Credits
[This email address is being protected from spambots. You need JavaScript enabled to view it.', 'Francisco Esquembre', 'Felix J. Garcia Clemente']
Sample Learning Goals
Project 8: WebEJS workshop Modeling Infectious Disease Spread with R0: A Visual Simulation by Hazel
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| https://sg.iwant2study.org/ospsg/index.php/interactive-resources/mathematics/1219"}">https://sg.iwant2study.org/ospsg/index.php/1219 |
Modeling Infectious Disease Spread with R0: A Visual Simulation
Infectious diseases have always been a major concern for public health, particularly with the advent of global pandemics. One of the critical parameters in understanding how infectious diseases spread is the basic reproduction number, known as R0 (pronounced R-naught). This number indicates the average number of secondary infections generated by one infected individual in a completely susceptible population.
Understanding R0 is crucial because it helps public health officials determine the potential for an outbreak and the level of intervention required to control it. In this blog post, we will explore an interactive model to visualize the spread of infections based on different R0 values, utilizing a network diagram and a graph plot.
Understanding R0
The R0 value can vary between diseases:
- R0 < 1: Each existing infection causes less than one new infection, leading to a decline in cases over time.
- R0 = 1: Each infection leads to one new infection, resulting in a stable number of infections over time.
- R0 > 1: Each infection causes more than one new infection, potentially leading to an epidemic.
For example, measles is highly contagious with an R0 of 12-18, whereas seasonal influenza is less contagious with an R0 of about 1.3.
Simulation Overview
The simulation presented in the image above provides a visual representation of how infections spread through a population based on a chosen R0 value. It consists of two primary components: a network diagram (left) and a graph plot (right).
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Network Diagram: This part of the simulation shows the progression of infections through a tree-like structure. Each node represents an infected individual, and the branches represent the transmission of the disease to others. As you adjust the R0 value using the slider, the diagram dynamically updates, demonstrating how quickly infections can multiply with higher R0 values.
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Graph Plot: On the right side, the plot visualizes the cumulative number of infections over time. The x-axis represents the rounds or cycles of infection, while the y-axis represents the total number of infections. The plot shows the exponential growth of infections as R0 increases, illustrating the potential severity of an outbreak.
Key Observations
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Exponential Growth: The plot clearly demonstrates the exponential growth pattern of infectious diseases with R0 > 1. For example, at R0 = 3, each infected individual infects three others, leading to rapid increases in total infections.
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Critical Threshold: The inflection point in the plot highlights the critical threshold where an outbreak transitions from a manageable situation to an uncontrolled epidemic.
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Impact of Interventions: By reducing the effective R0 (through vaccinations, social distancing, etc.), public health measures can significantly flatten the curve and control the spread.
Conclusion
This simulation underscores the importance of understanding and managing R0 to control infectious diseases effectively. Visual models like this are invaluable in educating the public and policymakers about the dynamics of disease transmission and the critical need for timely interventions.
As we continue to battle existing and emerging infectious diseases, tools like this R0 simulation are crucial for preparing and responding to public health challenges. They provide a tangible representation of abstract concepts, making it easier for everyone to grasp the importance of controlling disease spread.
For Teachers
20240718-24 Web EJS beta Workshop by Francisco Esquembre and Félix J. García Clemente supported by MOE CPDD1 Registration for Web EJS Workshop (18-24 July 2024)
Venue: MOEHQ Buona Vista, B3-02 (18 July) P2-01-02 (19,22,23,24 July) or
Contact organisers at This email address is being protected from spambots. You need JavaScript enabled to view it.
Research
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