Coronavirus Seasonality, Respiratory Infections and Weather

doi:10.21203/rs.3.rs-99539/v1

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Coronavirus Seasonality, Respiratory Infections and Weather

https://doi.org/10.21203/rs.3.rs-99539/v1

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published 26 Oct, 2021

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Background: The survival of coronaviruses are influenced by weather conditions and seasonal coronaviruses are more common in winter months. We examine the seasonality of respiratory infections in England and Wales and the associations between weather parameters and seasonal coronavirus cases.

Results: The seasonal distribution of 985,524 respiratory infections in England and Wales (1989-2019) showed coronavirus infections had a similar seasonal distribution to influenza A and bocavirus, with a winter peak between weeks 2-8. Seasonal coronaviruses from 2012 to 2019 were compared to weather parameters for the 28-day average before the patient’s specimen date. Ninety percent of infections occurred where the daily mean ambient temperatures were below 10^oC; where daily average global radiation exceeded 500kJ/m²/hour; where sunshine was less than 5 hours per day; or where relative humidity was above 80%. Coronavirus infections were significantly more common where daily average global radiation was under 300 kJ/m²/hour (OR 4.3; CI 3.9-4.6; p<0.001); where average relative humidity was over 84% (OR 1.9; CI 3.9-4.6; p<0.001); where average air temperature was below 10^oC (OR 6.7; CI 6.1-7.3; p<0.001) or where sunshine was below 4 hours (OR 2.4; CI 2.2-2.6; p<0.001) when compared to the distribution of weather values for the same time period. Seasonal coronavirus infections in children under three years old were more frequent at the start of an annual epidemic than at the end, suggesting that the size of the susceptible child population may be important in the annual cycle.

Conclusions: The dynamics of seasonal coronaviruses reflect immunological, weather, social and travel drivers of infection. Evidence from studies on different coronaviruses suggest that low temperature and low radiation/sunlight favour survival. This implies a seasonal increase in SARS-CoV-2 may occur in the UK and countries with a similar climate as a result of an increase in the R₀ associated with reduced temperatures and solar radiation. Increased measures to reduce transmission will need to be introduced in winter months for COVID-19.

Infectious Diseases

COVID-19

coronavirus

climate

respiratory viruses

seasonality

weather

pandemic

children

surveillance

virus survival

Coronaviruses (CoVs) are enveloped, single-strand, positive-sense RNA viruses that occur in a variety of animals, including birds, reptiles and amphibians (1), and humans, causing respiratory and gastrointestinal infections. They appear to have originated over three hundred million years ago (2). The seven coronaviruses that are known to infect humans are currently restricted to the Alphacoronaviruses and Betacoronaviruses, all deriving from animal sources (See Supplementary materials S1). There are four seasonal coronaviruses species (HCoV-NL63, HCoV-HKU1, HCoV-OC43, HCoV-229E). Bats are hosts of more than 30 sequenced CoVs and only Alphacoronavirus and Betacoronavirus have been detected in bats (3). Both genera have been found in bats in Asia, Europe, Africa, North and South America and Australasia; but Alphacoronaviruses are more widespread than Betacoronaviruses and their detection rate is also higher.

The outbreak of coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in Wuhan, China (4, 5), with subsequent person-to-person spread to many Chinese cities and then to cities across six continents initiated outbreaks of infection around the world that became a pandemic (6, 7). The clinical features of COVID-19 are varied, ranging from an asymptomatic state to acute respiratory distress syndrome and multiple organ dysfunction (8). The start of both the Severe Acute Respiratory Syndrome (SARS) in 2002/2003 and the COVID-19 epidemic in 2019/2020 occurred in cold dry winter seasons that coincided with major national public holidays (9).

With the rapid increase in cases of COVID-19 worldwide (10–12), the main strategies to limit the damage caused by SARS-CoV-2 have included a range of non-pharmaceutical interventions including closing air-routes and borders, case and contact tracing, isolation/quarantine, social distancing, increased use of personal protective equipment (PPE) and cleaning and disinfection procedure, restriction of non-essential activity in most countries, restriction of normal business activity to online/remote only, that is designed to delay or reduce the viral spread and lower the peak of the outbreak (13, 14). It is possible that the warm weather in European temperate northern-hemisphere summer conditions has contributed to keeping the virus at bay as a result of the same drivers that contribute to the occurrence of the seasonal coronaviruses. Winter is known to be the season with least capacity in the UK and overseas healthcare services for a number of reasons, including increased illness due to seasonal respiratory viruses such as influenza (15) and other factors related more directly to low temperatures (16). There is a known seasonal element to the spread of many respiratory infections, including seasonal coronaviruses, and a better understanding of this may aid predictive models for COVID-19. A previous study showed influenza A cases increase in Europe and Asia from December to March and in southern continents from June to October, while the seasonal peak respiratory syncytial virus (RSV) occurs slightly earlier than with influenza A (17). These are thought to be driven by the non-immune population size and the more competent transmission in cooler winter periods that may reflect better virus survival or spending more time within indoor environments. The areas of the world that have more warm, humid and tropical climates have one or two peaks, but with more cases throughout the year (18, 19).

This paper aims to contribute to the understanding of COVID-19 by reviewing the relationships between known seasonal coronavirus cases in England and Wales and weather, that might contribute to the seasonality. We relate this to the literature on coronavirus survival. The pathways linking season to transmission and human susceptibility of these viruses, including the dynamics of infections in children, may be informed by the survival characteristics of coronaviruses on surfaces, in aerosols, particles and droplets.

Respiratory virus disease data was extracted from the Second-Generation Surveillance System (SGSS) database. Individual case episodes of infectious diseases in England and Wales between 1989 and 2019 were diagnosed at local laboratory or national reference laboratory level. The cases occurring in the period of the year from day 43 (12 February) to day 224 (12 August) are when cases are declining from the late winter seasonal peak are here called the Down period and the days 225–366 and 1–42 (13th August to 11th February) are called the Up period when cases are increasing. This allows the associations between weather on climate to be compared across the year.

We used daily averages of climate observations obtained from the Met Office from within a box that covers England and Wales, specified by boundaries at 50 degrees N and 56 degrees N latitude and 6 degrees W and 2 degrees E longitude. Weather parameters included daily averages for air temperature (^oC), dewpoint temperature (^oC), relative humidity (%), hourly precipitation (mm/hour), hourly global radiation (kJ/m²/hour), and sunshine (hours/day). A range of lag periods were used to estimate an optimum for use across the six weather parameters (Supplementary materials S2), making assumptions that in a person-to-person transmitted outbreak it is likely that short-term weather influences will be more important than longer-term ones. We used 14-day averages for direct associations and 28-day averages for more seasonally smoothed associations. Some of the datasets were split into quantiles (e.g. deciles by temperature) and scatter plots for the whole coronavirus dataset had small random additions to data to prevent multiple values being expressed in a single dot (Fig. 3s-x only).

Analysis used Stata, Microsoft Excel and R. Tests for significance used Chi square to test coronavirus weather distributions with the distribution of weather in the days of the study period. Literature was reviewed using Endnote extractions from PubMed.

The number of weekly laboratory-confirmed cases of the common respiratory viral diseases in England and Wales between 1989 and 2019 is presented in Fig. 1. There were 985,524 patients with a viral respiratory infection over this period. Coronavirus infections (11,741 cases) from 41 laboratories in England and Wales in the period 2012–2019 included all age groups, although 29% of cases were in children under three years of age. Coronavirus infections had a similar seasonal distribution to influenza A and bocavirus, with a winter peak between weeks 2–8 (January and February). Influenza B and human metapneumovirus peak a few weeks earlier around the New Year, with infection extending into the spring. Parainfluenza 1 and 2 viruses peak in the autumn/winter period between weeks 40 and 50, parainfluenza 3 has a spring peak (weeks 10–25), human metapneumovirus and influenza B virus increase at the end of the year, with infection extending into the spring. Enteric viruses (coxsackievirus A and B; echovirus; enterovirus) have summer peaks (weeks 25–33), while adenovirus and rhinoviruses are relatively unseasonal and echovirus, coxsackie A and coxsackie B are more sporadic in nature. Most of the viral diseases exhibit an annual cycle of infection, with some of the diseases exhibiting a stronger biennial (i.e. every two years) cycle (e.g. parainfluenza 1 and 2; influenza B; Fig. 2).

The increase in seasonal coronavirus cases in England and Wales in the autumn/winter period was associated with a period in which the percentage of the cases in patients under 3 years increased from 20 to 50% (Fig. 2a). Such large increases are not seen in influenza A (Fig. 2b), respiratory syncytial virus (Fig. 2c), influenza B (Fig. 2d), human metapneumovirus (Fig. 2e), or human bocavirus (Fig. 2f), adenovirus (Fig. 2g), rhinovirus (Fig. 2h). Weekly reports of coronavirus between 2012–2019 show an annual increase in the percentage of cases in children in the weeks 43–50 (October to December) as the annual rise in cases begins (Fig. 2i). This suggests that the start of the seasonal outbreak of coronavirus is frequently associated with an increase in infections in young children (0–2 years old) that may drive, at least in part, the seasonal increase. This supports the idea that some of the seasonality of respiratory viruses is driven by the child population that results from a continuing increase in the susceptibility of this population cohort as a result of new births through the year. Data for influenza A before the H1N1 pandemic shows an increase in older children at the start of the pandemic (Fig. 2j), but an increase in the percentage of younger children can be seen in influenza cases before this period. (Fig. 2k).

There are strong correlations between coronavirus cases and global radiation (the total solar radiation), with little recorded infection where this exceeds 300 kJ/m²/hour (Fig. 3a, 3 m, 3 s) using a 2-week lag before the specimen date. The cases occurring in the period of the year from day 43 (12 February) to day 224 (12 August) when cases are declining after the late winter peak (here called the Down period) had a closer relationship with global radiation and few cases where this was over 300 kJ/m²/hour, compared to the Up period (days 225–366 and 1–42) where there were more cases over 300 kJ/m²/hour (Fig. 2g). The difference between Up and Down periods was less at longer lags, with the closest at 5 weeks but increased thereafter (Supplementary materials Figure S3 a-h). For global radiation of 300 kJ/m²/hour or more two weeks before, there were significantly more cases than that expected by the global radiation distribution based on days over the same time period (OR 4.3; CI 3.956–4.696; p < 0.001) and for global radiation below 300 kJ/m²/hour there were significantly lower case numbers (OR 0.231; CI 0.212–0.252; p < 0.001) (Fig. 3m). Using average global radiation over the previous four weeks the distribution of cases in relation to global radiation was a better fit than all other weather parameters (r² = 0.91). Air temperature was strongly inversely correlated with cases (Fig. 2c, 2o, 2u) and the relationship was relatively similar between Down and Up periods (Fig. 2i), with less than 10% of cases where the air temperature was over 10^oC (Fig. 3aa). For air temperatures of 10^oC or more two weeks before, there were significantly fewer cases than that expected by the air temperature distribution based on days over the same time period (OR 0.15; CI 0.13–0.164; p < 0.001) and for air temperatures below 10^oC there were significantly larger case numbers (OR 6.72; CI 6.135–7.366; p < 0.001) (Fig. 3o). Relative humidity also correlated with coronavirus cases (Fig. 2b, 2n, 2t) and the Up and Down periods showed different relationships (Fig. 2h). Infection was uncommon where the relative humidity was below 75%. For relative humidity of 84% or more in the two weeks before, there were significantly more cases than that expected by the relative humidity distribution based on days over the same time period (OR 1.906; CI 1.755–2.07; p < 0.001) and for relative humidity below 84% there were significantly smaller case numbers (OR 0.524; CI 0.483–0.569; p < 0.001) (Fig. 3n). Sunshine hours showed a similar relationship to global radiation, but with a less close correlation (Figs. 2d, 2p, 2v). The decline with sunshine hours was more gradual than with global radiation (Fig. 2p) and there was a similar difference between the Down and Up periods as was seen with global radiation (Fig. 2j). Infections were rare where daily sunshine exceeded 7 hours. For daily sunshine of 4 hours or more two weeks before, there were significantly fewer cases than that expected by the sunshine hours distribution based on days over the same time period (OR 0.406; CI 0.373–0.442; p < 0.001) and for sunshine below 4 hours there were significantly larger case numbers (OR 2.457; CI 2.257–2.674; p < 0.001) (Fig. 3p). Dewpoint temperature was similar to air temperature (Figs. 2e, 2q, 2w), showing no difference between Down and Up periods (Fig. 2k) and less than 10% of infections occurred where the dewpoint temperature was over 7^oC (Fig. 2ac). There was no evidence that rainfall had a significant impact on the occurrence of infections with coronavirus (Figs. 2f, 2 l, 2r, 2x, 2ad). The coronavirus cases were linked to average weather parameters in the previous four weeks, were sorted, separated into ten quantiles and expressed as the weather upper and lower standard deviations above the average, with maximum and minimum range and compared to all weather values over the days between 2012–2019 (Figs. 3y-3ad). Coronavirus infections had lower global radiation than expected (3y), relative humidity of cases was higher than expected (3z), air temperature of cases was lower than expected (3aa), sunshine hours of cases was lower than expected (3ab), dewpoint temperature was lower than expected (3ac), and precipitation was the same as expected (3ad).

When weather parameters were examined in combination for the Down and Up periods, there was consistent evidence that the Up period was much more closely correlated with weather variables than the Down period (Fig. 3g-3 l), and with the mass of infections in the early weeks of the year and a gradual decline through to summer months (Fig. 3s-3x). The association between global radiation and coronavirus infections was not constant and can be seen to cycle through the year (Supplementary materials Figure S3h). Combinations of air temperature and global radiation, air temperature and relative humidity, and air temperature and sunlight showed a closer association in Up than Down periods (Supplementary materials S3). The density of cases by individual weather parameters was also examined (Supplementary materials S3).

The development of a pandemic is dynamic and dominated by the immunological naivety of the world population to SARS-CoV-2. This study has found that seasonal coronavirus infections in England and Wales have a broadly similar distribution to influenza A and human bocavirus infections, but with a characteristic peak in days 23 to 54 (weeks 3 to 8; January to February) and reduced disease in days 131 to 303 (weeks 19 to 43; May to October). Parainfluenza 1, 2, 3, 4 and influenza B have a biannual cycle, differing from other respiratory infections occurring in England and Wales and confirming previously reported seasonality trends (20). Respiratory infections have repeating cycles every 1 to 2 years, suggesting that their seasonal distribution is driven by the degree of susceptibility of the population to each infection and the environmental determinants that change over the year, affecting virus survival and transmission and population behaviour. Some of the changed susceptibility may reflect gradual loss of immunity to circulating viruses, and some to new babies being born. The biennial viruses (e.g. Parainfluenza 1 and 2) are presumed to have too small a susceptible population in the year after a winter increase to allow an epidemic the following year (Figure 1).

In this study, the seasonal reductions in coronavirus showed disease associated with average outside air temperatures above 10^oC, global radiation over 300 kJ/m²/hour, average sunshine hours over 5 hours per day and no association with precipitation. The association with dewpoint temperature was similar to air temperature but at lower temperatures. The even relationship to average air temperature between spring (Down) and autumn (Up) periods suggests temperature is the principal environmental factor associated with disease occurrence. However, the different relationships with weather in periods after the annual epidemic (Down) and the period at the start of the new epidemic (Up) suggest that temperature and global radiation may inhibit the start of the epidemic more effectively than it reduces the end of one, perhaps reflecting the strength of respiratory transmission due to the susceptibility of the population.

Respiratory viruses, including coronaviruses, tend be more frequent in autumn/winter (November to March) in the Northern Hemisphere and April to August in the Southern Hemisphere (21-30). In more tropical countries, however, there is less evidence of any seasonality (31), or an increase in wet or dry seasons (32). In Brazil respiratory infections were increased at higher temperatures, although cases occurred year-round (33). In urban Italy, childhood infections with RSV were negatively correlated with temperature while positively correlated with humidity (34). Human metapneumovirus in Korea peaked between weeks 15 and 20 (35-38) which is later than for the UK (39). In China, human metapneumovirus infections were associated with sunshine duration, wind speed and diurnal temperature variation (39), and human bocavirus showed both summer and winter increases in different years (40). Infections with influenza are strongly seasonal, and the weather variables associated with these have been examined (41). Low temperature and UV indexes were the most predictive for influenza A virus seasonal epidemics in northern Europe (42). Diurnal temperature range has been found to correlate with influenza A in South Korea (43), although the mechanism for this is unclear. Temperature across the globe has been matched to influenza A and B (44).

The proposed mechanisms for increased respiratory disease in the winter include the effects of weather on virus survival, behavioural changes in winter months, and changes to people’s disease susceptibility. Behavioural changes that may influence cases of infection include increased winter crowding as a result of spending more time indoors in close proximity with other people (45), more time being spent outdoors due to longer daylight hours in summer and changes in schooling, travel and other behavioural, cultural and religious influences (45). The physiological elements that may change include day length, which has been suggested as a parameter contributing to influenza morbidity and mortality, host chilling which may increase susceptibility (45), transitions from cold exterior to hot interior environments that may cause alterations in nasal mucosal physiology, and seasonal effects from changing vitamin D levels that are a product of UV exposure. Day length is a standard seasonal parameter that is independent of the measured sunlight levels. Mortality in the 1918-1920 influenza pandemic was examined in a multiple regression model (46), showing enhanced survival in populations that experienced a short day-length (≤10 h light/day). It was suggested that exposure to short day lengths, typical of winter periods in non-tropical areas, yields “robust and enduring reductions in the magnitude of cytokine, febrile, and behavioural responses to infection” and suggested that a proportion of the global variance in morbidity and mortality may be explained by effects of day length. School attendance has also been linked to winter infections (e.g. increases in influenza) and day length (47). Day length and global radiation in the UK are likely to show a degree of correlation.

Possible mechanisms contributing to the seasonality of respiratory virus survival and increased viral transmission include sunlight and UV, which may reduce virus survival in aerosols and on surfaces (45, 48, 49). Ambient humidity has been examined as a factor contributing to influenza A and B occurrence (44). A study of droplet size and influenza survival suggested that virus survival at one hour is lower with increasing temperature and raised absolute humidity (50). The humidity may cause droplets to increase in size and come out of the air more quickly. However, the authors thought that relative humidity (which is strongly linked to temperature) is a better predictor of virus survival than absolute humidity. Indoor and outdoor absolute humidity are high in summer and low in winter and show similar values. Indoor relative humidity is lower in the winter and higher in summer, while outdoor relative humidity is higher in winter and lower in summer (51). The humidity of environments in buildings during the winter may encourage viral transmission (52).

Coronavirus seasonal characteristics

Coronaviruses are relatively robust enveloped RNA viruses. Their survival characteristics reflect this, and they are sensitive to surface-active agents. Survival of virus in nasal, salivary, respiratory or faecal secretions, or with cultured virus in a suitable medium, gives some insights into the mechanisms by which weather might contribute to seasonality. Virus survival studies suggests that environmental variables, such as humidity, temperature, sunlight and UV radiation, might contribute to the seasonal transmission of coronaviruses by influencing the inactivation of virus in air and on surfaces. Studies suggest that all coronaviruses tend to survive longer in colder and dryer conditions (for references see below).

SARS, MERS and COVID-19

The SARS outbreak showed little evidence of being influenced by climate. Risk of increased daily incidence of SARS in Hong Kong was 18.2 fold higher on days with lower temperature than days with higher temperature (53). SARS-CoV remained viable in faeces, serum and sputum for 72 hours, and survived on a variety of surfaces when tested using cytopathic effect in Vero-E6 cells (54). The virus was relatively stable at 4^oC, and survived for 2 hours at 20^oC and 37^oC, but was sensitive to exposure at 56^oC, at 67^oC and at 75^oC, for 90, 60 and 30 minutes respectively, and to UV radiation for 60 minutes. SARS-CoV can survive for 4 days in diarrheal stool samples with an alkaline pH, and it may remain infectious in respiratory specimens for >7 days at room temperature (55). A study on SARS-CoV in a liquid medium found inactivation at 65^oC or greater for 10 minutes using growth in Vero E6 cells (56) and another study of SARS-CoV in a liquid medium found inactivation by ultraviolet light (UVC) at (254 nm emitting 4016 mW/cm²) for 10 minutes using growth in Vero E6 cells but showed no inhibitory effect from UVA (365 nm emitting 2133 mW/cm²) (56).

The worldwide occurrence of MERS-CoV was also not very seasonal (57), was not strongly influenced by weather, but was more influenced by other risk factors such as exposure to camel milk, being male, older age, nosocomial transmission, super-spreaders and Arabian geography (58-60). The seasonality seen in Riyadh, Saudi Arabia, was linked to camel-related events rather than weather (61), although a case-crossover analysis of the impact of weather on primary cases of MERS in Saudi Arabia found cases were more likely to occur when conditions were relatively cold and dry (62). A study of the weather influences on 712 MERS cases found higher incidences of infection at times when there was higher temperatures, low relative humidity and low wind speed (63). MERS virus in fresh frozen plasma and platelets has been inactivated with a variety of UV based treatments (64-66) and there are protocols for large-scale UV inactivation of SARS-CoV for vaccine production purposes (67) and porcine epidemic diarrhoea virus for veterinary vaccines (68).

An examination of COVID-19 in cities in China showed evidence that low temperature, mild diurnal temperature range and low humidity favoured transmission (69). However, examining the associations between weather and disease during the early stages of the pandemic is complicated by the dynamics of the disease emergence. A Study of SARS-CoV-2 survival in simulated saliva or culture media dried onto stainless steel was reduced in count by 90% in 6.8 minutes and 14.3 minutes at room temperature when exposed to simulated sunlight (70). Similar results have been found for aerosols (71). The survival of SARS-CoV-2 on surfaces at room temperature is longer at low relative humidity than at high, and longer at 24^oC than at 37^oC (72). A review has examined the influence of climatic conditions on the stability and survival of SARS-CoV-2 and persistence at lower temperatures and lower relative humidity (73).

Seasonal coronaviruses

The seasonality of seasonal coronaviruses has been examined in 21 countries across the world and showed seasonal patterns similar to influenza and RSV in temperate climates (74). Heat maps of the four species show most cases occur within the months December to March, while southern hemisphere distributions were from July to September. It has been suggested that forecasting based on weather might help in targeting countries most at risk (75). Coronaviruses 229E and OC43 survived in phosphate buffered saline for up to six days, but were substantially reduced in titre on surfaces such as aluminium, latex gloves and sponges, surviving for hours rather than days (76). HCoV-229E was also able to survive on lettuce at 4^oC for a couple of days (77). Freeze dried coronavirus NL63 cultured in LLC-MK2 cells was viable for 14 days in liquid recovery media at ambient temperature (21^oC) but survived at higher concentrations at 4^oC (78). Human coronavirus 229E survival in a stabilised aerosol was found to remain viable for longest at 4^oC, with little difference made by relative humidity of 30%, 50% or 80%, while at 20^oC survival was better at 30% and 50% than at 80% relative humidity (79). Survival was lowest at room temperature with a high relative humidity (79), but although viral titres for HCoV-229E and SARS-CoV reduce at room temperature within a couple of days, lower numbers may survive for several days (80).

Studies of other coronaviruses

Other coronaviruses that have been used as potential surrogates of SARS-CoV, MERS-CoV and SARS-CoV-2 include porcine transmissible gastroenteritis virus (TGEV), bovine coronavirus (BCoV), canine coronavirus (CCV), turkey coronavirus (TCoV) and murine hepatitis virus (MHV). These viruses survived longest at low relative humidity (20%), and lowest at moderate to high humidity (>50% RH) (81). TGEV can survive on protective equipment for up to 24 days, but with significant log reduction of viral counts (82). CCV does not survive for long periods above 4^oC (83), can survive at 56^oC for 30 minutes but is inactivated at 75^oC (84). The enveloped bacteriophage Phi6 has also been used as a surrogate for the survival of influenza viruses and the SARS and MERS coronaviruses in droplets (85). The study found that relative humidity of 60-85% was associated with a significant loss in viability of the virus at 25^oC and 37^oC but not at 14^oC or 19^oC. TCoV shows reduced viral titres at room temperature compared to 4^oC (86). A cell culture of BCoV in growth medium survived for 14 days but for shorter periods in bovine faecal suspensions on the surface of romaine lettuce (87). A study of MHV found this virus to be highly susceptible to UV inactivation (88). Both SARS-CoV and the MHV-A59 were reduced in numbers by >5 logs to undetectable levels by UVC disinfection at 1.22 m distance. Review of the effects of ultraviolet light on different coronaviruses suggests much of the differences between studies reflect the experimental conditions and, overall, coronaviruses are very sensitive to ultraviolet light (89).

While many of the features of COVID-19 are different to influenza, some of the aspects of the survival and transmission of seasonal coronaviruses may well be similar to SARS-CoV-2. The dynamics of the COVID-19 pandemic suggest that at the start the high percentage of susceptible people means that the effects of weather are relatively small during the initial phase, but may increase in subsequent years as the virus becomes endemic and the percentage of the population that is susceptible declines (90). From April through to October seasonal coronavirus cases are relatively uncommon in England and Wales, suggesting that their ability to transmit within the community is reduced compared to winter months. We infer that there is a reduced R₀value in this period linked to temperatures above 10^oC and global radiation above 300 kJ/m²/hour, although the reductions are not sufficient to prevent transmission in summer months. For COVID-19, the seasonality in the pandemic is likely to have contributed to a reduction in R₀ during summer months but the size of the susceptible population means that these reductions are likely to break down and increase the risks of increased transmission during the winter. The demonstration of increased rates of transmission in young children (0 to 2 years old) at the start of the winter increase in seasonal coronaviruses suggests that additional measures to control transmission in young children should be considered.

Identifying and understanding the seasonal distribution of viral diseases and what factors drive their circulation and the severity of symptoms can help in preparing health care systems and populations to better protect those most at risk from serious complications from becoming infected, and support timing of interventions to help achieve this. Figure 4 shows the combined contributions of some of the main infection drivers for seasonal epidemics. Travel abroad, which also has a seasonal pattern (Supplementary Materials S4), is likely to introduce new viruses that differ from the currently circulating strains. A study from the Flu Watch cohort study showed eight infections with three different seasonal coronaviruses occurred in the following season, but all were of a different type (91), suggesting reinfection with the same species is uncommon over short timescales. The timing of the annual coronavirus epidemic is influenced by changing transmission dynamics through the year. Low temperature, low relative humidity indoors, short daylength and low UV contribute to better survival of the virus in winter months than in the summer, pushing the immunity driven epidemic to occur in the winter months.

The weather variables used are representative of conditions across a broad area, local variations in conditions (e.g. local microclimates) are not captured, and many of the factors auto-correlate because of their natural relationships in the environment (for example temperature and relative humidity). In England people spend the majority of their time indoors, and while there is a relationship between indoor and outdoor temperature, particularly for warm conditions (92), sufficient data on indoor conditions were not available for this study. The respiratory infections reported are likely to derive from the more severe infections such as pneumonia or infections in children or more vulnerable patients. The data may not capture a majority of the milder infections, such as the common cold that are better examined using syndromic surveillance sources. The respiratory cases are influenced by current practices over thirty years. For the coronaviruses we used data from seven years from 2012 to 2019 only. While it can be assumed that survival properties of the enveloped SARS-CoV-2 might be somewhat similar to SARS-CoV, MERS-CoV and other coronaviruses, examining the degree of similarity was beyond the scope of our study. A discussion of the similarities and differences between influenza, other respiratory viruses, seasonal coronaviruses and the SARS like coronaviruses would help to understand COVID-19 infections and a more generalised model of the factors contributing to respiratory virus seasonality is needed.

COVID-19 – The disease caused by SARS-CoV-2

CoVs – coronaviruses

Down period - the period between day of the year 43 (12 February) to day 224 (12 August) are when cases are declining from the late winter seasonal peak

MERS – Middle Eastern Respiratory Syndrome

MRC - UK Medical Research Council

NERC - UK Natural Environment Research Council

NIHR HPRU - National Institute for Health Research Health Protection Research Unit

PHE - Public Health England

RSV – respiratory syncytial virus

SARS – Severe Acute Respiratory Syndrome

SARS-CoV-2 – Severe Acute Respiratory Syndrome 2 virus

SGSS – Second Generation Surveillance System

Up period - the period between day of year 225-366 and 1-42 (13^th August to 11^th February) when cases are increasing

UV – ultraviolet light

Ethics approval and consent to participate: No ethics approval, consent to participate or consent for publication was required. Approval for the use of routine surveillance data for this study was through PHE Colidale.

Consent for publication: There was no consent for permission to publish sought. The data comprised around a million records of routine surveillance data that do not include any patient identifiable information and data linkage was arranged to ensure there was no possibility of patient identification. There were no competing interests.

Availability of data and material: The datasets generated and/or analysed during the current study are not publicly available due [the datasets of routine surveillance data are confidential and potentially identify a patients records if care is not taken to avoid deductive disclosure] but are available from the corresponding author on reasonable request.

Competing interests: All authors have indicated that they have no competing interests other than the funding from the National Institute for Health Research.

Funding: The research was funded in part by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Environmental Change and Health at the London School of Hygiene and Tropical Medicine in partnership with Public Health England (PHE), and in collaboration with the University of Exeter, University College London, and the Met Office; the UK Medical Research Council (MRC) and UK Natural Environment Research Council (NERC) for the MEDMI Project (https://www.data-mashup.org.uk). The NIHR HPRU-2 in Environmental Change and Health at the London School of Hygiene and Tropical Medicine in partnership with Public Health England (PHE) and University College London is also supporting this work.

Author contributions: Paper writing GN; EG; HM; SV; RP; SH; CS, data extraction and analysis AK; GN; CS; EG; HM, experimental design GN; SH, weather analysis EG; HM, GN, table construction GN, DA, figures GN; EG, literature review GN, SV; EG, discussion GN; EG; HM; SV, manuscript revision GN; EG; HM; SV; RP; CS; HS; RP.

Acknowledgments

We thank Aarti Khanna from PHE Colindale for providing us with the SGSS data. The views expressed are those of the authors not necessarily those of the NHS, the NIHR, the Department of Health and Social Care or Public Health England.

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Coronavirus Seasonality, Respiratory Infections and Weather

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