COVID-19 Social Lethality Characterization in Some Regions of Mexico through the Pandemic Years Using Data Mining

Abstract

In this chapter, an analysis of the data provided by the Federal Government of Mexico related to the COVID-19 disease during the pandemic years is described. For this study, nineteen significant variables were considered, which included the test result for detecting the presence of the SARS-CoV-2 virus, the alive/deceased people cases, and different comorbidities that affect a person’s health such as diabetes, hypertension, obesity, and pneumonia, among other variables. Thus, based on the KDD (Knowledge Discovery in Databases) process and data mining techniques, we undertook the task of preprocessing such data to generate classification models for identifying patterns in the data or correlations among the different variables that could have influence on COVID-19 deaths. The models were generated by using different classification algorithms, were selected based on a high correct classification rate, and were validated with the help of the cross-validation test. In this way, the period corresponding to the five SARS-CoV-2 infection waves that occurred in Mexico between March 2020 and October 2022 was analyzed with the main purpose of characterizing the COVID-19 social lethality in the most contagious regions of Mexico.

1. Introduction

Since the first case of SARS-CoV-2 in Mexico, diagnosed on February 28, 2020, five infection waves of this virus have occurred until October 2022, as shown in Figure 1. Associated with these infection waves are the COVID-19 death waves, which are shown in Figure 2, where it is observed that the second wave had the highest lethality although this wave was not the one with the highest infection rate. This fact can be observed by comparing both figures, obtained from [1].

Figure 1.

Five SARS-CoV-2 virus infection waves in Mexico.

Figure 2.

Behavior of COVID-19 deaths in Mexico.

From the previous figures, it can also be inferred that although the last two infection waves were the highest, they had at the same time the lowest lethality, which was a natural consequence of the growing application of anti-COVID vaccines, supplied every day to the different sectors of the Mexican population.

Thus, based on the data published by the Federal Government of Mexico during the pandemic years, which can be consulted on the General Directorate of Epidemiology website [2], a study was carried out to analyze them using data mining techniques, specifically classification algorithms, to detect patterns related to COVID-19 social lethality, particularly in the regions of Mexico with the highest SARS-CoV-2 virus infection rate.

2. Theoretical framework

In principle, this work was based on data mining techniques [3, 4, 5, 6] and on the Knowledge Discovery in Databases (KDD) process [7, 8, 9], which together allow to extract hidden knowledge from large data volumes. Thus, by using these techniques and process, we manage to extract knowledge from the COVID-19 dataset provided by the Federal Government of Mexico, which will be described later.

It is important to point out that we use classification algorithms to generate models containing knowledge in the form of rules, highlighting the use of Naïve Bayes and J48 classifiers, which are among the most widely used algorithms in the field of data mining research [10]. Thus, according to Taheri et al. [11], the Naïve Bayes classifier is useful for high dimensional data as the probability of each variable is estimated independently. Therefore, if C denotes the class of an observation of a set X of variables, X = {X₁, X₂, …, X_n}, then the class C can be predicted by using Bayes’ rule:

$$P\left(C/X\right)=\frac{P\left(C\right){\prod }_{i=1}^{n}P\left(X_i/C\right)}{P\left(X\right)}$$E1

In this way, we could predict different classes in our dataset, for instance, the class associated with alive or deceased patients, fundamental in our work.

Regarding the J48 classifier, it is worth mentioning that this is an C4.5 algorithm implementation [12], which allows to build decision trees from a set of training data, based on information entropy. This concept refers to the uncertainty measurement of an information source so that the source elements with less probability (less frequency) are those that provide more information. Thus, Shannon’s formula [13] for calculating the entropy of a random variable X that can take on x₁, x₂, …, x_n states is given by:

$$H\left(X\right)=-\sum _{i=1}^n{p}_i\log p_i$$E2

where p_i is the probability of x_i, i = 1, 2, …, n. This is how the J48 classifier operated on different variables of our dataset for predicting a certain class, considering that a negative entropy implies a lower level of information uncertainty.

On the other hand, to measure the classification reliability of nominal variables, Cohen’s Kappa Coefficient [14, 15] and Fleiss Kappa [16, 17] are commonly used measures, based on the agreement between what is observed in a dataset and what could happen randomly. Like most correlation statistics, the Kappa statistic can vary from −1 to +1, associating negative values to disagreement and positive values to agreement, so that values as low as 0,41 could be acceptable in terms of reliability according to Cohen [18]. Thus, by using different classifiers, several models were generated and validated with the cross-validation test, considered a widespread validation strategy because of its simplicity [19].

3. Related work

There are some interesting works related to the use of data mining and machine learning techniques focused on developing algorithms and models to analyze and forecast SARS-CoV-2 infections and COVID-19 disease behavior, some of which made use of epidemiological data referring to Mexico [20, 21, 22]. Thus, in [20, 21], classifiers such as decision tree, support vector machine, naïve Bayes, and random forest were used to generate forecasting models, while a multi-objective evolutionary algorithm was used in [22] for retrieving high-quality rules to identify the most susceptible groups to COVID-19 disease. Also, in [23], logistic regression models were employed to assess the association between demographic factors, comorbidities, wave and vaccination, and the risk of severe disease and in-hospital death. This work was carried out during the five COVID-19 waves in Mexico.

On the other hand, important works using the WEKA machine learning tool [24] (used in our work) were identified. In a generic way, such works [25, 26, 27] used several supervised machine learning algorithms (classifiers) available in this tool for building classification models using COVID-19 datasets. Other relevant works used different strategies and tools: python programming language in developing data mining models for predicting COVID-19 infected patients’ recovery using an epidemiological dataset of South Korea [28]; another generated its own dataset with the help of specialist physicians for predicting mortality in patients with COVID-19 based on data mining techniques [29]; another developed a model to predict the COVID-19 incidence rate in different regions of the world through a least-square classification algorithm [30]; another discovered rules on factors interrelated with COVID-19 pandemic using data mining methodologies [31], and one more used the RapidMiner Studio software [32] for creating a model to analyze and forecast the existence of COVID-19 using the so-called Kaggle dataset [33].

4. Methodology

As mentioned before, to carry out this work, the KDD process was used, shown in Figure 3. Thus, following the stages marked out in this process, the starting point was the data retrieval from the databases provided by the Federal Government of Mexico on SARS-CoV-2 infection cases and COVID-19 deaths.

Figure 3.

Used methodology for extracting knowledge from COVID-19 data.

It is important to note that the provided data were basically numbers associated to a catalog of codes, which contained omissions and errors. Therefore, it was necessary to preprocess the raw data so that they could be exploited with WEKA, the main analysis tool used in our study. A preprocessed data sample is shown in Figure 4.

Figure 4.

A COVID-19 preprocessed data sample.

To preprocess data, an extraction, transformation, and load (ETL) process was carried out using Microsoft Power BI and the Python programming language. That is, with the combination of these tools, different COVID-19 databases were integrated in a unique dataset containing clean, standardized, and transformed data, which were used to generate classification models. First, some preliminary models were generated to carry out a Variable Importance analysis using R and WEKA, whose results are shown in Figure 5.

Figure 5.

Variable importance analysis.

This analysis was realized taking the ALIVE_OR_DECEASED variable as the class to predict and considering only the SARS-CoV-2 positive cases. Thus, new models were generated, some of them using all the previous variables and others using the most significant ones. The best models are presented in the next section.

5. Results

In Figure 6, a preliminary analysis on all SARS-CoV-2 positive cases of Mexican population corresponding to the pandemic years (2020, 2021 and 2022) is shown. This analysis was realized by residence place so that it was possible to identify the regions of Mexico with the highest SARS-CoV-2 virus infection rate.

Figure 6.

Mexican population with a positive SARS-CoV-2 test.

About half of the 2,425,514 cases were concentrated in five States: Mexico City, Mexico State, Guanajuato, Nuevo Leon, and Jalisco. Thus, our work focused on these five regions, as well as the overall country, generating classification models through different classifier algorithms with different parameters, including the WEKA’s default parameters. Figure 7 shows an example of how the classifiers were tuned to improve their accuracy.

Figure 7.

Example of tuning a classifier algorithm.

In this way, a summary of the best classification models found in 2020 is shown in Figure 8. The models were selected based on the highest accuracy and validated with a 10-fold cross-validation test. It is important to point out that the selected models were compared with the preliminary models used to realize the Variable Importance analysis so that the best classifier identified in the preliminary models changed in some regions (Guanajuato and Jalisco) after a new classifier tuning.

Figure 8.

Summary of best classification models in 2020.

With respect to the findings identified in the best model for each region, related to deceased patients, the highlights are summarized below.

The main findings on deceased people in Guanajuato include 6,86% of lethality with respect to SARS-CoV-2 positive cases, in an approximate ratio of 2 to 1 between men and women (62,3% men and 37,7% women); 17,5% were intubated cases, 63% hospitalized cases (without intubating), and 19,5% ambulatory cases; pneumonia, diabetes, hypertension, and obesity emerge as the main comorbidities associated to lethality, and July and November appear as the months with the highest lethality.

In the case of Jalisco, the main findings on deceased people include 12.31% of lethality respect to SARS-CoV-2 positive cases, in an approximate ratio of 2 to 1 between men and women (63,6% men and 36,4% women); 26,6% were intubated cases, 63,4% hospitalized cases (without intubating), and 10% ambulatory cases; just like in Guanajuato, pneumonia, diabetes, hypertension, and obesity emerge as the main comorbidities associated to lethality, and June, July, August, November, and December appear as the months with the highest lethality. By comparing the findings of Guanajuato and Jalisco, it can be inferred that the main comorbidities associated to lethality are the same due in a certain way to their proximity and similar weather conditions.

Other regions with a high similarity, not only in weather but also in urban and demographic characteristics, are Mexico City and Mexico State. This is due to Mexico City and the most populated areas of Mexico State conform the same urban region (the metropolitan area of Mexico City). Therefore, as could be expected, much of the knowledge contained in their classification models is similar with respect to the characteristics of deceased patients. For instance, while in Mexico City, the risk of death for an intubated patient older than 56 years was 85%, in Mexico State, this risk for an intubated patient older than 54 years was 86%.

In the case of Nuevo Leon, the most important knowledge contained in its classification model respect to deceased people refers to both ambulatory and hospitalized (without intubating) patients older than 63 years. In the first case, the risk of death was 79%, and in the case of hospitalized patients, this risk was 74%, mainly in the period July–November.

For the year 2021, a summary of the best classification models found is shown in Figure 9. Again, the models were selected based on the highest accuracy and validated with a 10-fold cross-validation test.

Figure 9.

Summary of best classification models in 2021.

As can be observed, like in 2020, the best classifier in all cases was J48 compared to Random Forest and Naïve Bayes classifiers. The most important findings related to deceased patients were identified in these models and are described below.

In the case of Guanajuato, the risk of death for an intubated patient was 82%, no matter any other factor, while for a not-intubated patient (hospitalized), older than 64 years and suffering from pneumonia, the risk of death was 66%, mainly in January (winter). In Jalisco, also in January, the risk of death for a hospitalized patient older than 67 years was 66%, the same as Guanajuato. In Mexico City, the risk of death for an intubated patient older than 50 years was 79%; this pattern remains in a certain way like in 2020. In Mexico State, for an intubated patient older than 53 years and suffering from pneumonia, the risk of death was 85%, compared to the risk of 86% in 2020 for patients with similar characteristics; it can be inferred that this pattern remains the same. Finally, in Nuevo Leon, for an intubated patient older than 51 years and suffering from pneumonia, the risk of death was 85%.

Finally, a summary of the best classification models found for the year 2022 is shown in Figure 10. As in the years 2020 and 2021, the models were selected based on the highest accuracy and validated with a 10-fold cross-validation test.

Figure 10.

Summary of best classification models in 2022.

Again, the best classifier in all cases was J48; however, in this case, Kappa statistic could be considered low for the best two models in Mexico City, Mexico State, and Nuevo Leon. Therefore, considering that the third-best model, built with the Naïve Bayes classifier, has a more acceptable Kappa value [18] and a high enough classification percentage, the best rules were mainly searched in this model for the three mentioned places.

In the case of Guanajuato, the risk of death for an intubated patient older than 48 years was 85%, mainly in January, which is usually the coldest month in central Mexico. In Jalisco, the risk of death for an intubated patient older than 43 years and suffering from pneumonia was 86%. With respect to Mexico City, Mexico State, and Nuevo Leon, pneumonia, diabetes, and hypertension emerge as the main comorbidities associated to lethality and January as the most lethal month. Practically, 100% of deceased cases were hospitalized, mostly without being intubated. Besides, the mean age of death was 71 years in Mexico City, with a standard deviation of 16, which suggests people over 50 years as the most affected. For Mexico State and Nuevo Leon, the mean and standard deviation values were 67 and 18 and 69 and 16, respectively, which suggests people over 49 years in Mexico State and people over 53 years in Nuevo Leon were the most affected by death.

To finish the description of our work, as a tree-based representation example, Figure 11 shows a subtree of the classification model corresponding to the year 2020 (generated with the J48 classifier), in which the presence of most variables that have the highest impact on the ALIVE_OR_DECEASED class can be observed.

Figure 11.

Subtree of the classification model for 2020.

This model, like the classification models for 2021 and 2022, contains overall rules, in contrast with the models generated for the five analyzed regions, which contain more specific rules.

6. Conclusions

As can be read in the title of this chapter, the main objective of this work was to characterize the COVID-19 disease lethality in Mexico throughout the five SARS-CoV-2 virus infection waves occurred between March 2020 and October 2022, for which classification algorithms were used, as part of data mining techniques, to extract knowledge from the pandemic databases provided by the Government of Mexico. As a first stage to carry this out, an ETL process was executed on such databases to integrate them in a unique minable dataset.

Thus, from the consolidated dataset, some preliminary classification models were generated and used to realize a Variable Importance analysis for identifying the variables with the highest impact on the ALIVE_OR_DECEASED class, which was our base feature to characterize the COVID-19 disease lethality.

Our study focused on the five regions with the highest contagion rate in Mexico, identified through an analysis of the preprocessed data. In this way, Guanajuato, Jalisco, Mexico City, Mexico State, and Nuevo Leon emerged as the case studies, which together represented 42% of total SARS-CoV-2 infection cases in the pandemic period. For each of these regions, various classification models were generated in 2020, 2021, and 2022 using different classifiers, which were tuned by varying their parameters so that the best models could be found based on their accuracy and other metrics.

As an important part of the knowledge extracted from the classification models, various characteristics and conditions were identified in patients who died and whose test result had been confirmed as positive to SARS-CoV-2. For example, in 2020, the risk of death for intubated patients older than 54 years in Mexico City and Mexico State was 85 and 86%, respectively, in an approximate 1 to 2 women–men ratio. In 2021, this rule remained to some extent for Mexico City, dropping the risk of death to 79% for intubated patients older than 50 years, but remained practically unchanged for Mexico State with an 85% death risk for patients older than 53 years with pneumonia. As mentioned previously, the similarity of the lethality behavior in these populations is mainly because of the most populated regions of Mexico State are part of the metropolitan area of Mexico City.

In this way, the COVID-19 lethality in Mexico was characterized using different classifiers, highlighting WEKA’s J48 as the classifier with the best performance in all cases. Nonetheless, Random Forest and Naïve Bayes classifiers also helped extract important knowledge from the pandemic dataset.

To conclude, it is important to point out that in 2022, the COVID-19 lethality decreased drastically throughout Mexico as a natural consequence of the constant anti-COVID vaccination campaigns. However, the most affected population by this disease continued to be people over 50 years, according to what was described in this chapter.

Acknowledgments

We want to thank to the TECNOLÓGICO NACIONAL DE MÉXICO for its contribution and support to this work.