Spread Of COVID-19 In China Research Articles

Variable importance based interaction modelling with an application on initial spread of COVID-19 in China

Abstract Interaction selection for linear regression is useful in many fields of modern science, yet very challenging. Existing methods focus on finding one optimal model but they may perform poorly in terms of stability for high-dimensional data, and they do not typically deal with categorical predictors. In this paper, we introduce a variable importance based interaction modelling (VIBIM) procedure for learning interactions in a linear regression model with both continuous and categorical predictors. We apply the VIBIM procedure to a COVID-19 data and show that the VIBIM approach leads to better models in terms of interpretability, stability, reliability, and prediction.

Dynamic Modeling of Antibody Level Changes with Individual Age: The Case of COVID-19 Spread in China

Dynamic Modeling of Antibody Level Changes with Individual Age: The Case of COVID-19 Spread in China

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China

The human social and behavioral activities play significant roles in the spread of COVID-19. Social-distancing centered non-pharmaceutical interventions (NPIs) are the best strategies to curb the spread of COVID-19 prior to an effective pharmaceutical or vaccine solution. This study investigates various social-distancing measures’ impact on the spread of COVID-19 using advanced global and novel local geospatial techniques. Social distancing measures are acquired through website analysis, document text analysis, and other big data extraction strategies. A spatial panel regression model and a newly proposed geographically weighted panel regression model are applied to investigate the global and local relationships between the spread of COVID-19 and the various social distancing measures. Results from the combined global and local analyses confirm the effectiveness of NPI strategies to curb the spread of COVID-19. While global level strategies allow a nation to implement social distancing measures immediately at the beginning to minimize the impact of the disease, local level strategies fine tune such measures based on different times and places to provide targeted implementation to balance conflicting demands during the pandemic. The local level analysis further suggests that implementing different NPI strategies in different locations might allow us to battle unknown global pandemic more efficiently.

Impact of Lockdown on Air Pollution: Evidence from the “2+26” Cities in the Beijing-Tianjin-Hebei Region

To prevent the spread of COVID-19 in China, many cities were locked down after January 23, 2020. Based on the panel data of the “2+26” cities from 10 January to 15 March 2020, this paper took the lockdown as a quasi-natural experiment and established a multi-phase DID model to investigate whether the lockdown measures significantly reduced air pollution in locked-down cities in the Beijing-Tianjin-Hebei (BTH) region. The core innovation of this paper is that we considered the urban immigration scale index as a mediating variable , which is rarely adopted in the existing literature, and we identified the relationships between the lockdown, the intracity migration index, the urban immigration scale index and air pollution. The results showed that compared with the non-locked-down cities, the lockdown significantly reduced air pollution. Furthermore, it was found that the lockdown reduced air pollution by reducing intracity migration and the urban scale of immigration. Moreover, compared with the corresponding period in 2019, air pollution was significantly reduced in the locked-down cities of the “2+26” cities. Air pollution is closely related to human activity, and green production and technological innovations are critical for reducing air pollution in the BTH region.

Examining the Human Activity-Intensity Change at Different Stages of the COVID-19 Pandemic across Chinese Working, Residential and Entertainment Areas.

The COVID-19 pandemic has already resulted in more than 6 million deaths worldwide as of December 2022. The COVID-19 has also been greatly affecting the activity of the human population in China and the world. It remains unclear how the human activity-intensity changes have been affected by the COVID-19 spread in China at its different stages along with the lockdown and relaxation policies. We used four days of Location-based services data from Tencent across China to capture the real-time changes in human activity intensity in three stages of COVID-19-namely, during the lockdown, at the first stage of work resuming and at the stage of total work resuming-and observed the changes in different land use categories. We applied the mean decrease Gini (MDG) approach in random forest to examine how these changes are influenced by land attributes, relying on the CART algorithm in Python. This approach was also compared with Geographically Weighted Regression (GWR). Our analysis revealed that the human activity intensity decreased by 22-35%, 9-16% and 6-15%, respectively, in relation to the normal conditions before the spread of COVID-19 during the three periods. The human activity intensity associated with commercial sites, sports facilities/gyms and tourism experienced the relatively largest contraction during the lockdown. During the relaxations of restrictions, government institutions showed a 13.89% rise in intensity at the first stage of work resuming, which was the highest rate among all the working sectors. Furthermore, the GDP and road junction density were more influenced by the change in human activity intensity for all land use categories. The bus stop density was importantly associated with mixed-use land recovery during the relaxing stages, while the coefficient of density of population in entertainment land were relatively higher at these two stages. This study aims to provide additional support to investigate the human activity changes due to the spread of COVID-19 at different stages across different sectors.

Quantitatively evaluate the impact of domestic aviation control measures on the spread of COVID-19 in China

To quantitatively evaluate the impact of domestic aviation control measures on the spread of COVID-19 in China. The number of international flights from March to September 2019 simulated the number of flights from March to September 2020 without implementing aviation control measures. In addition, the proportion of asymptomatic persons and the delay in case reporting were adjusted to estimate the prevalence of each country during the same period and calculate the estimated imported cases. The estimated imported cases were assigned each day with weight, and the estimated daily reported cases were obtained based on the actual daily number of domestic cases in China. Effective Reproduction Number (R_t) was calculated based on delayed distribution, Basic Reproductive Number (R_0) distribution, and generation time distribution were reported in previous studies. Gaussian Process was used to estimate the effect of time-varying on R_t, and the estimated R_t was compared with the actual R_t. The estimated imported cases increased significantly compared with the actual number of imported cases. The estimated imported cases were mainly concentrated in North America and Europe from March to April and gradually increased in many East Asian countries from May to September. The difference between predicted R_t and actual R_t was statistically significant. The estimated imported cases and the estimated R_t have increased compared to the actual situation. This paper quantitatively proves that Chinese aviation control measures significantly suppress the COVID-19 epidemic, which is conducive to promoting and applying this measure.

Analysis of the Geographic Transmission Differences of COVID-19 in China Caused by Population Movement and Population Density.

The coronavirus disease (COVID-19) has led to a global pandemic and caused huge healthy and economic losses. Non-pharmaceutical interventions, especially contact tracing and social distance restrictions, play a vital role in the control of COVID-19. Understanding the spatial impact is essential for designing such a control policy. Based on epidemic data of the confirmed cases after the Wuhan lockdown, we calculate the invasive reproduction numbers of COVID-19 in the different regions of China. Statistical analysis indicates a significant positive correlation between the reproduction numbers and the population input sizes from Wuhan, which indicates that the large-scale population movement contributed a lot to the geographic spread of COVID-19 in China. Moreover, there is a significant positive correlation between reproduction numbers and local population densities, which shows that the higher population density intensifies the spread of disease. Considering that in the early stage, there were sequential imported cases that affected the estimation of reproduction numbers, we classify the imported cases and local cases through the information of epidemiological data and calculate the net invasive reproduction number to quantify the local spread of the epidemic. The results are applied to the design of border control policy on the basis of vaccination coverage.

Assessing impact of the COVID-19 pandemic on China’s TFP growth: Evidence from region-level data in 2020

This paper examines the first wave spread of COVID-19 in China and its impact on TFP growth for 2020, and assess the role of anti-epidemic lockdown policy in suppressing the pandemic. Methodologically, we systematically quantify the disparity in the pandemic’s productivity impact and the role of lockdown policies across regions, by combining the prefecture-level TFP growth for 422 regions (including 276 municipal cites and 146 county regions) with the daily statistics on the pandemic. Our results show that the negative impact of the COVID-19 pandemic on TFP growth are more likely to occur in municipal cities, compared to rural areas. Moreover, the anti-epidemic quarantine policy succeeded to bring the COVID-19 pandemic down in China, but it may generate additional costs through dampening TFP growth if overused. Given the regions either with a relative higher resilience level or in the remote rural areas suffered more from the strict regulation. A more flexible policy is required to be designed so as to mitigate the ongoing COVID-19 impacts in future. These findings provide useful insights for China, as well as other Asian developing countries, to cope with its continuing episodes.

Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted

BackgroundContact patterns play a key role in the spread of respiratory infectious diseases in human populations. During the COVID-19 pandemic, the regular contact patterns of the population have been disrupted due to social distancing both imposed by the authorities and individual choices. Many studies have focused on age-mixing patterns before the COVID-19 pandemic, but they provide very little information about the mixing patterns in the COVID-19 era. In this study, we aim at quantifying human heterogeneous mixing patterns immediately after lockdowns implemented to contain COVID-19 spread in China were lifted. We also provide an illustrative example of how the collected mixing patterns can be used in a simulation study of SARS-CoV-2 transmission.Methods and resultsIn this work, a contact survey was conducted in Chinese provinces outside Hubei in March 2020, right after lockdowns were lifted. We then leveraged the estimated mixing patterns to calibrate a mathematical model of SARS-CoV-2 transmission. Study participants reported 2.3 contacts per day (IQR: 1.0–3.0) and the mean per-contact duration was 7.0 h (IQR: 1.0–10.0). No significant differences in average contact number and contact duration were observed between provinces, the number of recorded contacts did not show a clear trend by age, and most of the recorded contacts occurred with family members (about 78%). The simulation study highlights the importance of considering age-specific contact patterns to estimate the COVID-19 burden.ConclusionsOur findings suggest that, despite lockdowns were no longer in place at the time of the survey, people were still heavily limiting their contacts as compared to the pre-pandemic situation.

Assessing the spread risk of COVID-19 associated with multi-mode transportation networks in China

The spatial spread of COVID-19 during early 2020 in China was primarily driven by outbound travelers leaving the epicenter, Wuhan, Hubei province. Existing studies focus on the influence of aggregated out-bound population flows originating from Wuhan; however, the impacts of different modes of transportation and the network structure of transportation systems on the early spread of COVID-19 in China are not well understood. Here, we assess the roles of the road, railway, and air transportation networks in driving the spatial spread of COVID-19 in China. We find that the short-range spread within Hubei province was dominated by ground traffic, notably, the railway transportation. In contrast, long-range spread to cities in other provinces was mediated by multiple factors, including a higher risk of case importation associated with air transportation and a larger outbreak size in hub cities located at the center of transportation networks. We further show that, although the dissemination of SARS-CoV-2 across countries and continents is determined by the worldwide air transportation network, the early geographic dispersal of COVID-19 within China is better predicted by the railway traffic. Given the recent emergence of multiple more transmissible variants of SARS-CoV-2, our findings can support a better assessment of the spread risk of those variants and improve future pandemic preparedness and responses.

Key links in network interactions: Assessing route-specific travel restrictions in China during the Covid-19 pandemic

We consider a model of network interactions where the outcome of a unit depends on the outcomes of the connected units. We determine the key network link, i.e., the network link whose removal results in the largest reduction in the aggregate outcomes, and examine a measure that quantifies the contribution of a network link to the aggregate outcomes. We provide an example examining the spread of Covid-19 in China. Travel restrictions were imposed to limit the spread of infectious diseases. As uniform restrictions can be inefficient and incur unnecessarily high costs, we examine the design of restrictions that target specific travel routes. Our approach may be generalized to multiple countries to guide policies during epidemics ranging from ex ante route-specific travel restrictions to ex post health measures based on travel histories, and from the initial travel restrictions to the phased reopening.

Influence of COVID-19 on the Tourism Industry in China: An Artificial Neural Networks Approach.

Prior to COVID-19, the tourism industry was one of the important sectors of the world economy. This study intends to measure the perception of Chinese tourists concerning the spread of COVID-19 in China. The crowding perception, xenophobia, and ethnocentrism are the measurement indicators of the study. A five-point Likert scale is used to predict the perception of the tourists in various destinations. The Kaiser–Mayer–Olkin test and Cronbach's alpha are conducted to ensure the validity and reliability of the corresponding items. SPSS version 21 is used to obtain factor loading, mean values, and standard deviation. Regression analysis is used to measure the strength of the constructs' relationship and prove the hypotheses. Questionnaires have been filled from 730 Chinese respondents. Artificial neural networks and confusion matrices are used for validation and performance evaluation, respectively. Results show that crowding perception, xenophobia, and ethnocentrism caused the spread of COVID-19 during the epidemic. Hence, the tourism industry in China is adversely affected by COVID-19. The crisis management stakeholders of the country need to adopt policies to reduce the spread of COVID-19. The tourism sector needs to provide confidence to the tourists. It will provide ground for the mental strength of the tourists in China.

Highly coordinated nationwide massive travel restrictions are central to effective mitigation and control of COVID-19 outbreaks in China.

COVID-19, the disease caused by the novel coronavirus 2019, has caused grave woes across the globe since it was first reported in the epicentre of Wuhan, Hubei, China, in December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that rendered more than 900 million people housebound for more than two months since the lockdown of Wuhan, and elsewhere, on 23 January 2020. Here, we assess the impact of China’s massive lockdowns and travel restrictions reflected by the changes in mobility patterns across and within provinces, before and during the lockdown period. We calibrate movement flow between provinces with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicentre Hubei. Moreover, we show that synchronous lockdowns and consequent reduced mobility lag a certain time to elicit an actual impact on suppressing the spread. Such highly coordinated nationwide lockdowns, applied via a top-down approach along with high levels of compliance from the bottom up, are central to mitigating and controlling early-stage outbreaks and averting a massive health crisis.

The effects of Wuhan highway lockdown measures on the spread of COVID-19 in China

To verify the effects of Wuhan highway lockdown measures on the spread of COVID-19 across China cities, we extracted the vehicle outflow from Wuhan to 245 cities from the Chinese highway toll system. A dynamic exponential risk model that considered the vehicle outflow, city gross domestic product, city population, and distance between two cities was established to characterize the spread of pandemics and quantify the blocking effects. Results showed that an early highway lockdown measure could indeed reduce the confirmed cases and vehicles with 1–9 seats played a leading role. The confirmed cases in Guangxi, Henan, and Shanxi could be reduced by more than 50%, as well as Hubei by 20% if the highway was closed 3 days in advance. The blocking effects on Fujian, Jiangxi, Guangdong, Hunan, and Shandong were not obvious, where the number of confirmed cases only decreased by a small proportion (below 10%). The findings could be used to help each provincial government to adjust policies properly and improve the effectiveness of epidemic control and prevention. Moreover, the proposed method could also be applied to various countries or regions affected by COVID-19, as well as other similar pandemics.

Prediction of the COVID-19 Spread in China Based on Long Short-Term Memory Network

The sudden outbreak of COVID-19 has caused great losses to the economy and the life of the masses. Long short-term memory (LSTM) network is a time recursive neural network, which is suitable for processing and predicting important events with relatively long interval and delay in time series. Using LSTM network to predict and analyze the development trend of epidemic situation, it is imperative to prevent epidemic situation from causing secondary harm to China’s development. In this paper, we first obtained the COVID-19 data published by China Health Net using crawler technology, which is the accurate value of infection trend after the outbreak of COVID-19 in China. Then, based on these data, the LSTM model is used to predict the development trend of the epidemic in one year, and the mean square error is used to calculate the error between the prediction and the real data. The experimental model is used to predict and analyze the development trend of COVID-19. The results show that the error between predicted data and real data is small and the effect is very good, which provides a reasonable basis and forecast for scientific prevention and control of epidemic situation.

Control strategies against COVID-19 in China: Significance of effective testing in the long run.

The COVID-19 pandemic has become a long-term crisis that calls for long-term solutions. We combined an augmented SEIR simulation model with real-time human mobility data to decompose the effects of lockdown, travel bans and effective testing measures in the curtailment of COVID-19 spread in China over different time horizons. Our analysis reveals that the significant growth in the detection rate of infectious cases, thanks to the expansion in testing efficiency, were as effective as city lockdowns in explaining the reduction in new infections up to mid-March. However, as we extended the analysis to July, increasing the detection rate to at least 50% is the only reliable way to bring the spread under control.

The role of high-speed rail and air travel in the spread of COVID-19 in China

BackgroundPublic transportation is a major facilitator of the spread of infectious diseases and has been a focus of policy interventions aiming to suppress the current COVID-19 epidemic. MethodsWe use a random-effects panel data model and a Difference-in-Differences in Reverse (DDR) model to examine how air and rail transport links with Wuhan as well as the suspension of these transport links influenced the development of the epidemic in China. ResultsWe find high-speed rail (HSR) and air connectivity with Wuhan resulted in 25.4% and 21.2% increases in the average number of daily new confirmed cases, respectively, while their suspension led to 18.6% and 13.3% decreases in that number. We also find that the suspension effect was dynamic, growing stronger over time and peaking 20–23 days after the Wuhan lockdown, then gradually wearing off. It took approximately four weeks for this effect to fully materialize, roughly twice the maximum incubation period, and similar dynamic patterns were seen in both HSR and air models. Overall, HSR had a greater impact on COVID-19 development than air transport. ConclusionsOur research provides important evidence for implementing transportation-related policies in controlling future infectious diseases.

Understanding the Spread of COVID-19 in China: Spatial–Temporal Characteristics, Risk Analysis and the Impact of the Quarantine of Hubei Province on the Railway Transportation Network

The rapid spread of COVID-19 and its global growth constitutes an international public-health emergency, posing a serious threat to global health, safety, and social economy. In this paper, we systematically studied the temporal and spatial characteristics of COVID-19, infectivity, and the impact of Hubei province’s quarantine on the national railway system on the basis of epidemic and national train data. This study found the following: (1) The overall growth of the epidemic was exponential, and the outbreak of Hubei had a strong spread in the eastern and southern directions. The epidemic was generally more serious in the capital or developed cities in each province, and the epidemic outside Hubei was under control after the imported growth ended. (2) On the basis of analyzing the disturbance of the spread of the epidemic by traffic control, the average incubation period of COVID-19 was approximately 4 days. The ratio of the number of cured people to the number of deaths gradually increased, indicating that, given sufficient medical service, the cure rate can be greatly improved. (3) The quarantine of Hubei had greater impact on cities with higher centrality, especially in the Yangtze River Delta region, and smaller impact on the overall connectivity of the national railway network. For local people, quarantine had great impact on the outflow of local people to neighboring provinces.

Uncertain Statistics and COVID-19 Spread in China

Uncertain statistics is a set of mathematical techniques for collecting, analyzing and interpreting data by uncertainty theory. In this paper, the main topics of uncertain statistics, including estimation of uncertainty distribution, uncertain regression analysis, uncertain times series, uncertain differential equation and uncertain hypothesis test, are reviewed. Furthermore, by the application to the COVID-19 spread in China, the advantages of those techniques in uncertain statistics are sorted out.

Adversity Tries Friends: A Multilevel Analysis of Corporate Philanthropic Response to the Local Spread of COVID-19 in China.

We examine corporate philanthropic decisions in response to the local spread of COVID-19. From a strategic perspective, firms may proactively undertake philanthropic efforts to limit the spread of the pandemic and avoid a degraded business environment. From the perspective of non-trivial costs, increased economic uncertainty can raise concerns about business survival and lead to conservative philanthropic strategies. Following the proverb “prosperity makes friends, adversity tries them,” at the provincial level, our results support the second perspective. Specifically, when the spread of the pandemic worsens in a province, local firms are less likely to make COVID-19-related donations in terms of likelihood and amount. Investors also react negatively, not only to the local spread of COVID-19 but also to COVID-19-related philanthropic donations. At the organizational level, our evidence indicates that there is at least some level of cost–benefit analysis underlying corporate philanthropic decisions. Specifically, corporate philanthropic donations, especially those made to the local business environment, are significantly affected by organizational-level factors, such as pre-existing resource availability and motives to acquire political and reputational resources. Overall, our multilevel study presents a comprehensive picture of corporate philanthropic decisions amid the COVID-19 crisis.