scholarly journals STUDY OF BIO-MEDICAL WASTE GENERATION AND MANAGEMENT IN VARIOUS HOSPITALS IN DAVANGERE CITY OF KARNATAKA, INDIA

2013 ◽  
Vol 03 (03) ◽  
pp. 022-024
Author(s):  
S. Thirumala
Keyword(s):  
Medical Waste ◽  
Survey Result ◽  
Waste Generation ◽  
Biomedical Waste ◽  
Research Article

AbstractThis research article is to survey the practice of biomedical waste such as collection, storage, transportation and disposal along with the amount of generated biomedical waste in various hospitals in Davangere city, and create awareness among the staff and patient about biomedical wastes. The survey result on biomedical waste generation, disposal and methods adopted in various hospitals of Davangere city are discussed.

scholarly journals Medical Waste Generation during COVID-19 (SARS-CoV-2) Pandemic and Its Management: An Indian Perspective

2020 ◽  
pp. 10-15 ◽  
Author(s):  
Manju Rawat Ranjan ◽  
Ashutosh Tripathi ◽  
Ganga Sharma
Keyword(s):  
Waste Treatment ◽  
Medical Waste ◽  
Waste Generation ◽  
Sample Collection ◽  
Biomedical Waste ◽  
Novel Virus ◽  
The Common ◽  
The Government ◽  
Indian Perspective ◽  
Take All

The generation of biomedical waste has increased many times after the SARS Cov2 commencement. The biomedical waste generated from COVID-19 Patients is very infectious and contaminated. Thus, it is a big challenge with all stakeholders to avoid spreading of COVID-19 through it. This requires monitoring the complete cycle to the grave to be monitored from the cradle, if the spreading needs to be controlled. The COVID-19 waste generation, collection, storage, transportation and disposal is a big challenge withall stakeholders including isolation wards, quarantine centres, sample collection centres, laboratories, urban local bodies, and the Common Bio-medical Waste Treatment Facility (CBWTF) respectively. As its a novel virus and WHO has instructed that proper guidelines need to be followed with regards to COVID-19biomedical waste generation and its safe disposal. The Government of India has separately developed the Guidelines for the handling of COVID-19 biomedical waste, which needs to be followed besides BiomedicalRules, 2016 so that Corona spread through this can be controlled. Owing to its novel origin and least information about its behaviour, thus it is extremely important to take all precautions possible till we get some medical treatment.

scholarly journals Risk of Covid-19 Transmission due to Domestic Biomedical Waste – A Review

2021 ◽  
Vol 9 (10) ◽  
pp. 410-413
Author(s):  
Aneri Tank
Keyword(s):  
Medical Waste ◽  
Municipal Waste ◽  
Waste Generation ◽  
Hospital Waste ◽  
Surface Stability ◽  
Biomedical Waste ◽  
Advantages And Disadvantages ◽  
The Past

Abstract: Biomedical waste is said to be a type of waste generated during treatment or diagnosis. Apart from hospitals, clinics, and laboratories it is generated domestically as well. Amount of Biomedical waste in household has increased significantly in the past year due to Covid-19 pandemic, People were effectively educated regarding usage of masks, face covers, PPEs and sanitizers, but were not acknowledged regarding waste generated and disposal methods due to usage of such disposable items in ample quantity. Awareness should be spread across regarding Covid-19 related medical waste generation and its ways of disposal. Waste which is contaminated with Covid-19 virus has certain hours on surface stability of it, due to which risk of contamination increases. Therefore, one must be always aware regarding advantages and disadvantages of the items they use, consume or throw away. Keywords: Biomedical Waste, Hospital Waste, Covid-19, domestic Biomedical waste, Municipal Waste

Assessment of Awareness About Biomedical Waste Among Doctors and Nurses of Children Hospital Lahore

2021 ◽  
Author(s):  
Taimoor Hassan ◽  
Sidra Siddique ◽  
Sana Saeed ◽  
Muhammad Moazzam ◽  
Azmat Tahira ◽  
...  
Keyword(s):  
Sampling Technique ◽  
Medical Waste ◽  
Basic Knowledge ◽  
Human Beings ◽  
Biomedical Waste ◽  
Staff Members ◽  
Children's Hospital ◽  
Children’S Hospital ◽  
Study Population ◽  
Doctors And Nurses

Bio Medical waste refers to any type of waste which is generated during the diagnosis, treatment or immunization of human beings or animals or in research purposes pertaining to or in the fabrication or testing of biologicals. Objective: To assess the awareness about bio-medical waste management among Doctors and Nurses of Children Hospital.  Methods: Descriptive study was conducted in Children's Hospital and Institute of Child Health Lahore. Convenient Sampling Technique was applied to gather data. This was a hospital-based study in which staff members of both genders were included. The study population divided into two strata and these strata consisted of doctors and nurses. A total of 139 staff members were involved out of which there were 77 doctors, 62 nurses. Their responses checked by a Performa about problems in the management of biomedical waste. Results: The result showed that majority (62%) staff members had knowledge about bio-medical waste. The remaining staff had very basic knowledge about bio-medical waste. Conclusions: The awareness about BMW management among Children's Hospital Operation theater staff is satisfactory. But still, they need to improve their knowledge to ensure more patient safety by organizing seminars, workshops.

scholarly journals Awareness & attitude regarding biomedical waste disposal among post-graduate students, under-graduate students & auxiliary staff of a dental college – a questionnaire survey

2017 ◽  
Vol 5 (1) ◽  
pp. 64
Author(s):  
Rahul Chopra ◽  
Shivani Mathur ◽  
Vidya Dodwad ◽  
Nikhil Sharma ◽  
Siddharth Tevatia
Keyword(s):  
Graduate Students ◽  
Waste Disposal ◽  
Cross Sectional Study ◽  
Health Care Facilities ◽  
Waste Generation ◽  
Sectional Study ◽  
Cross Sectional ◽  
Biomedical Waste ◽  
Care Facilities ◽  
Indiscriminate Disposal

Purpose: Indiscriminate disposal of bio medical waste poses a serious threat to environment and human health and is currently a burning issue with increasing health care facilities and associated waste generation. Hence this study assesses the awareness levels and attitude regarding biomedical waste disposal among post-graduates, under-graduates & auxiliary staff of a dental college.Materials and Method: This was a cross-sectional study conducted among post-graduates, under-graduates & auxiliary staff using a questionnaire. A total of 120 participants, 40 in each group answered the questionnaire. The answers were analyzed and graded for each group.Results: The results depict satisfactory awareness about biomedical waste disposal among post-graduates and under-graduates. However, the auxiliary staff lacks the awareness about proper biomedical waste disposal.Conclusion: The study reveals that there is a need to increase knowledge among the auxiliary staff regarding biomedical waste management by continuing training program.

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.

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

2016 ◽  
pp. 224-251 ◽  
Author(s):  
J. Jagan ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
System Modeling ◽  
Medical Waste ◽  
Least Square ◽  
Support Vector ◽  
Waste Generation ◽  
Nonlinear System Modeling ◽  
Hospital Capacity ◽  
Regression Techniques ◽  
Rate Type

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

2020 ◽  
pp. 990-1012
Author(s):  
J. Jagan ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
System Modeling ◽  
Medical Waste ◽  
Least Square ◽  
Support Vector ◽  
Waste Generation ◽  
Nonlinear System Modeling ◽  
Hospital Capacity ◽  
Regression Techniques ◽  
Rate Type

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

2020 ◽  
pp. 808-829
Author(s):  
J. Jagan ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
System Modeling ◽  
Medical Waste ◽  
Least Square ◽  
Support Vector ◽  
Waste Generation ◽  
Nonlinear System Modeling ◽  
Hospital Capacity ◽  
Regression Techniques ◽  
Rate Type

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

Determination of Rate of Medical Waste Generation Using RVM, MARS and MPMR

2016 ◽  
pp. 1-18
Author(s):  
Jagan J. ◽  
Pijush Samui ◽  
Barnali Dixon
Keyword(s):  
Bayesian Learning ◽  
Piecewise Linear ◽  
Regression Function ◽  
Relevance Vector Machine ◽  
Medical Waste ◽  
Waste Generation ◽  
Learning Framework ◽  
Adaptive Regression ◽  
Regression Techniques

The prediction of medical waste generation is an important task in hospital waste management. This article uses Relevance Vector Machine (RVM), Multivariate Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR) for prediction of rate of medical waste generation. Type of hospital, Capacity and Bed Occupancy has been used as inputs of RVM, MARS and MPMR. RVM is a probabilistic bayesian learning framework. MARS builds flexible model by using piecewise linear regressions. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. MARS, RVM and MPMR have been used as regression techniques. The results show that the developed RVM, MPMR and MARS give excellent models for determination of rate of medical waste generation.

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