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3586. Find COVID Recovery Patients

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Database

Table: patients

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| patient_id  | int     |
| patient_name| varchar |
| age         | int     |
+-------------+---------+
patient_id is the unique identifier for this table.
Each row contains information about a patient.

Table: covid_tests

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| test_id     | int     |
| patient_id  | int     |
| test_date   | date    |
| result      | varchar |
+-------------+---------+
test_id is the unique identifier for this table.
Each row represents a COVID test result. The result can be Positive, Negative, or Inconclusive.

Write a solution to find patients who have recovered from COVID - patients who tested positive but later tested negative.

  • A patient is considered recovered if they have at least one Positive test followed by at least one Negative test on a later date
  • Calculate the recovery time in days as the difference between the first positive test and the first negative test after that positive test
  • Only include patients who have both positive and negative test results

Return the result table ordered by recovery_time in ascending order, then by patient_name in ascending order.

The result format is in the following example.

 

Example:

Input:

patients table:

+------------+--------------+-----+
| patient_id | patient_name | age |
+------------+--------------+-----+
| 1          | Alice Smith  | 28  |
| 2          | Bob Johnson  | 35  |
| 3          | Carol Davis  | 42  |
| 4          | David Wilson | 31  |
| 5          | Emma Brown   | 29  |
+------------+--------------+-----+

covid_tests table:

+---------+------------+------------+--------------+
| test_id | patient_id | test_date  | result       |
+---------+------------+------------+--------------+
| 1       | 1          | 2023-01-15 | Positive     |
| 2       | 1          | 2023-01-25 | Negative     |
| 3       | 2          | 2023-02-01 | Positive     |
| 4       | 2          | 2023-02-05 | Inconclusive |
| 5       | 2          | 2023-02-12 | Negative     |
| 6       | 3          | 2023-01-20 | Negative     |
| 7       | 3          | 2023-02-10 | Positive     |
| 8       | 3          | 2023-02-20 | Negative     |
| 9       | 4          | 2023-01-10 | Positive     |
| 10      | 4          | 2023-01-18 | Positive     |
| 11      | 5          | 2023-02-15 | Negative     |
| 12      | 5          | 2023-02-20 | Negative     |
+---------+------------+------------+--------------+

Output:

+------------+--------------+-----+---------------+
| patient_id | patient_name | age | recovery_time |
+------------+--------------+-----+---------------+
| 1          | Alice Smith  | 28  | 10            |
| 3          | Carol Davis  | 42  | 10            |
| 2          | Bob Johnson  | 35  | 11            |
+------------+--------------+-----+---------------+

Explanation:

  • Alice Smith (patient_id = 1):
    • First positive test: 2023-01-15
    • First negative test after positive: 2023-01-25
    • Recovery time: 25 - 15 = 10 days
  • Bob Johnson (patient_id = 2):
    • First positive test: 2023-02-01
    • Inconclusive test on 2023-02-05 (ignored for recovery calculation)
    • First negative test after positive: 2023-02-12
    • Recovery time: 12 - 1 = 11 days
  • Carol Davis (patient_id = 3):
    • Had negative test on 2023-01-20 (before positive test)
    • First positive test: 2023-02-10
    • First negative test after positive: 2023-02-20
    • Recovery time: 20 - 10 = 10 days
  • Patients not included:
    • David Wilson (patient_id = 4): Only has positive tests, no negative test after positive
    • Emma Brown (patient_id = 5): Only has negative tests, never tested positive

Output table is ordered by recovery_time in ascending order, and then by patient_name in ascending order.

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