Most areas around the world have experienced fluctuating cases of the coronavirus disease 2019 (COVID-19); however, the ability to predict such surges is limited as a result of the lack of knowledge about the associated risk factors. A new preprint on the medRxiv* server finds that environmental factors, as well as changing trends in human mobility, predict seasonal changes in COVID-19 rates, just as in influenza and similar diseases.
Study: Environmental Factors and Mobility Predict COVID-19 Seasonality. Image Credit: olhaliladiy / Shutterstock.com
Like several other respiratory viral infections and pandemics, COVID-19 shows a seasonal trend. Moreover, the nadir of each wave appears to coincide with the onset of allergy season in the temperate zone. The factors that are responsible for seasonal patterns in the earlier known respiratory illnesses could thus be common to COVID-19 seasonality as well.
The researchers of the current study examined factors related to the weather conditions including sunlight hours, humidity and temperature, the allergy season reflected by pollen/hay fever incidence, and human movement. They also obtained the reproduction number of COVID-19 (Rt), a daily estimate based on the number of positive COVID-19 tests.
This study, which was carried out in the Netherlands, was conducted between February 17, 2020 to September 21, 2020, which coincided with the first allergy season. The mobility trend was then added to enhance the accuracy of prediction.
The current study found that the incidence of hay fever, temperature, solar radiation, and the incidence of visits to indoor recreation centers significantly predicted the transmission of COVID-19. Conversely, a higher humidity, which is linked to rain or fog, and reduced solar radiation as well as temperature, are associated with a higher spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
The model thus created explained 88% of the difference in the rates of new cases and performed better than one based only on environmental factors. However, models based solely on environmental factors still explained 77% of the variance.
Lockdowns and restrictions on businesses and schools helped drive down the Rt; however, human behaviors continue to show a strong link with the seasons. When there is good weather, people tend to move outdoors. This will be associated with increased solar radiation, higher allergen levels, and higher numbers of hay fever cases.
While indoor recreational centers such as restaurants, cafes, retail centers, libraries, and movie theaters are associated with the higher transmission, staying at home is inversely related to viral spread. Indoor centers are not only independently related to spread but represent other mobility sources including transit stations and workplaces. Not surprisingly, outdoor recreational places are negatively related to transmission.
A possible explanation is that daylight may regulate the levels of the hormone melatonin in the brain which, in turn, improves lung immunity according to the body’s circadian rhythms. With higher sun exposure, the virus may degrade faster; however, the clinical implications of this effect are controversial at present.
What about allergies? Some scientists believe that allergies are associated with lower levels of the host cell membrane-bound angiotensin-converting enzyme 2 (ACE-2), which acts as the receptor for SARS-CoV-2 attachment and cell entry, along with higher eosinophils and modulated cytokine release. The resulting reduction in the inflammatory response is accompanied by a T-cell response to the allergen that may also counter COVID-19.
In contrast, some research indicates a spike in COVID-19 cases as the pollen levels rise; however, this is yet to be validated.
The researchers also point out that the use of Rt is superior to COVID-19 incidence as a metric of correlation with the aforementioned potential risk factors. The Rt is more sensitive to changes, accommodates lags due to the virus incubation time, and does not change with the season.
For a temperate country like the Netherlands, a combination of mobility and environmental factors can be used to set up a highly predictive model. This can help predict a lower transmission rate of COVID-19 in relation to lower sunlight, colder weather, and low incidence of hay fever.
Adding changes in mobility is linked to a higher predictive value, not only because it compensates for the effects of lockdown restrictions, but also because it brings out the key role of seasonal behavioral trends. A higher number of visits to all indoor (but not outdoor) recreation centers was linked to a greater spread of the virus.
“This finding suggests that outdoor transmission of SARS-CoV-2 is far less likely than indoor transmission, and that restrictive policies that limit visiting Outdoor Recreation locations have less added value.”
In contrast, lockdown measures are effective in containing the Rt.
Overall, the temperature was not an independent factor in the spread of COVID-19; however, its effects were mediated by changes in human movement to crowded indoor locations where social distancing is unlikely, and in seasonal allergens like pollen. When parties involving drinking and drugs are allowed, in the words of the authors, “social distancing becomes a distant reality.”
Further research might uncover differences in Rt per virus variant, while immunity levels in the population could be included to increase the predictive power.
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information