Modeling the Impact of Air Pollution and Meteorological Variables on COVID-19 Transmission in Western Cape, South Africa

John Kamwele Mutinda, A. Langat
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Abstract

Understanding the factors that influence COVID-19 transmission is essential in assessing and mitigating the spread of the pandemic. This study focuses on modeling the impact of air pollution and meteorological parameters on the risk of COVID-19 transmission in Western Cape Province, South Africa. The data used in this study consist of air pollution parameters, meteorological variables, and COVID-19 incidence observed for 262 days from April 26, 2020, to January 12, 2021. Lagged data were prepared for modeling based on a 6-day incubation period for COVID-19 disease. Based on the overdispersion property of the incidence, negative binomial (NB) and generalised Poisson (GP) regression models were fitted. Stepwise regression was used to select the significant predictors in both models based on the Akaike information criterion (AIC). The residuals of both NB and GB regression models were autocorrelated. An autoregressive integrated moving average (ARIMA) model was fitted to the residuals of both models. ARIMA (7, 1, 5) was fitted to the residuals of the NB model while ARIMA (1, 1, 6) was fitted for the residuals of the GP model. NB + ARIMA (7, 1, 5) and GP + ARIMA (1, 1, 6) models were tested for performance using root mean square error (RSME). GP + ARIMA (1, 1, 6) was selected as the optimal model. The results from the optimal model suggest that minimum temperature, ambient relative humidity, ambient wind speed, PM2.5, and NO2 at various lags are positively associated with COVID-19 incidence while maximum relative humidity, minimum relative humidity, solar radiation, maximum temperature, NO, PM load, PM10, SO2, and NOX at various lags have a negative association with COVID-19 incidence. Ambient wind direction and temperature showed a nonsignificant association with COVID-19 at all lags. This study suggests that meteorological and pollution parameters play a vital independent role in the transmission of the SARS-CoV-2 virus.
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模拟空气污染和气象变量对 COVID-19 在南非西开普省传播的影响
了解影响 COVID-19 传播的因素对于评估和减轻该流行病的传播至关重要。本研究的重点是模拟空气污染和气象参数对南非西开普省 COVID-19 传播风险的影响。本研究使用的数据包括从 2020 年 4 月 26 日至 2021 年 1 月 12 日的 262 天内观察到的空气污染参数、气象变量和 COVID-19 发病率。根据 COVID-19 疾病的 6 天潜伏期,准备了滞后数据用于建模。根据发病率的过度分散特性,拟合了负二项(NB)和广义泊松(GP)回归模型。根据阿凯克信息准则(AIC),采用逐步回归法选择两个模型中的重要预测因子。NB 和 GB 回归模型的残差都是自相关的。对两个模型的残差都拟合了一个自回归综合移动平均(ARIMA)模型。NB 模型的残差拟合了 ARIMA(7,1,5),而 GP 模型的残差则拟合了 ARIMA(1,1,6)。使用均方根误差(RSME)对 NB + ARIMA (7, 1, 5) 和 GP + ARIMA (1, 1, 6) 模型进行了性能测试。GP + ARIMA (1, 1, 6) 被选为最优模型。最优模型的结果表明,不同滞后期的最低气温、环境相对湿度、环境风速、PM2.5 和 NO2 与 COVID-19 的发生率呈正相关,而不同滞后期的最大相对湿度、最小相对湿度、太阳辐射、最高气温、NO、PM 负荷、PM10、SO2 和 NOX 与 COVID-19 的发生率呈负相关。在所有滞后期,环境风向和温度与 COVID-19 的关系均不显著。这项研究表明,气象和污染参数在 SARS-CoV-2 病毒传播中发挥着重要的独立作用。
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