Table 2.

What OR models and methods can help fight against COVID-19

Stages	Parties	Related areas	Methods (related literature)
Before	GOVT	BG1. Treatment preparation; BG2. Disease prevention	BG1. Simulation (Habbema et al. 1987) BG2. Simulation (Davies et al. 2003)
	HNPO	BH1. Staffing at mass-immunization clinics; BH2. Vaccination	BH1. Simulation (Beeler et al. 2014) BH2. Randomized algorithm (Ventresca and Aleman 2014)
	BUSS	–	–
During	GOVT	DG1. Mass screening for diseases; DG2. Demand management; DG3. Predict spread of disease; DG4. People mobility; DG5. Logistics for pandemic	DG1. Forecasting model (Van Oortmarssen et al. 1981) DG2. Predict excess demand (Nikolopoulos et al. 2020) DG3. Multivariate Reed-Frost model (Sun et al. 2009) DG4. Spatial–temporal model with network dynamics (Zhang et al. 2020a) DG5. MIP (Büyüktahtakin et al. 2018; Liu et al. 2020)
	HNPO	DH1. Healthcare demand management; DH2. Healthcare staffing and resource allocation; DH3. Treatment; DH4. Controling spread of diseases by healthcare; DH5. Risk factor for disease progression	DH1. Fuzzy inference system (Govindan et al. 2020) DH2. Discrete-time Markov chain (Chen et al. 2020); bi-objective optimization (Sun et al. 2014) DH3. Stochastic dynamic programming (Mondschein et al. 2019) DH4. The generalized Markov model (Yaesoubi and Cohen 2011) DH5. Regression models (Fu et al. 2012)
	BUSS	DB1. Bring-service-near-your-home operations	DB1. Analytical modeling (Choi 2020)
After	GOVT	AG1. Future disease prevention	AG1. Data envelopment analysis (Zanakis et al. 2007)
	HNPO	AH1. Food distribution	AH1. Heuristics (Ekici et al. 2014)
	BUSS	AB1. Supply chain management; AB2. Sustainable supply chain opetations	AB1. Building resilent using supply chain theories (Remko 2020) AB2. The stepwise weight assessment ratio analysis (Sharma et al. 2020)

(P.S.: For the code, e.g., BG1 means the first related area (“1”) under “Before + GOVT” (i.e., BG))