Non-linear effects of meteorological factors on COVID-19: An analysis of 440 counties in the americas

Abstract

Background: In the last three years, COVID-19 has caused significant harm to both human health and economic stability. Analyzing the causes and mechanisms of COVID-19 has significant theoretical and practical implications for its prevention and mitigation. The role of meteorological factors in the transmission of COVID-19 is crucial, yet their relationship remains a subject of intense debate.

Methods: To mitigate the issues arising from short time series, large study units, unrepresentative data and linear research methods in previous studies, this study used counties or districts with populations exceeding 100,000 or 500,000 as the study unit. The commencement of local outbreaks was determined by exceeding 100 cumulative confirmed cases. Pearson correlation analysis, generalized additive model (GAM) and distributed lag nonlinear model (DLNM) were used to analyze the relationship and lag effect between the daily new cases of COVID-19 and meteorological factors (temperature, relative humidity, solar radiation, surface pressure, precipitation, wind speed) across 440 counties or districts in seven countries of the Americas, spanning from January 1, 2020, to December 31, 2021.

Results: The linear correlations between daily new cases and meteorological indicators such as air temperature, relative humidity and solar radiation were not significant. However, the non-linear correlations were significant. The turning points in the relationship for temperature, relative humidity and solar radiation were 5 °C and 23 °C, 74 % and 750 kJ/m², respectively.

Conclusion: The influence of meteorological factors on COVID-19 is non-linear. There are two thresholds in the relationship with temperature: 5 °C and 23 °C. Below 5 °C and above 23 °C, there is a positive correlation, while between 5 °C and 23 °C, the correlation is negative. Relative humidity and solar radiation show negative correlations, but there is a change in slope at about 74 % and 750 kJ/m², respectively.

Keywords: COVID-19; DLNM; GAM; Meteorological factors; Non-linear analysis.

Graphical abstract

Fig. 1

Location and distribution of 440 counties (districts) across 7 countries in the Americas.

Fig. 2

Pearson correlation coefficients between meteorological variables and daily new cases per million. T: mean temperature; RH: relative humidity; SP: surface pressure; SR: solar radiation; P: precipitation; WS: wind speed; DNC: daily new cases per million. All associations between variables passed the significance test, ***:p ≤ 0.001.

Fig. 3

Exposure–response curves between daily new cases and meteorological factors using GAM. (a) mean temperature; (b) relative humidity; (c) surface pressure; (d) solar radiation; (e) precipitation; (f) wind speed. The X-axis show the meteorological factors; the Y-axis shows the contribution value after fitting using the spline function.

Fig. 4

Contour map of exposure–lag–response relationship between daily new cases and meteorological variables. (a) Mean temperature; (b) relative humidity; (c) surface pressure; (d) solar radiation; (e) precipitation; (f) wind speed. The Y-axis shows the lag days, ranging from 0 to 21. The X-axis shows the range of the observed values of each variable. The color gradient represents the relative risk (RR). The red color gradient represents higher strength of RR, above 1, and the blue gradient represents lower strength of RR, below 1. The white color represents no difference, at RR = 1.

Fig. 5

Exposure–response curve of the relationship between various meteorological variables and daily new cases under different lag conditions. (a) Mean temperature; (b) relative humidity; (c) surface pressure; (d) solar radiation; (e) precipitation; (f) wind speed. The X-axis shows the range of the observed values of each variable. The Y-axis shows relative risk (RR). The blue line represents the situation without lag; the red and purple lines represent the situation with a lag of 7 days and 14 days, respectively.

Fig. 6

Contour map of exposure–lag–response relationships between daily new cases and meteorological variables at different latitudes. Only (a) temperature, (b) relative humidity and (c) solar radiation are displayed.