# Short-Term Prediction of COVID-19 Cases Using Machine Learning Models
## 4 Discussion  
In this work, we constructed a short-term forecasting model that trains in a cloud server using data on the cumulative number of reported cases for the COVID-19 pandemic in Bangladesh. It forecasted the infection and fatality cases for the following couple of days quickly and accurately. There have previously been some attempts at this type of statistical modeling [43] around COVID-19 in Bangladesh that have shown some success. However, our proposed improved model employs a comprehensive machine learning prediction model to investigate cumulative cases which yielded a very good fit to the epidemic curves when it conducts its evaluations using RMSE, MAE and \(R^{2}\) values. It is notable that individual rounds provide trends of the epidemic curve, which are observed in this analysis. In the 1 st round, LR showed the best result because the cumulative cases were not much increased during this period. However, the trend or growth factor increased markedly from the 1 st to the 4 th round, showed the best performance for SVM, PR, MLP, or Poly-MLP using hyperparameter tuning. Through the rest of the rounds (for the 5 th, 6 th, 7 th, 8 th, 9 th and 10 th), we investigated the results of machine learning regression models, and found that the prophet model gave the best results for both infections and mortalities. From the 5 th to the 9 th rounds, it indicated the increased rate of growth factor for confirmed infection than death cases. However, their differences are not large. Nevertheless, the growth rate of fatalities is increased compared to the confirmed infection rate in the 10 th round. However, the growth factor did not vary more, hence prophet showed the best results consistently from the 5 th to the 10 th round. Besides, Table 4 shows the average execution times of different methods where prophet algorithms takes more time than others. The runtime of the prophet algorithm is also higher from the 31 st to the 34 th round, respectively. Nevertheless, this algorithm generated a low error rate in forecasting associated cases, hence it has been taken as the best model in most of the rounds. To justify the consistency of these results, we split primary dataset into 75:25 ratio where 75% instances were trained and 25% records were used as test sets. Similar models were then implemented into these datasets and manipulated the error rates (see Table 5). Without the 1 st and 4 th rounds, all regression models showed consistent results as well. Numerous research groups have attempted to forecast infectious cases,such as [6, 44, 45], but did not verify their models using several intervals or rounds. Some of these [25] did not evaluate their work using appropriate evaluation metrics. Our model is implemented on a cloud based web portal ([[https://corona.nstu.edu.bd/](https://corona.nstu.edu.bd/)]([https://corona.nstu.edu.bd/](https://corona.nstu.edu.bd/)), accessed on 21 January 2021) and dynamically predicted confirmed both infection and fatality cases with high consistency [46]. In previous studies of diseases, epidemiological models require a number of parameters related to social mixing to fit models well. However, contact tracing has found this is non-stationary through time due to unreliable social interaction parameters. In SIR or SEIR, social mixing alters the number of susceptible individuals and the reproductive number \(R_{0}\). If \(R_{0}\) is greater than 1, then it will spread. When it is less than 1, then the epidemic may decrease and eventually disappear. In addition, it is clear that such models must be adapted to the local situation based on insights into susceptibility to confirmed infection. For Bangladesh, conventional epidemiological models face substantial limits to obtaining a generalized prediction ability and robustness. Therefore, machine learning has gained a great deal of attention, resulting in numerous attempts to develop a disease outbreak prediction tool that shows high generalization and reliability.