scholarly journals Assessing the forecasting model ability in measuring the prevention transmission of COVID-19 pandemic: An application of visibility analysis using Inductive logic

2022 ◽  
Vol 11 (2) ◽  
pp. 159-166
Author(s):  
Yuyun Hidayat ◽  
Titi Purwandari Sukono ◽  
Jumadil Saputra
Keyword(s):  
Model Development ◽  
Inductive Logic ◽  
Integral Approach ◽  
Limited Attention ◽  
Forecasting Model ◽  
Prevention Measures ◽  
Visibility Analysis ◽  
Forecasting Performance ◽  
Future Events ◽  
Transmission Prevention

Forecasting is an integral approach due to its ability to make informed act decisions and develop data-driven strategies. It's also used to make decisions related to current circumstances and predictions on future conditions. An integral part has been developed using visibility analysis for COVID-19 Outbreak, a lesson from Indonesia. The author identified that its topic has limited attention, especially in assessing the forecasting models. The issue comes from predicted results that are questionable or cannot be trusted without applying the visibility analysis in the forecasting model. The visibility analysis is required to assess the model's ability to forecast future events. In conjunction with the issue, this paper introduces the analysis of visibility error with the different concepts during model development for the transmission prevention measures in making the decision. This study applied a statistical approach to assess the visibility error of forecasting performance in determining how long periods of forecasting and deciding for transmission prevention measures COVID-19 pandemics. Also, we developed the visibility error of time-variant using inductive logic. The result indicated that the number of data required to perform forecasting work on the basis of forecasting model specifications. In conclusion, this study has been completed to develop the statistical formula for identifying the largest time horizon in forecasting model N = V + 2. Also, this developed model can assist the stakeholder in forecasting the number of transmission prevention and making the decision in case of COVID-19 pandemic.

scholarly journals Research Progress of Desertification and Its Prevention in Mongolia

2021 ◽  
Vol 13 (12) ◽  
pp. 6861
Author(s):  
Xiya Liang ◽  
Pengfei Li ◽  
Juanle Wang ◽  
Faith Ka Shun Chan ◽  
Chuluun Togtokh ◽  
...  
Keyword(s):  
Model Development ◽  
Mitigation Strategies ◽  
Research Progress ◽  
Prevention Measures ◽  
Interdisciplinary Approaches ◽  
The World ◽  
Impacting Factors ◽  
Decoupling Analysis ◽  
And Control ◽  
The Relationship

Mongolia is a globally crucial region that has been suffering from land desertification. However, current understanding on Mongolia’s desertification is limited, constraining the desertification control and sustainable development in Mongolia and even other parts of the world. This paper studied spatiotemporal patterns, driving factors, mitigation strategies, and research methods of desertification in Mongolia through an extensive review of literature. Results showed that: (i) remote sensing monitoring of desertification in Mongolia has been subject to a relatively low spatial resolution and considerable time delay, and thus high-resolution and timely data are needed to perform a more precise and timely study; (ii) the contribution of desertification impacting factors has not been quantitatively assessed, and a decoupling analysis is desirable to quantify the contribution of factors in different regions of Mongolia; (iii) existing desertification prevention measures should be strengthened in the future. In particular, the relationship between grassland changes and husbandry development needs to be considered during the development of desertification prevention measures; (iv) the multi-method study (particularly interdisciplinary approaches) and desertification model development should be enhanced to facilitate an in-depth desertification research in Mongolia. This study provides a useful reference for desertification research and control in Mongolia and other regions of the world.

scholarly journals Forecasting Wheat Production in Pakistan

2008 ◽  
Vol 13 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Falak Sher ◽  
Eatzaz Ahmad
Keyword(s):  
Production Function ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Forecasting Model ◽  
Future Prospects ◽  
Wheat Production ◽  
Steady Growth ◽  
Forecasting Performance ◽  
The Future ◽  
Out Of Sample

This study analyzes the future prospects of wheat production in Pakistan. Parameters of the forecasting model are obtained by estimating a Cobb-Douglas production function for wheat, while future values of various inputs are obtained as dynamic forecasts on the basis of separate ARIMA estimates for each input and for each province. Input forecasts and parameters of the wheat production function are then used to generate wheat forecasts. The results of the study show that the most important variables for predicting wheat production per hectare (in order of importance) are: lagged output, labor force, use of tractors, and sum of the rainfall in the months of November to March. The null hypotheses of common coefficients across provinces for most of the variables cannot be rejected, implying that all variables play the same role in wheat production in all the four provinces. Forecasting performance of the model based on out-of-sample forecasts for the period 2005-06 is highly satisfactory with 1.81% mean absolute error. The future forecasts for the period of 2007-15 show steady growth of 1.6%, indicating that Pakistan will face a slight shortage of wheat output in the future.

scholarly journals Model development in practice: A comprehensive update to the boundary layer schemes in HARMONIE-AROME cycle 40

2021 ◽  
Author(s):  
Wim C. de Rooy ◽  
Pier Siebesma ◽  
Peter Baas ◽  
Geert Lenderink ◽  
Stephan de Roode ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Climate Model ◽  
Current Model ◽  
Weather Prediction ◽  
Model Development ◽  
Integral Approach ◽  
General Interest ◽  
Strong Impact ◽  
Low Clouds ◽  
Boundary Layer Processes

Abstract. The parameterised description of subgrid-scale processes in the clear and cloudy boundary layer has a strong impact on the performance skill in any Numerical Weather Prediction (NWP) or climate model and is still a prime source of uncertainty. Yet, improvement of this parameterised description is hard because operational models are highly optimised and contain numerous compensating errors. Therefore, improvement of a single parameterised aspect of the boundary layer often results in an overall deterioration of the model as a whole. In this paper we will describe a comprehensive integral revision of three parameterisation schemes in the HARMONIE-AROME model that together parameterise the boundary layer processes: the cloud scheme, the turbulence scheme, and the shallow cumulus convection scheme. One of the major motivations for this revision is the poor representation of low clouds in the current model cycle. The new revised parametric descriptions provide not only an improved prediction of low clouds but also of precipitation. Both improvements can be related to a stronger accumulation of moisture under the atmospheric inversion. The three improved parameterisation schemes are included in a recent update of the HARMONIE-AROME configuration, but its description and the insights in the underlying physical processes are of more general interest as the schemes are based on commonly applied frameworks. Moreover, this work offers an interesting look behind the scenes of how parameterisation development requires an integral approach and a delicate balance between physical realism and pragmatism.

scholarly journals Comparison of Fuzzy Time Series and ARIMA to Forecast Tourist Arrivals to Homestay in Pahang

2021 ◽  
Vol 6 (4) ◽  
pp. 80-89
Author(s):  
Maizatul Akhmar Jafridin ◽  
Nur Fatihah Fauzi ◽  
Rohana Alias ◽  
Huda Zuhrah Ab Halim ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Demand Forecasting ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Strategic Decisions ◽  
Forecasting Performance ◽  
Future Events ◽  
Time Series Method

Predictions of future events must be incorporated into the decision-making process. For tourism demand, forecasting is very important to help directors and investors to make decisions in operational, tactical, and strategic decisions. This study focuses on forecasting performance between Fuzzy Time Series and ARIMA to forecast the tourist arrivals in homestays in Pahang. The main objective of this study is to compare and identify the best method between Fuzzy Time Series and Autoregressive Integrated Moving Average (ARIMA) in forecasting the arrival of tourists based on the secondary data of tourist arrivals to homestay in Pahang from January 2015 to December 2018. ARIMA models are flexible and widely used in time-series analysis and Fuzzy Time Series which do not need large samples and long past time series. These two methods have been compared by using the mean square error (MSE) and mean absolute percentage error (MAPE) as the forecast measures of accuracy. The results show that Fuzzy Time Series outperforms the ARIMA. The lowest value of MSE and MAPE was obtained from using the Fuzzy Time Series method at values 2192305.89 and 11.92256, respectively.

Analysis of the projected image of tourism destinations on photographs: A literature review to prepare for the future

2017 ◽  
Vol 25 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Patricia Picazo ◽  
Sergio Moreno-Gil
Keyword(s):  
Literature Review ◽  
Destination Image ◽  
Integral Approach ◽  
Future Research ◽  
Limited Attention ◽  
Projected Image ◽  
Reference Guide ◽  
Comprehensive Literature Review ◽  
Tourism Destinations ◽  
The Right

Making the right impression is paramount to succeed in today’s very competitive market, where photographs have acquired a prominent role in doing so. The evaluation and analysis of destination image have been tackled mainly from the perspective of the tourists’ perceptions. However, the projected image of destinations has received limited attention in the literature and the topic has not yet been successfully operationalized. Moreover, existing literature on the projected image on photographs (PIP) is scattered and lacking of an integral approach. Thus, the aim of this article is to fill this gap by carrying a comprehensive literature review on the destination’s PIP, covering information sources analyzed, destinations included, number of pictures, time framework, methodology, and specially categorization (people, activities represented, and tourism context) and specific attributes used. As a result, this article provides researchers with a reference guide to understand the current situation of the research on this topic, context, methods, and focus of previous studies. Finally, it identifies trends and reflections on future research.

scholarly journals Effectiveness of antithrombotic prophylaxis in paediatric cardiosurgical patients

2021 ◽  
Vol 25 (1) ◽  
pp. 97
Author(s):  
N. B. Karakhalis
Keyword(s):  
Conflict Of Interest ◽  
Arterial Thrombosis ◽  
Perioperative Management ◽  
Integral Approach ◽  
Prevention Measures ◽  
Antithrombotic Drug ◽  
Antithrombotic Prophylaxis ◽  
Shunt Thrombosis ◽  
Heparin Administration ◽  
Therapeutic Measures

<p><strong>Aim.</strong> To evaluate the effectiveness of prevention measures for thrombotic catheter-associated events in the perioperative management of patients undergoing cardiac surgery.</p><p><strong>Methods.</strong> A total of 433 paediatric and neonatal patients were included in the study during the period from January to December 2018. All patients received antithrombotic prophylaxis via systemic heparin administration.</p><p><strong>Results.</strong> Thirty-six patients displayed signs of thrombosis during the postoperative period (8.31%): 28 patients had venous thrombosis, while 6 had the Blalock-Taussig shunt thrombosis, and 2 had arterial thrombosis. The mortality rate was higher in the group with registered thrombosis than in the group without thrombosis (p = 0.01).</p><p><strong>Conclusion.</strong> The dosage regimen for children and neonatal patients should be according to age-associated antithrombotic drug standards. It requires an integral approach for evaluating the effectiveness of preventive and therapeutic measures.</p><p>Received 25 August 2020. Revised 18 December 2020. Accepted 22 December 2020.</p><p><strong>Funding:</strong> The study did not have sponsorship.</p><p><strong>Conflict of interest:</strong> Author declares no conflict of interest.</p>

scholarly journals COVID-19 is in the air: Why are we still ignoring the importance of ventilation?

2021 ◽  
Vol 41 (3) ◽  
pp. e97492
Author(s):  
Nestor Y. Rojas ◽  
Laura A. Rodríguez-Villamizar
Keyword(s):  
Early Stage ◽  
Transmission Mechanism ◽  
Indoor Environments ◽  
Prevention Measures ◽  
Air Filtration ◽  
Adequate Ventilation ◽  
Long Time ◽  
Transmission Prevention ◽  
Indoor Spaces

The main transmission mechanism of the SARS-CoV-2 virus is airborne, particularly in poorly ventilated indoor environments. Recognizing the importance of this mechanism has taken a long time, despite the evidence generated by aerosol scientists from an early stage of the pandemic. Hence, measures applied more widely by the population have focused on the disinfection of surfaces, often in an exaggerated way, while measures focused on reducing the concentration of aerosols in indoor environments, such as adequate ventilation and air filtration, have been timidly promoted. In addition to the progress of the National Vaccination Plan, it is necessary to intensify transmission prevention measures for a safer reopening of the economy. It is therefore urgent, to educate and generate clear guidelines for the evaluation and improvement of ventilation in indoor spaces.

scholarly journals Fast and Accurate Influenza Forecasting in the United States with Inferno

2021 ◽  
Author(s):  
Dave Osthus
Keyword(s):  
Situational Awareness ◽  
Model Development ◽  
Predictive Performance ◽  
The United States ◽  
Primary Objective ◽  
Forecasting Model ◽  
Accurate Information ◽  
Disease Forecasting ◽  
Minimal Impact ◽  
Forecasting Models

AbstractInfectious disease forecasting is an emerging field and has the potential to improve public health through anticipatory resource allocation, situational awareness, and mitigation planning. By way of exploring and operationalizing disease forecasting, the U.S. Centers for Disease Control and Prevention (CDC) has hosted FluSight since the 2013/14 flu season, an annual flu forecasting challenge. Since FluSight’s onset, forecasters have developed and improved forecasting models in an effort to provide more timely, reliable, and accurate information about the likely progression of the outbreak. While improving the predictive performance of these forecasting models is often the primary objective, it is also important for a forecasting model to run quickly, facilitating further model development, improvement, and scalability. In this vein I introduce Inferno, a fast and accurate flu forecasting model inspired by Dante, the top performing model in the 2018/19 FluSight challenge. When compared to all models that participated in FluSight 2018/19, Inferno would have placed 2nd in both the national and state challenges, behind only Dante. Inferno, however, runs in minutes and is trivially parallelizable, while Dante takes hours to run, representing a significant operational improvement with minimal impact to performance. A future consideration for forecasting competitions like FluSight will be how to encourage improvements to secondarily important properties of forecasting models, such as runtime, generalizability, and interpretability.

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