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1.
JMIR Public Health Surveill ; 7(3): e27317, 2021 03 29.
Article in English | MEDLINE | ID: covidwho-2197905

ABSTRACT

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.


Subject(s)
Air Travel/statistics & numerical data , COVID-19 , Global Health , Public Health , Spatio-Temporal Analysis , Coronavirus Infections/epidemiology , Disease Outbreaks/statistics & numerical data , Humans
2.
Sci Rep ; 12(1): 17817, 2022 Oct 24.
Article in English | MEDLINE | ID: covidwho-2087284

ABSTRACT

The purposes of our study are to map high-risk areas in Canada as well as quantifying the effects of vaccination intervention and socio-demographic factors on the transmission rates of infection, recovery, and death related to COVID-19. The data of this research included weekly number of COVID­19 cases, recovered, and dead individuals from 2020 through 2021 in Canada at health region and provincial levels. These data were associated with cumulative rates of partial and full vaccination and socio-demographic factors. We applied the spatio-temporal Susceptible-Exposed-Infected-Removed (SEIR), and Susceptible-Exposed-Infected-Removed-Vaccinated (SEIRV) models. The results indicated the partial vaccination rate has a greater effect compared with full vaccination rate on decreasing the rate of infectious cases (risk ratio (RR) = 0.18; 95%CrI: 0.16-0.2; RR = 0.60; 95%CrI: 0.55-0.65, respectively) and increasing the rate of recovered cases (RR = 1.39; 95%CrI: 1.28-1.51; RR = 1.21; 95%CrI: 1.23-1.29, respectively). However, for mortality risk reduction, only increasing full vaccination rate was significantly associated (RR = 0.09; 95%CrI: 0.05-0.14). In addition, our results showed that regions with higher rates of elderly and aboriginal individuals, higher population density, and lower socioeconomic status (SES) contribute more to the risk of infection transmission. Rates of elderly and aboriginal individuals and SES of regions were significantly associated with recovery rate. However, elderly individuals rate of regions was only a significant predictor of mortality risk. Based on the results, protection against mild and severe COVID-19 infection after the primary vaccination series decreased.


Subject(s)
COVID-19 , Aged , Humans , Canada/epidemiology , COVID-19/epidemiology , COVID-19/prevention & control , Vaccination , Vaccination Coverage , Spatio-Temporal Analysis
3.
Comput Intell Neurosci ; 2022: 8491628, 2022.
Article in English | MEDLINE | ID: covidwho-2083052

ABSTRACT

In order to explore the spatial and temporal distribution characteristics of COVID-19 in Chongqing from January 22 to February 25, 2010, and provide a series of suggestions for scientific prevention and control of epidemic situation, we will mainly analyze the epidemic situation data of Chongqing Municipal Health Committee members and improve the descriptive analysis. Regional distribution and spatiotemporal scans were analyzed for COVID-19 outbreaks using ArcGIS10.2 and SaTScan9. 5 software. After the analysis, a total of 576 novel coronavirus pneumonia patients were confirmed in Chongqing. The incidence trend increased rapidly from January 22 to January 31, then decreased gradually, and there were no new cases until February 25. The purely spatial scanning results were consistent with spatiotemporal scanning, and a first-level accumulation area was detected by spatiotemporal scanning in the east and northeast of Chongqing from January 22 to February 10. From January 22 to February 25, 2020,COVID-19 occurred in the eastern and northeast regions of Chongqing. It is recommended to strengthen the detection of cluster areas to prevent another outbreak of COVID-19 risk.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , China/epidemiology , Cluster Analysis , Humans , Incidence , Spatio-Temporal Analysis
4.
Int J Environ Res Public Health ; 19(19)2022 Sep 27.
Article in English | MEDLINE | ID: covidwho-2065939

ABSTRACT

Rural resilience is not only a comprehensive reflection of "thriving businesses, pleasant living environments, social etiquette and civility, effective governance, and prosperity". It is also the unity of resilience in industry, ecology, culture, organization and livelihood. This paper uses the entropy weight-TOPSIS method to measure the rural resilience level in 31 regions in China and analyzes the configuration of influencing factors with the Fuzzy-set qualitative comparative analysis (fsQCA). The results of the study are as follows: (1) The level of rural resilience in China showed a stable increase from 2010 to 2019, but the overall level was low, with large regional disparities, showing a significant positive spatial correlation. (2) In the high-level rural resilience explanatory path, labor-driven, cultural-driven and market-labor-technology linkage-driven play a core role, while administrative force is not playing a significant role. In the explanation path of non-high level rural resilience, the market-labor absent, administrative-market absent and cultural absent hinder the improvement of rural resilience. In summary, we put forward the following suggestions. Policy renovation and support should be strengthened. Adaption to local conditions should be considered in order to achieve sustainable and differentiated development. Development should be coordinated and balanced in different regions so as to achieve an overall resilience level in rural areas.


Subject(s)
Social Planning , Sustainable Development , China , Ecology , Spatio-Temporal Analysis
5.
J Theor Biol ; 554: 111279, 2022 Dec 07.
Article in English | MEDLINE | ID: covidwho-2036334

ABSTRACT

Shanghai suffered a large outbreak of Omicron mutant of COVID-19 at the beginning of March 2022. To figure out the spatiotemporal patterns of the epidemic, a retrospective statistical investigation, coupled with a dynamic model, is implemented in this study. The hotspots of SARS-CoV-2 transmissions are identified, and strong aggregative effects in the decay stage are found. Besides, the visualization of disease diffusion is provided to show how COVID-19 disease invades all districts of Shanghai in the early stage. Furthermore, the calculations from the dynamic model manifest the effect of detections to suppress the epidemic dissemination. These results reveal the strategies to improve the spatial control of disease.


Subject(s)
COVID-19 , COVID-19/epidemiology , China/epidemiology , Disease Outbreaks , Humans , Retrospective Studies , SARS-CoV-2 , Spatio-Temporal Analysis
6.
Front Public Health ; 10: 897784, 2022.
Article in English | MEDLINE | ID: covidwho-2022934

ABSTRACT

Based on the epidemic data of COVID-19 in 50 states of the United States (the US) from December 2021 to January 2022, the spatial and temporal clustering characteristics of COVID-19 in the US are explored and analyzed. First, the retrospective spatiotemporal analysis is performed by using SaTScan 9.5, and 17 incidence areas are obtained. Second, the reliability of the results is tested by the circular distribution method in the time latitude and the clustering method in the spatial latitude, and it is confirmed that the retrospective spatiotemporal analysis accurately measures in time and reasonably divides regions according to the characteristics in space. Empirical results show that the first-level clustering area of the epidemic has six states with an average relative risk of 1.28 and the second-level clustering area includes 18 states with an average relative risk of 0.86. At present, the epidemic situation in the US continues to expand. It is necessary to do constructive work in epidemic prevention, reduce the impact of epidemic, and effectively control the spread of the epidemic.


Subject(s)
COVID-19 , Humans , Incidence , Reproducibility of Results , Retrospective Studies , Spatio-Temporal Analysis , United States
7.
J Urban Health ; 99(5): 873-886, 2022 10.
Article in English | MEDLINE | ID: covidwho-2007234

ABSTRACT

Monitoring the spatial and temporal course of opioid-related drug overdose mortality is a key public health determinant. Despite previous studies exploring the evolution of drug-related fatalities following the stay-at-home mandates during the COVID-19 pandemic, little is known about the spatiotemporal dynamics that mitigation efforts had on overdose deaths. The purpose of this study was to describe the spatial and temporal dynamics of overdose death relative risk using a 4-week interval over a span of 5 months following the implementation of the COVID-19 lockdown in the city of Chicago, IL. A Bayesian space-time model was used to produce posterior risk estimates and exceedance probabilities of opioid-related overdose deaths controlling for measures of area-level deprivation and stay-at-home mandates. We found that area-level temporal risk and inequalities in drug overdose mortality increased significantly in the initial months of the pandemic. We further found that a change in the area-level deprivation from the first to the fourth quintile increased the relative risk of a drug overdose risk by 44.5%. The social distancing index measuring the proportion of persons who stayed at home in each census block group was not associated with drug overdose mortality. We conclude by highlighting the importance of contextualizing the spatial and temporal risk in overdose mortality for implementing effective and safe harm reduction strategies during a global pandemic.


Subject(s)
COVID-19 , Drug Overdose , Analgesics, Opioid , Bayes Theorem , Communicable Disease Control , Drug Overdose/drug therapy , Humans , Pandemics , Physical Distancing , Spatio-Temporal Analysis
8.
Proc Natl Acad Sci U S A ; 119(33): e2203042119, 2022 08 16.
Article in English | MEDLINE | ID: covidwho-1984599

ABSTRACT

A common feature of large-scale extreme events, such as pandemics, wildfires, and major storms is that, despite their differences in etiology and duration, they significantly change routine human movement patterns. Such changes, which can be major or minor in size and duration and which differ across contexts, affect both the consequences of the events and the ability of governments to mount effective responses. Based on naturally tracked, anonymized mobility behavior from over 90 million people in the United States, we document these mobility differences in space and over time in six large-scale crises, including wildfires, major tropical storms, winter freeze and pandemics. We introduce a model that effectively captures the high-dimensional heterogeneity in human mobility changes following large-scale extreme events. Across five different metrics and regardless of spatial resolution, the changes in human mobility behavior exhibit a consistent hyperbolic decline, a pattern we characterize as "spatiotemporal decay." When applied to the case of COVID-19, our model also uncovers significant disparities in mobility changes-individuals from wealthy areas not only reduce their mobility at higher rates at the start of the pandemic but also maintain the change longer. Residents from lower-income regions show a faster and greater hyperbolic decay, which we suggest may help account for different COVID-19 rates. Our model represents a powerful tool to understand and forecast mobility patterns post emergency, and thus to help produce more effective responses.


Subject(s)
COVID-19 , Human Migration , Models, Statistical , Natural Disasters , Pandemics , COVID-19/epidemiology , Forecasting , Human Migration/trends , Humans , Income , Seasons , Spatio-Temporal Analysis , United States
9.
J Med Virol ; 94(11): 5354-5362, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-1941182

ABSTRACT

The Omicron variant was first reported to the World Health Organization (WHO) from South Africa on November 24, 2021; this variant is spreading rapidly worldwide. No study has conducted a spatiotemporal analysis of the morbidity of Omicron infection at the country level; hence, to explore the spatial transmission of the Omicron variant among the 220 countries worldwide, we aimed to the analyze its spatial autocorrelation and to conduct a multiple linear regression to investigate the underlying factors associated with the pandemic. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the local indicators of spatial association (LISA) were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the LISA were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. The value of Moran's I was positive (Moran's I = 0.061, Z-score = 3.772, p = 0.007), indicating a spatial correlation of the morbidity of Omicron at the country level. From November 26, 2021 to February 26, 2022; the morbidity showed obvious spatial clustering. Hotspot clustering was observed mostly in Europe (locations in High-High category: 24). Coldspot clustering was observed mostly in Africa and Asia (locations in Low-Low category: 32). The result of joinpoint regression showed an increasing trend from December 21, 2021 to January 26, 2022. Results of the multiple linear regression analysis demonstrated that the morbidity of Omicron was strongly positively correlated with income support (coefficient = 1.905, 95% confidence interval [CI]: 1.354-2.456, p < 0.001) and strongly negatively correlated with close public transport (coefficient = -1.591, 95% CI: -2.461 to -0.721, p = 0.001). Omicron outbreaks exhibited spatial clustering at the country level worldwide; the countries with higher disease morbidity could impact the other countries that are surrounded by and close to it. The locations with High-High clustering category, which referred to the countries with higher disease morbidity, were mainly observed in Europe, and its adjoining country also showed high spatial clustering. The morbidity of Omicron increased from December 21, 2021 to January 26, 2022. The higher morbidity of Omicron was associated with the economic and policy interventions implemented; hence, to deal with the epidemic, the prevention and control measures should be strengthened in all aspects.


Subject(s)
Disease Outbreaks , Pandemics , Cluster Analysis , Humans , Incidence , South Africa/epidemiology , Spatio-Temporal Analysis
10.
Transbound Emerg Dis ; 69(5): e2731-e2744, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1932580

ABSTRACT

The transmission of coronavirus disease-2019 (COVID-19) epidemic is a global emergency, which is worsened by the genetic mutations of SARS-CoV-2. However, till date, few statistical studies have researched the COVID-19 spread patterns in terms of the variant cases. Hence, this paper aims to explore the associated risk factors of Delta variant, the most contagious strain of COVID-19. The study collected the state-level COVID-19 Delta variant cases in the United States during a 12-week period and included potential environmental, socioeconomic, and public prevention factors as independent variables. Instead of regarding the covariate effects as constant, this paper proposes a flexible Bayesian hierarchical model with spatio-temporally varying coefficients to account for data heterogeneity. The method enables us to cluster the states into distinctive groups based on the temporal trends of the coefficients and simultaneously identify significant risk factors for each cluster. The findings contribute novel insight into the dynamics of covariate effects on the COVID-19 Delta variant over space and time, which could help the government develop targeted prevention measures for vulnerable regions based on the selected risk factors.


Subject(s)
COVID-19 , Animals , Bayes Theorem , COVID-19/epidemiology , COVID-19/veterinary , Risk Factors , SARS-CoV-2/genetics , Spatio-Temporal Analysis , United States/epidemiology
11.
Int J Environ Res Public Health ; 19(14)2022 07 06.
Article in English | MEDLINE | ID: covidwho-1917494

ABSTRACT

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/epidemiology , Humans , Pandemics , Risk Factors , Spatio-Temporal Analysis
12.
Sci Rep ; 12(1): 666, 2022 01 13.
Article in English | MEDLINE | ID: covidwho-1900550

ABSTRACT

The worldwide spread of the COVID-19 pandemic is a complex and multivariate process differentiated across countries, and geographical distance is acceptable as a critical determinant of the uneven spreading. Although social connectivity is a defining condition for virus transmission, the network paradigm in the study of the COVID-19 spatio-temporal spread has not been used accordingly. Toward contributing to this demand, this paper uses network analysis to develop a multidimensional methodological framework for understanding the uneven (cross-country) spread of COVID-19 in the context of the globally interconnected economy. The globally interconnected system of tourism mobility is modeled as a complex network and studied within the context of a three-dimensional (3D) conceptual model composed of network connectivity, economic openness, and spatial impedance variables. The analysis reveals two main stages in the temporal spread of COVID-19, defined by the cutting-point of the 44th day from Wuhan. The first describes the outbreak in Asia and North America, the second stage in Europe, South America, and Africa, while the outbreak in Oceania intermediates. The analysis also illustrates that the average node degree exponentially decays as a function of COVID-19 emergence time. This finding implies that the highly connected nodes, in the Global Tourism Network (GTN), are disproportionally earlier infected by the pandemic than the other nodes. Moreover, countries with the same network centrality as China are early infected on average by COVID-19. The paper also finds that network interconnectedness, economic openness, and transport integration are critical determinants in the early global spread of the pandemic, and it reveals that the spatio-temporal patterns of the worldwide spreading of COVID-19 are more a matter of network interconnectivity than of spatial proximity.


Subject(s)
COVID-19/economics , COVID-19/transmission , Global Health/economics , Pandemics/economics , Disease Outbreaks/economics , Humans , SARS-CoV-2/pathogenicity , Spatio-Temporal Analysis
13.
Sci Rep ; 12(1): 9369, 2022 Jun 07.
Article in English | MEDLINE | ID: covidwho-1878546

ABSTRACT

Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/epidemiology , Humans , Incidence , Pandemics , Remote Sensing Technology , Spatio-Temporal Analysis
14.
Sci Rep ; 12(1): 9364, 2022 Jun 07.
Article in English | MEDLINE | ID: covidwho-1878545

ABSTRACT

The first case of coronavirus disease 2019 (COVID-19) in South Korea was confirmed on January 20, 2020, approximately three weeks after the report of the first COVID-19 case in Wuhan, China. By September 15, 2021, the number of cases in South Korea had increased to 277,989. Thus, it is important to better understand geographical transmission and design effective local-level pandemic plans across the country over the long term. We conducted a spatiotemporal analysis of weekly COVID-19 cases in South Korea from February 1, 2020, to May 30, 2021, in each administrative region. For the spatial domain, we first covered the entire country and then focused on metropolitan areas, including Seoul, Gyeonggi-do, and Incheon. Moran's I and spatial scan statistics were used for spatial analysis. The temporal variation and dynamics of COVID-19 cases were investigated with various statistical visualization methods. We found time-varying clusters of COVID-19 in South Korea using a range of statistical methods. In the early stage, the spatial hotspots were focused in Daegu and Gyeongsangbuk-do. Then, metropolitan areas were detected as hotspots in December 2020. In our study, we conducted a time-varying spatial analysis of COVID-19 across the entirety of South Korea over a long-term period and found a powerful approach to demonstrating the current dynamics of spatial clustering and understanding the dynamic effects of policies on COVID-19 across South Korea. Additionally, the proposed spatiotemporal methods are very useful for understanding the spatial dynamics of COVID-19 in South Korea.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Pandemics , Republic of Korea/epidemiology , Spatial Analysis , Spatio-Temporal Analysis
15.
Int J Environ Res Public Health ; 19(11)2022 05 30.
Article in English | MEDLINE | ID: covidwho-1869615

ABSTRACT

Spatio-temporal models need to address specific features of spatio-temporal infection data, such as periods of stable infection levels (endemicity), followed by epidemic phases, as well as infection spread from neighbouring areas. In this paper, we consider a mixture-link model for infection counts that allows alternation between epidemic phases (possibly multiple) and stable endemicity, with higher AR1 coefficients in epidemic phases. This is a form of regime-switching, allowing for non-stationarity in infection levels. We adopt a generalised Poisson model appropriate to the infection count data and avoid transformations (e.g., differencing) to alternative metrics, which have been adopted in many studies. We allow for neighbourhood spillover in infection, which is also governed by adaptive regime-switching. Compared to existing models, the observational (in-sample) model is expected to better reflect the balance between epidemic and endemic tendencies, and short-term extrapolations are likely to be improved. Two case study applications involve COVID area-time data, one for 32 London boroughs (and 96 weeks) since the start of the COVID epidemic, the other for a shorter time span focusing on the epidemic phase in 144 areas of Southeast England associated with the Alpha variant. In both applications, the proposed methods produce a better in-sample fit and out-of-sample short term predictions. The spatial dynamic implications are highlighted in the case studies.


Subject(s)
COVID-19 , Epidemics , COVID-19/epidemiology , England , Humans , SARS-CoV-2 , Spatio-Temporal Analysis
16.
Spat Spatiotemporal Epidemiol ; 42: 100521, 2022 08.
Article in English | MEDLINE | ID: covidwho-1867801

ABSTRACT

Severe acute respiratory syndrome - coronavirus 2 (SARS-CoV-2) continues to effect communities across the world. One way to combat these effects is to enhance our collective ability to remotely monitor community spread. Monitoring SARS-CoV-2 in wastewater is one approach that enables researchers to estimate the total number of infected people in a region; however, estimates are often made at the sewershed level which may mask the geographic nuance required for targeted interdiction efforts. In this work, we utilize an apportioning method to compare the spatial and temporal trends of daily case count with the temporal pattern of viral load in the wastewater at smaller units of analysis within Austin, TX. We find different lag-times between wastewater loading and case reports. Daily case reports for some locations follow the temporal trend of viral load more closely than others. These findings are then compared to socio-demographic characteristics across the study area.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Humans , Spatio-Temporal Analysis , Waste Water
17.
Chaos ; 32(4): 041106, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1830316

ABSTRACT

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing-Tianjin-Hebei (BTH), Chengdu-Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.


Subject(s)
Air Pollution , COVID-19 , Traffic-Related Pollution , Air Pollution/analysis , COVID-19/epidemiology , Humans , Spatio-Temporal Analysis , Traffic-Related Pollution/analysis
18.
Bol Med Hosp Infant Mex ; 79(2): 91-99, 2022.
Article in English | MEDLINE | ID: covidwho-1811941

ABSTRACT

BACKGROUND: Initial publications of COVID-19 (2019 coronavirus disease) focused on the adult population until March 2020, when the first series in children was reported. Our objective was to analyze the spatiotemporal behavior of the pediatric population with COVID-19 in the state of Jalisco. METHODS: We conducted a cross-sectional study including subjects < 18 years of age with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection confirmed by reverse transcription-polymerase chain reaction, registered in the RADAR platform. We investigated the prevalence, incidence rate, age, sex, outpatient or inpatient status, distribution of cases by time, municipality of residence, and geographical region. Descriptive statistics were used for data analysis. RESULTS: Of 58,231 subjects studied, 1,515 were children (3%): 768 males (51%) and 747 females (49%). The mean age was 12 ± 5 years; outpatients predominated (94%). The Central region concentrated the largest cases with 1,257 (82%) and was the second-highest incidence rate, behind the Occidental Coastal-Mountain region. The most affected municipality was Guadalajara. The distribution of new cases increased proportionally to mobility: after the holiday weekend in May, it rose from 28 to 161 cases; after the opening of beaches and recreational sites in June and July, to 539; and after the opening of movie theaters in August, to 673 cases. CONCLUSIONS: Although with a lower incidence, the pediatric population is not exempt from SARS-CoV-2 infection. We observed an increase in cases as restrictions on social activities diminished.


INTRODUCCIÓN: Las publicaciones iniciales de COVID-19 (enfermedad por coronavirus de 2019) se enfocaron en población adulta, hasta marzo de 2020, cuando se informó la primera serie en niños. Nuestro objetivo fue analizar el comportamiento espacio-temporal de la población pediátrica con COVID-19 en el estado de Jalisco. MÉTODOS: Se llevó a cabo un estudio transversal en el que se incluyeron sujetos < 18 años con infección por SARS-CoV-2 (coronavirus tipo 2 del síndrome respiratorio agudo grave) confirmada por reacción en cadena de la polimerasa con retrotranscriptasa, registrados en la plataforma RADAR. Se investigó la prevalencia, tasa de incidencia, edad, sexo, paciente ambulatorio u hospitalizado, distribución de casos por tiempo, municipio de residencia y región geográfica. Se utilizó estadística descriptiva para el análisis de los datos. RESULTADOS: De 58,231 sujetos estudiados, se encontraron 1,515 pacientes pediátricos (3%): 768 de sexo masculino (51%) y 747 de sexo femenino (49%). La media de edad fue de 12 ± 5 años; predominaron los pacientes ambulatorios (94%). La región Centro concentró la mayor cantidad de casos con 1,257 (82%) y fue la segunda con mayor tasa de incidencia, detrás de la región Costa-Sierra Occidental. El municipio más afectado fue Guadalajara. La distribución de nuevos casos incrementó al aumentar la movilidad: después del puente vacacional de mayo subió de 28 a 161 casos; tras la apertura de playas y sitios de recreación en junio y julio, a 539 casos, y posterior a la apertura de cines en agosto, a 673 casos. CONCLUSIONES: Aunque con una incidencia menor, la población pediátrica no está exenta de la infección por SARS-CoV-2. Se observó un incremento de los casos a medida que disminuyeron las restricciones para las actividades sociales.


Subject(s)
COVID-19 , Adolescent , Adult , COVID-19/epidemiology , Child , Cross-Sectional Studies , Female , Humans , Incidence , Male , SARS-CoV-2 , Spatio-Temporal Analysis
19.
Spat Spatiotemporal Epidemiol ; 41: 100498, 2022 06.
Article in English | MEDLINE | ID: covidwho-1805212

ABSTRACT

The COVID-19 epidemic has emerged as one of the most severe public health crises worldwide, especially in Europe. Until early July 2021, reported infected cases exceeded 180 million, with almost 4 million associated deaths worldwide, almost a third of which are in continental Europe. We analyzed the spatio-temporal distribution of the disease incidence and mortality rates considering specific periods in this continent. Further, we applied Global Moran's I to examine the spatio-temporal distribution patterns of COVID-19 incidence rates and Getis-Ord Gi* hotspot analysis to represent high-risk areas of the disease. Additionally, we compiled a set of 40 demographic, socioeconomic, environmental, transportation, health, and behavioral indicators as potential explanatory variables to investigate the spatial variations of COVID-19 cumulative incidence rates (CIRs). Ordinary Least Squares (OLS), Spatial Lag model (SLM), Spatial Error Model (SLM), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) regression models were implemented to examine the spatial dependence and non-stationary relationships. Based on our findings, the spatio-temporal distribution pattern of COVID-19 CIRs was highly clustered and the most high-risk clusters of the disease were situated in central and western Europe. Moreover, poverty and the elderly population were selected as the most influential variables due to their significant relationship with COVID-19 CIRs. Considering the non-stationary relationship between variables, MGWR could describe almost 69% of COVID-19 CIRs variations in Europe. Since this spatio-temporal research is conducted on a continental scale, spatial information obtained from the models could provide general insights to authorities for further targeted policies.


Subject(s)
COVID-19 , Aged , COVID-19/epidemiology , Geographic Information Systems , Humans , Incidence , Spatial Regression , Spatio-Temporal Analysis
20.
Front Public Health ; 10: 843862, 2022.
Article in English | MEDLINE | ID: covidwho-1776053

ABSTRACT

From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018-2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further, contemplate the governance measures during the period of the 14th Five-Year Plan (2021-2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25-35 µg/m3 increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the pollution's gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran's I and Local Geti's G were applied to identify hotspots with a high degree of aggregation. Integrating national population density, hotspots were classified into four areas: the Beijing-Tianjin-Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.


Subject(s)
Environmental Monitoring , Particulate Matter , Socioeconomic Factors , China/epidemiology , Cities , Environmental Monitoring/methods , Humans , Particulate Matter/analysis , Policy , Spatio-Temporal Analysis
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