Hematologic analysis of hospitalized patients and outpatients infected with SARS-CoV-2 and possible use as a prognostic biomarker.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global health problem; this has caused thousands of deaths around the world. This infection induces hematologic alterations, and it is necessary to recognize predictive biomarkers to address the need for hospitalization or the severity of the disease. This study aimed to analyze different parameters in outpatients and hospitalized patients infected with SARS-CoV-2 and determine whether hematic biometry can be used for prognosis rapidly. We analyzed 689 patients, of whom 355 were outpatients (162 women and 193 men) and 334 required hospitalization (197 men and 137 women). The average age of the hospitalized patients was 46 years (men, 49 years; women, 52 years), whereas the average age of the outpatients was 49 years (men, 51 years; women, 44 years). Hematologic parameters were analyzed and compared between the outpatients and hospitalized patients. The patients were divided into groups by age and sex. We found that in the hospitalized patients, the erythrocyte, hematocrit, and hemoglobin levels decreased, whereas the outpatients did not experience changes in the erythroid series. In leukocytes, these increased significantly, as they did in neutrophils; however, lymphocytopenia was observed. In the outpatients, we observed normal levels of neutrophils and lymphopenia. We can conclude that hematic biometry can be used as a biomarker, and the relation between neutrophils and lymphocytes is indicated for understanding the development and prognosis of the disease.

SARS-CoV-2 Transmission Risk Model in an Urban Area of Mexico, Based on GIS Analysis and Viral Load.

The COVID-19 pandemic highlighted health systems vulnerabilities, as well as thoughtlessness by governments and society. Due to the nature of this contingency, the use of geographic information systems (GIS) is essential to understand the SARS-CoV-2 distribution dynamics within a defined geographic area. This work was performed in Tepic, a medium-sized city in Mexico. The residence of 834 COVID-19 infected individuals was georeferenced and categorized by viral load (Ct). The analysis took place during the maximum contagion of the first four waves of COVID-19 in Mexico, analyzing 158, 254, 143, and 279 cases in each wave respectively. Then heatmaps were built and categorized into five areas ranging from very low to very high risk of contagion, finding that the second wave exhibited a greater number of cases with a high viral load. Additionally, a spatial analysis was performed to measure urban areas with a higher risk of contagion, during this wave this area had 19,203.08 km2 (36.11% of the city). Therefore, a kernel density spatial model integrated by meaningful variables such as the number of infected subjects, viral load, and place of residence in cities, to establish geographic zones with different degrees of infection risk, could be useful for decision-making in future epidemic events.