Table 1 Summary of optimal resource allocation models for COVID-19

Total number of articles reviewed

22

100

[20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]

Africa

3

13.64%

[20,21,22]

North America

6

27.27%

[22, 23, 25, 30, 35, 37]

Asia

7

31.82%

[24, 25, 31, 34, 36, 38, 39]

Europe

6

27.27%

[20, 25, 26, 28, 32, 34]

South America

1

4.55%

[25]

Numerical simulation

4

18.18%

[27, 29, 33, 40]

1

18

81.82%

[20,21,22,23,24, 26, 28, 29, 31,32,33,34,35,36, 38,39,40,41]

2

1

4.55%

[37]

3 or more

3

13.64%

[25, 27, 30]

Tests

4

18.18%

[21, 26,27,28]

Vaccines

6

27.27%

[29, 31, 32, 36, 40, 41]

Beds

8

36.36%

[20, 25, 27, 30, 34, 37,38,39]

Apparatus and Instruments

3

13.64%

[23, 30, 37]

PPE (Personal Protective Equipment)

2

9.09%

[25, 33]

Health specialists

3

13.64%

[24, 25, 27]

Therapeutic Drug

3

13.64%

[23, 30, 37]

Lockdown

1

4.55%

[22]

confirmed cases/Infection rate

14

63.64%

[20,21,22,23, 25, 27,28,29,30,31, 33, 36, 40, 41]

Deaths/the mortality rate

9

40.91%

[25, 26, 30, 32,33,34, 38, 40, 41]

the recovery rate

4

18.18%

[24, 30, 35, 39]

cost-effectiveness

4

18.18%

[27, 37, 40, 41]

the duration of the epidemic

1

4.55%

[21]

Method for modeling

differential equation (SEIR)

13

59.09%

[21, 22, 25,26,27,28,29, 31,32,33,34, 39,40,41]

optimization function

8

36.36%

[20, 22, 24, 32, 35, 36, 38]

machine learning algorithm

7

31.82%

[22, 23, 25, 26, 30, 37, 38]