In this project, I showcase my expertise in statistics and data analysis, particularly focusing on COVID-19 data. The project's primary objective was to analyze the waves of COVID-19 cases, measuring their intensity and duration.
- Proficiency: Excel was the primary tool used for data manipulation, calculations, and visualizations throughout the project.
- Data Processing: I leveraged Excel to process and analyze the COVID-19 data effectively.
- Statistical Models: I applied discrete-time Markov chain models to mimic the patterns observed during the COVID-19 pandemic.
- Probability Calculations: Probability calculations were essential for determining transition probabilities and expected lengths of COVID-19 case inflation and decline periods.
- Data Sources: Real-world COVID-19 data from the CDC was utilized as the basis for transition probabilities.
- Data Extrapolation: I extrapolated weekly transition probabilities to create a week-by-week transition structure for our model.
- Transition probabilities for each state were assumed, with a focus on four separate states: 10% and 50% increases in diagnosis rates and 10% and 50% reductions in diagnosis rates.
- Acceptable ranges for transition probabilities were established to improve the realism of the model.
- To determine the expected lengths of periods of case inflation and decline, a geometric probability model was employed.
- Expected values for the lengths of increase and decrease periods were calculated using transition probabilities.
- I applied an Excel-based method to calculate the most occurring state in our model.
- The steady-state vector for each state was calculated, and it was determined that the most occurring state was a 50% increase in COVID cases.
My analysis revealed that the most frequently occurring state during the COVID-19 pandemic was a 50% increase in cases. This finding corresponds to the numerous surges and closures experienced across California during the pandemic.
Overall, the project successfully developed a Markov chain model to simulate COVID-19 case patterns. I demonstrated my ability to make assumptions using real-world data, calculate transition probabilities, and determine the most occurring state. Our model aligned with actual data trends, making it a reliable tool for understanding the dynamics of COVID-19 case fluctuations.
While the project achieved its goals, I encountered challenges, such as determining the most occurring state and refining transition probability ranges. Further refinement and validation could enhance the accuracy of the model. Additionally, a more standardized process for extrapolating data rates may streamline future analyses.
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