# Short-Term Prediction of COVID-19 Cases Using Machine Learning Models
###### Abstract
[31] applied seven regression models into infectious cases which are exponential, polynomial, quadratic, third degree, fourth degree, fifth degree, sixth degree and logit growth model, respectively. Again, [PERSON] et al. [32] implemented autoregressive integrated moving average (ARIMA), support vector regressor (SVR), LSTM, Bi-LSTM to manipulate COVID-19 instances, respectively. In the current circumstances, most of the standard epidemiological models, such as SIR [33] or SEIR models, can be used to estimate future case numbers and locations. The effect of COVID-19 on various country-specific sectors has also been studied using computational modeling [34]. Often, these models have not given useful results because they do not properly take into account the non-stationary social mixing parameters prevailing in Bangladesh [27]. Epidemiological data is limited, and it is required to estimate parameters and elaborate automatic transmission models that cannot be accurately interpreted. Therefore, short-term forecasting may be of greater utility in guiding us to understand the patterns of infection spread through the community and suggest the best interventions for minimizing the epidemic [35]. Machine learning models can be used as an alternative means to conventional models for generating short term forecasting based on the reported cases. In addition, these models are also needed to inform rapid actions to counter COVID-19 spread. The integration of cloud services with machine learning models provides an opportunity to greatly improve clinical outcomes above what can be achieved by normal infrastructure alone. Thus, this expected morbidity framework that we propose could greatly assist medical staff to better understand the local and temporal aspects of a dynamic epidemic situation, and so act and communicate accordingly. The objective of this work is therefore to construct a web tool resource that can provide useful forecasts of case numbers and COVID-19-related deaths over the short term future (i.e., 7 days ahead) that employs cloud services readily available in Bangladesh. This work has been shared on the public repository using this following link: [[https://github.com/shahriariit/Short-Term-Forecasting-BD](https://github.com/shahriariit/Short-Term-Forecasting-BD)]([https://github.com/shahriariit/Short-Term-Forecasting-BD](https://github.com/shahriariit/Short-Term-Forecasting-BD)), accessed on 6 April 2021. The contribution of this paper is summarized as follows:  
1. The number of COVID-19 infected people is currently still increasing inBanglades but there are only incomplete data on the number and location of cases. There are many strategies for reducing the spread that could be implemented by the local authorities but a lack of understanding of the spreading pattern hinders their design and implementation. We therefore designed software based modeling that can be trained to perform short-term forecasting. 2. Many previous studies have considered many epidemiological models where several pandemic parameters depended on the rate of social mixing of people. In the current circumstances in Bangladesh, we cannot determine or measure such parameters precisely. Therefore, various machine learning models can be a useful approach to forecast infectious cases without needing such parameter precision. However, it is important to note that predictions of infection levels are sensitive to non-linear changes of parameters so that long term prediction tends to give poor results. For this reason, we have focused on implementing short-term forecasting models where accuracy is more likely to be achieved. 3. The analysis with different sliding windows (rounds) helps to estimate the prediction capability of individual machine learning models and assist in the exploration of the best models that provide the most accurate predictions. This model will assist governmental authorities to take more effective steps against COVID-19 spread and fatalities. 4.