Background The World Health Organization (WHO) has called for the elimination of cervical cancer. Unfortunately, the implementation of cost-effective prevention and control strategies has faced significant barriers, such as insufficient guidance on best practices for resource and operations planning. Therefore, we demonstrate the value of discrete event simulation (DES) in implementation science research and practice, particularly to support the programmatic and operational planning for sustainable and resilient delivery of healthcare interventions. Our specific example shows how DES models can inform planning for scale-up and resilient operations of a new HPV-based screen and treat program in Iquitos, an Amazonian city of Peru. Methods Using data from a time and motion study and cervical cancer screening registry from Iquitos, Peru, we developed a DES model to conduct virtual experimentation with “what-if” scenarios that compare different workflow and processing strategies under resource constraints and disruptions to the screening system. Results Our simulations show how much the screening system’s capacity can be increased at current resource levels, how much variability in service times can be tolerated, and the extent of resilience to disruptions such as curtailed resources. The simulations also identify the resources that would be required to scale up for larger target populations or increased resilience to disruptions, illustrating the key tradeoff between resilience and efficiency. Thus, our results demonstrate how DES models can inform specific resourcing decisions but can also highlight important tradeoffs and suggest general “rules” for resource and operational planning. Conclusions Multilevel planning and implementation challenges are not unique to sustainable adoption of cervical cancer screening programs but represent common barriers to the successful scale-up of many preventative health interventions worldwide. DES represents a broadly applicable tool to address complex implementation challenges identified at the national, regional, and local levels across settings and health interventions—how to make effective and efficient operational and resourcing decisions to support program adaptation to local constraints and demands so that they are resilient to changing demands and more likely to be maintained with fidelity over time.
Data mining and machine learning have become crucial and inspiring fields of work for today's researchers. Mostly in every field, there is considerable use of machine learning to make micro to massive operations more feasible and possible. And with the world witnessing the worst ever Pandemic that has affected all section of society but specifically the economic sector i.e., businesses sector and with now people relying less on human-human physical interaction and more on technological means because of obvious reasons, our project has provided a better and more reliable platform for providing the upcoming entrepreneur as well as big investors such as government and private sector to fulfill their shattered dreams by knowing best location they can start/ restart their businesses. The model is majorly based on K-mean clustering. The users can provide the details of the individual sector they are interested in to begin a business and find the best opportunity around them.
There has been a sudden increase in population and in the establishment of different industries due to which we are having waste pollution problems, including plastic waste. Discarding of plastic waste is a major problem, as it is non-biodegradable. When we mix plastic waste with bitumen, we are able to enhance bitumen's physical properties for a specific road mix. Then, bitumen's stability and water resisting capacity is upgraded. Plus, it acts as a more superior binding material than without the addition of plastic waste. Through this work, we are able to find the optimum percentage of bitumen to be substituted with plastic waste. This will also act as a part of smart waste disposal in smart cities and improve the air quality with increase environmental, economic parameters of the place leading to the improvement in human health in urban areas.
The suburban rail project in Bengaluru city in the Karnataka state of India will address the local mass travel needs from Bengaluru city to nearby towns or satellite cities. The chapter discusses the development stages of the Bengaluru Suburban Rail Project. The necessity, essential features, and significant advantages of the suburban rail project are presented. The project is expected to reduce traffic congestion problems in Bengaluru city and its nearby towns. Supplementary benefits related to time savings and increased passenger comfort are also estimated. Further, the challenges and risks faced by the project are discussed. Some future potential extensions of the project are considered.
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