PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■在过去的三年里,COVID-19对人类健康和经济稳定都造成了重大损害。分析COVID-19的原因和机制对其预防和缓解具有重要的理论和实践意义。气象因素在COVID-19传播中的作用至关重要，然而，他们的关系仍然是激烈辩论的主题。
■为了缓解短时间序列带来的问题，大型研究单位，以往研究中没有代表性的数据和线性研究方法，这项研究使用人口超过10万或50万的县或地区作为研究单位。当地爆发的开始取决于累计确诊病例超过100例。皮尔逊相关分析，采用广义加性模型(GAM)和分布滞后非线性模型(DLNM)分析了COVID-19每日新发病例与气象因素(温度，相对湿度,太阳辐射,表面压力,降水,风速)跨越美洲七个国家的440个县或地区，从2020年1月1日到2021年12月31日。
■每日新病例与气温等气象指标之间的线性相关性，相对湿度和太阳辐射不显著。然而,非线性相关性显著。温度关系的转折点，相对湿度和太阳辐射分别为5°C和23°C，74%和750kJ/m2，分别为。
■气象因素对COVID-19的影响是非线性的。与温度的关系有两个阈值：5°C和23°C。低于5°C和高于23°C，存在正相关，在5°C至23°C之间，相关性是负的。相对湿度和太阳辐射呈负相关，但是坡度的变化分别约为74％和750kJ/m2。
■为了缓解短时间序列带来的问题，大型研究单位，以往研究中没有代表性的数据和线性研究方法，这项研究使用人口超过10万或50万的县或地区作为研究单位。当地爆发的开始取决于累计确诊病例超过100例。皮尔逊相关分析，采用广义加性模型(GAM)和分布滞后非线性模型(DLNM)分析了COVID-19每日新发病例与气象因素(温度，相对湿度,太阳辐射,表面压力,降水,风速)跨越美洲七个国家的440个县或地区，从2020年1月1日到2021年12月31日。
■每日新病例与气温等气象指标之间的线性相关性，相对湿度和太阳辐射不显著。然而,非线性相关性显著。温度关系的转折点，相对湿度和太阳辐射分别为5°C和23°C，74%和750kJ/m2，分别为。
■气象因素对COVID-19的影响是非线性的。与温度的关系有两个阈值：5°C和23°C。低于5°C和高于23°C，存在正相关，在5°C至23°C之间，相关性是负的。相对湿度和太阳辐射呈负相关，但是坡度的变化分别约为74％和750kJ/m2。
