Negative-Binomial and quasi-poisson regressions between COVID-19, mobility and environment in São Paulo, Brazil.

Brazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to monitor quarantines and prevent the transmissions of SARS-CoV-2. In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, Brazil. We applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between RMI and COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 4 and 21 days, and 2) the association between COVID-19 and exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility. We found that an RMI of 45.28% results in 1212 cases (95% CI: 1189 to 1235) and 44 deaths (95% CI: 40 to 47). Increasing the isolation from 45.28% to 50% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 µg⋅m-³ of PM2.5 results in a risk of 1.140 (95% CI: 1.021 to 1.274) for cases and 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3 produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively. We compared our results with observations and literature review, finding well agreement. Policymakers can use such mobility indices as tools to control social distance activities. Spatial distancing is an important factor to control COVID-19, however, measuring face-mask usage would enhance the understanding the pandemic dynamic. Small increments of air pollution result in an increased number of COVID-19 cases and deaths.

Impacts of teleconnection patterns on South America climate.

Oceanic heat sources disturb the atmosphere, which, to come back to its initial state, disperses waves. These waves affect the climate in remote regions, characterizing the teleconnection patterns. In this study, we describe eight teleconnection patterns that affect South America climate: the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO), the Tropical Atlantic Dipole (TAD), the South Atlantic Dipole (SAD), the Southern Annular Mode (SAM), the Madden-Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD). Precipitation and winds at 850-hPa anomalies, considering these teleconnection patterns in ENSO neutral periods, are also presented. Overall, southeastern South America and the north sector of the North and Northeast regions of Brazil are the most affected areas by the teleconnection patterns. In general, there is a precipitation dipole pattern between these regions during each teleconnection pattern.