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1.
PLoS One ; 17(1): e0260836, 2022.
Article in English | MEDLINE | ID: covidwho-1613339

ABSTRACT

In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.


Subject(s)
COVID-19/epidemiology , Humans , Japan/epidemiology , Markov Chains , Models, Statistical , Monte Carlo Method , Normal Distribution , Poisson Distribution , Regression Analysis , SARS-CoV-2/pathogenicity , Spatial Analysis , Spatio-Temporal Analysis
2.
Sci Rep ; 11(1): 24470, 2021 12 28.
Article in English | MEDLINE | ID: covidwho-1594859

ABSTRACT

A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial-temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial-temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.


Subject(s)
COVID-19/epidemiology , Algorithms , COVID-19/mortality , COVID-19/virology , Humans , Republic of Korea/epidemiology , SARS-CoV-2/isolation & purification , Spatio-Temporal Analysis , Time Factors
3.
Nat Microbiol ; 7(1): 97-107, 2022 01.
Article in English | MEDLINE | ID: covidwho-1596437

ABSTRACT

Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.


Subject(s)
COVID-19/epidemiology , Models, Statistical , SARS-CoV-2/isolation & purification , Basic Reproduction Number , Bias , COVID-19/diagnosis , COVID-19/transmission , COVID-19 Testing/statistics & numerical data , Forecasting , Humans , Prevalence , Reproducibility of Results , SARS-CoV-2/genetics , Spatio-Temporal Analysis , United Kingdom/epidemiology
5.
Public Health ; 202: 80-83, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1517447

ABSTRACT

OBJECTIVES: Among the few studies examining patterns of COVID-19 spread in border regions, findings are highly varied and partially contradictory. This study presents empirical results on the spatial and temporal dynamics of incidence in 10 European border regions. We identify geographical differences in incidence between border regions and inland regions, and we provide a heuristic to characterise spillover effects. STUDY DESIGN: Observational spatiotemporal analysis. METHODS: Using 14-day incidence rates (04/2020 to 25/2021) for border regions around Germany, we delineate three pandemic 'waves' by the dates with the lowest recorded rates between peak incidence. We mapped COVID-19 incidence data at the finest spatial scale available and compared border regions' incidence rates and trends to their nationwide values. The observed spatial and temporal patterns are then compared to the time and duration of border controls in the study area. RESULTS: We observed both symmetry and asymmetry of incidence rates within border pairs, varying by country. Several asymmetrical border pairs feature temporal convergence, which is a plausible indicator for spillover dynamics. We thus derived a border incidence typology to characterise (1) symmetric border pairs, (2) asymmetric border pairs without spillover effects, and (3) asymmetric with spillover effects. In all groups, border control measures were enacted but appear to have been effective only in certain cases. CONCLUSIONS: The heuristic of border pairs provides a useful typology for highlighting combinations of spillover effects and border controls. We conclude that border control measures may only be effective if the timing and the combination with other non-pharmaceutical measures is appropriate.


Subject(s)
COVID-19 , Humans , Incidence , Pandemics , SARS-CoV-2 , Spatio-Temporal Analysis
6.
Spat Spatiotemporal Epidemiol ; 39: 100461, 2021 11.
Article in English | MEDLINE | ID: covidwho-1510319

ABSTRACT

With the whole world being affected by the pandemic, it is a matter of great importance that studies about spatial and spatio-temporal aspects of the COVID-19 (Sars-Cov-2) pandemic should be conducted, therefore the main goal of this paper is to present the Global Moran's I and the Local Moran's I used to evaluate spatial association in the number of deaths and infections by COVID-19, and a spatio-temporal Poisson scan statistic used to identify emerging or "alive" clusters of infections by Sars-Cov-2 in space and time. As of January 2021 vaccination against COVID-19 already started, since the use of spatial clustering methods to identify non-vaccinated populations is not new among studies on vaccination coverage strategies, this paper also aims to discuss the implementation of spatial and spatio-temporal clustering methods in early vaccination.


Subject(s)
COVID-19 , Cluster Analysis , Humans , SARS-CoV-2 , Spatial Analysis , Spatio-Temporal Analysis , Vaccination
7.
PLoS One ; 16(11): e0259031, 2021.
Article in English | MEDLINE | ID: covidwho-1496523

ABSTRACT

With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.


Subject(s)
COVID-19 , Physical Distancing , Travel , Humans , Quarantine , Spatio-Temporal Analysis , United States
8.
PLoS Comput Biol ; 17(10): e1009473, 2021 10.
Article in English | MEDLINE | ID: covidwho-1496327

ABSTRACT

Infectious diseases attack humans from time to time and threaten the lives and survival of people all around the world. An important strategy to prevent the spatial spread of infectious diseases is to restrict population travel. With the reduction of the epidemic situation, when and where travel restrictions can be lifted, and how to organize orderly movement patterns become critical and fall within the scope of this study. We define a novel diffusion distance derived from the estimated mobility network, based on which we provide a general model to describe the spatiotemporal spread of infectious diseases with a random diffusion process and a deterministic drift process of the population. We consequently develop a multi-source data fusion method to determine the population flow in epidemic areas. In this method, we first select available subregions in epidemic areas, and then provide solutions to initiate new travel flux among these subregions. To verify our model and method, we analyze the multi-source data from mainland China and obtain a new travel flux triggering scheme in the selected 29 cities with the most active population movements in mainland China. The testable predictions in these selected cities show that reopening the borders in accordance with our proposed travel flux will not cause a second outbreak of COVID-19 in these cities. The finding provides a methodology of re-triggering travel flux during the weakening spread stage of the epidemic.


Subject(s)
COVID-19/epidemiology , Epidemics , SARS-CoV-2 , Travel , COVID-19/prevention & control , COVID-19/transmission , China/epidemiology , Cities , Computational Biology , Humans , Mathematical Concepts , Models, Biological , Spatio-Temporal Analysis , Travel/statistics & numerical data
9.
Medicine (Baltimore) ; 100(43): e27685, 2021 Oct 29.
Article in English | MEDLINE | ID: covidwho-1494094

ABSTRACT

ABSTRACT: To analyze the epidemiological characteristics of coronavirus disease 2019 (COVID-19) in Jiangxi Province, China, from January 21 to April 9, 2020.COVID-19 epidemic information was obtained from the official websites of the Jiangxi Provincial Health Committee, Hubei Provincial Health Committee, and National Health Commission of the People's Republic of China. ArcGIS 10.0 was used to draw a map of the spatial distribution of the cases.On January 21, 2020, the first COVID-19 confirmed case in Jiangxi was reported. By January 27, COVID-19 had spread rapidly to all cities in Jiangxi. The outbreak peaked on February 3, with a daily incidence of 85 cases. The last indigenous case reported on February 27. From January 21 to April 9, a total of 937 confirmed cases of COVID-19 were reported, with a cumulative incidence of 2.02/100,000. Of those, 936 patients (99.89%) were cured, and 1 (0.11%) died due to COVID-19. The COVID-19 epidemic trend in Jiangxi was basically consistent with the national epidemic trend (except Hubei). Throughout the epidemic prevention and control phase, Jiangxi province has taken targeted prevention and control measures based on the severity of the spread of COVID-19.The COVID-19 epidemic in Jiangxi was widespread and developed rapidly. In less than 1 month, the epidemic situation was effectively controlled, and the epidemic situation shifted to a low-level distribution state. All these proved that the COVID-19 prevention and control strategies and measures adopted by Jiangxi Province were right, positive and effective.


Subject(s)
COVID-19/epidemiology , Adaptation, Psychological , COVID-19/prevention & control , China/epidemiology , Communicable Disease Control/organization & administration , Epidemics , Humans , Retrospective Studies , SARS-CoV-2 , Spatio-Temporal Analysis
10.
Lancet Public Health ; 6(11): e805-e816, 2021 11.
Article in English | MEDLINE | ID: covidwho-1467001

ABSTRACT

BACKGROUND: High-resolution data for how mortality and longevity have changed in England, UK are scarce. We aimed to estimate trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6791 middle-layer super output areas (MSOAs) in England. METHODS: We performed a high-resolution spatiotemporal analysis of civil registration data from the UK Small Area Health Statistics Unit research database using de-identified data for all deaths in England from 2002 to 2019, with information on age, sex, and MSOA of residence, and population counts by age, sex, and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs, and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA. FINDINGS: In 2002-06 and 2006-10, all but a few (0-1%) MSOAs had a life expectancy increase for female and male sexes. In 2010-14, female life expectancy decreased in 351 (5·2%) of 6791 MSOAs. By 2014-19, the number of MSOAs with declining life expectancy was 1270 (18·7%) for women and 784 (11·5%) for men. The life expectancy increase from 2002 to 2019 was smaller in MSOAs where life expectancy had been lower in 2002 (mostly northern urban MSOAs), and larger in MSOAs where life expectancy had been higher in 2002 (mostly MSOAs in and around London). As a result of these trends, the gap between the first and 99th percentiles of MSOA life expectancy for women increased from 10·7 years (95% credible interval 10·4-10·9) in 2002 to reach 14·2 years (13·9-14·5) in 2019, and for men increased from 11·5 years (11·3-11·7) in 2002 to 13·6 years (13·4-13·9) in 2019. INTERPRETATION: In the decade before the COVID-19 pandemic, life expectancy declined in increasing numbers of communities in England. To ensure that this trend does not continue or worsen, there is a need for pro-equity economic and social policies, and greater investment in public health and health care throughout the entire country. FUNDING: Wellcome Trust, Imperial College London, Medical Research Council, Health Data Research UK, and National Institutes of Health Research.


Subject(s)
Life Expectancy/trends , Mortality/trends , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , Child , Child, Preschool , England/epidemiology , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Registries , Residence Characteristics/statistics & numerical data , Risk Assessment , Spatio-Temporal Analysis , Young Adult
11.
BMJ Open ; 11(10): e050574, 2021 Oct 04.
Article in English | MEDLINE | ID: covidwho-1450605

ABSTRACT

OBJECTIVES: To evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies. DESIGN: A geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19. PARTICIPANTS: All hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020. OUTCOME MEASURES: Predictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates. RESULTS: Peak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%-20% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%. CONCLUSIONS: Local demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.


Subject(s)
COVID-19 , Communicable Disease Control , Hospitalization , Humans , SARS-CoV-2 , Spatio-Temporal Analysis , United Kingdom/epidemiology
12.
Int J Mycobacteriol ; 10(3): 234-242, 2021.
Article in English | MEDLINE | ID: covidwho-1449033

ABSTRACT

Background: This study aimed to describe the spatiotemporal distribution, to build a forecasting model, and to determine the seasonal pattern of tuberculosis (TB) in Algeria. Methods: The Box-Jenkins methodology was used to develop predictive models and GeoDa software was used to perform spatial autocorrelation. Results: Between 1982 and 2019, the notification rate per 100,000 population of smear-positive pulmonary TB (SPPTB) has dropped 62.2%, while that of extrapulmonary TB (EPTB) has risen 91.3%. For the last decade, the mean detection rate of PTB was 82.6%. At around, 2% of PTB cases were yearly reported in children under 15 years old, a peak in notification rate was observed in the elderly aged 65 and over, and the sex ratio was in favor of men. Between 52% and 59% of EPTB cases were lymphadenitis TB and between 15% and 23% were pleural TB. About two-third of EPTB cases were females and around 10% were children under the age of 15. The time series analysis showed that (1,1, 2) × (1, 1, 0)4 (respectively (0, 1, 2) × (1, 1, 0)4, (3, 1, 0) × (1, 1, 0)4) offered the best forecasting model to quarterly TB (respectively EPTB, SPPTB) surveillance data. The most hit part was the Tell followed by high plateaus which accounted for 96.6% of notifications in 2017. Significant hot spots were identified in the central part for EPTB notification rate and in the northwestern part for SPPTB. Conclusions: There is a need to reframe the set objectives in the state strategy to combat TB taking into account seasonality and spatial clustering to ensure improved TB management through targeted and effective interventions.


Subject(s)
Tuberculosis, Pleural , Tuberculosis, Pulmonary , Adolescent , Aged , Algeria/epidemiology , Child , Female , Forecasting , Humans , Male , Spatio-Temporal Analysis , Tuberculosis, Pulmonary/epidemiology
13.
BMC Infect Dis ; 21(1): 816, 2021 Aug 14.
Article in English | MEDLINE | ID: covidwho-1440911

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China. OBJECTIVE: To analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China. METHODS: A spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space-time scan statistic were conducted. RESULTS: The high incidence stage of China's COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran's I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P < 0.01) included 6 cities in Hubei province with Wuhan as the centre. Overall, the clusters with larger coverage were in the early stage of the epidemic, while it changed to only gather in a specific city in the later period. The pattern and scope of clusters changed and reduced over time in China. CONCLUSIONS: Spatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.


Subject(s)
COVID-19 , China/epidemiology , Cities/epidemiology , Humans , Pandemics , SARS-CoV-2 , Spatio-Temporal Analysis
14.
Sci Rep ; 11(1): 18614, 2021 09 20.
Article in English | MEDLINE | ID: covidwho-1428902

ABSTRACT

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking Henan Province before and after COVID-19 pandemic as an example, the NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of Henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction.


Subject(s)
Air Pollution/analysis , Algorithms , COVID-19/epidemiology , Models, Theoretical , Nitrogen Dioxide/analysis , Pandemics , Air Pollutants/analysis , China/epidemiology , Diffusion , Environmental Monitoring , Geography , Humans , Multivariate Analysis , Seasons , Spatio-Temporal Analysis
15.
Eur Rev Med Pharmacol Sci ; 25(17): 5547-5555, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1417452

ABSTRACT

OBJECTIVE: The aim of the study was to analyze spatiotemporal changes of CT manifestations in patients with COVID-19 pneumonia. PATIENTS AND METHODS: In this retrospective review, 110 patients with confirmed COVID-19 by RT-PCR form February 16, 2020, to March 28, 2020 were included. A total of 449 CT scans were reviewed. We analyze the type and distribution of lung abnormalities, and CT general assessment and lesion area statistics were performed. Patients were divided into mild, moderate, and severe disease based on Chinese guidelines: mild (patients with minimal symptoms, CT scans showed no pneumonia or a small area of pneumonia infection), moderate (different extent of clinical manifestations and CT scans showed multiple pneumonia infections in both lungs), severe disease (respiratory distress, CT scans lesion area exceeds 50%, and the lesion contains consolidation). The proportion of patients with mild, moderate and severe diseases was counted. RESULTS: The CT score and the area involved reached a peak (median 10) on illness days 7-12, and then, continued to be at a high level. The main abnormal pattern after symptoms appeared GGO (36/94 [36%] to 40/65 [62%] in different periods). The proportion of mixed reached its peak on illness days 13-18 (36/93 [39%]). Pure GGO was the most common subtype of GGO (24 of 60 CT scans [40%] to 23 of 33 CT scans [70%]) after symptoms onset. The ratio of GGO with irregular lines and interfaces peaked on illness days 7-12 (6/34 [18%]). The lesions are mainly distributed on both sides and under the pleura. 76/84 (90%) of discharged patients had residual lesions on the final CT scans. 4 confirmed patients' CT scans did not show lesions (on illness days 1-24 days). There were 47 mild cases (42.7%), 46 moderate cases (41.8%), and 7 severe cases (6.3%). CONCLUSIONS: The degree of lung abnormality on the CT of the patients reached the peak on the 7th to 12th days of the disease. CT performance changes with time have a certain regularity, which may indicate the progress and recovery of the disease. 90% of patients still observed residual lung abnormalities in CT images at the time of discharge. There were 4 confirmed cases where the CT images did not show the lesion; hence, CT cannot be used as a basis for judging COVID-19 as a single tool.


Subject(s)
COVID-19/diagnostic imaging , SARS-CoV-2 , Adult , COVID-19/pathology , Female , Humans , Lung/diagnostic imaging , Lung/pathology , Male , Middle Aged , Retrospective Studies , Severity of Illness Index , Spatio-Temporal Analysis , Tomography, X-Ray Computed
16.
PLoS One ; 16(9): e0257587, 2021.
Article in English | MEDLINE | ID: covidwho-1416906

ABSTRACT

BACKGROUND: Zhejiang Province is one of the five provinces in China that had the highest incidence of novel coronavirus disease (COVID-19). Zhejiang, ranked fourth highest in COVID-19 incidence, is located in the Yangtze River Delta region of southeast China. This study was undertaken to identify the space-time characteristics of COVID-19 in Zhejiang. METHODS: Data on COVID-19 cases in Zhejiang Province from January to July 2020 were obtained from this network system. Individual information on cases and deaths was imported, and surveillance information, including demographic characteristics and geographic and temporal distributions, was computed by the system. The Knox test was used to identify possible space-time interactions to test whether cases that are close in distance were also close in time. Network analysis was performed to determine the relationship among the cases in a transmission community and to try to identify the key nodes. RESULTS: In total, 1475 COVID-19 cases and 1 fatal case were reported from January to July 2020 in Zhejiang Province, China. Most of the cases occurred before February 15th, which accounted for 90.10%. The imported cases increased and became the main risk in Zhejiang Province after February 2020. The risk areas showed strong heterogeneity according to the Knox test. The areas at short distances within 1 kilometer and at brief periods within 5 days presented relatively high risk. The numbers of subcommunities for the four clusters were 12, 9, 6 and 4. There was obvious heterogeneity in the modularity of subcommunities. The maximum values of the node centrality for the four clusters were 2.9474, 4.3706, 4.1080 and 2.7500. CONCLUSIONS: COVID-19 was brought under control over a short period in Zhejiang Province. Imported infections from outside of mainland China then became a new challenge. The effects of spatiotemporal interaction exhibited interval heterogeneity. The characteristics of transmission showed short range and short term risks. The importance to the cluster of each case was detected, and the key patients were identified. It is suggested that we should focus on key patients in complex conditions and in situations with limited control resources.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19/transmission , China/epidemiology , Humans , Incidence , Spatio-Temporal Analysis
17.
PLoS One ; 16(9): e0257291, 2021.
Article in English | MEDLINE | ID: covidwho-1416893

ABSTRACT

The outbreak of a novel coronavirus pneumonia (COVID-19), wherein more than 200 million people have been infected and millions have died, poses a great threat to achieving the United Nations 2030 sustainable development goal (SDGs). Based on the Baidu index of 'novel coronavirus', this paper analyses the spatial and temporal characteristics of and factors that influenced the attention network for COVID-19 from January 9, 2020, to April 15, 2020. The study found that (1) Temporally, the attention in the new coronavirus network showed an upward trend from January 9 to January 29, with the largest increase from January 23 to January 29 and a peak on January 29, and then a slow downward trend. The level of attention in the new coronavirus network was basically flat when comparing January 22 and March 4. (2) Spatially, first, from the perspective of regional differences, the network attention in the eastern and central regions decreased in turn. The network users in the eastern region exhibited the highest attention to the new coronavirus, especially in Guangdong, Shandong, Jiangsu and other provinces and cities. The network attention in Tibet, Xinjiang, Qinghai and Ningxia in the western region was the lowest in terms of the national network attention. Second, from the perspective of interprovincial differences, the attention in the new coronavirus network was highly consistent with the Hu Huanyong line of China's population boundary. The east of the Hu Huanyong line is densely populated, and the network showed high concern, mostly ranking at the third to fifth levels. (3) The number of Internet users in the information technology field, the population, and the culture and age characteristics of individuals are important factors that influence the novel coronavirus attention network.


Subject(s)
COVID-19/prevention & control , Information Dissemination/methods , Internet/statistics & numerical data , Online Social Networking , Spatio-Temporal Analysis , Algorithms , COVID-19/epidemiology , COVID-19/virology , China/epidemiology , Epidemics , Geography , Humans , Internet/trends , Models, Theoretical , Public Health/methods , Public Health/statistics & numerical data , Public Health/trends , SARS-CoV-2/physiology , Time Factors
18.
Nat Commun ; 12(1): 3358, 2021 06 07.
Article in English | MEDLINE | ID: covidwho-1397869

ABSTRACT

Early stages of embryogenesis depend on subcellular localization and transport of maternal mRNA. However, systematic analysis of these processes is hindered by a lack of spatio-temporal information in single-cell RNA sequencing. Here, we combine spatially-resolved transcriptomics and single-cell RNA labeling to perform a spatio-temporal analysis of the transcriptome during early zebrafish development. We measure spatial localization of mRNA molecules within the one-cell stage embryo, which allows us to identify a class of mRNAs that are specifically localized at an extraembryonic position, the vegetal pole. Furthermore, we establish a method for high-throughput single-cell RNA labeling in early zebrafish embryos, which enables us to follow the fate of individual maternal transcripts until gastrulation. This approach reveals that many localized transcripts are specifically transported to the primordial germ cells. Finally, we acquire spatial transcriptomes of two xenopus species and compare evolutionary conservation of localized genes as well as enriched sequence motifs.


Subject(s)
Cell Tracking/methods , Embryo, Nonmammalian/metabolism , RNA, Messenger/genetics , Transcriptome/genetics , Zebrafish/genetics , Animals , Embryo, Nonmammalian/cytology , Embryo, Nonmammalian/embryology , Female , Gene Expression Regulation, Developmental , Oocytes/cytology , Oocytes/metabolism , RNA, Messenger/metabolism , Single-Cell Analysis/methods , Spatio-Temporal Analysis , Species Specificity , Xenopus/embryology , Xenopus/genetics , Xenopus laevis/embryology , Xenopus laevis/genetics , Zebrafish/embryology
19.
Int J Environ Res Public Health ; 18(1)2021 01 02.
Article in English | MEDLINE | ID: covidwho-1389357

ABSTRACT

Infectious diseases have caused some of the most feared plagues and greatly harmed human health. However, despite the qualitative understanding that the occurrence and diffusion of infectious disease is related to the environment, the quantitative relations are unknown for many diseases. Zika virus (ZIKV) is a mosquito-borne virus that poses a fatal threat and has spread explosively throughout the world, impacting human health. From a geographical perspective, this study aims to understand the global hotspots of ZIKV as well as the spatially heterogeneous relationship between ZIKV and environmental factors using exploratory special data analysis (ESDA) model. A geographically weighted regression (GWR) model was used to analyze the influence of the dominant environmental factors on the spread of ZIKV at the continental scale. The results indicated that ZIKV transmission had obvious regional and seasonal heterogeneity. Population density, GDP per capita, and landscape fragmentation were the dominant environmental factors affecting the spread of ZIKV, which indicates that social factors had a greater influence than natural factors on the spread of it. As SARS-CoV-2 is spreading globally, this study can provide methodological reference for fighting against the pandemic.


Subject(s)
Zika Virus Infection , Animals , Humans , Mosquito Vectors , Spatio-Temporal Analysis , Zika Virus , Zika Virus Infection/epidemiology , Zika Virus Infection/transmission
20.
J Hosp Infect ; 110: 172-177, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1385938

ABSTRACT

BACKGROUND: Studying the spatiotemporal distribution of SARS-CoV-2 infections among healthcare workers (HCWs) can aid in protecting them from exposure. AIM: To describe the spatiotemporal distributions of SARS-CoV-2 infections among HCWs in Wuhan, China. METHODS: In this study, an open-source dataset of HCW diagnoses was provided. A geographical detector technique was then used to investigate the impacts of hospital level, type, distance from the infection source, and other external indicators of HCW infections. FINDINGS: The number of daily HCW infections over time in Wuhan followed a log-normal distribution, with its mean observed on January 23rd, 2020, and a standard deviation of 10.8 days. The implementation of high-impact measures, such as the lockdown of the city, may have increased the probability of HCW infections in the short term, especially for those in the outer ring of Wuhan. The infection of HCWs in Wuhan exhibited clear spatial heterogeneity. The number of HCW infections was higher in the central city and lower in the outer city. CONCLUSION: HCW infections displayed significant spatial autocorrelation and dependence. Factor analysis revealed that hospital level and type had an even greater impact on HCW infections; third-class and general hospitals closer to infection sources were correlated with especially high risks of infection.


Subject(s)
COVID-19/epidemiology , Disease Outbreaks/statistics & numerical data , Health Personnel/statistics & numerical data , Occupational Diseases/epidemiology , Adult , China/epidemiology , Factor Analysis, Statistical , Female , Humans , Male , Middle Aged , Spatio-Temporal Analysis
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