George Mason University  World Bank Group  Johns Hopkins University MASH-Pandemics



The MASH-Pandemics multi-unit hospital models are patient-based and resource-constrained. They help with assessing hospital preparedness to serve patient surges during a pandemic, mass casualty incident, or disaster event.

Upon arrival to an emergency entrance, patients are assigned a care pathway depending on their condition. Patients are prioritized according to the level of service needed and their likelihood of survival, factoring in the wait time for needed services. Care paths are updated as patient conditions evolve. If circumstances warrant, lowest priority arrivals may be turned away without treatment (i.e. they may be triaged).

This approach explicitly recognizes that the performance of the whole hospital depends on the functionality of its units and the specific needs of patients. In times of surge demand, as in a pandemic, hospital resources (staff, stuff and space) are often stretched thin.


Shortfalls in one unit can negatively affect services provided in another unit. MASH-Pandemics’ patient-based approach illustrates the possibility of shortages arising between units due to shared resources including staff, stuff or space. Where patients with different care pathways rely on a shared, limited resource (such as laboratory testing), bottlenecks can arise indirectly in one unit due to limitations in another.

Our models include patients who arrive at or are transferred to the hospital through the emergency department, have scheduled elected surgical procedures and directly enter pre-operation rooms or internal general wards for inpatient day-ahead procedures, or are placed directly in critical care units. Patients are tracked from their entry through to their discharge.

Sample models (see below) show how resources can be reallocated to pandemic patients during emergencies. These reallocation strategies include canceling all or some elective procedures, speeding-up patient care, discharging patients early when possible, omitting some patient care services, changing triage tactics and other demand management strategies, moving staff across units, repurposing space to meet event-related medical needs, and altering planned patient care paths.

Features of the models include replication of capacity enhancement strategies, modified operations, crisis standards of care, and regional collaboration strategies.

SEE MODEL SCHEMATICS (Click on images below to view the full size)

Snapshots from One of the Hospital Models

Coalition Policies
Hospital Coalition Policies


coalition strategies
Modeling Coalition Policies


  1. TariVerdi, M., E. Miller-Hooks, and T. Kirsch (2018). “Strategies for Improved Hospital Response to Mass Casualty Incidents,” Disaster Medicine and Public Health Preparedness 12 (6), 778-790.
  2. TariVerdi, M., E. Miller-Hooks, T. Kirsch and S. Levin (2019). “A Resource-Constrained, Multi-Unit Hospital Model for Operational Strategies under Routine and Surge Demand Scenarios,” IIE Transactions on Healthcare Systems Engineering, 9(2) 103-119.
  3. Shahverdi, B., M. TariVerdi and E. Miller-Hooks (2020). “Assessing Hospital System Resilience to Disaster Events involving Physical Damage and Demand Surge,” Socio-Economic Planning Sciences 70, 100729.
  4. Shahverdi, B., E. Miller-Hooks, M. Tariverdi, H. Ghayoomi, D. Prentiss and T. Kirsch (2022). “Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic,” in press in Disaster Medicine and Public Health Preparedness.

Also relevant:

  1. TariVerdi, M., H. Fotouhi, S. Moryadee and E. Miller-Hooks (2019). “Health Care System Disaster Resilience given its Reliance on Interdependent Critical Lifelines”, ASCE Journal of Infrastructure Systems 25(1), 04018044-1 to 04018044-16.
  2. Ghayoomi, H., K. Laskey, E. Miller-Hooks, C. Hooks and M. Tariverdi (2021). "Assessing Resilience of Hospitals to Cyberattack”, Digital Health 7, 1-15.

Data Collection

The hospital models were designed based on in-depth interviews with key hospital personnel. The interviews were conducted over multiple visits in the course of approximately one year at the Johns Hopkins Hospital, Suburban Hospital, and the Johns Hopkins Office of Critical Event Preparedness and Response. The primary sources included the director of operations and an administrative director of ED/trauma, safety, security and employee health services, among others. Extensive discussions regarding multiple hazard scenarios were held and findings were translated into modeling parameters. The models were constructed over a 4-year period and details have been published.

Mathematical Basis

MASH-Pandemics is a set of discrete-event simulation models developed in the ExtendSim Simulation Software environment. The models assume an open-queueing network of the hospital’s key units for comprehensive care. Closed loops and parallel processes facilitate replication of key, potentially interconnected services, including in-hospital laboratory and imaging services.

We welcome requests for more information or adopting the models for specific facility configurations or demand scenarios from hospital administrators and others working in an official capacity: REQUEST ANALYSIS.