# COVID-19 Outbreak Prediction with Machine Learning
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Logistic** & **Linear by** & **Logarithmic** & **Quadratic** & **Power by** & **MLP** & **ANFIS** \\  & **by GWO** & **GWO** & **by GWO** & **GWO** & **GWO** & **MLP** & **ANFIS** \\ \hline Day 20 th & 242.6091 & 869.8855 & \(-\)156.73 & 456.0663 & 309.616 & 244.0038 & 243.6504 \\ \hline Day 40 th & 21,951.15 & 2406.562 & 15.85698 & 6383.264 & 1031.324 & 21,942.25 & 21,948.25 \\ \hline Day 60 th & 32,547.08 & 3943.238 & 116.8138 & 18,366.35 & 2084.876 & 32,552.6 & 32,548.47 \\ \hline Day 80 th & 32,604.34 & 5479.914 & 188.4437 & 36,405.33 & 3435.319 & 32,606.19 & 32,604.47 \\ \hline Day 100 th & 32,604.55 & 7016.591 & 244.0043 & 60,500.21 & 5060.548 & 32,606.63 & 32,604.72 \\ \hline Day 120 th & 32,604.55 & 8553.267 & 289.4005 & 90,650.97 & 6944.676 & 32,606.7 & 32,604.76 \\ \hline Day 140 th & 32,604.55 & 10,089.94 & 327.7825 & 126,857.6 & 9075.446 & 32,606.78 & 32,604.8 \\ \hline Day 150 th & 32,604.55 & 10,858.28 & 344.9611 & 147,231.9 & 10,230.16 & 32,606.81 & 32,604.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 21: The outbreak prediction for the USA for 150 days. it is not possible to select the most suitable scenario. Therefore, both daily and weekly sampling can be used in machine learning modeling. Comparison between analytical and machine learning models using the deviation from the target value (Figures 17-21) indicated that the MLP in both scenarios delivered the most accurate results. Extrapolation for long-term prediction of up to 150 days using the ML models was tested. The actual prediction of MLP and ANFIS for the five countries was reported and showed the progression of the outbreak. This paper evaluated the applicability of two machine learning models, MLP and ANFIS, for predicting the COVID-19 outbreak. The models showed promising results in terms of predicting the time series without the assumptions that epidemiological models require. Machine learning models, as an alternative to epidemiological models, showed potential in predicting COVID-19, as they did for modeling other outbreaks (see Table 1). Considering the availability of only a small amount of training data, it is expected that machine learning will be developed further as the basis for, or a component of, future outbreak prediction models. Here, it is worth mentioning that machine learning can also be found useful in dealing with the challenges that SEIR models face for COVID-19. For example, the number of cases reported by worldometer is not the number of infected (\(E\) in the SEIR model). For example, the number of cases reported by worldometer for the UK situation is the number of people tested. In addition, data for the number of infectious people (\(I\) in SEIR) is a challenging matter because many people who might be infectious may not choose to be for tested if, for example, their symptoms are mild. Although better data exist on the number of people who are admitted to hospital and the number who die, these also do not represent \(R\) because it is generally accepted that most people with COVID-19 recover without entering hospital. Considering this data problem, it is extremely difficult to fit SEIR models satisfactorily. Considering such challenges, for future research, the ability of machine learning for estimation of the missing information on the number of exposed \(E\) or infected can be evaluated. Furthermore, the temporal non-stationarity data in control measures can also be investigated using machine learning.