Pandemic Prediction Literatur Review

Get Started. It's Free
or sign up with your email address
Pandemic Prediction Literatur Review by Mind Map: Pandemic Prediction Literatur Review

1. Statistical Modeling

1.1. ARIMA ( Atawneg,2020)

1.2. SARIIMA (Caurasia, 2020)

1.3. Holt Winter (Caurasia, 2020)

1.4. SSA (Kalantari, 2020)

2. Machine Learning Modeling

2.1. Optimization method for forecasting confirmed cases of COVID-19 in China (ANFIS-SSA)

2.1.1. Limitation: Calculate only Linear Data

2.2. Hybrid Machine Learning Model with MLP-ICA and ANFIS algorithm [Pinter, 2020]

2.2.1. calculate only Linear Data, RMSE 1.92

2.3. A Hybrid Production Model using the EEMD + ANN method [Hasan, 2020]

2.3.1. calculate only Linear Data R2 0.9

2.4. COVID-19 Outbreak Prediction with Machine Learning (ANFIS-MLP) [alQaness 2020]

2.4.1. calculate only Linear Data

2.5. Coronavirus disease (COVID-19) global prediction using hybrid artificial intelligence method of ANN trained with grey wolf optimizer

2.5.1. calculate Stracture data, MAPE 11,4

2.6. prediction model with LSTM-RNN [Yang,2020

2.6.1. only numeric, time consuming

2.7. A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast [saif 2021]

2.7.1. only for Covid 19, time consuming

2.8. Tabel 1 Literatur Review limitation page 10-13

3. Epidemology Modeling

3.1. SIR (chakraboury, 2020 - Ismail, 2020 - Shide, 2020 )

3.2. SIRD (Ardabili, 2020 - Pnter, 2020)

3.3. SEIR ( Tabataba 2017))

4. However, it should be emphasized that SIR, SIRD, SEIR models do not operate effectively when the contact network is not stationary over time [53]. (has less accuracy becouse not accomodate all pandemic variable)

5. only able to handle small data, (big data+unstructured and uncertain data=less accuracy)

6. The prediction model with machine learning technique has weaknesses in data processing time / time consuming and the output are unstable (when the variables and the amount of data change). ANFIS and hybrid method optimization has been carried out by previous researchers. Why the prediction still missing??

7. The complexity of covid 19 data Non-linear, unstructured, and uncertain data creates problems in the accuracy of machine learning time series predictions