Environmental exposure and element-by-element analysis of pandemic waves at the city level: Exposome algorithm for analysis of short-term temperature drops on surges in COVID-19-associated hospitalizations

Abstract

Amid the COVID-19 pandemic, it is crucial to understand the complex network of factors that contribute to its pathogenesis and epidemiology in order to be able to prevent, predict, and minimize the consequences of the burden on urban services, particularly the critical surge in hospitalized patients.Based on a hypothesis of compromised airway epithelium, the principle of wave superposition, and mechanistic and causal interface approaches, this study aims to design a concept of element-by-element analysis of pandemic waves to isolate and analyze the impact of short-term drops in ambient temperature (as one important factor) on the spikes in hospitalizations due to the exacerbation of the disease course in SARS-CoV-2 infected individuals during the Omicron-associated wave.A new concept and analysis algorithm were proposed and tested. Results showed that every short-term drop in daily temperature of more than 3 °C led to an increase in the number of hospitalizations due to a worsening of disease progression in infected individuals with a 1–3 day lag. The analysis also revealed an influence with a 7.6-day lag, which may indicate an incubation period in patients at risk, such as the elderly and patients with comorbidities.The findings have implications for public health initiatives and urban planning to prevent overwhelming the healthcare system. Further research is recommended.