Current computer systems are accumulating huge amounts of information in several application domains. The outbreak of COVID-19 has increased rekindled interest in the use of data mining techniques for the analysis of factors that are related to the emergence of an epidemic. Data mining techniques are being used in the analysis and interpretation of information, which helps in the discovery of patterns, planning of isolation policies, and even predicting the speed of proliferation of contagion in a viral disease such as COVID-19. This research provides a comprehensive study of various data mining algorithms that are used in conjunction with epidemiological prediction models. The document considers that there is an opportunity to improve or develop tools that offer an accurate prognosis in the management of viral diseases through the use of data mining tools, based on a comparative study of 35 research papers.

The COVID-19 pandemic was the first health crisis that involved the entire world in this century. The statistical data revealed a lack of organization and control in sanitary measures, containment, and mitigation policies, as well as a lack of planning and coordination in the use of medical supplies. This situation led to the development of prediction models that provided predictive information about the pandemic evolution. In this paper, time series of accumulated cases of infection were generated through official data provided by the Ministry of Health of the Government of Mexico. Six deterministic and stochastic predictive models were applied to this information with the purpose of comparing its efficiency to predictive cases of infection of COVID-19. These models were applied to data from two cities in Mexico, Colima and the State of Mexico. The study concludes that the ARIMA and ANN MLP models adapt better to the data that are generated daily, therefore, they have an improved prediction capacity.

Artificial Intelligence area has penetrated the medical field with the purpose of supporting the automatic recognition of brain imaging. One of the diseases that has been increasing in the last decade are Brain Stroke, whose set of diseases, affect the cerebral blood vessels. Given that effects that can cause this type of disease, it is important to characterize the sources that generate medical images and the methods used to improve and interpret them. This paper describes the advances reported in the literature in relation to the sources that obtain the images and the stages followed for their recognition from 2001 to 2016 period. Moreover considers that there is an opportunity to improve and development of advanced algorithms, which will offer a higher level in the detection of diseases in brain images.