Review: A COVID-19 infection risk model for frontline health care workers

This study formulates a theoretical model to calculate the risk of being infected in health care facilities and suggest ways to minimize these risks.

The number of confirmed COVID-19 cases admitted in hospitals is continuously increasing in the Philippines. Frontline health care workers are faced with imminent risks of getting infected.

Using data from the Philippines, the authors formulate a model to investigate how many frontline workers are expected to be infected under certain scenarios. The model considers:

  • the average number of encounters with a suspected COVID-19 patient per hour,
  • interaction time for each encounter,
  • work shift duration or exposure time,
  • crowd density, which may depend on the amount of space available in a given location, and
  • availability and effectiveness of protective gear (e.g., isolation booths, N95 masks, face and eye shields) including level of protection to reduce exposure to aerosolized particles (e.g., for those tasked to do intubation either via direct or via video laryngoscopy, to do nasopharyngeal or oropharyngeal swabs).

Based on the simulation results, the authors recommend the following:

  1. Decrease the rate of patient encounter per frontline health care worker to a maximum of 3 encounters per hour in a 12-hour work shift duration. Multiple frontline triage nurses, multiple queues, multiple entrances, and proper referral systems will mitigate the risk of infection.
  2. Decrease the interaction time between the frontline health care worker and the patients, e.g., less than 40 minutes for the whole day. In other words, reduce work shift durations for frontline workers.
  3. Increase the clean and safe space for social distancing, e.g., maximum of 10% crowd density, and if possible, implement compartmentalization of patients.
  4. Provide effective PPE that the frontline health care workers can use during their shift.
|2020-04-01T13:55:33-04:00April 1st, 2020|COVID-19 Literature|Comments Off on Review: A COVID-19 infection risk model for frontline health care workers

About the Author: Erika Cheng

Erika Cheng

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