Spatiotemporal analysis of SARS-CoV-2 variants of concern in the city of São Paulo

Abstract

The availability of viral genome sequencing techniques and projections by means of mathematical, statistical, and computational models allow tracking the emergence and spread of infectious diseases on a local and global scale. Since the first confirmed case of SARS-CoV-2 infection, these techniques and analyses are responsible for an active and continuous monitoring of the epidemiological evolution in the human population. These projections depend directly on epidemiological and clinical surveillance with actual data from confirmed cases and sequenced genomes. In particular, in the case of SARS-CoV-2, the emergence and dissemination of the so-called "variants of concern" require active surveillance, especially in areas of high population density, such as the municipality of São Paulo. Different analyses have demonstrated that these variants may have higher transmissibility, virulence, and lethality than the original SARS-CoV-2 lineage. For example, for the Gamma variant, originally identified in Amazonas, there are data and simulations that suggest that its transmissibility is more than twice as high and a greater possibility of reinfection, resulting in an unprecedented hospital demand, such as the one that occurred in Manaus. Likewise, other variants that may arise or be received in municipalities such as São Paulo can be easily disseminated throughout the Brazilian territory. Through genotyping and sequencing of representative samples distributed by the Regional Health Coordinators in the municipality of São Paulo, it is possible to georeference the cases by building heat maps to identify spatial patterns of emergence, making a real-time monitoring of epidemiological surveillance data and classifying them in a database with cases by different regions of the municipality. By building compartmental and age-structured models, it is possible to use mathematical models of infectious disease dissemination that take into account demography, social contact patterns, and human mobility to simulate the future spatio-temporal behaviour of the variants of concern. The analysis of time series and projections through these data serve to guide actions in public health, being possible to develop a robust system of collection, analysis, and epidemiological surveillance. (AU)