# COVID-19 Outbreak Prediction with Machine Learning
## 1 Introduction
Figure 1 shows the inaccuracy of conventional models applied to the outbreak in Italy by comparing the actual number of confirmed infections and epidemiological model predictions (this trend obviously depends on the approach chosen to model the outbreak; for example, the SEIR model performs usually better than SIR model). SEIR models, by considering the significant incubation period during which individuals are infected, showed increased model accuracy for the Varicella and Zika outbreaks [13,14]. SEIR models assume that the incubation period is a random variable and, similarly to the SIR model, there is a disease-free equilibrium [15,16]. It should be noted, however, that standard SIR and SEIR models will not fit well where the parameters related to social mixing and, thus, the contact network, are non-stationary through time [17]. A key cause of non-stationarity is where the social mixing (which determines the contact network) changes through time. Social mixing determines the reproductive number \(R_{0}\), which is the number of susceptible individuals that an infected person will infect. When \(R_{0}\) is less than 1 the epidemic will die out; when it is greater than 1 it will spread. \(R_{0}\) for COVID-19 prior to lockdown was estimated as a massive 4 [1], representing a pandemic. It is expected that lockdown measures should bring \(R_{0}\) down to less than 1. The key reason why SEIR models are difficult to fit for COVID-19 is non-stationarity of mixing, caused by nudging (step-by-step) intervention measures. A further drawback of conventional epidemiological models is the short lead time. To evaluate the performance of the models, the median success of the outbreak prediction presents useful information. The median prediction factor can be calculated as follows (Equation (5)):  
\[f=\frac{Prediction}{True\ value} \tag{5}\]  
As the lead-time increases, the accuracy of the model declines. For instance, for the COVID-19 outbreak in Italy, the accuracy of the model for more than 5 days hence reduces from \(f=1\) for the first five days to \(f=0.86\) for day 6 [12]. Overall, the standard epidemiological models can be effective and reliable only if (a) the social interactions are stationary through time (i.e., no changes in interventions or control measures), and (b) there exists a great deal of knowledge of class \(R\) with which to compute Equation (3). To acquire information on class \(R\), several novel models included data from social media or call data records (CDR), which showed promising results [18, 19, 20, 21, 22, 23, 24, 25]. However, observation of the behavior of COVID-19 in several countries demonstrates a high degree of uncertainty and complexity [26]. Thus, for epidemiological models to be able to deliver reliable results, they must  
Figure 1: Italy’s COVID-19 outbreak: the actual number of confirmed infections vs. epidemiological model. be adapted to the local situation based on insights into susceptibility to infection due to changes in public health interventions, and the various states in the SIR/SEIR model [27]. This imposes a huge limit on the generalization ability and robustness of conventional models. Advancing accurate models with a great generalization ability to be scalable to model both the regional and global pandemic is, thus, essential [28]. Due to the complexity and the large-scale nature of the problem in developing epidemiological models, machine learning (ML) has recently gained attention for building outbreak prediction models. ML approaches aim at developing models with higher generalization ability and greater prediction reliability for longer lead times [29; 30; 31; 32; 33]. Although ML methods were used in modeling former pandemics (e.g., Ebola, cholera, swine fever, H1N1 influenza, dengue fever, Zika, oyster norovirus [8; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]), there is a gap in the literature for peer-reviewed papers dedicated to COVID-19. Table 1 represents notable ML methods used for outbreak prediction.