Analysis of the Healthcare MERS-CoV Outbreak in King Abdulaziz Medical Center, Riyadh, Saudi Arabia, June-August 2015 Using a SEIR Ward Transmission Model.
Tamer OrabyMichael G TyshenkoHanan H BalkhyYasar TasnifAdriana Quiroz-GasparZeinab MohamedAyesha ArayaSusie ElsaadanyEman Al-MazroaMohammed A AlhelailYaseen M ArabiMustafa Al-ZoughoolPublished in: International journal of environmental research and public health (2020)
Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging zoonotic coronavirus that has a tendency to cause significant healthcare outbreaks among patients with serious comorbidities. We analyzed hospital data from the MERS-CoV outbreak in King Abdulaziz Medical Center, Riyadh, Saudi Arabia, June-August 2015 using the susceptible-exposed-infectious-recovered (SEIR) ward transmission model. The SEIR compartmental model considers several areas within the hospital where transmission occurred. We use a system of ordinary differential equations that incorporates the following units: emergency department (ED), out-patient clinic, intensive care unit, and hospital wards, where each area has its own carrying capacity and distinguishes the transmission by three individuals in the hospital: patients, health care workers (HCW), or mobile health care workers. The emergency department, as parameterized has a large influence over the epidemic size for both patients and health care workers. Trend of the basic reproduction number (R0), which reached a maximum of 1.39 at the peak of the epidemic and declined to 0.92 towards the end, shows that until added hospital controls are introduced, the outbreak would continue with sustained transmission between wards. Transmission rates where highest in the ED, and mobile HCWs were responsible for large part of the outbreak.
Keyphrases
- respiratory syndrome coronavirus
- saudi arabia
- sars cov
- emergency department
- healthcare
- coronavirus disease
- intensive care unit
- end stage renal disease
- adverse drug
- ejection fraction
- chronic kidney disease
- newly diagnosed
- prognostic factors
- acute care
- primary care
- electronic health record
- machine learning
- case report
- deep learning
- patient reported outcomes
- artificial intelligence