Correlations between COVID-19 cases and temperature, air humidity, and social isolating rate with cross wavelet transform and wavelet coherence: Case study of New York and Sao Paulo cities

Author(s)

Magrini, Luciano Aparecido ; Baroni, Mariana Pelissari Monteiro Aguiar ; Goulart, Amari ; Gadotti, Marta Cilene

• Document type: Journal article
• Source: Chaos; v. 33, n. 8, p. 13-pg., 2023-08-01.

Abstract

The COVID-19 pandemic originated in 2019 and has become an endemic disease that we must learn to live with, similar to other strains of influenza. The Organization (WHO) declared on May 5, 2023, in Geneva, Switzerland, the end of the Public Health Emergency of International Concern regarding COVID-19. As vaccines become more widely available and the pandemic appears to be improved, our focus shifts to the challenges we still face. Understanding how external factors like temperature, air humidity, and social isolation impact the spread of the SARS-CoV-2 virus remains a crucial challenge beyond our control. In this study, potential links between the number of COVID-19 cases in Sao Paulo City (SPC) and New York City (NWC) were explored. Our analysis was carried out utilizing the continuous wavelet transform, alongside other tools such as cross-wavelet transform and wavelet coherence. Based on our findings, there appears to be a correlation between the variables related to low frequencies, which aligns with previous research on the topic. Particularly, our research has revealed a connection between COVID-19 cases and factors such as temperature, air humidity, and social isolation rates. Regarding the latter, our findings indicate that implementing social distancing measures was a wise public policy decision, although the correlation with daily COVID-19 cases requires careful analysis. For this study, we analyzed data from February of 2020, when the first cases were reported in the cities under investigation, SPC and NWC, up until December 31, 2022, by which time the vaccination campaign was well under way. (AU)

• FAPESP's process: 21/12708-4 - Qualitative and numerical analysis of epidemiological models of COVID-19 via differential equations with delay
• Grantee: Marta Cilene Gadotti
• Support Opportunities: Regular Research Grants