1.72 latent or infected persons per 10,000 Wuhan travelers to other locations until January 20th, 2020. The space-time basic reproduction number (R0) estimates indicate an initial value between 2 and 3.5 in most cities on this date. The mean period for R0 estimates to decrease to 80%, and 50% of initial values in cities were an average of 14.73 and 19.62 days, respectively. CONCLUSIONS: Our model estimates the complete spatiotemporal epidemiological characteristics of the outbreak in a space-time domain. These findings will help enhance a comprehensive understanding of the outbreak and inform the strategies of prevention and control in other countries worldwide.

Spatiotemporal heterogeneity and its determinants of COVID-19 transmission in typical labor export provinces of China.

BACKGROUND: Previous studies have indicated that the risk of infectious disease spread is greatest in locations where a population has massive and convenient access to the epicenter of an outbreak. However, the spatiotemporal variations and risk determinants of COVID-19 in typical labor export regions of China remain unclear. Understanding the geographical distribution of the disease and the socio-economic factors affecting its transmission is critical for disease prevention and control. METHODS: A total of 2152 COVID-19 cases were reported from January 21 to February 24, 2020 across the 34 cities in Henan and Anhui. A Bayesian spatiotemporal hierarchy model was used to detect the spatiotemporal variations of the risk posed by COVID-19, and the GeoDetector q statistic was used to evaluate the determinant power of the potential influence factors. RESULTS: The risk posed by COVID-19 showed geographical spatiotemporal heterogeneity. Temporally, there was an outbreak period and control period. Spatially, there were high-risk regions and low-risk regions. The high-risk regions were mainly in the southwest areas adjacent to Hubei and cities that served as economic and traffic hubs, while the low-risk regions were mainly in western Henan and eastern Anhui, far away from the epicenter. The accessibility, local economic conditions, and medical infrastructure of Wuhan in Hubei province all played an important role in the spatiotemporal heterogeneity of COVID-19 transmission. The results indicated that the q statistics of the per capita GDP and the proportion of primary industry GDP were 0.47 and 0.47, respectively. The q statistic of the population flow from Wuhan was 0.33. In particular, the results showed that the q statistics for the interaction effects between population density and urbanization, population flow from Wuhan, per capita GDP, and the number of doctors were all greater than 0.8. CONCLUSIONS: COVID-19 showed significant spatiotemporal heterogeneity in the labor export regions of China. The high-risk regions were mainly located in areas adjacent to the epicenter as well as in big cities that served as traffic hubs. Population access to the epicenter, as well as local economic and medical conditions, played an important role in the interactive effects of the disease transmission.

Risk of Coronavirus Disease 2019 Transmission in Train Passengers: an Epidemiological and Modeling Study.

BACKGROUND: Train travel is a common mode of public transport across the globe; however, the risk of coronavirus disease 2019 (COVID-19) transmission among individual train passengers remains unclear. METHODS: We quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2334 index patients and 72 093 close contacts who had co-travel times of 0-8 hours from 19 December 2019 through 6 March 2020 in China. We analyzed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time. RESULTS: The attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI], 5.3%-19.0%), with a mean of 0.32% (95% CI, .29%-.37%). Passengers in seats on the same row (including the adjacent passengers to the index patient) as the index patient had an average attack rate of 1.5% (95% CI, 1.3%-1.8%), higher than that in other rows (0.14% [95% CI, .11%-.17%]), with a relative risk (RR) of 11.2 (95% CI, 8.6-14.6). Travelers adjacent to the index patient had the highest attack rate (3.5% [95% CI, 2.9%-4.3%]) of COVID-19 infection (RR, 18.0 [95% CI, 13.9-23.4]) among all seats. The attack rate decreased with increasing distance, but increased with increasing co-travel time. The attack rate increased on average by 0.15% (P = .005) per hour of co-travel; for passengers in adjacent seats, this increase was 1.3% (P = .008), the highest among all seats considered. CONCLUSIONS: COVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when traveling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.

Risk assessment of the step-by-step return-to-work policy in Beijing following the COVID-19 epidemic peak.

Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. As the virus spread worldwide causing a global pandemic, China reduced transmission at considerable social and economic cost. Post-lockdown, resuming work safely, that is, while avoiding a second epidemic outbreak, is a major challenge. Exacerbating this challenge, Beijing hosts many residents and workers with origins elsewhere, making it a relatively high-risk region in which to resume work. Nevertheless, the step-by-step approach taken by Beijing appears to have been effective so far. To learn from the epidemic progression and return-to-work measures undertaken in Beijing, and to inform efforts to avoid a second outbreak of COVID-19, we simulated the epidemiological progression of COVID-19 in Beijing under the real scenario of multiple stages of resuming work. A new epidemic transmission model was developed from a modified SEIR model for SARS, tailored to the situation of Beijing and fitted using multi-source data. Because of strong spatial heterogeneity amongst the population, socio-economic factors and medical capacity of Beijing, the risk assessment was undertaken spatiotemporally with respect to each district of Beijing. The epidemic simulation confirmed that the policy of resuming work step-by step, as implemented in Beijing, was sufficient to avoid a recurrence of the epidemic. Moreover, because of the structure of the model, the simulation provided insights into the specific factors at play at different stages of resuming work, allowing district-specific recommendations to be made with respect to monitoring at different stages of resuming work . As such, this research provides important lessons for other cities and regions dealing with outbreaks of COVID-19 and implementing return-to-work policies.