Predictive Analytics in COVID-19 Hospitalization Forecasting

Predictive analytics is a method that uses Data and statistical techniques to make forecasts about future events. In the context of diseases, it helps us to predict how many people might get sick and how many hospital beds will be needed. One specific case of interest is COVID-19, which has affected many people worldwide. Understanding how to analyze data related to COVID-19 can provide valuable insights into managing healthcare resources.

#Understanding the Data

To study disease forecasting, data is essential. For this particular study, data from a region in Ontario, Canada was used. This data included the daily number of COVID-19 Hospitalizations. However, the initial outbreak in 2020 showed an enormous spike in hospitalizations due to the sudden increase in cases. To get a clearer picture of trends over time, the data from 2020 was not included in the analysis. Instead, the focus was on the data from January 1, 2021, to December 31, 2022.

To make the data easier to analyze, daily hospitalizations were grouped into sets covering ten days. For example, the first group covered hospitalizations from January 1 to January 10. This grouping made it easier to spot changes and patterns over two years. Each ten-day group was assigned a number, resulting in a total of 73 groups over the studied period.

#Identifying Outliers

When looking at the data, some extreme values, known as outliers, were noticed. Outliers can distort the results of data analysis and lead to less accurate predictions. Therefore, it is useful to identify and remove these outlier values. In this case, a method was employed to determine which values were outliers by calculating certain statistical boundaries.

After examining the data, it was found that there were no very low values. However, some hospitalization numbers were significantly higher than others and were therefore considered outliers. These outliers were replaced with average values from adjacent data points to maintain the overall trend while improving the accuracy of the analysis.

#Preparing for Analysis

Before performing any predictive analysis, it is crucial to check how well the model can predict outcomes based on existing data. This is where a technique called Test Train Split comes in. In simple terms, this method divides the data into two parts: one part is used to create the Predictive Model, while the other part tests the model's accuracy.

In this case, 80% of the data was used to build the model, and 20% was set aside for testing. This division ensures that the model can be validated with fresh data that it hasn’t seen before, providing a nuanced understanding of its reliability.

#Building the Predictive Model

To create a predictive model, the goal is to find a relationship between the time (number of ten-day groups) and the number of hospitalizations. A common way to express this relationship is through a linear equation, which predicts the number of hospitalizations based on the time period.

However, a linear model may not always fit the data well, especially in cases like a pandemic where trends can change rapidly. In this analysis, the aim was to minimize the errors between what the model predicts and the actual hospitalizations. This involves some complex calculations, but the key takeaway is that the model's effectiveness depends on how closely it can match actual results.

#Evaluating Accuracy

To see how well the predictive model worked, the Mean Absolute Error was calculated. This figure shows how far off the predictions were from the actual data. In this case, the Mean Absolute Error was higher than expected, pointing to a need for a better-fitting model.

The analysis identified that a linear approach might not represent the data accurately. Often, data from health crises do not follow a straight line. Instead, a logarithmic or polynomial model could be more suitable for this kind of data. These models can accommodate trends that change more dramatically over time.

#Comparing Different Models

To understand which model fits the data best, different regression models were tested. The R-squared value, which indicates how well the model explains the data, was calculated for each model. This value ranges from 0 (no fit) to 1 (perfect fit).

In the analysis, the R-squared value for the linear model was found to be quite low. However, when logarithmic and polynomial models were tested, they produced much higher R-squared Values. This indicates that these models captured the trends in the data much better than the linear model.

#Conclusion

The study of predictive analytics in disease forecasting, especially for COVID-19, shows that using the right model is crucial for accuracy. While linear regression can be useful, it may not always be the best approach for complex data like that of a pandemic.

By recognizing the limitations of a linear model and exploring other options such as logarithmic and polynomial regressions, better predictions can be made. This can lead to improved preparations for healthcare systems in managing hospitalizations during such crises.

In summary, the study highlights the importance of using appropriate models to analyze and forecast disease trends. With the right tools and understanding, healthcare providers can make better decisions and prepare for future healthcare challenges more effectively.