Analyses were performed through Poisson regression models with distributed lag non-linear models.

Exposure risks were evaluated by multivariate distributed lag nonlinear models and a meta-regression model.

Single lag and constrained polynomial distributed lag models were explored.

Distributed lag nonlinear models, Pearson's correlation coefficient and wavelet transform coherence were employed to appraise the relationship between meteorological factors and COVID-19 cases.

A distributed lag function was used to model the delayed response in heat production associated with support metabolism with changes in plane of nutrition.

The association between mortality (due to different causes) and exposure was analyzed using single and distributed lag models and adjusting for potential confounders.

With the distributed lag model, DLM 0-3, PM10 ambient concentrations were associated with an increased risk of mortality in all cities except Concepci6n and Temuco.

To assess the association between exposure to air pollution and risk of death, the PM10 and O3 data were fit using distributed lag models (DLMs).