In this study, we found the ambient temperature was significantly associated with the incidence of influenza in Wuhan, China. Our study indicated that there were significant nonlinear and delayed effects of cold and hot temperatures on influenza. Hot temperatures showed a considerably higher risk for influenza than cold temperatures. We found an acute and strong association between high temperature and influenza, while low temperature was observed to have a mild but lasting effect on influenza. Our findings provide further epidemiological evidence to explore the unclear mechanism of how ambient temperature affects influenza outbreaks.
This study found that the association between temperature and the incidence of influenza presented an approximate ‘S’ shape from January 1st, 2014 to December 31st, 2017, with the highest cumulative RR observed at 23.57℃ (RR = 2.604, 95%CI: 1.462–62639). A study conducted in another subtropical region-Shanghai also used DLNM to explore the relationship between climate change and influenza from 2012 to 2018 (Zhang et al., 2020). Consistent with our findings, this study showed the similar shape between temperature and influenza A, with two peaks at 1.4°C and 25.8°C. However, various previous studies observed both linear and non-linear exposure-response relationships between temperature and influenza. Some spotted linear patterns of temperature on influenza (Liu et al., 2019, Soebiyanto et al., 2014) while most others noticed the non-linear relationships between temperature and influenza (Guo et al., 2019, Zhang et al., 2015, Peci et al., 2019, Liu et al., 2018, Lytras et al., 2019). For example, a study in Jiangsu Province, China observed an M-shaped relationship for influenza-like illness and influenza A virus, and an inverted U-shaped pattern for influenza B virus (Dai et al., 2018). A Chinese multi-city study including six cities reported that different cities had different exposure-response shapes, although only one city found a statistically significant relationship (Lau et al., 2018). In addition to the different model settings, the heterogeneity in the shape of non-linear relationships might result from differences in various geographic and climatic characteristics including latitudes, atmospheric dispersal, sunspots, sunlight, and vitamin D levels (Tang et al., 2010, Tamerius et al., 2013).
We found the cold effect became predominant at lag 2 and lasted for 3 days, while the hot effect was strong immediate after exposure from lag 0 to lag 1. Similar to our findings, others also reported the acute and shorter-term effect of high temperature on influenza (Ma et al., 2019, Islam et al., 2017). We might be able to attribute it to that some people preferred to seek medical attention soon after infection while others would not do it unless severe symptoms appeared (Apostolidis et al., 2009). Thus, the hot temperature may prompt patients to seek medical treatment faster (Dai et al., 2018). Moreover, a previous study found the positive association between temperature and host activity (Belanger et al., 2009). Therefore, high temperature may increase the chance of contact with the contaminated objects and make it easier for contact transmission spread immediately (Dai et al., 2018).
We observed different patterns of the lagged effects for extremely low temperatures. The extremely cold temperature exhibited a protective effect at lag 0 and then started to show a negative effect at lag 1. The protective effect may be explained by the awareness of seeking medical assistance. On cold days, people tend to avoid medical attention immediately after infection and this led to the underestimate of reported cases. Consistent with previous studies, we have also observed persistent cold effects (Peci et al., 2019, Guo et al., 2019). Various hypotheses were proposed to explain the phenomenon that low temperatures favored increased influenza cases. Low temperature may prolong the survival of viral particles, and make more people crowded indoors to increase exposure (Zhang et al., 2020, Chong et al., 2015). Eccles et al. reported that inhalation of cold air was associated with a reduction in the nasal epithelium temperature which was sufficient to inhibit immune defenses against infection (Eccles, 2002). Besides, a laboratory study using guinea pig models has indicated that the cold temperature enhances the spread of influenza viruses in the air by increasing and prolonging the virus emissions of the vaccinated animals (Pica et al., 2012).
We also observed an increased risk of influenza on hot days which was consistent with many previous studies as well (Firestone et al., 2012, Lytras et al., 2019). For instance, the study conducted in Australia observed a higher risk of infection at a variety of high temperatures (> 28℃). However, a study in Guilin, China showed no significant association between the hot effect and influenza (Guo et al., 2019). Some studies indicated that high temperature was negatively associated with the influenza risk in different countries (Peci et al., 2019, Lau et al., 2019, Liu et al., 2019). the underlying reason for the association remains unclear, but the findings of previous studies may help to make possible explanations. Paul K.S. Chan claimed that the peak of influenza epidemic in summer is the direct or indirect result of the increase in the use of indoor air-conditioning (Chan et al., 2009). People like spending more time indoors in a more crowded and air-conditioned environment where the conditions are cooler and dryer, contributing to influenza epidemic (Tang et al., 2010). In addition, an animal study found that higher temperatures (20–30℃) blocked aerosol transmission of influenza and the contact or short-range spread became the major transmission route under hot conditions (Lowen et al., 2008).
Unlike previous studies (Lytras et al., 2019, Guo et al., 2019, Zhang et al., 2020), we found the hot effect was generally stronger than cold effect. This result remained robust after we adjusted the lag period from lag0-1 to lag0-6. It may be a result of the existing potential confounding factors such as relative humidity. Guinea pig models have shown that transmission of human influenza viruses was most efficient under cold temperature and low relative humidity conditions (Lowen and Steel, 2014, Lowen et al., 2007, Chan et al., 2009). Wuhan often experiences humid weather regardless of cold winter and hot summer with the mean RH equals to 78.83%. Animal models have shown that transmission frequency at high temperature (20 or 23℃) was higher than at low temperature (5℃) among guinea pigs and ferrets at the constant high RH (> 70%) (Lowen and Steel, 2014, Gustin et al., 2015). As the relative humidity in Wuhan was high all year around, the observed stronger hot effects than cold effects is reasonable.
This study has some limitations that should be acknowledged. Firstly, this is a single-city study. So, without parallel studies conducted in other cities we cannot ascertain the generalizability of the results. Secondly, due to the limited data, we cannot estimate the risk of the specific influenza subtype. Third, the meteorological and air pollutant data were collected from outdoor stations and they may not fully represent the accurate indoor climate and individual conditions. Finally, due to no data available our analysis does not include other important risk factors that may affect influenza outbreaks, for example, demographic characteristics and contact patterns (Schmidt-Ott et al., 2016).