Multivariate spatio-temporal analysis of the global COVID-19 pandemic

AbstractThe COVID-19 pandemic has caused significant mortality and disruption on a global scale not seen in living memory. Understanding the spatial and temporal vectors of transmission as well as similarities in the trajectories of recorded cases and deaths across countries can aid in understanding the benefit or otherwise of varying interventions and control strategies on virus transmission. It can also highlight emerging globa trends as they occur. Data on number of cases and deaths across the globe have been made available through a variety of databases and provide a wide range of opportunities for the application of multivariate statistical methods to extract information on similarity or difference from them. Here we conduct spatial and temporal multivariate statistical analyses of global COVID-19 cases and deaths for the period spanning January to August 2020, using a variety of distance based multivariate methods to cluster countries according to similar temporal trends in cases and deaths resulting from COVID-19. We also use novel air passenger data as a proxy for movement between countries. The air passenger movement can act as an important vector of transmission and thus scaling covariance matrices before conducting dimension reduction techniques can account for known structures in the data and help highlight important residual spatial and/or temporal trends that may then be attributable to the success of interventions or other cultural differences. Global temporal structure is found to be of significantly more importance than local spatial structure in terms of global dynamics. Our results highlight a significant global change in case and mortality daynamics from early-August, consistent in timing with the emergence of new strains with highger levels of transmission. We propose the methodology offers great potential in real-time analysis of complex, noisy spatio-temporal data and the extraction of emerging changes in pandemic dynamics that can support policy and decision makers.

Rainfall spatio-temporal correlation and intermittency structure from micro-γ to meso-β scale in the Netherlands

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0311.1 ◽

2021 ◽

Author(s):

Thomas C. van Leth ◽

Hidde Leijnse ◽

Aart Overeem ◽

Remko Uijlenhoet

Keyword(s):

The Netherlands ◽

Spatial Scales ◽

Rainfall Variability ◽

Temporal Structure ◽

Temporal Correlation ◽

Exponential Model ◽

Rain Gauges ◽

Wide Range ◽

Spatio Temporal ◽

Two Parameter

AbstractWe investigate the spatio-temporal structure of rainfall at spatial scales from 7m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatio-temporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

Mapping Evapotranspiration, Vegetation and Precipitation Trends in the Catchment of the Shrinking Lake Poopó

Remote Sensing ◽

10.3390/rs12010073 ◽

2019 ◽

Vol 12 (1) ◽

pp. 73 ◽

Author(s):

Juan Torres-Batlló ◽

Belén Martí-Cardona ◽

Ramiro Pillco-Zolá

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Temporal Trends ◽

Temporal Analysis ◽

Mountain Range ◽

Water Losses ◽

Essential Information ◽

General Decline ◽

Increasing Trend ◽

Lake Poopó is located in the Andean Mountain Range Plateau or Altiplano. A general decline in the lake water level has been observed in the last two decades, coinciding roughly with an intensification of agriculture exploitation, such as quinoa crops. Several factors have been linked with the shrinkage of the lake, including climate change, increased irrigation, mining extraction and population growth. Being an endorheic catchment, evapotranspiration (ET) losses are expected to be the main water output mechanism and previous studies demonstrated ET increases using Earth observation (EO) data. In this study, we seek to build upon these earlier findings by analyzing an ET time series dataset of higher spatial and temporal resolution, in conjunction with land cover and precipitation data. More specifically, we performed a spatio-temporal analysis, focusing on wet and dry periods, that showed that ET changes occur primarily in the wet period, while the dry period is approximately stationary. An analysis of vegetation trends performed using 500 MODIS vegetation index products (NDVI) also showed an overall increasing trend during the wet period. Analysis of NDVI and ET across land cover types showed that only croplands had experienced an increase in NDVI and ET losses, while natural covers showed either constant or decreasing NDVI trends together with increases in ET. The larger increase in vegetation and ET losses over agricultural regions, strongly suggests that cropping practices exacerbated water losses in these areas. This quantification provides essential information for the sustainable planning of water resources and land uses in the catchment. Finally, we examined the spatio-temporal trends of the precipitation using the newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS-v2) product, which we validated with onsite rainfall measurements. When integrated over the entire catchment, precipitation and ET showed an average increasing trend of 5.2 mm yr−1 and 4.3 mm yr−1, respectively. This result suggests that, despite the increased ET losses, the catchment-wide water storage should have been offset by the higher precipitation. However, this result is only applicable to the catchment-wide water balance, and the location of water may have been altered (e.g., by river abstractions or by the creation of impoundments) to the detriment of the Lake Poopó downstream.

Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17072563 ◽

2020 ◽

Vol 17 (7) ◽

pp. 2563 ◽

Cited By ~ 8

Author(s):

Wentao Yang ◽

Min Deng ◽

Chaokui Li ◽

Jincai Huang

Keyword(s):

Temporal Dynamics ◽

Spatial Interaction ◽

Temporal Trends ◽

Temporal Patterns ◽

Temporal Analysis ◽

Hubei Province ◽

Incidence Rates ◽

County Level ◽

Spatial Clusters ◽

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

Spatio-temporal analysis and visualisation of the anthrax epidemic situation in livestock in Kazakhstan over the period 1933-2016

Geospatial health ◽

10.4081/gh.2017.589 ◽

2017 ◽

Author(s):

Sarsenbay K. Abdrakhmanov ◽

Yersyn Y. Mukhanbetkaliyev ◽

Fedor I. Korennoy ◽

Bolat Sh. Karatayev ◽

Aizada A. Mukhanbetkaliyeva ◽

...

Keyword(s):

Temporal Trends ◽

Small Ruminants ◽

Temporal Analysis ◽

Space Time ◽

Decision Makers ◽

Annual Number ◽

Spatio Temporal ◽

The Republic ◽

Increasing Trends ◽

Historical Periods

An analysis of the anthrax epidemic situation among livestock animals in the Republic of Kazakhstan over the period 1933-2016 is presented. During this time, 4,064 anthrax outbreaks (mainly in cattle, small ruminants, pigs and horses) were recorded. They fall into five historical periods of increase and decrease in the annual anthrax incidence (1933-1953; 1954-1968; 1969-1983; 1984- 2001; and 2002-2016), which has been associated with changes in economic activity and veterinary surveillance. To evaluate the temporal trends of incidence variation for each of these time periods, the following methods were applied: i) spatio-temporal analysis using a space-time cube to assess the presence of hotspots (i.e., areas of outbreak clustering) and the trends of their emergence over time; and ii) a linear regression model that was used to evaluate the annual numbers of outbreaks as a function of time. The results show increasing trends during the first two periods followed by a decreasing trend up to now. The peak years of anthrax outbreaks occurred in 1965-1968 but outbreaks still continue with an average annual number of outbreaks of 1.2 (95% confidence interval: 0.6-1.8). The space-time analysis approach enabled visualisation of areas with statistically significant increasing or decreasing trends of outbreak clustering providing a practical opportunity to inform decision-makers and allowing the veterinary services to concentrate their efforts on monitoring the possible risk factors in the identified locations.

Spatio-Temporal Analysis of Heating and Cooling Degree-Days Over Iran

10.21203/rs.3.rs-441421/v1 ◽

2021 ◽

Author(s):

Amin Sadeqi ◽

Hossein Tabari ◽

Yagob Dinpashoh

Keyword(s):

Time Series ◽

Energy Demand ◽

Temporal Trends ◽

Temporal Analysis ◽

Degree Days ◽

Heating And Cooling ◽

Cooling Degree Days ◽

Spatio Temporal ◽

Heating Degree Days ◽

Cooling Energy Demand

Abstract Climate change affects the energy demand in different sectors of the society. To investigate this possible impact, in this research, temporal trends and change points in heating degree-days (HDD), cooling degree-days (CDD), and their simultaneous combination (HDD+CDD) were analysed for a 60-year period (1960-2019) in Iran. The results show that less than 20% of the study stations had significant trends (either upward or downward) in HDD time series, while more than 80% of the stations had significant increasing trends in CDD and HDD+CDD time series. Abrupt changes in HDD time series mostly occurred in the early 1980s, but those in CDD time series were mostly observed in the 1990s. The cooling energy demand in Iran has dramatically increased as CDD values have raised up from 690 ºC-days to 1010 ºC-days in the last 60 years. HDD, however, almost remained constant in the same period. The results suggest that if global warming continues with the current pace, cooling energy demand in the residential sector will considerably increase in the future, calling for a change in residential energy consumption policies.

Spatio-temporal trends in the risk of adolescent pregnancy in Minas Gerais, Brazil, 2000–2015

International Journal of Adolescent Medicine and Health ◽

10.1515/ijamh-2018-0027 ◽

2018 ◽

Vol 0 (0) ◽

Author(s):

Daiane Leite da Roza ◽

Carla Maria Teixeira de Oliveira ◽

Maria de Fátima Rodrigues Pereira de Pina ◽

Denisa Maria de Melo Vasques de Mendonça ◽

Edson Zangiacomi Martinez

Keyword(s):

Relative Risk ◽

Human Development ◽

Adolescent Mothers ◽

Temporal Trends ◽

Temporal Analysis ◽

Minas Gerais ◽

The State ◽

Local Index ◽

Live Births ◽

Abstract Purpose To investigate, through a spatio-temporal analysis, the association between the percentages of live births of adolescent mothers (LBAM) and the human development index (HDI), including the three components: income, education and longevity. Methods The percentage of LBAM was obtained from the Brazilian Live Births Information System for the state of Minas Gerais, Brazil in the period 2000–2015 and the HDI data and its components were obtained from United Nations Development Program’s (UNDP) Human Development Reports. A generalized additive model (GAM) was used to estimate the relative risk of LBAM in relation to the HDI and to identify spatial clusters of the geographical distribution of LBAM, the Moran global and local index was used. Results There is an association between the HDI and its components with LBAM. The high values of relative risk are spatially concentrated in the northern part of the state of Minas Gerais. The graphs indicated a nonlinear relationship between LBAM over the years. Conclusions There is a strong spatial dependence of LBAM in Minas Gerais, which suggests that a geographical location plays a fundamental role in understanding it. The regional disparity confirmed in this study is inherent in the process of human development, it is important for planning actions aimed at the development of these regions in order to minimize existing disparities.

The full impulse response of two-dimensional jet/wake flows and implications for confinement

Journal of Fluid Mechanics ◽

10.1017/s0022112007007975 ◽

2007 ◽

Vol 590 ◽

pp. 163-185 ◽

Cited By ~ 28

Author(s):

MATTHEW P. JUNIPER

Keyword(s):

Impulse Response ◽

Temporal Analysis ◽

Strong Mixing ◽

Combustion Chambers ◽

Fuel Injectors ◽

Wake Flows ◽

Wide Range ◽

Spatio Temporal ◽

High Reynolds ◽

Density Ratios

In this theoretical study, a linear spatio-temporal analysis is performed on unconfined and confined inviscid jet/wake flows in order to determine whether they are absolutely or convectively unstable. The impulse response is evaluated in the entire outer fluid, rather than just at the point of impulse, over a wide range of density ratios. This confirms that the dominant saddle point can validly migrate into the plane of diverging eigenfunctions. This reveals that, at certain density ratios and shear numbers, the response can grow upstream in some directions with a cross-stream component, even though it decays directly upstream of, and at, the point of impulse. This type of flow is convectively unstable when unconfined, but becomes absolutely unstable when confined. Other effects of confinement are described in a previous paper. Together, these articles have important implications for the design of fuel injectors, which often employ confined shear flows at high Reynolds number and large density ratios to generate strong mixing in combustion chambers.

Spatio-temporal analysis of flood data from South Carolina

Journal of Statistical Distributions and Applications ◽

10.1186/s40488-020-00112-x ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Haigang Liu ◽

David B. Hitchcock ◽

S. Zahra Samadi

Keyword(s):

South Carolina ◽

Temporal Structure ◽

Daily Rainfall ◽

Temporal Analysis ◽

The United States ◽

Environmental Data ◽

Temporal Properties ◽

Proximity Matrix ◽

Spatio Temporal ◽

Emergency Decision

AbstractTo investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatio-temporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall. In this respect, a proximity matrix based on watershed information was used to capture the spatial structure of gage height measurements in and around South Carolina. The temporal structure was handled by a first-order autoregressive term in the model. Several covariates, including the elevation of the sites and effects of seasonality, were examined, along with daily rainfall amount. A non-normal error structure was used to account for the heavy-tailed distribution of maximum gage heights. The proposed model captured some key features of the flood process such as seasonality and a stronger association between precipitation and flooding during summer season. The model is able to forecast short term flood gage height which is crucial for informed emergency decision. As a byproduct, we also developed a Python library to retrieve and handle environmental data provided by some main agencies in the United States. This library can be of general usefulness for studies requiring rainfall, flow, and geomorphological information over specific areas of the conterminous US.

A spatio-temporal modelling of Covid-19 infections in Toronto’s neighbourhoods

University of Toronto Journal of Public Health ◽

10.33137/utjph.v2i2.36812 ◽

2021 ◽

Vol 2 (2) ◽

Author(s):

Sze Hang Fu

Keyword(s):

Population Density ◽

Temporal Trends ◽

Social Factor ◽

Age Groups ◽

Temporal Analysis ◽

Disease Spread ◽

Lower Socioeconomic ◽

Neighbourhood Level ◽

Spatio Temporal ◽

Control Disease

Introduction & Objective: Besides age and sex as established risk factors of COVID-19 infection, social factor is found to be a determinant, with people of lower socioeconomic status suffer disproportionately from the disease. The city of Toronto has one of the highest COVID-19 infection rates in Canada. This analysis aims to explore the socioeconomic correlates associated with COVID-19 infection and the temporal trends among different age groups in Toronto using geospatial modeling. Methods: A Bayesian spatio-temporal analysis was conducted using public COVID-19 cases data for Toronto. The case data were modeled using the Besag-York-Mollie (BYM) model, implemented in R-INLA. The model adjusted for age, sex, neighbourhood-level socioeconomic factors, crime rates, and population density. Random effects were included to account for neighbourhood-level variation and for spatial autocorrelation. Temporal trends of COVID-19 cases were modelled using second-order random walks to allow non-parametric estimations. Results: The model estimates showed that men are at higher risk of COVID-19 infection. Among neighbourhood factors, higher home prices, education level, and population density are at lower risks, while belonging to an improvement area showed elevated risks. The temporal trends differed by age, with ages 20-59 showed increased risks over time, compared to the youngest and older age groups. Model predictions showed that northwest Toronto has higher risk compared to the rest of Toronto. Conclusion: The higher COVID-19 infection risks in the Northwest will require increase public health effort to control disease spread in this area. The ecological correlates identified in this analysis will also help to guide the ongoing vaccination plans.

Why Does It Always Rain on Me? A Spatio-Temporal Analysis of Precipitation in Austria

Austrian Journal of Statistics ◽

10.17713/ajs.v41i1.190 ◽

2016 ◽

Vol 41 (1) ◽

Author(s):

Nikolaus Umlauf ◽

Georg Mayr ◽

Jakob Messner ◽

Achim Zeileis

Keyword(s):

Temporal Structure ◽

Temporal Analysis ◽

Weekend Effect ◽

Quality Data ◽

Data Set ◽

Data Driven Approach ◽

Spatio Temporal ◽

Ordered Response ◽

Additive Regression ◽

Spatially Varying

It is popular belief that the weather is “bad” more frequently on weekends than on other days of the week and this is often perceived to be associated with an increased chance of rain. In fact, the meteorological literature does report some evidence for such human-induced weekly cycles although these findings are not undisputed. To contribute to this discussion, a modern data-driven approach using structured additive regression modelsis applied to a newly available high-quality data set for Austria. The analysis investigates how an ordered response of rain intensities is influenced by a (potential) weekend effect while adjusting for spatio-temporal structure using spatially varying effects of overall level and seasonality patterns. The underlying data are taken from the HOMSTART project which provides daily precipitation quantities over a period of more than 60 years and a dense netof more than 50 meteorological stations all across Austria.