Evidence and magnitude of seasonality in SARS-CoV-2 transmission: Penny wise, pandemic foolish?

Abstract

Importance: Intensity and duration of the COVID-19 pandemic, and planning required to balance concerns of saving lives and avoiding economic collapse, could depend significantly on whether SARS-CoV-2 transmission is sensitive to seasonal changes. Objective: Hypothesis is that increasing temperature results in reduced SARS CoV-2 transmission and may help slow the increase of cases over time. Setting: Fifty representative Northern Hemisphere countries meeting specific criteria had sufficient COVID-19 case and meteorological data for analysis. Methods: Regression was used to find relationship between the log of number of COVID-19 cases and temperature over time in 50 representative countries. To summarize the day-day variability, and reduce dimensionality, we selected a robust measure, Coefficient of Time (CT), for each location. The resulting regression coefficients were then used in a multivariable regression against meteorological, country-level and demographic covariates. Results: Median minimum daily temperature showed the strongest correlation with the reciprocal of CT (which can be considered as a rate associated with doubling time) for confirmed cases (adjusted R2=0.610, p= 1.45E-06). A similar correlation was found using median daily dewpoint, which was highly colinear with temperature, and therefore was not used in the analysis. The correlation between minimum median temperature and the rate of increase of the log of confirmed cases was 47% and 45% greater than for cases of death and recovered cases of COVID-19, respectively. This suggests the primary influence of temperature on SARS-CoV2 transmission more than COVID-19 morbidity. Based on the correlation between temperature and the rate of increase in COVID-19, it can be estimated that, between the range of 30 to 100 degrees Fahrenheit, a one degree increase leads to a 1% decrease--and a one degree decrease leads to a 3.7% increase--in the rate of increase of the log of daily confirmed cases. Conclusion: The results suggest that boreal summer months are associated with slower rates of COVID-19 transmission, with the reverse true in winter months. Knowledge of COVID-19 seasonality could prove useful in local planning for phased reductions social interventions and help to prepare for the timing of possible pandemic resurgence during cooler months.

On the Environmental Determinants of COVID‐19 Seasonality

“…On the other hands, several studies have emphasized the effect of atmospheric humidity on the prevalence of this disease, consistent with influenza dynamics (Baker et al., 2020; Ma et al., 2020; Ward et al., 2020). By contrast, the spread of COVID‐19 was found to be more correlated with temperature than with humidity in certain areas (Bherwani et al., 2020; Kaplin et al., 2020; Tosepu et al., 2020; Xie and Zhu, 2020). In addition, attention has also paid to ultraviolet (UV) radiation as a potential environmental factor impacting VRD transmission (Carleton et al., 2021).…”

On the Environmental Determinants of COVID-19 Seasonality

“…On the other hands, several studies have emphasized the effect of atmospheric humidity on the prevalence of this disease, consistent with influenza dynamics (Baker et al, 2020; Ward et al, 2020; Ma et al, 2020). By contrast, the spread of COVID-19 was found to be more correlated with temperature than humidity in certain areas (Bherwani et al, 2020; Kaplin et al, 2020; Tosepu et al, 2020; Xie and Zhu, 2020). In addition, attention has also focused on Ultraviolet (UV) radiation as a potential environmental factor impacting VRD transmission (Carleton et al, 2021).…”

Feature Analysis and Machine Learning Techniques to determine severity of COVID-19 infections

“…First of all only data from the first 6 months of the pandemic were collected, where COVID-19 was still relatively new to treat and no vaccinations available. Additionally the periodical behaviour of the disease was unknown and is not represented in the data [21][22][23][24]. Although our data set is larger than for most other research in this topic, it is still relatively small and more data would increase the significance and quality of the models.…”

“…Because the data were collected in the first months of the pandemic, seasonal behaviour of COVID-19 could not be observed [21][22][23][24] and is not represented in the data. To avoid overfitting due to coincidental correlations between time features and the resulting outcome, all time features were removed.…”

“…Possible extensions of this work could be done by repeating the experiments with more data collected over a longer time period or different locations. This might result in a more representative evaluation of the actual importance of time features as SARS-CoV-2 seems to be correlated to seasonal behaviour, but also regarding environmental influences and accessibility of health institutions [21][22][23][24]. Additionally aspects like SARS-CoV-2 mutations, times of vaccination and date of last vaccination could be included and improve the reliability of the model by now.…”