Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities

Runkle, Jennifer D.; Sugg, Margaret M.; Leeper, Ronald D.; Rao, Yuhan; Matthews, Jessica L.; Rennie, Jared

doi:10.1016/j.scitotenv.2020.140093

Cited by 108 publications

(110 citation statements)

References 43 publications

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“…cases [3]. This study was similar to ours that humidity was a risk factor under higher humidity conditions and there was no correlation under lower humidity conditions.…”

Section: Discussionsupporting

confidence: 90%

“…1 , 2 were the vector of regression coefficients for cb.Temp, cb.RH, which were the cross-basis matrix of temperature, relative humidity. The maximum lag day was set as 7 days, which was based on previous studies [3]. We allowed for non-linear relationships by using a natural cubic spline with 3 degrees of freedom (df), and the lagged effects were modeled using a natural cubic spline with an intercept and three internal knots placed at equally-spaced log-values.…”

Section: Resultsmentioning

confidence: 99%

“…Another laboratory study also reported that the viability of coronaviruses reduced rapidly when the temperature or relative humidity increased [6]. Most epidemiological studies [3,[7][8][9]] also suggested that the weather with low temperature and low humidity likely favored the transmission of COVID-19. However, a Brazilian study [10] showed that higher mean temperature and average relative humidity favored the COVID-19 transmission, differently from reports from coldest countries or periods under cool temperatures.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, a study based on Hong Kong, Guangzhou, Beijing, and Taiyuan indicated that the outbreaks of SARS were significantly related to temperature [2]. Another study in US cities indicated that humidity was the best predictor of COVID-19 transmission [3].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Temperature and Humidity on COVID-19 Transmission in California, US: A Time-Series Study

Fang

Wang

Pan

2020

Preprint

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Background: Previous researches inferred that high temperatures and high humidity might weaken COVID-19 transmission. However, with the warming weather coming, the COVID-19 pandemic is still intensifying. Methods: This study aims to evaluate the associations between daily temperature, relative humidity, and COVID-19 cases using the Distributed Lag Non-linear Model (DLNM) from Jan 27th to July 15th, 2020, in California, US.Results: There was a statistically significant difference between COVID-19 and temperature from 6 °C to 9 °C, relative humidity from 80% to 98%. It increased the risk of 95.4% at 6 °C (RR:1.954; CI: 1.032-3.701). It increased the risk of 70.3% when the humidity was 98% (RR: 1.703, CI: 1.049-2.765). At low temperature group, it increased the risk of 46.3% (RR = 1.463, 95%CI: 1.054-2.030) on lag 0-4 days. At high humidity group, it increased the risk of 42.3% (RR = 1.423, 95%CI: 1.070-1.892) on lag 0-6 days.Conclusions: We found that low temperature and high humidity were the risky factors of COVID-19 transmission, and higher temperature and lower humidity had no effect on the transmission of COVID-19, which indicated that it might not slow down due to weather factors in summer in the Mediterranean climate.

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“…cases [3]. This study was similar to ours that humidity was a risk factor under higher humidity conditions and there was no correlation under lower humidity conditions.…”

Section: Discussionsupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effects of Temperature and Humidity on COVID-19 Transmission in California, US: A Time-Series Study

Fang

Wang

Pan

2020

Preprint

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“…In contrast, one study using multivariate analyses for three regions in Italy that compared climatic factors in relation to daily COVID-19 confirmed cases demonstrated an inverse correlation of temperature with daily incidence (Pirouz et al, 2020). A number of studies have also attempted to model lag effects of climatic predictors, though these have so far been limited to select regions (Runkle et al, 2020), relatively narrow temperature ranges (Passerini et al, 2020;Bashir et al, 2020;Tosepu et al, 2020), relatively short time series (Shi et al, 2020) and lag periods (Tobías and Molina, 2020), all of which may induce significant biases.…”

Section: Introductionmentioning

confidence: 99%

Delayed positive association between air temperature and covid-19 incidence in Italy

Fong¹,

Goodson²,

Smith³

2020

Preprint

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The association between air temperature and covid-19 incidence is unclear, particularly regarding lag effects. Here we address this research gap using high resolution data from Italy. We obtained daily covid-19 cases, populations at risk, and mean daily air temperature from 97 Italian cities for the period 24 February through 21 September 2020. We fitted a mixed-effects distributed lag non-linear model, presenting the effects as relative risks (RR) and cumulative relative risks (RRcum).Negative increments in mean daily temperature produced approximately inverted U- shaped lag-responses, though for large positive increments in temperature, the peak RR occurred at the maximal lag of 14 days. The temperature exposure response curves generally showed an increased RR with increasing temperature, though the shape varied according to the lag period. Positive and negative increments in temperature caused increases and decreases in the RRcum respectively, though the plateau effect for negative increments was not observed above small positive increments in temperature.We postulate that latent variables correlated with temperature, such as frequency and duration of social activities, are the underlying cause of our observed trends. Nonetheless, our statistical model can be utilised to forecast cumulative covid-19 incidence rates assuming specified air temperature increments at the city level.

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Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID‐19) in a hot, arid climate

Yassin

Al‐Dashti

2021

Integr Envir Assess & Manag

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The rapid outbreak of the coronavirus disease (COVID-19) has affected millions of people all over the world and killed hundreds of thousands. Atmospheric conditions can play a fundamental role in the transmission of a virus. The relationship between several atmospheric variables and the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are therefore investigated in this study, in which the State of Kuwait, which has a hot arid climate, is considered during free movement (without restriction), partial lockdown (partial restrictions) and full lockdown (full restriction). The relationship between the infection rate, growth rate, and doubling time for SARS-CoV-2 and atmospheric variables are also investigated in this study. Daily data

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Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities

Cited by 108 publications

References 43 publications

Effects of Temperature and Humidity on COVID-19 Transmission in California, US: A Time-Series Study

Effects of Temperature and Humidity on COVID-19 Transmission in California, US: A Time-Series Study

Delayed positive association between air temperature and covid-19 incidence in Italy

Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID‐19) in a hot, arid climate

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