Table 2 Optimal resource allocation models for COVID-19 included in review
From: Optimal resource allocation model for COVID-19: a systematic review and meta-analysis
Study identifier /year of publication | Geographic focus | Types of resources | Optimization technique | Optimization goal(s) |
|---|---|---|---|---|
Evans et al. (2023) | Madagascar | Testing capacity | Epidemic Model (SEIR model) | Maximize testing efficiency |
Xia,Zeyu et al. (2023) | numerical simulation | Testing Capacity; Beds; doctors and nurses | SEIR model | The cost-optimal solution for effective epidemic control |
Jin Zhu et al. (2023) | England | Vaccines | the multi-period two-dose vaccine allocation model | Minimize lower vaccine supply levels and minimize the daily number of deaths |
Barnieh L et al. (2023) | US | Patient-treating drugs; Beds | decision tree model、a Markov model | Minimize treatment (hospitalization、quality-adjusted life year) costs |
Kai Zong et al. (2022) | US | Lockdown resource allocation | MARAAC structure、the advantage function、SEAIRD model | Minimize the economic loss while keeping the number of individuals |
Khan A A et al. (2022) | Pakistan | Vaccines | a compartment epidemic model、 the compartmental-based COVID-19 vaccine model | Maximize vaccination |
Schmidt et al. (2021) | Munich | Beds | A Planning Model for Intrahospital Resource Allocation | Maximize hospitalization rate |
Apornak et al. (2021) | Iran | nurses | the linear programming technique | Maximize nurse service timer period |
Libin et al. (2021) | Belgian | Testing capacity | extend the STRIDE model | Maximize testing efficiency |
Daniel Kim et al. (2021) | numerical simulation | Vaccines | Extended SIR-D model | Maximize vaccine efficacy and reach |
Jeongmin Kim et al. (2021) | Korea | ICU Beds | Multivariate logistic regression (LR) and XGBoost | Maximize hospitalization rate |
Worby et al. (2020) | numerical simulation | masks | the “resource allocation”model、 the “supply & demand” model (SEIR model) | Maximize mask use |
Michail et al. (2020) | Switzerland | Testing capacity | a sequential optimization algorithm、SEIrIuR epidemiological model | Minimize prediction uncertainty, Maximize information gain of unreported infections |
Arunmozhi et al. (2022) | 10 countries | Ventilators; PPE; ICU Beds; Health specialists | the Probability Queueing Theory (PQT) and K-Mean clustering Machine Learning (ML) | Increasing Capacity |
Majid et al. (2023) | Iran | Vaccines | a two-stages model with uncertainty demand | Minimize the total cost of meeting demand、the maximum coverage index |
Lin Wang et al. (2022) | US | ICU Beds; Ventilators; treatments for symptoms | a novel Lasso Logistic Regression model based on feature-based time series data | Reducing the mortality rate of hospitalized COVID-19 patients |
Bing Xue et al. (2022) | US | ECMO | Multi-horizon machine learning prediction models | Maximize ECMO use |
Ying-Qi Zeng et al. (2022) | 4 countries | Beds | COVID-19 patient admission model | Maximize hospitalization rate |
Mehrotra et al. (2020) | US | Ventilators | a multi-period planning model | Minimize ventilators’ shortage |
Zhou D et al.(2022) | numerical simulation | Vaccines | a transmission dynamic-model | Minimizing the size of infection |
Sean Shao et al.(2022) | Singapore | Beds | Beds resource planning model | Increasing beds Capacity |
Krishna P. R et al.(2021) | South Africa | Vaccines | Micro simulation model | Minimize treatment costs |