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

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.

Author(s):  
Ashwin Aravindakshan ◽  
Jörn Boehnke ◽  
Ehsan Gholami ◽  
Ashutosh Nayak

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.


2021 ◽  
Vol 26 (28) ◽  
Author(s):  
Paul R Hunter ◽  
Felipe J Colón-González ◽  
Julii Brainard ◽  
Steven Rushton

Introduction The current pandemic of coronavirus disease (COVID-19) is unparalleled in recent history as are the social distancing interventions that have led to a considerable halt on the economic and social life of so many countries. Aim We aimed to generate empirical evidence about which social distancing measures had the most impact in reducing case counts and mortality. Methods We report a quasi-experimental (observational) study of the impact of various interventions for control of the outbreak through 24 April 2020. Chronological data on case numbers and deaths were taken from the daily published figures by the European Centre for Disease Prevention and Control and dates of initiation of various control strategies from the Institute of Health Metrics and Evaluation website and published sources. Our complementary analyses were modelled in R using Bayesian generalised additive mixed models and in STATA using multilevel mixed-effects regression models. Results From both sets of modelling, we found that closure of education facilities, prohibiting mass gatherings and closure of some non-essential businesses were associated with reduced incidence whereas stay-at-home orders and closure of additional non-essential businesses was not associated with any independent additional impact. Conclusions Our findings are that schools and some non-essential businesses operating ‘as normal’ as well as allowing mass gatherings were incompatible with suppressing disease spread. Closure of all businesses and stay at home orders are less likely to be required to keep disease incidence low. Our results help identify what were the most effective non-pharmaceutical interventions in this period.


2021 ◽  
Author(s):  
Yong Ge ◽  
Wenbin Zhang ◽  
Haiyan Liu ◽  
Corrine W Ruktanonchai ◽  
Maogui Hu ◽  
...  

Abstract Worldwide governments have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic, together with the large-scale rollout of vaccines since late 2020. However, the effect of these individual NPI and vaccination measures across space and time has not been sufficiently explored. By the decay ratio in the suppression of COVID-19 infections, we investigated the performance of different NPIs across waves in 133 countries, and their integration with vaccine rollouts in 63 countries as of 25 March 2021. The most effective NPIs were gathering restrictions (contributing 27.83% in the infection rate reductions), facial coverings (16.79%) and school closures (10.08%) in the first wave, and changed to facial coverings (30.04%), gathering restrictions (17.51%) and international travel restrictions (9.22%) in the second wave. The impact of NPIs had obvious spatiotemporal variations across countries by waves before vaccine rollouts, with facial coverings being one of the most effective measures consistently. Vaccinations had gradually contributed to the suppression of COVID-19 transmission, from 0.71% and 0.86% within 15 days and 30 days since Day 12 after vaccination, to 1.23% as of 25 March 2021, while NPIs still dominated the pandemic mitigation. Our findings have important implications for continued tailoring of integrated NPI or NPI-vaccination strategies against future COVID-19 waves or similar infectious diseases.


2021 ◽  
Author(s):  
Ibrahim NJOUENWET ◽  
Lucie A. Djiotang Tchotchou ◽  
Brian Odhiambo Ayugi ◽  
Guy Merlin Guenang ◽  
Derbetini A. Vondou ◽  
...  

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.


Author(s):  
Dengyue Zhao ◽  
Mingzhu Xiao ◽  
Chunbo Huang ◽  
Yuan Liang ◽  
Ziyue An

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.


BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yang Liu ◽  
◽  
Christian Morgenstern ◽  
James Kelly ◽  
Rachel Lowe ◽  
...  

Abstract Background Non-pharmaceutical interventions (NPIs) are used to reduce transmission of SARS coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19). However, empirical evidence of the effectiveness of specific NPIs has been inconsistent. We assessed the effectiveness of NPIs around internal containment and closure, international travel restrictions, economic measures, and health system actions on SARS-CoV-2 transmission in 130 countries and territories. Methods We used panel (longitudinal) regression to estimate the effectiveness of 13 categories of NPIs in reducing SARS-CoV-2 transmission using data from January to June 2020. First, we examined the temporal association between NPIs using hierarchical cluster analyses. We then regressed the time-varying reproduction number (Rt) of COVID-19 against different NPIs. We examined different model specifications to account for the temporal lag between NPIs and changes in Rt, levels of NPI intensity, time-varying changes in NPI effect, and variable selection criteria. Results were interpreted taking into account both the range of model specifications and temporal clustering of NPIs. Results There was strong evidence for an association between two NPIs (school closure, internal movement restrictions) and reduced Rt. Another three NPIs (workplace closure, income support, and debt/contract relief) had strong evidence of effectiveness when ignoring their level of intensity, while two NPIs (public events cancellation, restriction on gatherings) had strong evidence of their effectiveness only when evaluating their implementation at maximum capacity (e.g. restrictions on 1000+ people gathering were not effective, restrictions on < 10 people gathering were). Evidence about the effectiveness of the remaining NPIs (stay-at-home requirements, public information campaigns, public transport closure, international travel controls, testing, contact tracing) was inconsistent and inconclusive. We found temporal clustering between many of the NPIs. Effect sizes varied depending on whether or not we included data after peak NPI intensity. Conclusion Understanding the impact that specific NPIs have had on SARS-CoV-2 transmission is complicated by temporal clustering, time-dependent variation in effects, and differences in NPI intensity. However, the effectiveness of school closure and internal movement restrictions appears robust across different model specifications, with some evidence that other NPIs may also be effective under particular conditions. This provides empirical evidence for the potential effectiveness of many, although not all, actions policy-makers are taking to respond to the COVID-19 pandemic.


2014 ◽  
Vol 10 (4) ◽  
pp. 1633-1644 ◽  
Author(s):  
P. Bakker ◽  
H. Renssen

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.


2015 ◽  
Vol 15 (2) ◽  
pp. 57-64 ◽  
Author(s):  
Dibas Shrestha ◽  
Rashila Deshar

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


Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Benoît Paix ◽  
Nicolas Layglon ◽  
Christophe Le Poupon ◽  
Sébastien D’Onofrio ◽  
Benjamin Misson ◽  
...  

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.


2019 ◽  
Author(s):  
Didier G. Leibovici ◽  
Shaun Quegan ◽  
Edward Comyn-Platt ◽  
Gary Hayman ◽  
Maria Val Martin ◽  
...  

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.


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