Use of Weather Variables in SARS-CoV-2 Transmission Studies

Babin, Steven M.

doi:10.1016/j.ijid.2020.09.032

Cited by 19 publications

(25 citation statements)

References 37 publications

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“…Finally, this study showed an association between SARS-CoV-2 R0 and temperature/absolute humidity, but due to the strong correlation between absolute humidity and temperature in the seasonal conditions analysed here, it is difficult to determine which parameter is more important and they could not be analysed in combination [18]. The continent-specific analysis suggests that the relationship between absolute humidity and R0 was more stable than that of temperature, which was strong in the US but less so in South Europe.…”

Section: Discussionmentioning

confidence: 99%

Colder and drier winter conditions are associated with greater SARS-CoV-2 transmission: a regional study of the first epidemic wave in north-west hemisphere countries

Landier

Paireau

Legendre

et al. 2021

Preprint

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Higher transmissibility of SARS-CoV-2 in cold and dry weather conditions has been hypothesized since the onset of the COVID-19 pandemic but the level of epidemiological evidence remains low.During the first wave of the pandemic, Spain, Italy, France, Portugal, Canada and USA presented an early spread, a heavy COVID-19 burden, and low initial public health response until lockdowns. In a context when testing was limited, we calculated the basic reproduction number (R0) in 63 regions from the growth in regional death counts. After adjusting for population density, early spread of the epidemic, and age structure, temperature and humidity were negatively associated to SARS-CoV-2 transmissibility. A reduction of mean absolute humidity by 1g/m3 was associated with a 0.15-unit increase of R0. Below 10°C, a temperature reduction of 1°C was associated with a 0.16-unit increase of R0.Our results confirm a dependency of SARS-CoV-2 transmissibility to weather conditions in the absence of control measures during the first wave. The transition from summer-to winter-like conditions likely contributed to the intensification of the second wave in north-west hemisphere countries. Adjustments of the levels of social mobility restrictions need to account for increased SARS-CoV-2 transmissibility in winter conditions.

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Section: Discussionmentioning

confidence: 99%

Colder and drier winter conditions are associated with greater SARS-CoV-2 transmission: a regional study of the first epidemic wave in north-west hemisphere countries

Landier

Paireau

Legendre

et al. 2021

Preprint

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“…Secondly, our study is limited to the lag effects of mean temperature. A recent study (Babin, 2020) reported that min/max temperature might actually be a better predictor of COVID-19 incidence; this would be a worthy avenue for future research. Third, our results suggest the lag effect may extend longer than 14 days and so future work should consider this.…”

Section: Discussionmentioning

confidence: 94%

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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“…Quantitative climate assessments are affected by complex, interdependent sets of variables [26]. Up to 89 distinct parameters are required for meteorological classification alone (apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc/).…”

Section: Resultsmentioning

confidence: 99%

“…Hence, fundamental sources of uncertainty are associated to climate modeling [11]. We thus resorted to utilizing the Köppen–Geiger climate classification ( koeppen-geiger.vu-wien.ac.at/present.htm ), as drawn over 30+ years of observations, and robustly validated [22, 26, 28, 29]. This was summarized as a tripartite classification of country/region/province, which was compounded as an independent variable versus COVID-19 spreading velocity (Table 1).…”

Section: Resultsmentioning

confidence: 99%

“…However, climate areas are associated to much more complex sets of variables, among them indoor versus outdoor temperature profiles, specific/relative/absolute humidity [13, 26, 27], UV exposure versus daily time/season/latitude [20, 27], weather structure and ventilation, together with social behavior, inter-individual distancing, indoor-crowding, lifestyle, outside physical activity, (papers.ssrn.com/sol3/papers.cfm?abstract_id=3556998) [27], versus community structure, socio-economical and healthcare factors (www.medrxiv.org/content/10.1101/2020.03.23.20040501v4). Recent studies have begun to dissect such determinants, indicating impact of UV exposure on COVID-19 transmission [20], while suggesting no role of temperature and humidity (SSRN 3567840, 2020 - papers.ssrn.com) [20, 29].…”

Section: Discussionmentioning

confidence: 99%

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