RESUMO
Meteorology is a critical factor affecting respiratory infectious diseases such as MERS, SARS, and influenza, but its effect on the spread of the COVID-19 disease remains controversial. Nevertheless, since the infected people cough-jets produce plumes of droplets and aerosols that can travel for several meters in the atmosphere, the possible influence of wind circulation and atmospheric turbulence on the infectious plume's fate cannot be ignored. This paper applied cluster analysis for identifying the near surface wind circulation patterns and associated temperature and humidity distributions in the Mexico City Metropolitan Area (MCMA), then their influence on the spread of the COVID-19 disease during the 2020 pandemic was discussed. Meteorology data and daily numbers of confirmed COVID-19 infections were obtained from public sources. An intense infection activity occurred from October to December 2020, and notable spreading of the disease toward the southwest and south MCMA was observed. In the same period, temperature and humidity conditions that could favor the virus stability and replication were detected in the same sectors, besides 60% of the wind observations revealed considerable northerly components. These findings suggested the existence of correlations between both phenomena. For assessing the possible relationship, the Pearson coefficients between the daily confirmed infections and the temperature and inward flux were estimated, and values from -0.32 to -0.55 and 0.62 to 0.70 were obtained. Correlation was negligible for relative humidity. Multilinear regression for the daily infections in response to the meteorological variables produced coefficients of determination from 0.3839 to 0.6138. Because of its implications for public health, this topic deserves a more in-depth investigation.
RESUMO
In this work, we propose a 2D lattice gas model for infection spreading, and we apply it to study the COVID-19 pandemic in the Mexico City Metropolitan Area (MCMA). We compared the spatially averaged results of this model against the MCMA available data. With the model, we estimated the numbers of daily infected and dead persons and the epidemic's duration in the MCMA. In the simulations, we included the small-world effects and the impact of lifting/strengthen lockdown measures. We included some indicators of the goodness of fit; in particular, the Pearson correlation coefficient resulted larger than 0.9 for all the cases we considered. Our modeling approach is a research tool that can help assess the effectiveness of strategies and policies to address the pandemic phenomenon and its consequences.