a. Dual Container Simulation

• Container A (Susceptible Bacteria):

  • Population: Starts with a defined number of susceptible bacteria (e.g., 6).
  • Mechanism: Each antibiotic dose affects susceptible bacteria based on a probabilistic model (e.g., rolling a dice where outcomes 1-4 kill a bacterium).

• Container B (Antibiotic-Resistant Bacteria):

  • Population: Starts with a smaller number of resistant bacteria (e.g., 4).
  • Mechanism: Resistant bacteria are affected differently (e.g., rolling a dice where outcome 6 kill a bacterium).

b. Simulation Cycles and Doses

• Cycles: The simulation runs through multiple cycles (e.g., 100), each consisting of a set number of antibiotic doses (e.g., 10 per cycle).
• Dose Application: For each dose within a cycle, the simulation rolls dice to determine which bacteria survive or are killed.

c. Death Dose Recording

• Objective: Track and record the specific dose at which all bacteria in each container are eradicated within a cycle.
• Data Recording: Maintains separate records for susceptible and resistant bacteria, indicating the dose number where they were completely wiped out.

d. Interactive Data Display

• Detailed Simulation Table: Shows the number of surviving bacteria after each antibiotic dose for both susceptible and resistant populations.
• Death Dose Record Table: Compiles and displays the dose numbers at which bacteria populations were entirely eliminated across all cycles.
• Toggle Functionality: Users can switch between the detailed simulation view and the death dose records seamlessly during the simulation.

e. Control Mechanisms

• Pause and Resume: Users can pause the simulation at any point and resume it, allowing for interactive exploration and observation.
• Reset Functionality: After completing a set of doses, the simulation resets for the next cycle, maintaining consistent conditions for comparison.

a. Simulation Logic

• Dice Rolling Mechanism: Uses JavaScript intervals and timeouts to simulate the probabilistic killing of bacteria based on dice outcomes.
• Cycle Management: Tracks the number of completed cycles and manages the transition between cycles, ensuring accurate data recording and reset processes.
• Death Dose Tracking: Monitors when bacteria populations reach zero and records the corresponding dose number for each cycle.

b. Data Management

• Arrays for Tracking: Maintains arrays (normalX, normalY, resistantX, resistantY, etc.) to store data points for each dose and cycle.
• Dynamic Table Population: Utilizes DOM manipulation to dynamically add rows to tables, reflecting real-time simulation results.

c. User Interface (UI)

• Responsive Tables: Designed with CSS to ensure tables are scrollable and headers remain fixed, enhancing readability and usability.
• Interactive Controls: Includes buttons or controls for toggling between different data views and managing the simulation state (pause/resume).

Educational Impact

This simulation serves as an educational tool to:

• Demonstrate Antibiotic Resistance: Visually and interactively show how antibiotic-resistant bacteria can survive treatments that eliminate susceptible bacteria.
• Highlight the Importance of Proper Antibiotic Use: Emphasize the consequences of overusing or misusing antibiotics, which can lead to the proliferation of resistant strains.
• Provide Insight into Population Dynamics: Allow users to observe how different factors (e.g., dosage frequency, bacteria populations) influence the overall effectiveness of antibiotic treatments.