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Melissa Al-Shaer
and
Charles Ivan Niswander II
In this 1-hour long project-based course, you will learn basic principles of agent-based SIR models and how one can be implemented in Python. Together, we will explore basic Python implementations of SIR differential equations and agent-based modeling from...
Read more
In this 1-hour long project-based course, you will learn basic principles of agent-based SIR models and how one can be implemented in Python. Together, we will explore basic Python implementations of SIR differential equations and agent-based modeling from...
Read more
In this 1-hour long project-based course, you will learn basic principles of agent-based SIR models and how one can be implemented in Python. Together, we will explore basic Python implementations of SIR differential equations and agent-based modeling from scratch. This will be accomplished using Matplotlib and Numpy.
I encourage learners to experiment. What other variables can be added? Can our variables be represented better? What limitations do we come across? The learner is highly encouraged to experiment beyond the scope of the course.
After you finish the course, you will have the basic knowledge of building a mathematical simulation to model the transmission of a pandemic contagion.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/6a3qdu/simulating
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Good to know
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what to watch for , and
possible dealbreakers
Provides hands-on experience through Python coding and exercises
Incorporates widely used tools for data analysis and visualization, making it relevant for data scientists and researchers
Encourages active exploration and experimentation, promoting a deeper understanding of the concepts
Taught by experienced professionals with expertise in the field, ensuring up-to-date knowledge and insights
Suitable for students and professionals seeking a fundamental understanding of agent-based modeling for simulating pandemic spread
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Save Simulating Viral Pandemics in Python to your list so you can find it easily later:
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Reviews summary
Pandemic modeling in python
This course provides a basic understanding of agent-based SIR models and their implementation in Python. While some learners found the content superficial and lacking in explanations, others appreciated the opportunity to experiment and build a mathematical simulation. Overall, this course may be suitable for learners with some prior knowledge of Python and modeling techniques who are interested in a project-based approach.
Promotes experimentation and exploration.
"I encourage learners to experiment."
Hands-on project to build a pandemic simulation.
"... you will learn basic principles of agent-based SIR models and how one can be implemented in Python."
Insufficient guidance in code implementations.
"Also the code part does not have enough explanations about the SIR model and its implementations. You need a good understanding of libraries and good Python programming skills to understand the code."
Limited depth in explanations.
"Very superficial"
"The project is superficial in the SIR model explanations."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Simulating Viral Pandemics in Python with these
activities:
Connect with Experts in Agent-Based Modeling
Show steps
Enhance your learning by connecting with experienced professionals who can provide guidance and support.
Browse courses on
Python
Show steps
-
Attend virtual conferences or workshops to meet experts.
-
Reach out to researchers or practitioners in your field through LinkedIn or email.
Run Simulations with Small Changes
Show steps
Practice and deepen your understanding of agent-based SIR models by running simulations with small changes to variables.
Browse courses on
Simulation
Show steps
-
Modify the transmission or recovery rate in the model.
-
Run the simulation and observe the changes in the output.
-
Repeat steps 1-2 with different parameter changes.
Organize and Review Course Notes and Materials
Show steps
Solidify your understanding by organizing and reviewing the materials covered in the course.
Show steps
-
Compile your notes, assignments, and quizzes into a coherent document.
-
Review the materials regularly to reinforce concepts and identify areas for improvement.
Three other activities
Expand to see all activities and additional details
Show all six activities
Write a Blog Post Summarizing the Course
Show steps
Reinforce your understanding of the concepts covered in the course by writing a blog post that summarizes the key takeaways.
Browse courses on
Python
Show steps
-
Summarize the main principles of agent-based SIR models.
-
Provide examples of how these models can be applied in various domains.
-
Share your insights on the limitations and potential improvements of these models.
Explore Online Tutorials on Advanced Agent-Based Modeling Techniques
Show steps
Enhance your skills by seeking out and completing tutorials on advanced topics related to agent-based modeling.
Browse courses on
Python
Show steps
-
Identify online platforms or repositories that offer tutorials on agent-based modeling.
-
Select tutorials that align with your learning goals and skill level.
-
Complete the tutorials and apply the concepts to your own projects.
Develop a Custom SIR Model for a Specific Scenario
Show steps
Apply your knowledge of agent-based SIR models to a real-world scenario by developing a custom model for a specific disease or context.
Browse courses on
Python
Show steps
-
Define the parameters and assumptions of your custom model.
-
Implement the model in Python using Matplotlib and Numpy.
-
Run simulations and analyze the results to draw meaningful conclusions.
Connect with Experts in Agent-Based Modeling
Show steps
Enhance your learning by connecting with experienced professionals who can provide guidance and support.
Browse courses on
Python
Show steps
Show steps
Show steps
- Attend virtual conferences or workshops to meet experts.
- Reach out to researchers or practitioners in your field through LinkedIn or email.
Run Simulations with Small Changes
Show steps
Practice and deepen your understanding of agent-based SIR models by running simulations with small changes to variables.
Browse courses on
Simulation
Show steps
Show steps
Show steps
- Modify the transmission or recovery rate in the model.
- Run the simulation and observe the changes in the output.
- Repeat steps 1-2 with different parameter changes.
Organize and Review Course Notes and Materials
Show steps
Solidify your understanding by organizing and reviewing the materials covered in the course.
Show steps
Show steps
Show steps
- Compile your notes, assignments, and quizzes into a coherent document.
- Review the materials regularly to reinforce concepts and identify areas for improvement.
Three other activities
Expand to see all activities and additional details
Show all six activities
Write a Blog Post Summarizing the Course
Show steps
Reinforce your understanding of the concepts covered in the course by writing a blog post that summarizes the key takeaways.
Browse courses on
Python
Show steps
Show steps
Show steps
- Summarize the main principles of agent-based SIR models.
- Provide examples of how these models can be applied in various domains.
- Share your insights on the limitations and potential improvements of these models.
Explore Online Tutorials on Advanced Agent-Based Modeling Techniques
Show steps
Enhance your skills by seeking out and completing tutorials on advanced topics related to agent-based modeling.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify online platforms or repositories that offer tutorials on agent-based modeling.
- Select tutorials that align with your learning goals and skill level.
- Complete the tutorials and apply the concepts to your own projects.
Develop a Custom SIR Model for a Specific Scenario
Show steps
Apply your knowledge of agent-based SIR models to a real-world scenario by developing a custom model for a specific disease or context.
Browse courses on
Python
Show steps
Show steps
Show steps
- Define the parameters and assumptions of your custom model.
- Implement the model in Python using Matplotlib and Numpy.
- Run simulations and analyze the results to draw meaningful conclusions.
Career center
Learners who complete Simulating Viral Pandemics in Python will develop knowledge and skills
that may be useful to these careers:
Disease Modeler
Disease Modeler
Disease Modelers use their knowledge of mathematics and computer science to create models of disease transmission. These models can be used to predict the spread of disease and to evaluate the effectiveness of public health interventions. This course can help you enter the field of Disease Modeling. It will provide you with a strong foundation in the mathematical modeling of disease transmission.
Epidemiologist
Epidemiologist
Epidemiologists are responsible for studying the patterns, causes, and effects of health conditions in defined populations. The work of an Epidemiologist can take them anywhere in the world; they are often called upon to help control disease outbreaks. This course can help you enter the field of Epidemiology. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Epidemiologists who are interested in specializing in mathematical modeling of disease transmission.
Mathematician
Mathematician
Mathematicians use their knowledge of math to solve problems in various fields, such as science, engineering, and business. Their work can range from developing new mathematical models to solving complex equations. This course can help you enter the field of Mathematics. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Mathematicians who are interested in specializing in mathematical modeling of disease transmission.
Statistician
Statistician
Statisticians use their knowledge of statistics to collect, analyze, and interpret data. Their work can range from designing surveys to developing statistical models. This course can help you enter the field of Statistics. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Statisticians who are interested in specializing in mathematical modeling of disease transmission.
Biostatistician
Biostatistician
Biostatisticians use their knowledge of statistics to solve problems in the field of biology. Their work can range from designing clinical trials to analyzing genetic data. This course can help you enter the field of Biostatistics. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Biostatisticians who are interested in specializing in mathematical modeling of disease transmission.
Public Health Analyst
Public Health Analyst
Public Health Analysts use their knowledge of public health to improve the health of communities. Their work can range from developing health promotion programs to evaluating the effectiveness of public health interventions. This course can help you enter the field of Public Health Analysis. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Public Health Analysts who are interested in specializing in mathematical modeling of disease transmission.
Physicist
Physicist
Physicists are responsible for studying the fundamental laws of nature. Their work can range from studying the behavior of subatomic particles to the evolution of the universe. This course can help you enter the field of Physics. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Physicists who are interested in specializing in mathematical modeling of disease transmission.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts use their knowledge of data analysis to improve the quality and efficiency of healthcare. Their work can range from analyzing patient data to developing predictive models. This course can help you enter the field of Healthcare Data Analysis. It will provide you with a strong foundation in Python, as well as an introduction to epidemiology. This course may also be helpful for Healthcare Data Analysts who are interested in specializing in mathematical modeling of disease transmission.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics and statistics to solve problems in the financial industry. Their work can range from pricing financial instruments to developing trading strategies. This course can help you enter the field of Quantitative Finance. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Quantitative Analysts who are interested in specializing in healthcare finance.
Computer Scientist
Computer Scientist
Computer Scientists use their knowledge of computer science to solve problems in a variety of fields, such as healthcare, finance, and manufacturing. This course can help you enter the field of Computer Science. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Computer Scientists who are interested in specializing in health informatics.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and computer science to solve problems in a variety of fields, such as healthcare, logistics, and manufacturing. This course can help you enter the field of Operations Research. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Operations Research Analysts who are interested in specializing in healthcare.
Actuary
Actuary
Actuaries use their knowledge of mathematics and statistics to assess risk. Their work can range from pricing insurance policies to developing retirement plans. This course can help you enter the field of Actuarial Science. It will provide you with a strong foundation in the mathematical modeling of disease transmission. This course may also be helpful for Actuaries who are interested in specializing in health insurance.
Data Scientist
Data Scientist
Data Scientists use their expertise in a range of programming languages to transform raw data into usable information. Their work helps businesses and organizations make data-driven decisions. Because of the high volume of data that is generated every second of every hour of every single day, the demand for Data Scientists has risen sharply over the last decade and will continue to do so. This course is a great way for you to enter the field of Data Science. It will provide you with a strong foundation in Python, as well as an introduction to epidemiology. This course may also be helpful for Data Scientists who are interested in specializing in healthcare analytics.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain computer software. Their work can range from creating new applications to fixing bugs in existing systems. This course can help you enter the field of Software Engineering. It will provide you with a strong foundation in Python, as well as an introduction to epidemiology. This course may also be helpful for Software Engineers who are interested in specializing in healthcare software development.
For more career information including salaries, visit:
OpenCourser.com/course/6a3qdu/simulating
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Simulating Viral Pandemics in Python.
This classic in epidemiology is used extensively as a textbook for introductory infectious disease modeling. It covers all the necessary mathematical models used in COVID-19 analysis and is an essential reference.
This advanced graduate textbook provides a rigorous introduction to stochastic processes, including Markov processes.
Provides a practical guide to using mathematical models to simulate and analyze infectious disease spread.
This advanced textbook provides a comprehensive overview of mathematical models for infectious disease spread at the graduate level.
Provides a comprehensive introduction to mathematical modeling in the biomedical sciences, including coverage of infectious disease spread.
Provides a thorough introduction to differential equations, including the mathematical theory behind the SIR model used in the course.
Provides an introduction to stochastic processes, including the theory behind Markov processes used in the course. It is suitable for advanced undergraduate or beginning graduate students.
Save
Provides a comprehensive introduction to mathematical modeling of biological systems at the undergraduate level.
Practical guide to using Python for data analysis, including extensive coverage of the NumPy and Pandas libraries used in the course.
Is the user guide for Matplotlib, the plotting library used in the course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6a3qdu/simulating
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructors
Melissa Al-Shaer
Charles Ivan Niswander II
Good to know
Provides hands-on experience through Python coding and exercises
Incorporates widely used tools for data analysis and visualization, making it relevant for data scientists and researchers
Encourages active exploration and experimentation, promoting a deeper understanding of the concepts
Taught by experienced professionals with expertise in the field, ensuring up-to-date knowledge and insights
Suitable for students and professionals seeking a fundamental understanding of agent-based modeling for simulating pandemic spread
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