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| Sr. No. | Model | Measures/predictions |
|
| 1 | Susceptible-Exposed-Infected-Recovered (SEIR) compartment model | Possible peak might be reached in early summer and will not be ended quickly in Japan [4] |
| 2 | Flower Pollination Algorithm Salp Swarm Algorithm-Adoptive Neuro-Fuzzy Interference System (FPASSA-ANFIS) | Predicting the number of corona cases in upcoming 10 days [5] |
| 3 | Susceptible-Exposed-Infected-Recovered metapopulation model | It was forecasting the extent of domestic as well as global public health risks of the epidemic disease [13] |
| 4 | Heuristic and WHO-based situation reports | It was forecasting the number of cases outside of China [6] |
| 5 | Lotka-Volterra model coupled with Kaman Filter Algorithm | Epidemic could be controlled with restriction of traveling and social distancing [8] |
| 6 | Generalized Logistic or Richards Growth Models and Subepidemic Wave Model | It was concluded that the cases are decreasing in Zhejiang and Guangdong provinces [9] |
| 7 | Susceptible-Infected-Resistant (SIR) model, Adaptive Network-Based Fuzzy Interference System (ANFIS), and Multi-Layered Perception-Imperialist Competitive Algorithm (MLP-ICA) | In Hungary, the death rate and overall corona cases dropped [10] |
| 8 | Susceptible-Exposed-Infected-Recovered (SEIR) compartment model [11] | A decreased number of cases in Emilia Romagna compared to Lombardy in Italy were predicted [10] |
| 9 | Verhulst equation-based model | It was concluded that China had reported a fewer number of cases than Italy and Spain due to adhering to the precautionary measures and imposing complete lockdown [14] |
| 10 | Motivation, Context, Interactivity, Evaluation, and Connectivity (MCIEC) model | It was predicted that e-learning is the best method compared to the expository method in adults [15] |
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