This work presents the modeling and prediction of cases of COVID-19 infection in Mexico through mathematical and computational models using only the confirmed cases provided by the daily technical report COVID-19 MEXICO until May 8^th. The mathematical models: Gompertz and Logistic, as well as the computational model: Artificial Neural Network were applied to carry out the modeling of the number of cases of COVID-19 infection from February 27^th to May 8^th. The results show a good fit between the observed data and those obtained by the Gompertz, Logistic and Artificial Neural Networks models with an R² of 0.9998, 0.9996, 0.9999, respectively. The same mathematical models and inverse Artificial Neural Network were applied to predict the number of cases of COVID-19 infection from May 9^th to 16^th in order to analyze tendencies and extrapolate the projection until the end of the epidemic. The Gompertz model predicts a total of 47,576 cases, the Logistic model a total of 42,131 cases, and the inverse artificial neural network model a total of 44,245 as of May 16^th. Finally, to predict the total number of COVID-19 infected until the end of the epidemic, the Gompertz, Logistic and inverse Artificial Neural Network model were used, predicting 469,917, 59,470 and 70,714 cases, respectively.

^{这项工作仅使用截至 5 月 8日}的每日技术报告 COVID-19 MEXICO 提供的确诊病例，通过数学和计算模型对墨西哥的 COVID-19 感染病例进行建模和预测。^{应用数学模型：Gompertz和Logistic以及计算模型：人工神经网络对2月27日}至5月8^日期间的COVID-19感染病例数进行建模。结果表明，观测数据与 Gompertz、Logistic 和人工神经网络模型获得的数据吻合良好，R ²分别为 0.9998、0.9996、0.9999。^{应用相同的数学模型和逆人工神经网络来预测 5 月 9日}至 16^日期间的 COVID-19 感染病例数，以便分析趋势并推断直至疫情结束的预测。^{截至5月16日}，Gompertz模型总共预测了47,576例，Logistic模型总共预测了42,131例，逆人工神经网络模型总共预测了44,245例。最后，为了预测截至疫情结束时的 COVID-19 感染总数，使用了 Gompertz、Logistic 和逆人工神经网络模型，分别预测了 469,917、59,470 和 70,714 例病例。