scholarly journals Restarting after COVID-19: A Data-driven Evaluation of Opening Scenarios

2020 ◽  
Author(s):  
Ashwin Aravindakshan ◽  
Jörn Boehnke ◽  
Ehsan Gholami ◽  
Ashutosh Nayak
Keyword(s):  
Public Space ◽  
Temporal Variations ◽  
International Travel ◽  
Disease Spread ◽  
Disease Propagation ◽  
Essential Services ◽  
Accurate Forecast ◽  
Policy Measures ◽  
Spatio Temporal ◽  
The Impact

AbstractTo contain the COVID-19 pandemic, several governments introduced strict Non-Pharmaceutical Interventions (NPI) that restricted movement, public gatherings, national and international travel, and shut down large parts of the economy. Yet, the impact of the enforcement and subsequent loosening of these policies on the spread of COVID-19 is not well understood. Accordingly, we measure the impact of NPI on mitigating disease spread by exploiting the spatio-temporal variations in policy measures across the 16 states of Germany. This quasi-experiment identifies each policy’s effect on reducing disease spread. We adapt the SEIR (Susceptible-Exposed-Infected-Recovered) model for disease propagation to include data on daily confirmed cases, intra- and inter-state movement, and social distancing. By combining the model with measures of policy contributions on mobility reduction, we forecast scenarios for relaxing various types of NPIs. Our model finds that, in Germany, policies that mandated contact restrictions (e.g., movement in public space limited to two persons or people co-living), initial business closures (e.g., restaurant closures), stay-at-home orders (e.g., prohibition of non-essential trips), non-essential services (e.g., florists, museums) and retail outlet closures led to the sharpest drops in movement within and across states. Contact restrictions were the most effective at lowering infection rates, while border closures had only minimal effects at mitigating the spread of the disease, even though cross-border travel might have played a role in seeding the disease in the population. We believe that a deeper understanding of the policy effects on mitigating the spread of COVID-19 allows a more accurate forecast of the disease spread when NPIs are (partially) loosened, and thus also better informs policymakers towards making appropriate decisions.

scholarly journals Preparing for a future COVID-19 wave: insights and limitations from a data-driven evaluation of non-pharmaceutical interventions in Germany

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ashwin Aravindakshan ◽  
Jörn Boehnke ◽  
Ehsan Gholami ◽  
Ashutosh Nayak
Keyword(s):  
Public Space ◽  
Temporal Variations ◽  
International Travel ◽  
Disease Spread ◽  
Educational Institutions ◽  
Disease Propagation ◽  
Accurate Forecast ◽  
Spatio Temporal ◽  
The Impact ◽  
Pharmaceutical Interventions

AbstractTo contain the COVID-19 pandemic, governments introduced strict Non-Pharmaceutical Interventions (NPI) that restricted movement, public gatherings, national and international travel, and shut down large parts of the economy. Yet, the impact of the enforcement and subsequent loosening of these policies on the spread of COVID-19 is not well understood. Accordingly, we measure the impact of NPIs on mitigating disease spread by exploiting the spatio-temporal variations in policy measures across the 16 states of Germany. While this quasi-experiment does not allow for causal identification, each policy’s effect on reducing disease spread provides meaningful insights. We adapt the Susceptible–Exposed–Infected–Recovered model for disease propagation to include data on daily confirmed cases, interstate movement, and social distancing. By combining the model with measures of policy contributions on mobility reduction, we forecast scenarios for relaxing various types of NPIs. Our model finds that in Germany policies that mandated contact restrictions (e.g., movement in public space limited to two persons or people co-living), closure of educational institutions (e.g., schools), and retail outlet closures are associated with the sharpest drops in movement within and across states. Contact restrictions appear to be most effective at lowering COVID-19 cases, while border closures appear to have only minimal effects at mitigating the spread of the disease, even though cross-border travel might have played a role in seeding the disease in the population. We believe that a deeper understanding of the policy effects on mitigating the spread of COVID-19 allows a more accurate forecast of disease spread when NPIs are partially loosened and gives policymakers better data for making informed decisions.

scholarly journals Spatio-temporal Variability and Trends of Mean and Extreme Rainfall Events in the Sudano-sahelian Region of Cameroon

2021 ◽  
Author(s):  
Ibrahim NJOUENWET ◽  
Lucie A. Djiotang Tchotchou ◽  
Brian Odhiambo Ayugi ◽  
Guy Merlin Guenang ◽  
Derbetini A. Vondou ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Rainfall ◽  
Temporal Variations ◽  
Annual Rainfall ◽  
Drought Risk ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Precipitation Concentration ◽  
Spatio Temporal ◽  
The Impact

Abstract The Sudano-Sahelian region of Cameroon is mainly drained by the Benue, Chari and Logone rivers, which are very useful for water resources, especially for irrigation, hydropower generation, and navigation. Long-term changes in mean and extreme rainfall events in the region may be of crucial importance in understanding the impact of climate change. Daily and monthly rainfall data from twenty-five synoptic stations in the study area from 1980 to 2019 and extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) measurements were estimated using the non-parametric Modified Mann-Kendall test and the Sen slope estimator. The precipitation concentration index (PCI), the precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to explore the spatio-temporal variations in the characteristics of rainfall concentrations. An increase in extreme rainfall events was observed, leading to an upward trend in mean annual. Trends in consecutive dry days (CDD) are significantly increasing in most parts of the study area. This could mean that the prevalence of drought risk is higher in the study area. Overall, the increase in annual rainfall could benefit the hydro-power sector, agricultural irrigation, the availability of potable water sources, and food security.

scholarly journals Mitigating isolation: The use of rapid antigen testing to reduce the impact of self-isolation periods

2021 ◽  
Author(s):  
Declan Bays ◽  
Timothy Whiteley ◽  
Matt Pindar ◽  
Johnathon Taylor ◽  
Brodie F Walker ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Fixed Time ◽  
Disease Spread ◽  
Essential Services ◽  
Testing Technology ◽  
Antigen Tests ◽  
Widespread Access ◽  
The Individual ◽  
The Impact ◽  
Number Of Individuals

Isolating, either enforced or self-guided, is a well-recognised and used technique in the limitation and reduction of disease spread. This usually balances the societal harm of disease transmission against the individual harm of being isolated and is typically limited to a very small number of individuals. With the widespread transmission of SARS-CoV-2 and requirements to self-isolate when symptomatic or having tested positive, the number of people affected has grown very large causing noticeable individual cost, and disruption to the provision of essential services. With widespread access to reliable rapid antigen tests (also known as LFD or LFTs), in this paper we examine strategies to utilise this testing technology to limit the individual harm whist maintaining the protective effect of isolation. We extend this work to examine how isolation may be improved and mitigate the release of infective individuals into the population caused by fixed time-periods.

scholarly journals Landscape Dynamics Improved Recreation Service of the Three Gorges Reservoir Area, China

2021 ◽  
Vol 18 (16) ◽  
pp. 8356
Author(s):  
Dengyue Zhao ◽  
Mingzhu Xiao ◽  
Chunbo Huang ◽  
Yuan Liang ◽  
Ziyue An
Keyword(s):  
Three Gorges Reservoir ◽  
Temporal Variations ◽  
Landscape Dynamics ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
The Three Gorges ◽  
Vegetation Landscape ◽  
Recreation Service ◽  
Spatio Temporal ◽  
The Impact

Spatio-temporal variations of recreation service not only could help to understand the impact of cultural services on human well-being but also provides theoretical and technical support for regional landscape management. However, previous studies have avoided deeply quantifying and analyzing it or have simply focused on assessing recreational service at a single period in time. In this study, we used the InVEST model to evaluate the spatio-temporal variations of recreation service in the Three Gorges Reservoir Area and demonstrated the impact of recreation service on landscape dynamics. The results demonstrated that recreation service increased significantly and presented a significant spatial heterogeneity. Although afforestation and urban expansion both could significantly increase recreation service, the recreation service proxy of the non-vegetation landscape is far higher than that of the vegetation landscape. This finding indicated that human landscape is more attractive to tourists than the natural landscape, so we recommend to strengthen the infrastructure construction for enhancing the accessibility of natural landscapes. Moreover, we propose other constructive suggestions and landscape-design solutions for promoting recreation service. This study shifted the static environmental health assessment to the analysis of recreation service dynamics, bridging the regulatory mechanisms of ecosystem services involved in cultural services.

scholarly journals Last interglacial model–data mismatch of thermal maximum temperatures partially explained

2014 ◽  
Vol 10 (4) ◽  
pp. 1633-1644 ◽  
Author(s):  
P. Bakker ◽  
H. Renssen
Keyword(s):  
Climate Models ◽  
Temporal Variations ◽  
Model Data ◽  
Last Interglacial ◽  
Underlying Assumption ◽  
Thermal Maximum ◽  
Spatio Temporal ◽  
Maximum Temperatures ◽  
The Impact ◽  
Global Mean

Abstract. The timing of the last interglacial (LIG) thermal maximum across the globe remains to be precisely assessed. Because of difficulties in establishing a common temporal framework between records from different palaeoclimatic archives retrieved from various places around the globe, it has not yet been possible to reconstruct spatio-temporal variations in the occurrence of the maximum warmth across the globe. Instead, snapshot reconstructions of warmest LIG conditions have been presented, which have an underlying assumption that maximum warmth occurred synchronously everywhere. Although known to be an oversimplification, the impact of this assumption on temperature estimates has yet to be assessed. We use the LIG temperature evolutions simulated by nine different climate models to investigate whether the assumption of synchronicity results in a sizeable overestimation of the LIG thermal maximum. We find that for annual temperatures, the overestimation is small, strongly model-dependent (global mean 0.4 ± 0.3 °C) and cannot explain the recently published 0.67 °C difference between simulated and reconstructed annual mean temperatures during the LIG thermal maximum. However, if one takes into consideration that temperature proxies are possibly biased towards summer, the overestimation of the LIG thermal maximum based on warmest month temperatures is non-negligible with a global mean of 1.1 ± 0.4 °C.

scholarly journals Spatial Variations in the Diurnal Pattern of Precipitation over Nepal Himalayas

2015 ◽  
Vol 15 (2) ◽  
pp. 57-64 ◽  
Author(s):  
Dibas Shrestha ◽  
Rashila Deshar
Keyword(s):  
Summer Monsoon ◽  
Diurnal Cycle ◽  
Monsoon Season ◽  
Tropical Rainfall Measuring Mission ◽  
Early Morning ◽  
Temporal Variations ◽  
Diurnal Cycles ◽  
Diurnal Cycle Of Precipitation ◽  
Spatio Temporal ◽  
The Impact

The Central Himalayan Region (Nepal Himalayas), comprised of two clear sub-parallel mountain ranges, is atypical location for studying the impact of rugged topography on spatio temporal variations of precipitation. The relationship between topography and diurnal cycles of rainfall have been investigated utilizing 13-year (1998–2010) high resolution (0.05° × 0.05°) Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. An investigation of diurnal cycle of precipitation revealed an afternoon maximum during the pre-monsoon season (March–May) and midnight–early morning maximum during the summer monsoon season (June–August)over the southern slopes of the Himalayas. The summer monsoon exhibited a robust spatial variation of diurnal cycle of precipitation, during afternoon-evening time, primary rainfall peak appeared along the Lesser Himalayas (~2,000–2,200 m above mean sea level), while early-morning rain in contrast showed maximum concentration along the southern margin of the Himalayas (~500–700 m above MSL). An afternoon-evening rainfall peak was attributed to higher rain frequency, whereas early-morning rainfall peak was attributed to fewer but rather intense rainfall. It is suggested that, confluence between down slope and moist south easterly monsoon flow triggers convection near the foothills of the Himalayas during early morning period. The results further suggested the morning precipitation moves southward in the mature monsoon season.DOI: http://dx.doi.org/njst.v15i2.12116Nepal Journal of Science and Technology Vol. 15, No.2 (2014), 57-64

scholarly journals Integration of spatio-temporal variations of surface metabolomes and epibacterial communities highlights the importance of copper stress as a major factor shaping host-microbiota interactions within a Mediterranean seaweed holobiont

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Benoît Paix ◽  
Nicolas Layglon ◽  
Christophe Le Poupon ◽  
Sébastien D’Onofrio ◽  
Benjamin Misson ◽  
...  
Keyword(s):  
Trace Metal ◽  
Relative Proportion ◽  
Geographical Area ◽  
Environmental Parameters ◽  
High Specificity ◽  
Temporal Variations ◽  
Mediterranean Coast ◽  
Copper Stress ◽  
Spatio Temporal ◽  
The Impact

Abstract Background Although considered as holobionts, macroalgae and their surface microbiota share intimate interactions that are still poorly understood. Little is known on the effect of environmental parameters on the close relationships between the host and its surface-associated microbiota, and even more in a context of coastal pollutions. Therefore, the main objective of this study was to decipher the impact of local environmental parameters, especially trace metal concentrations, on an algal holobiont dynamics using the Phaeophyta Taonia atomaria as a model. Through a multidisciplinary multi-omics approach combining metabarcoding and untargeted LC-MS-based metabolomics, the epibacterial communities and the surface metabolome of T. atomaria were monitored along a spatio-temporal gradient in the bay of Toulon (Northwestern Mediterranean coast) and its surrounding. Indeed, this geographical area displays a well-described trace metal gradient particularly relevant to investigate the effect of such pollutants on marine organisms. Results Epibacterial communities of T. atomaria exhibited a high specificity whatever the five environmentally contrasted collecting sites investigated on the NW Mediterranean coast. By integrating metabarcoding and metabolomics analyses, the holobiont dynamics varied as a whole. During the occurrence period of T. atomaria, epibacterial densities and α-diversity increased while the relative proportion of core communities decreased. Pioneer bacterial colonizers constituted a large part of the specific and core taxa, and their decrease might be linked to biofilm maturation through time. Then, the temporal increase of the Roseobacter was proposed to result from the higher temperature conditions, but also the increased production of dimethylsulfoniopropionate (DMSP) at the algal surface which could constitute of the source of carbon and sulfur for the catabolism pathways of these taxa. Finally, as a major result of this study, copper concentration constituted a key factor shaping the holobiont system. Thus, the higher expression of carotenoids suggested an oxidative stress which might result from an adaptation of the algal surface metabolome to high copper levels. In turn, this change in the surface metabolome composition could result in the selection of particular epibacterial taxa. Conclusion We showed that associated epibacterial communities were highly specific to the algal host and that the holobiont dynamics varied as a whole. While temperature increase was confirmed to be one of the main parameters associated to Taonia dynamics, the originality of this study was highlighting copper-stress as a major driver of seaweed-epibacterial interactions. In a context of global change, this study brought new insights on the dynamics of a Mediterranean algal holobiont submitted to heavy anthropic pressures.

scholarly journals Spatio-Temporal Variations and Uncertainty in Land Surface Modelling for High Latitudes: Univariate Response Analysis

2019 ◽  
Author(s):  
Didier G. Leibovici ◽  
Shaun Quegan ◽  
Edward Comyn-Platt ◽  
Gary Hayman ◽  
Maria Val Martin ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Temporal Pattern ◽  
Temporal Variations ◽  
The Other ◽  
Surface Modelling ◽  
Land Surface Modelling ◽  
Spatio Temporal ◽  
The Times ◽  
The Impact

Abstract. A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate sensitive infections (CSIs), agriculture crop modelling, etc., make use of Land Surface Modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and, depending on the application, the choice of LSM and its sensitivity will have different impacts. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multi-way data analysis, which extends Singular Value Decomposition (SVD) to multi-dimensional tables, and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fenno-Scandinavian and north-west Russian regions, and the approach is explored by comparing Net Primary Production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits, but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences, and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT respectively differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction.

scholarly journals Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis

2020 ◽  
Vol 17 (7) ◽  
pp. 1821-1844
Author(s):  
Didier G. Leibovici ◽  
Shaun Quegan ◽  
Edward Comyn-Platt ◽  
Garry Hayman ◽  
Maria Val Martin ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Temporal Pattern ◽  
Temporal Variations ◽  
The Other ◽  
Surface Modelling ◽  
Land Surface Modelling ◽  
Spatio Temporal ◽  
The Times ◽  
The Impact

Abstract. A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, make use of land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and this may introduce predictive uncertainty when LSM outputs are used as inputs to inform a given application. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fennoscandian and north-west Russian regions, and the approach is explored by comparing net primary production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high-latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction.

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