Time-series-based hybrid mathematical modelling method adapted to forecast automotive and medical waste generation: Case study of Lithuania

2018 ◽  
Vol 36 (5) ◽  
pp. 454-462 ◽  
Author(s):  
Aistė Karpušenkaitė ◽  
Tomas Ruzgas ◽  
Gintaras Denafas
Keyword(s):  
Time Series ◽  
Mathematical Modelling ◽  
Error Rate ◽  
Medical Waste ◽  
Waste Generation ◽  
Prediction Horizon ◽  
Modelling Method ◽  
Forecasting Method ◽  
Hybrid Forecasting

The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used ‘pure’ time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%–4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models’ abilities to forecast short- and mid-term forecasts were tested using prediction horizon.

Forecasting medical waste generation using short and extra short datasets: Case study of Lithuania

2016 ◽  
Vol 34 (4) ◽  
pp. 378-387 ◽  
Author(s):  
Aistė Karpušenkaitė ◽  
Tomas Ruzgas ◽  
Gintaras Denafas
Keyword(s):  
Medical Waste ◽  
Waste Generation

scholarly journals Effect of COVID-19 pandemic on medical waste management: a case study

2021 ◽  
Author(s):  
Roshanak Rezaei Kalantary ◽  
Arsalan Jamshidi ◽  
Mohammad Mehdi Golbini Mofrad ◽  
Ahmad Jonidi Jafari ◽  
Neda Heidari ◽  
...  
Keyword(s):  
Waste Management ◽  
Medical Waste ◽  
Public Hospitals ◽  
Waste Generation ◽  
Waste Composition ◽  
Private And Public ◽  
Solid Waste Generation ◽  
Medical Waste Management ◽  
Infectious Waste

AbstractCovid-19 Pandemic leads to medical services for the society all over the world. The Covid-19 pandemic influence the waste management and specially medical waste management. In this study, the effect of the Covid-19 outbreak on medical waste was evaluated via assessing the solid waste generation, composition, and management status in five hospitals in Iran. The results indicated that the epidemic Covid-19 leads to increased waste generation on average 102.2 % in both private and public hospitals. In addition, the ratio of infectious waste in the studied hospitals increased by an average of 9 % in medical waste composition and 121 % compared with before COVID-19 pandemic. Changes in plans and management measurement such as increasing the frequency of waste collection per week leads to lower the risk of infection transmission from medical waste in the studied hospitals. The results obtained from the present research clearly show the changes in medical waste generation and waste composition within pandemic Covid-19. In addition, established new ward, Covid-19 ward with high-infected waste led to new challenges which should be managed properly by change in routine activities.

scholarly journals Graph-Demonstration of The Solutions of Engineering Tasks

2018 ◽  
Vol 9 (1) ◽  
pp. 103-106
Author(s):  
Fruzsina Horváth ◽  
László Pokorádi
Keyword(s):  
Mathematical Model ◽  
Mathematical Modelling ◽  
Main Part ◽  
Technical Education ◽  
Modelling Method ◽  
Calculation Task ◽  
Logical Tree ◽  
Optimal Set

Abstract During technical education it is a very difficult yet essential task to develop the good logical engineering thinking of students or pupils. One main part of this thinking is the determination of the optimal set of required input parameters for the calculation task mentioned above. The LogTreeMM (Logical Tree of Mathematical Modelling) method can help to solve this task. The aim of this paper is to show modification of the LogTreeMM method to determine the required parameters of a mathematical model by a simple case study.

scholarly journals Description of the Characteristics of Solid Medical Waste in the Environment During the COVID - 19 Pandemic: Case Study Hospital X Covid-19 Referral in Semarang City

2021 ◽  
Vol 940 (1) ◽  
pp. 012042
Author(s):  
N Himayati ◽  
T Joko ◽  
M Raharjo
Keyword(s):  
Health Care ◽  
Health Service ◽  
Medical Waste ◽  
Health Care Facilities ◽  
Waste Generation ◽  
Health Service Provider ◽  
Study Results ◽  
Care Facilities ◽  
Study Hospital

Abstract Characteristics of Solid Medical Waste As long as the hospital as a health service provider is a source of solid medical waste generation. The current COVID-19 pandemic can potentially increase the number of medical waste generation in health care facilities. The COVID-19 pandemic has had an impact on changing the characteristics of the medical waste produced. This study describes the characteristics of hospital solid medical waste during the COVID-19 pandemic at the X Referral Covid Hospital in Semarang City. The study results show that the ratio of increasing solid medical waste during the 2020 pandemic ranges from 1.39 to 2.08 kg/bed/day. Handling medical waste in this condition is a challenge that needs to be appropriately managed.

scholarly journals The Hybrid Forecasting Method SVR-ESAR for Covid-19

2020 ◽  
Author(s):  
Juan Frausto-Solis ◽  
Jose Enrique Olvera Vazquez ◽  
Juan Javier Gonzalez-Barbosa ◽  
Guadalupe Castilla-Valdez ◽  
Juan Paulo Sanchez-Hernandez ◽  
...  
Keyword(s):  
Time Series ◽  
Health Services ◽  
Economic Consequences ◽  
Support Vector ◽  
Modern Times ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
The Us ◽  
The World ◽  
Hybrid Forecasting

We know that SARS-Cov2 produces the new COVID-19 disease, which is one of the most dangerous pandemics of modern times. This pandemic has critical health and economic consequences, and even the health services of the large, powerful nations may be saturated. Thus, forecasting the number of infected persons in any country is essential for controlling the situation. In the literature, different forecasting methods have been published attempting to solve the problem. However, a simple and accurate forecasting method is required for its implementation in any part of the world. This paper presents a precise and straightforward forecasting method named SVR-ESAR (Support Vector regression hybridized with the classical Exponential smoothing and ARIMA). We applied this method to the infected time series in four scenarios: the Whole World, China, the US, and Mexico. We compared our results with those of the literature showing the proposed method has the best accuracy.

Medical Waste Management: A Case Study on Rajshahi City Corporation in Bangladesh

2015 ◽  
Vol 6 (1) ◽  
pp. 173-178 ◽  
Author(s):  
MZ Alam ◽  
MS Islam ◽  
MR Islam
Keyword(s):  
Waste Management ◽  
Medical Waste ◽  
Generation Rate ◽  
Waste Generation ◽  
Different Types ◽  
Medical Waste Management ◽  
Waste Generation Rate ◽  
Rajshahi City ◽  
Medical Wastes

The management of medical waste (MW) is of great importance due to its impact on human health and environment. The present practices of improper management of generated medical wastes in different Healthcare Establishment (HCEs) in Rajshahi City Corporation (RCC) is playing a contributing role to create vulnerable condition in spreading out the Diarrhea, Hepatitis and various skin related diseases. The objectives of the study are to identify different types of wastes, its generation rate and assess the existing waste management in various HCEs. The study was carried out in 14 different HCEs that generated much portion of MW of total generated MW in RCC. The methodology of this project was descriptive and consisted of the use of field survey and interviews with the relevant authorities and personnel involved in the management of MW. It was found that the surveyed HCEs generate a total of 1495 kg/day of MW; of which about 1328.6 kg/day (88.87%) are non-infectious and about 166.4 kg/day (11.13%) are infectious. The average waste generation rate for surveyed HCEs is 1.54 kg/bed/day or 0.30 kg/patient/day. It was found from the survey that there is no proper and systematic management of medical wastes. The study reveals that lack of awareness; financial support and willingness are responsible for improper management of MW. So the RCC and HCEs authorities should adopt appropriate policy regarding this issue and provide training program on relevant personnel who are engaged in medical waste management.DOI: http://dx.doi.org/10.3329/jesnr.v6i1.22062 J. Environ. Sci. & Natural Resources, 6(1): 173-178 2013

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