Barati Chermahini Melinaz, Hoeppner Vernon
Public Health Sciences Department, Queen's University, 62 Fifth Field Company Lane, Kingston, ON K7L 3N6, Canada.
College of Medicine, University of Saskatchewan, 107 Wiggins Rd, Saskatoon, SK S7N 5E5M, Canada.
Diseases. 2025 Aug 19;13(8):269. doi: 10.3390/diseases13080269.
We aimed to examine the relationship between COVID-19 cases and Public Health Interventions (PHIs), vaccine coverage, and temperature. We compared our findings with those of other studies that used different methodologies, such as mathematical models. : We developed monthly PHI scores using the Oxford COVID-19 Government Response Tracker from May 2020 to May 2021. We calculated PHI scores by summing the highest monthly score of each intervention and expressed the PHI score as a percentage of the maximum. We obtained vaccine coverage and temperature data from January 2021 to September 2023. We calculated Spearman's rank-order correlation coefficients to examine correlations. : The correlation between cases and PHI was positive (ρ = 0.947, < 0.0001). The correlation between cases and vaccine coverage was approximately zero (ρ = 0.0165, = 0.957) from January 2021 to January 2022 and was negative from February 2022 to September 2023 (ρ= -0.816, < 0.0001). The correlation for cases and temperature was negative from January 2021 to January 2022 (ρ = -0.676, = 0.0112) and was almost zero from February 2022 to September 2023 (ρ = -0.162, = 0.494). The models showed a negative correlation between PHI and vaccine coverage, and mixed results for temperature. : There was a positive correlation between cases and PHI. Prior to reaching the vaccine threshold coverage, there was no correlation for vaccination and a negative correlation for temperature. Post-vaccine threshold, there was a negative correlation for vaccination and no correlation for temperature. Correlation results for PHI and temperature differed from those of the mathematical models.
我们旨在研究新冠病毒感染病例与公共卫生干预措施(PHIs)、疫苗接种率及温度之间的关系。我们将研究结果与其他采用不同方法(如数学模型)的研究结果进行了比较。:我们使用牛津新冠病毒政府应对追踪器,计算了2020年5月至2021年5月期间的月度公共卫生干预措施得分。我们通过将每项干预措施的月度最高得分相加来计算公共卫生干预措施得分,并将公共卫生干预措施得分表示为最高分的百分比。我们获取了2021年1月至2023年9月的疫苗接种率和温度数据。我们计算了斯皮尔曼等级相关系数以检验相关性。:病例数与公共卫生干预措施之间呈正相关(ρ = 0.947,P < 0.0001)。2021年1月至2022年1月,病例数与疫苗接种率之间的相关性约为零(ρ = 0.0165,P = 0.957),而在2022年2月至2023年9月期间呈负相关(ρ = -0.816,P < 0.0001)。2021年1月至2022年1月,病例数与温度之间的相关性为负(ρ = -0.676,P = 0.0112),而在2022年2月至2023年9月期间几乎为零(ρ = -0.162,P = 0.494)。模型显示公共卫生干预措施与疫苗接种率之间呈负相关,而温度方面的结果则参差不齐。:病例数与公共卫生干预措施之间呈正相关。在达到疫苗阈值覆盖率之前,疫苗接种无相关性而温度呈负相关。在疫苗阈值之后,疫苗接种呈负相关而温度无相关性。公共卫生干预措施与温度的相关结果与数学模型不同。