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One of the key indicators to measure the sustainability and resilience of a city during a public health crisis is how well it can meet the daily needs of its residents. During the COVID-19 lockdown in Shanghai in 2022, e-commerce shopping and delivery became the most important method for ensuring the city's material supplies. This article uses the distribution data of a fresh e-commerce platform's pre-warehouse and static population distribution data to establish a basic material supply system evaluation model for the city and explore its resilience potential. Focusing on the central urban area of Shanghai, this study uses a population heat map with geographic coordinates to reflect the static distribution of residents and obtains the distribution data of the e-commerce pre-warehouses. Using kernel density analysis, the relationship between the pre-warehouses and the residents' needs is established. Through analysis, it was found that the supply capacity of fresh food in different areas of Shanghai during the lockdown could be categorized as insufficient, adequate, or excessive. Based on these three categories, improvement strategies were proposed. Finally, this article suggests establishing a scientific supply security system to promote urban sustainability and prepare for future challenges.
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Given the turbulent conditions of the early 21st century and the release of data from the 2020 Census, it is an appropriate time to examine contemporary population redistribution trends in nonmetropolitan America. Analysis centers on the major demographic components of population change: migration;and natural increase. The analysis demonstrates that the turbulent economic, social, and now epidemiological conditions of recent years altered traditional demographic trends in nonmetropolitan America. For the first time in history, nonmetropolitan America lost population between 2010 and 2020 because of shifts in migration trends and diminishing natural increase. In contrast, post‐censal population estimates suggest that nonmetropolitan population gains exceeded those in metropolitan areas for the first time in 50 years between 2020 and 2021. The recent widespread nonmetropolitan population increases are the result of substantial net migration gains that offset the growing natural decrease fostered by COVID‐19. Sustained net migration gains in nonmetro areas provides a demographic lifeline to many counties that would otherwise face depopulation because of accelerating natural decrease. Whether these migration patterns can be sustained remains to be seen.
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Taylor's law states that the spatial variance of the population density varies as the power function of the mean population density. This law is tested on daily Covid-19 infection density for five periods between February 25, 2020 and March 15, 2021. The Italian provinces are grouped by geography into three ensembles. A simultaneous-equation model accounts for correlations between the ensembles, between Italian provinces within each ensemble, and for temporal autocorrelations. The selected periods show ensembles with all Taylor's law slopes below 2 (reflecting State interventions at the national level), or all above 2 (reflecting interventions at the local level), or some ensembles above while others were below. Slope of Taylor's law and average density trend indicate whether the infection density is highly concentrated in a few provinces (when the slope is greater than 2 with increasing density, and when the slope is less than 2 with decreasing density) or spread evenly among all provinces in an ensemble (when the slope is greater than 2 with decreasing density, and when the slope is less than 2 with increasing density), which allows the government and epidemiologists to design disease control policies for targeted provinces and ensembles in Italy. © 2023 Taylor & Francis.
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Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still not widely available due to the limited knowledge of complex human activity patterns. The emergence of location-based service big data provides additional opportunities to solve this problem. In this study, we integrated ambient population data, nighttime light data, and building volume data;innovatively proposed a spatial downscaling framework for Baidu heat map data during work time and sleep time;and mapped the population distribution with high spatiotemporal resolution (i.e., hourly, 100 m) in Beijing. Finally, we validated the generated population distribution maps with high spatiotemporal resolution using the highest-quality validation data (i.e., mobile signaling data). The relevant results indicate that our proposed spatial downscaling framework for both work time and sleep time has high accuracy, that the distribution of the population in Beijing on a regular weekday shows "centripetal centralization at daytime, centrifugal dispersion at night" spatiotemporal variation characteristics, that the interaction between the purpose of residents' activities and the spatial functional differences leads to the spatiotemporal evolution of the population distribution, and that China's "surgical control and dynamic zero COVID-19" epidemic policy was strongly implemented. In addition, our proposed spatial downscaling framework can be transferred to other regions, which is of value for governmental emergency measures and for studies about human risks to environmental issues.
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Based on the classical SIR model and CEMM intercity model, a new model was established by adding "population density"parameter to analyze and predict the spread of virus. In addition, the current trend of the epidemic and forecast data can be referenced to the public in an intuitive web view to improve the perception of risk information in the society. The real-time epidemic data interface was adopted to analyze the real-time pneumonia epidemic data captured by the deployment of timing crawler combined with the regional population density to build a model. Then, the diversified charts, Python and Web front-end technologies were used to realize the visualization of epidemic information. COVID-19 grows exponentially without obstruction, and when a place has a high population density, the spread of the virus accelerates and the number of people infected increases. The research shows that the integration of population density parameters can further improve the epidemic prediction function, provide epidemic data reference in a more effective and accurate way, and further improve the public's ability to perceive social risk information. © 2022 IEEE.
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Evaluating park equity can help guide the advancement of sustainable and equitable space policies. Previous studies have mainly considered accessibility when evaluating park equity while ignoring the selectivity and convenience of entering parks and residents’ recognition of parks. Measuring equity based mainly on spatial thinking has resulted in the social aspects of parks receiving insufficient attention. In this study, we therefore integrated the spatial and social equity of parks and developed a multidimensional framework to evaluate park equity in four dimensions: accessibility (Ai), diversity (Di), convenience (Ci), and satisfaction (Si). Empirical analysis from Yangzhou, China showed that: (1) in Yangzhou’s built-up districts, 23.43% of the communities received high- or relatively high-level park access but 17.72% received little or no park access. (2) The Gini coefficient indicated that all three dimensions showed a mismatch with population distribution, except for satisfaction (Si), which showed a relatively reasonable match. (3) Park access was generally better in communities with better locations, environments, and facilities. High-income groups enjoyed significantly better park access than low- and middle-income groups. These findings could help urban planners and policymakers develop effective policies to reduce inequality in park access.
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Introduction: The human circadian clock is daily entrained by both light exposure and daily social schedules, which were severely affected during the pandemic-associated lockdown. In a previous work we found that after one month of lockdown, Argentinian residents exhibited later chronotypes compared with a pre-pandemic situation, despite they slept longer and showed less social jetlag (Leone M.J. et al, Current Biology). In this study, we collected an independent set of local data with the aim to develop an evidence-based mobile app that offers customized recommendations to improve and maintain healthy sleep and circadian rhythms. Materials and methods: Data was collected throughout a phone/website survey between July and September 2020 (n=4460, after 4-6 months into lockdown) in Argentina. The survey included questions about demographic factors, habits, and previously standardized and validated questionnaires (MEQ, MCTQ, PSQI). Data from Buenos Aires city and suburbs (n=3246) was calibrated to match the population distribution and it was used to run the main analyses. The rest of the database was used to validate results. We conducted a cross validation process using linear models, which included a feature selection process to find the most relevant regressors to fit each chronotype and sleep-related variable. For a given age and gender, each model predicted a set of optimal values for the regressors (e.g. sunlight exposure, regular activities) where the dependent variable is maximized (or minimized). Finally, the recommendation system is based on the comparison between optimal and actual values for each predictor, considering the most affected variables. Results: The final calibrated sample (age: 41.3±15.5, 67% female) shows late chronotypes (MSFsc: 06:00±11min, MEQ score: 49.42±11.3), low levels of social jetlag (0.99h±1.09) and considerable long sleep duration on weekdays (7.31h±1.43). The regressors which significantly affect at least one variable were light exposure, use of alarm, naps and regular activities (and its timing, i.e. work, study, dinner, other activities) as well as age and gender (and interactions). We found no effects of cohabitation, exercise timing and use of screens. The optimal levels of the selected regressors were used to build the recommendation system (i.e. algorithm) on which the mobile app MiRelojInterno is based (available for both Android and iOS platforms, www.mirelojinterno.org). Conclusions: We developed a mobile app based on local evidence that inquires about habits, chronotype and sleep, returns to its users an overview of their current state -including all variables and predictors- along with customized recommendations with the aim to create awareness and improve and maintain healthy sleep and circadian rhythms depending on the age, gender and habits. Acknowledgements: This research project was supported by CONICET and Agencia I+D+i (IP-COVID19-679).
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Objective To analyze the work situation of the personnel in Beijing Centers for Disease Prevention and Control during the novel coronavirns disease 2019 (C0VID-19) epidemic，and to provide references for improving the construction of the capital5 s disease control and prevention system. Methods Cross-sectional survey and cluster sanpling methods were used. A total of 422 municipal-level and 664 district-level professional technicians from CDCs who were mainly involved in epidemic prevention and control in Beijing were included in the study. Self-designed questionnaires were used to collect the basic information, work intensity and satisfaction and other data. The statistical description and test analysis were carried out. Results Among professionals, 64. 36% had nornal workload, and 76. 89% had overload during the epidemic prevention and control period. The proportion of disease control personnel expressing dissatisfaction "with the usual salary level "was 54. 51%, and the satisfaction with the professional title promotion w-as mostly at the average level (45. 58%). The proportions of satisfaction with the prevention and control work arrangements and logistical support during the COVID-19 epidemic were 49. 08% and 54. 42%, respectively. Only 21. 73% professionals were satisfied with the temporar w-ork subsidy. From the perspective of population distribution, staffs at the municipal and district levels and in different job positions were mainly dissatisfied with the salar level (all P<0. 05). Most of staffs who undertook different prevention and control responsibilities were satisfied with the work arrangements and logistics support (all P<0. 05), but they w-ere dissatisfied with the temporar work subsidies (H = 27. 076, P = 0. 012). Among the survey respondents, 44.48% had thoughts of resigning. Regardless of the municipal and district levels, different professional titles or positions, the wdllingness to resign was generally high (all P>0. 05). The primar reason for wanting to leave was the low salary level, followed by difficulty in promotion of professional titles and poor development prospects which were also major considerations. Conclusion It is suggested to improve the stability of CDCs staffs and promote the high-quality and sustainable development of the disease control and prevention system by improving the personnel allocation, strengthening the interdisciplinary talent reserve, improving the salary system and optimizing the professional title appointment mechanism.
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Objective: To analyze the epidemiological characteristics and effect of prevention on imported COVID-19 cases in Beijing, and provide scientific evidences for the prevention and control of imported COVID-19.
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The Unmanned Aerial Vehicle (UAV) has been used for the delivery of medical supplies in urban logistical distribution, due to its ability to reduce human contact during the global fight against COVID-19. However, due to the reliability of the UAV system and the complex and changeable operation scene and population distribution in the urban environment, a few ground-impact accidents have occurred and generated enormous risks to ground personnel. In order to reduce the risk of UAV ground-impact accidents in the urban logistical scene, failure causal factors, and failure modes were classified and summarized in the process of UAV operation based on the accumulated operation data of more than 20,000 flight hours. The risk assessment model based on the Bayesian network was built. According to the established network and the probability of failure causal factors, the probabilities of ground impact accidents and intermediate events under different working conditions were calculated, respectively. The posterior probability was carried out based on the network topology to deduce the main failure inducement of the accidents. Mitigation measures were established to achieve the equivalent safety level of manned aviation, aiming at the main causes of accidents. The results show that the safety risk of the UAV was reduced to 3.84 × 10−8 under the action of risk-mitigation measures.
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It has been little over a year since the global surge of the COVID-19 pandemic. Governments and private efforts to develop a vaccine that will curb the spread of the virus have been made across the globe particularly in developed countries. Withal, there has been worries about equitable access to the vaccines once they have been fully developed and approved for wider population distribution particularly in poorer countries throughout the African continent. This calls for these countries to look for alternative therapeutics through the collaboration of western-trained scientists and indigenous health care practitioners and knowledge holders. A lot has been published on the possible contribution of traditional medicine across the globe, however, little has been done to provide perspectives from African indigenous knowledge systems. The main aim of this paper, is to explore what the current situation in Africa is with regards to the contribution of African indigenous knowledge systems, to the development of a vaccine for COVID-19 and the existing therapeutics used by local communities in the management of the illness. This is done through a preliminary analysis of current literature published in scholarly journals. The article concludes that African traditional medicine has played a huge role in providing primary health care services even though it is under developed compared to Asian traditional medical systems. Therefore, harnessing and developing the ever-present potential of indigenous health care systems towards providing solutions to health challenges posed by the COVID-19 pandemic is vital. Perceptions of African people towards Western manufactured vaccines are equally important in order to provide an understanding on the level of acceptability within African communities.
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BACKGROUND: As a multi-ethnic country, the US is increasingly concerned about ethnic minorities facing disproportionate health risks of the coronavirus disease 2019 (COVID-19) pandemic. This study attempted to provide a macro picture of the associations between population distribution by ethnicity and the vulnerability to COVID-19 in terms of infection risk and vaccination coverage in the US. METHODS: This study used multi-source data from New York Times, County Health Rankings & Roadmap Program (2020), and the Center for Disease Control and Prevention. Multiple linear regressions were performed at equidistant time points (May 2020-Jan 2021, with one-month interval between each time point) to reveal the association between population distribution by ethnicities and the infection risk and the dynamics over time. Besides, multiple linear regressions were also conducted at equidistant time points (Jan 2021-Aug 2021) to reveal whether health disparities between ethnicities would hold true for the COVID-19 vaccination coverage (in total population, and among those > 12, > 18, and > 65 years of age). RESULTS: Both the COVID-19 confirmed cases (population standardized) and the vaccination coverage (in total population, and among those > 12, > 18, and > 65 years of age) were significantly associated with the population distribution by ethnicity (e.g., population percentage of ethnic minorities). Above associations were statistically significant for non-Hispanic blacks and Hispanics, but not for Asian Americans. CONCLUSIONS: A proportion of socioeconomically-disadvantageous population could be a key intuitive reflection of the risk level of this public health crisis. The policy focusing on the vulnerable population is important in this pandemic.
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Are stay-at-home orders effective during a global pandemic? Although stay-at-home orders should help to slow the spread of contagious diseases (like COVID-19) by reducing person-to-person contact outside a household, these orders are only effective if people actually stay at home. This study uses data on the mobility of (anonymized) smartphones within states before and after the enactment of stay-at-home orders to understand the effects of stay-at-home orders on mobility. The dataset contains over ten million observations on the movements of smartphones across all states within the US, which allows for an analysis of compliance with stay-at-home orders without worrying about the potential for self-reporting biases. By using a quasi-experimental method, this study overcomes biases that can come from comparing pre and post policy trends (due to unmeasured differences across states). The results from a difference-in-difference analysis suggest that stay-at-home orders are associated with a 4.6% increase in the percent of smartphones that remained at home during the late spring of 2020 across the US. Although there is a statistically significant difference across states with and without stay-at-home orders, it is important to note that the average percent of smartphones that remained at home was 40% in stay-at-home states compared to 36% in states without stay-at-home orders. The results also show that penalties (jail time and fines) had no significant effect on compliance with stay-at-home orders (considering all states with such orders), while compliance with stay at home orders in Republican controlled states was 5.3% lower than in Democratic controlled states. Further, states with higher population densities had the highest percent of smartphones that remained at home after stay-at-home orders went into effect (at 45%). Overall, the results in this paper suggest that stay-at-home orders had a small but significant impact on mobility, while also suggesting that studies of individual behaviors and choices will be necessary to understand when and why people may be more or less willing to shelter at home during a global pandemic. © 2021 IEEE.
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The Chinese government has curbed the rapid transmission of COVID-19 through a population flow control rarely seen in history. What is the effect of population flow control on pandemic prevention and control? How does it affect China's population mobility and short-term population distribution? In this paper, an SEIR model of virus transmission dynamics is used to evaluate the effectiveness of the control measures, and mobile location data are employed to track the temporal and spatial changes of population mobility in China, in order to review the positive and negative effects of population flow control during the major outbreaks of COVID-19: (1) Population flow control has significantly stabilized the daily new infection, serving as an essential part of China's non-pharmacological intervention measures in response to major public emergencies of COVID-19. Population flow control postponed the arrival of the peak day of daily new infections in China by 1.9 times, and reduced the number of newly infected people on that day by 63.4%. In the selected 5 provinces, 5 cities in Hubei, and 6 cities outside Hubei, the peak days were postponed by 1.4-8 times, 5.6-16.7 times, and 2.3-7.2 times, respectively, and the number of newly infected people on that day was reduced by 56.9%-85.5%, 62.2%-89.2%, and 67.1%-86.2%, respectively. Therefore, population flow control bought valuable buffer time for the prevention and control of the pandemic, and greatly weakened the impact of concentrated transmissions on medical facilities. (2) Population flow control limited intercity population flow. From January to April 2020, the average daily population flow intensity in China decreased by 40.18% compared with the same period in 2019. In particular, the coming-back-to-work flow after the Spring Festival travel rush in 2020 (from January 25 to February 18) decreased by 66.4% on average. (3) Population flow control and people's fear of the pandemic greatly affected the Spring Festival travel rush in 2020, and the spatial and temporal and distribution of China's population was changed for a short period. This paper helps the understanding of the impact of the population flow control strategy introduced by the government on major public emergencies, as well as the influences of geographical characteristics upon on the population flow and distribution. © 2022, Science Press. All right reserved.
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OBJECTIVE: To explore the pandemic situation of COVID-19 among global health care workers, and to compare the effect of infection control and prevention methods among health workers in various countries. METHODS: Infection rates of health care workers in 5 developed and 5 developing G20 countries were calculated. The spatial, temporal, population distribution and risk factors for COVID-19 prevalence among health care workers were analyzed, and the data were compared between China and other countries. RESULTS: There are differences in the spatial and temporal distribution of health care worker infection between developed and developing countries. The infection rates of health care workers in 5 developed countries are generally lower than those in 5 developing countries. Mainland China's health care worker infection rate did not exceed 0.040% which is much lower than those of the 10 G20 countries selected in this study. CONCLUSION: Global medical staff are at risk of infection and China has achieved good results in infection prevention and control. China's experience is worth learning from, and the prevention and control work of medical workers around the world needs to be strengthened.
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The outbreak of Coronavirus disease-2019 (Covid-19) poses a severe threat around the world. Although several studies of modelling Covid-19 cases have been done, there appears to have been limited research into modelling Covid-19 using Bayesian hierarchical spatial models. This study aims to examine the most suitable Bayesian spatial CAR Leroux models in modelling the number of confirmed Covid-19 cases without and with covariates namely distance to the capital city and population density. Data on the number of confirmed positive cases of Covid-19 (March 20, 2020 - August 30, 2021) in 15 sub-districts in Makassar City, the number of populations, population density, and distance to the city are used. The best model selection is based on several criteria, namely Deviance Information Criteria (DIC), Watanabe Akaike Information Criteria (WAIC), residuals from Moran's I Modification (MMI), and the 95% credible interval does not contain zero. The results showed that the best model in modelling Covid-19 is spatial CAR Leroux with hyperprior Inverse-Gamma (0.5, 0.05) model with the incorporation of distance to the capital city. It is found that there was a negative correlation between the distance to the capital city and Covid-19 risk, but the association between population density and the relative risk of Covid-19 was not statistically significant. Ujung Pandang district and Sangkarrang Island have the highest and the lowest relative risk respectively. © 2021 Institute of Physics Publishing. All rights reserved.
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Thrombotic and thromboembolic complications in patients diagnosed with coronavirus disease 2019 (COVID-19) are emerging as important sequelae that contribute to mortality, including disseminated intravascular coagulation, pulmonary embolism, deep vein thrombosis, ischemic stroke, and myocardial infraction. Reported incidence of thrombotic and thromboembolic complications in moderate/severe COVID-19 patients is from 21% to 49%, while even higher incidence in non-surviving COVID-19 patients. However, the underlying mechanism between thrombosis and COVID-19 is still unclear. Tissue-type plasminogen activator (tPA) plays an important role on initiating fibrinolysis by converting zymogen plasminogen to plasmin, a serine protease that degrades the fibrin clot, and therefore preventing excessive pathological blood clots. A homologous protein to plasminogen is apolipoprotein(a) [apo(a)], a major component of lipoprotein(a). The apo(a) inhibits fibrinolysis and exacerbates thrombosis through blocking the conversion from Glu-plasminogen to Lys-plasminogen, which has a higher binding affinity to fibrin and is a better substrate to tPA. The population distribution of plasma apo(a) level is positively skewed (most values are clustered around the left tail of the distribution close to zero), and the plasma apo(a) level in most people is less than 300 μg/mL. High plasma concentration of apo(a) (> 300 μg/mL), or genetic variants of LPA, the gene that encodes for apo(a), correlates with thrombotic cardiovascular risk and thromboembolic risk in many population-based clinical or genetic studies. To investigate the potential correlation between infection of SARS-COV-2 and thrombosis, we tested de-identified plasma samples collected from hospitalized patients with or without positivity of SARS-CoV-2 testing results and COVID-19 diagnosis (ICD10CM:U07.1) through the COVD-19 Tissue Bank at the Medical College of Wisconsin. The tPA enzymatic activity was measured by the release of p-nitroaniline chromophore from a plasmin-specific synthetic substrate with exogenous human plasminogen, with the intensity of color proportional to tPA activity. The apo(a) concentration is measured by ELISA capturing total apo(a) antigen. Our results show that the SARS-CoV-2-positive inpatients have higher plasma tPA concentration than the SARS-CoV-2 negative inpatients (6.0 versus 3.0 ng/mL, p<0.05), while plasma tPA enzymatic activity is lower in SARS-CoV-2-positive inpatients than the SARS-CoV-2 negative inpatients (15.2 versus 25.5 ΔA/min/mL/10 4, p<0.0001) (Figure A). The plasma apo(a) concentration is significantly higher in SARS-CoV-2-positive inpatients than in the plasma from SARS-CoV-2-negative inpatients (the median of the two groups are 114.8 versus 34.4 μg/mL, p<0.05) (Figure B). Among the 20 hospitalized patients with COVID-19, 13 survived. The 13 survived patients have one additional plasma sample collected after recovering from COVID-19 (date range between the two blood collections of onset and after recovery is from 19 to 87 days, the mean duration is 42 days). After recovery, 11 out of 13 surviving patients have increased plasma tPA enzymatic activity (the mean value at onset versus recovery is 5.2 versus 7.1 ΔA/min/mL/10 4, p<0.05) (Figure C). Consistently, 11 out of 13 surviving patients have decreased plasma apo(a) concentration compared to the plasma collected during the onset of COVID-19 from the same individuals (the median values of the onset and recovery are 141.1 versus 106.5 μg/mL, p<0.001) (Figure D). In summary, our study shows lower tPA enzymatic activity and higher apo(a) concentration in SARS-CoV-2-positive hospitalized patients compared to SARS-CoV-2-negative hospitalized patients. Among the survived patients, the reduction of apo(a) concentration after recovering from COVID-19 is accordance with the increase of tPA enzymatic activity. Considering the role of apo(a) in inhibiting fibrinolysis through limiting tPA-mediated plasminogen to plasmin conversion, the alteration in apo(a) concentration provide a possible explana ion of change of tPA activity in patients with severe COVID-19. [Formula presented] Disclosures: Baumann Kreuziger: CSL Behring: Consultancy;Quercegen Pharmaceuticals: Consultancy;Vaccine Injury Compensation Program: Consultancy.
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Predicting the population density in certain key areas of the city is of great importance. It helps us rationally deploy urban resources, initiate regional emergency plans, reduce the spread risk of infectious diseases such as Covid-19, predict travel needs of individuals, and build intelligent cities. Although current researches focus on using the data of point-of-interest (POI) and clustering belonged to unsupervised learning to predict the population density of certain neighboring cities to define metropolitan areas, there is almost no discussion about using spatial-temporal models to predict the population density in certain key areas of a city without using actual regional images. We 997 key areas in Beijing and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of three parts, which are the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the Data Fountain platform, we evaluate the model and compare it with some typical models. Experimental results show that the Spatial Convolution Layer can merge features of the nodes and edges to reflect the spatial correlation, the Temporal Convolution Layer can extract the temporal dependence, and the Feature Component can enhance the importance of other attributes that affect the population density of the area. In general, the WE-STGCN is better than baselines and can complete the work of predicting population density in key areas. © 2021 SPIE.
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The coronavirus disease 2019 (COVID-19) first identified at the end of 2019, significantly impacts the regional environment and human health. This study assesses PM2.5 exposure and health risk during COVID-19, and its driving factors have been analyzed using spatiotemporal big data, including Tencent location-based services (LBS) data, place of interest (POI), and PM2.5 site monitoring data. Specifically, the empirical orthogonal function (EOF) is utilized to analyze the spatiotemporal variation of PM2.5 concentration firstly. Then, population exposure and health risks of PM2.5 during the COVID-19 epidemic have been assessed based on LBS data. To further understand the driving factors of PM2.5 pollution, the relationship between PM2.5 concentration and POI data has been quantitatively analyzed using geographically weighted regression (GWR). The results show the time series coefficients of monthly PM2.5 concentrations distributed with a U-shape, i.e., with a decrease followed by an increase from January to December. In terms of spatial distribution, the PM2.5 concentration shows a noteworthy decline over the Central and North China. The LBS-based population density distribution indicates that the health risk of PM2.5 in the west is significantly lower than that in the Middle East. Urban gross domestic product (GDP) and urban green area are negatively correlated with PM2.5; while, road area, urban taxis, urban buses, and urban factories are positive. Among them, the number of urban factories contributes the most to PM2.5 pollution. In terms of reducing the health risks and PM2.5 pollution, several pointed suggestions to improve the status has been proposed.
Subject(s)
Big Data , Coronavirus Infections , Environmental Exposure/analysis , Pandemics , Particulate Matter/analysis , Pneumonia, Viral , Risk Assessment , Betacoronavirus , COVID-19 , China/epidemiology , Humans , Middle East , SARS-CoV-2 , Spatio-Temporal AnalysisABSTRACT
OBJECTIVES: To analyze the regional epidemic features of coronavirus disease 2019 (COVID-19) in Henan Province, China. METHODS: According to the data of COVID-19 patients and the resident population at the end of 2018 in Henan Province, statistical description and analysis of epidemiological characteristics of COVID-19 in Henan Province were conducted, including the time distribution, population distribution, and regional distribution. RESULTS: The cumulative incidence of COVID-19 in Henan Province was 1.32/100 000, the cure rate was 98.03%, and the fatality rate was 1.73% by March 9, 2020. The incidence curve showed that the epidemic peak reached from January 24 to January 28. The high-incidence area was Xinyang, with a standardized cumulative incidence rate of 4.36/100 000. There were 580 female COVID-19 patients (45.60%), 688 males (54.09%) in Henan Province. The incidence of males was 1.41/100 000, while the incidence of females was 1.23/100 000. The age with the highest incidence of COVID-19 in Henan Province was 20-69 years old (88.68%). The incidence rate was highest in men aged 30-39 (2.51/ 100 000), while the lowest rate in women aged 0-9 (0.16/100 000). There were 1 225 local patients (96.31%), and the rural patients (45.73%) were slightly higher than the urban patients (44.02%) in Henan Province. A total of 63.60% patients had traveled or lived in Hubei or contacted with people who came from Hubei to Henan. The proportion of patients whose family members suffered from COVID-19 was 32.70%. Global spatial autocorrelation analysis suggested that there was a statistically significant positive correlation in the spatial distribution of COVID-19 patients in Henan Province (Moran's I=0.248, Z=2.955, P<0.01). CONCLUSIONS: There are differences in the morbidity and mortality of COVID-19 patients in different areas of Henan Province, with epidemic peak reaching from January 24 to January 28. Henan is dominated by local patients, male patients, and patients with contact history in Hubei. The space appears to be moderately clustered.