You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:

- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.
- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.
- Prevention: The content covers measures to prevent the spread of COVID-19.
- Mechanism: The paragraph explains the biological mechanisms of the virus.
- Case Report: The content is a report of a COVID-19 case.
- Transmission: The paragraph discusses how COVID-19 is transmitted.
- Diagnosis: The content is about diagnosing COVID-19 in individuals.

Your classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: ["Topic1", "Topic2", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.

Article:

In this paper, we aim at understanding the broad spectrum of factors influencing the survival of infected patients and the correlations between these factors to create a predictive probabilistic score for surviving the COVID-19 disease. Initially, 510 hospital admissions were counted in the study, out of which 310 patients did not survive. A prediction model was developed based on this data by using a Bayesian approach. Following the data collection process for the development study, the second cohort of patients totaling 541 was built to validate the risk matrix previously created. The final model has an area under the curve of 0.773 and predicts the mortality risk of SARS-CoV-2 infection based on nine disease groups while considering the gender and age of the patient as distinct risk groups. To ease medical workers' assessment of patients, we created a visual risk matrix based on a probabilistic model, ranging from a score of 1 (very low mortality risk) to 5 (very high mortality risk). Each score comprises a correlation between existing comorbid conditions, the number of comorbid conditions, gender, and age group category. This clinical model can be generalized in a hospital context and can be used to identify patients at high risk for whom immediate intervention might be required.
["Diagnosis", "Treatment"]