scholarly journals Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment

2021 ◽  
Author(s):  
Sangeeta Bhatia ◽  
Kris V Parag ◽  
Jack Wardle ◽  
Natsuko Imai ◽  
Sabine L Van Elsland ◽  
...  
Keyword(s):  
Situational Awareness ◽  
Credible Interval ◽  
Medium Term ◽  
Short Term ◽  
Forecast Horizon ◽  
Health Measures ◽  
Population Immunity ◽  
Retrospective Assessment ◽  
Relative Errors ◽  
Modelling And Forecasting

Background: As of July 2021, more than 180,000,000 cases of COVID-19 have been reported across the world, with more than 4 million deaths. Mathematical modelling and forecasting efforts have been widely used to inform policy-making and to create situational awareness. Methods and Findings: From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for countries with evidence of sustained transmission. The estimates and forecasts were based on an ensemble model comprising of three models that were calibrated using only the reported number of COVID-19 cases and deaths in each country. We also developed a novel heuristic to combine weekly estimates of transmissibility and potential changes in population immunity due to infection to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. Conclusions: During the 39-week period covered by this study, we produced short- and medium-term forecasts for 81 countries. Both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax stringent public health measures that were implemented to contain the pandemic.

scholarly journals Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment

2021 ◽  
Author(s):  
Sangeeta Bhatia ◽  
Kris Parag ◽  
Jack Wardle ◽  
Natsuko Imai ◽  
Sabine van Elsland ◽  
...  
Keyword(s):  
Public Health ◽  
Coverage Probability ◽  
Credible Interval ◽  
Planning Tool ◽  
Medium Term ◽  
Short Term ◽  
Forecast Horizon ◽  
Health Measures ◽  
Retrospective Assessment ◽  
Relative Errors

Abstract From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for 81 countries with evidence of sustained transmission. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3\% and 45.6\% of the observations lying in the 50\% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9\% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.

Is money the creation of the state or the market? (On the “modern monetary theory” as described in the textbook by W. Mitchell, L. R. Wray and M. Watts “Macroeconomics”)

2020 ◽  
pp. 121-134
Author(s):  
S. A. Andryushin
Keyword(s):  
Public Debt ◽  
Scientific Community ◽  
Full Employment ◽  
Term Effect ◽  
Scientific Concept ◽  
Monetary Theory ◽  
Left Wing ◽  
Medium Term ◽  
Short Term ◽  
Stimulation Of

In 2019, a textbook “Macroeconomics” was published in London, on the pages of which the authors presented a new monetary doctrine — Modern Monetary Theory, MMT, — an unorthodox concept based on the postulates of Post-Keynesianism, New Institutionalism, and the theory of Marxism. The attitude to this scientific concept in the scientific community is ambiguous. A smaller part of scientists actively support this doctrine, which is directly related to state monetary and fiscal stimulation of full employment, public debt servicing and economic growth. Others, the majority of economists, on the contrary, strongly criticize MMT, arguing that the new theory hides simple left-wing populism, designed for a temporary and short-term effect. This article considers the origins and the main provisions of MMT, its discussions with the mainstream, criticism of the basic tenets of MMT, and also assesses possible prospects for the development of MMT in the medium term.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Multidimensional assessment of women after severe maternal morbidity: the COMMAG cohort study

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e041138
Author(s):  
Elton C Ferreira ◽  
Maria Laura Costa ◽  
Rodolfo C Pacagnella ◽  
Carla Silveira ◽  
Carla B Andreucci ◽  
...  
Keyword(s):  
Cohort Study ◽  
Sexual Function ◽  
Screening Test ◽  
Maternal Morbidity ◽  
Severe Maternal Morbidity ◽  
Medium Term ◽  
Short Term ◽  
Multidimensional Assessment ◽  
Index Pregnancy ◽  
Sf 36

ObjectivesTo perform a multidimensional assessment of women who experienced severe maternal morbidity (SMM) and its short-term and medium-term impact on the lives and health of women and their children.DesignA retrospective cohort study.SettingA tertiary maternity hospital from the southeast region of Brazil.ParticipantsThe exposed population was selected from intensive care unit admissions if presenting any diagnostic criteria for SMM. Controls were randomly selected among women without SMM admitted to the same maternity and same time of childbirth.Primary and secondary outcome variablesValidated tools were applied, addressing post-traumatic stress disorder (PTSD) and quality of life (SF-36) by phone, and then general and reproductive health, functioning (WHO Disability Assessment Schedule), sexual function (Female Sexual Function Index (FSFI)), substance abuse (Alcohol, Smoking and Substance Involvement Screening Test 2.0) and growth/development (Denver Developmental Screening Test) of children born in the index pregnancy in a face-to-face interview.ResultsAll instruments were applied to 638 women (315 had SMM; 323 were controls, with the assessment of 264 and 307 children, respectively). SF-36 score was significantly lower in the SMM group, while PTSD score was similar between groups. Women who had SMM became more frequently sterile, had more abnormal clinical conditions after the index pregnancy and a higher score for altered functioning, while proportions of FSFI score or any drug use were similar between groups. Furthermore, children from the SMM group were more likely to have weight (threefold) and height (1.5 fold) for age deficits and also impaired development (1.5-fold).ConclusionSMM impairs some aspects of the lives of women and their children. The focus should be directed towards monitoring these women and their children after birth, ensuring accessibility to health services and reducing short-term and medium-term repercussions on physical, reproductive and psychosocial health.

scholarly journals Short-term and medium-term health effects of 9/11

The Lancet ◽  
2011 ◽  
Vol 378 (9794) ◽  
pp. 925-934 ◽  
Author(s):  
Sharon E Perlman ◽  
Stephen Friedman ◽  
Sandro Galea ◽  
Hemanth P Nair ◽  
Monika Erős-Sarnyai ◽  
...  
Keyword(s):  
Health Effects ◽  
Medium Term ◽  
Short Term

Sampling estuarine copepods at different scales and resolutions: a study in Rio de la Plata

2018 ◽  
Vol 99 (5) ◽  
pp. 1059-1064 ◽  
Author(s):  
Sourav Paul ◽  
Danilo Calliari
Keyword(s):  
Sampling Effort ◽  
Rio De La Plata ◽  
Population Densities ◽  
Medium Term ◽  
Short Term ◽  
Río De La Plata ◽  
La Plata ◽  
Resource Limitations ◽  
Paracalanus Parvus

AbstractIn the Rio de la Plata salinity, temperature, chlorophyll a (chl a), and densities (ind. m−3) of the copepods Acartia tonsa and Paracalanus parvus were measured from January to November in 2003 by following a nested weekly and monthly design. Such sampling yielded two separate datasets: (i) Yearly Dataset (YD) which consists of data of one sampling effort per month for 11 consecutive months, and (ii) Seasonal Weekly Datasets (SWD) which consists of data of one sampling effort per week of any four consecutive weeks within each season. YD was assumed as a medium-term low-resolution (MTLR) dataset, and SWD as a short-term high-resolution (STHR) dataset. The hypothesis was, the SWD would always capture (shorter scales generally captures more noise in data) more detail variability of copepod populations (quantified through the regression relationships between temporal changes of salinity, temperature, chl a and copepod densities) than the YD. Analysis of both YD and SWD found that A. tonsa density was neither affected by seasonal cycles, nor temporal variability of salinity, temperature and chl a. Thus, compared to STHR sampling, MTLR sampling did not yield any further information of the variability of population densities of the perennial copepod A. tonsa. Analysis of SWD found that during summer and autumn the population densities of P. parvus had a significant positive relationship to salinity but their density was limited by higher chl a concentration; analysis of YD could not yield such detailed ecological information. That hints the effectiveness of STHR sampling over MTLR sampling in capturing details of the variability of population densities of a seasonal copepod species. Considering the institutional resource limitations (e.g. lack of long-term funding, manpower and infrastructure) and the present hypothesis under consideration, the authors suggest that a STHR sampling may provide useful complementary information to interpret results of longer-term natural changes occurring in estuaries.

scholarly journals Towards the best future for humankind: ZIPAR whitepaper.

2018 ◽  
Author(s):  
Marko Kovic ◽  
Christian Caspar ◽  
Adrian Rauchfleisch
Keyword(s):  
Medium Term ◽  
Short Term ◽  
Profound Impact ◽  
Future Challenges ◽  
The Way

Humankind is facing major challenges in the short-term, medium-term, and long-term future. Those challenges will have a profound impact on humankind’s future progress and wellbeing. In this whitepaper, we outline our understanding of humankind’s future challenges, and we describe the way in which we work towards identifying as well as managing them. In doing so, we pursue the overall goal of ZIPAR: We want to make the best future for humankind (ever so slightly) more probable.

scholarly journals Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data

2019 ◽  
Author(s):  
soumya banerjee
Keyword(s):  
Big Data ◽  
Forecasting Model ◽  
Short Term ◽  
Port Development ◽  
Forecasting Accuracy ◽  
Port Throughput ◽  
Infrastructure Investments ◽  
The Cost ◽  
Modelling And Forecasting

Modelling and forecasting port throughput enables stakeholders to make efficient decisions ranging from management of port development, to infrastructure investments, operational restructuring and tariffs policy. Accurate forecasting of port throughput is also critical for long-term resource allocation and short-term strategic planning. In turn, efficient decision-making enhances the competitiveness of a port. However, in the era of big data we are faced with the enviable dilemma of having too much information. We pose the question: is more information always better for forecasting? We suggest that more information comes at the cost of more parameters of the forecasting model that need to be estimated. We comparemultiple forecasting models of varying degrees of complexity and quantify the effect of the amount of data on model forecasting accuracy. Our methodology serves as a guideline for practitioners in this field. We also enjoin caution that even in the era of big data more information may not always be better. It would be advisable for analysts to weigh the costs of adding more data: the ultimate decision would depend on the problem, amount of data and the kind of models being used.

scholarly journals The effectiveness of M-Health associated with cardiac rehabilitation on functional capacity and cardiovascular risk factors: a systematic review and meta-analysis

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
A C Campagnolo Goncalves Toledo ◽  
N Soares De Almeida ◽  
A Pierucci ◽  
A Straioto Salomao ◽  
I Ribeiro Lemes ◽  
...  
Keyword(s):  
Functional Capacity ◽  
Meta Analysis ◽  
Medium Term ◽  
Short Term ◽  
High Quality ◽  
Quality Of Evidence ◽  
High Quality Evidence ◽  
Quality Evidence

Abstract Introduction Smartphone applications for health (M-Health) seem to overcome barriers to access Cardiovascular Rehabilitation Programs (CRP), because of their high degree of acceptance and also their potential to influence the frequency of physical exercise in weight loss. Objective To analyze the effectiveness of the combination of M-Health and CRP compared to CRP alone on functional capacity, adherence to CRP, management of cardiovascular risk factors in cardiac patients. Design Systematic Review and Meta-Analysis. Methods The following databases were used Medline via Ovid, EMBASE, Central, PEDro and SPORTDiscus via EBSCOhost from their inception until July/2020. We included randomized controlled trials investigating the effects of M-Health in combination with CRP compared to CRP alone in adults with heart disease, and the interventions with M-Health consisted of text messages, e-mails, and applications. The primary outcome of this review was functional capacity, measured by VO2peak, or self-reported physical activity (METs.min/week). PEDro scale was used to assess the methodological quality of the studies and the GRADE approach to assess the overall quality of evidence. Pooled estimates were calculated using a random effect model to obtain mean difference (MD) or standardized mean differences (SMD) and their respectives 95% confidence intervals (CI). Results Nineteen RCTs were eligible, the median risk of bias was 7 out of 10 points. The primary endpoint was analyzed by subgroups, time of intervention and kind of type CRP, eigthteen of these studies assessed functional capacity. CRP in combination with a m-health intervention was more effective than CRP alone in improving VO2peak, ml/min/kg, (MD: 0.84, CI: 0.30 to 1.38; I2=0%, high quality evidence, 12 trials, n=1889) at short-term follow-up, but at medium-term follow-up (MD: 0.84, CI: −0.26 to 1.41; I2=0%, high quality evidence, 8 trials, n=927,). Similarly, CRP associated with m-health was superior to CRP alone in increasing self-reported at short-term, METs.min/week, (MD:1.31, CI: −0.24 to 2.37; I2 = not aplicable, very low quality evidence, 1 trial, n=18), and at medium-term follow-up (MD: 0.18, CI: −0.01 to 0.36; I2=56%, moderate quality evidence, 4 trials, n=1107). Conclusion High quality of evidence shows that M-Health improves cardiorespiratory fitness at short-term follow-up. In addition, supervised program showed to be more effective than non-supervised. Funding Acknowledgement Type of funding sources: None.

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