Introduction

The current COVID-19 outbreak is a global pandemic caused by the novel coronavirus, which can result in severe acute respiratory syndrome coronavirus 2 and has affected more than 103 million people globally, including 206 countries, and has resulted in over 2 million deaths worldwide as of January 31, 2021 (Dong et al. 2020). The new pandemic has become one of the worst public health crises, arousing considerable concern throughout the world. The new virus is mainly transmitted when people are in close contact, often via small droplets that are produced by coughing, sneezing, and talking, which exposes the virus to the external environment. Usually, instead of remaining in the air for a long period of time, droplets are peculiarly prone to falling to the ground or surfaces (Srivastava 2021). Its mechanism of rapid transmission and virological characteristics have not been fully explored and understood, but we know that, historically, many viruses have possessed different stabilities in different environments and that some infectious diseases have changed with the weather. For example, Middle East respiratory syndrome coronavirus was observed to be more stable under low temperature or low humidity conditions and could still be recovered after 48 h in the laboratory (van Doremalen et al. 2013). Historically, human-to-human transmissions of coronavirus and positive viscous infectious diseases have been mostly reported in subtropical monsoon climates or in winter and spring festivals in the Northern Hemisphere, whereas Flavivirus infectious diseases have been mostly detected in tropical regions, as well as in hot and rainy summers and autumns (Wang et al. 2020b). Additionally, the transmission of rotavirus has been shown to peak in December or January in the southwestern USA, but it has also been shown to peak in April and May in the Northeast (Mo 2020). At present, some scholars have found that COVID-19 is sensitive to high temperature and humidity conditions in the laboratory. For example, Casanova et al. have found that there was greater survival at low temperatures and low relative humidity for SARS-CoV-2 under laboratory conditions (Casanova et al. 2010). In cell cultures, the new coronavirus was observed to be highly stable at 4°C. Moreover, its survival was found to be related to the concentration of the virus, and the high concentration of the virus could survive for 7 days at 22.5°C, whereas the virus only remained completely alive for 1 day at 37°C (Cui et al. 2021). This in vitro study showed that SARS-CoV-2 obtained from a COVID-19 patient could be rapidly inactivated via irradiation with a deep ultraviolet light-emitting diode (DUV-LED) of 280 ± 5 nm wavelength (Inagaki et al. 2020). These results suggest that the epidemic of COVID-19 may be associated with meteorological variables, such as ultraviolet light and temperature.

Therefore, the exploration of the climate factors affecting the spread of the new coronavirus has become one of the key research issues in academic circles. Various methods, including mathematical models and machine learning algorithms, have been used to identify the epidemiologic relationship between COVID-19 prevalence and weather in different temporal and spatial dimensions. In this systematic review, we comprehensively collected and analyzed the studies involved in the epidemic status of COVID-19 and climate to summarize the methods of ecological studies and the results regarding the association between weather variables and COVID-19 incidence, which could be useful in better predicting the incidence of COVID-19.

Materials and methods

Study selection

A systematic search in Web of Science, PubMed (www.ncbi.nlm.nih.gov/pubmed), and Chinese National Knowledge Infrastructure (www.cnki.net) was conducted to collect publications concerning the correlation between COVID-19 incidence and weather throughout the world. Data that ranged from the start of the pandemic to Feb 1, 2021, were retrieved by means of the keywords “COVID-19” and “wind” or “humidity” or “temperature” or “rainfall” or “precipitation” or “UV” or “weather” or “climate” or “seasonality” in both English and Chinese. Titles and abstracts were scanned for relevance, and further relevant studies were identified from the references. The literature retroactive method was also used to extend the literature search. The last search was performed on Feb 1, 2021.

Inclusion and exclusion criteria

All of the identified studies were subjected to the following six self-established criteria to ensure consistency with the research objectives: (i) new daily, weekly, or monthly confirmed cases of COVID-19 (or other incidence and transmission index that could describe the dynamics of disease) were presented; (ii) meteorological indexes were presented; (iii) the underlying geographical scale information, research period, and temporal data aggregation unit were presented; (iv) the statistical analysis methods that had been used and the results were clearly presented; (v) studies from peer-reviewed dissertations or journals; and (vi) for the included studies, the time range of the data was more than sixty consecutive days, except for the studies concerning spread and decay durations; however, the duration of the studies about China only required more than 30 days. Additionally, the epidemic in China has been generally controlled much better than in other countries. Therefore, epidemiological studies that only provided COVID-19 mortality or admission rate data or studies that did not clearly describe methods or weather indexes were removed, and reviews and comment were also removed. If the studies were repeatedly published, then a dissertation with more detailed information was selected.

Data extraction

The following information was extracted from each included study, which was based on our self-designed information extraction list: first author and publication year, region and period, the type of COVID-19 data, climate indexes (with the lag time considered), temporal data aggregation unit (monthly, weekly, or daily), the statistical method that was used, major results regarding the correlation between climate and COVID-19 activity, and limitations.

To improve the reliability, we adopted the standard Cochrane methods (Cumpston et al. 2019). Two review authors (ZHL and GZL) independently screened for potentially eligible studies by glancing over the titles, abstracts, and full texts; additionally, they created a shortlist and determined final eligibility by using the predetermined inclusion and exclusion criteria. Subsequently, two review authors (ZHL and GZL) independently extracted data from the included studies and entered the data into the well-established data extraction form. We resolved any disagreement with the help of a third review author (WW) who acted as an arbiter. Included publications were considered to be qualified only when the data were extracted and double-checked.

Risk assessment of study bias

In consideration of the PRISMA statement (Moher et al. 2009), the modified criteria from BioMed Central (Wang et al. 2018), the Joanna Briggs Institute (JBI) Critical Appraisal Checklist tool (Mecenas et al. 2020), and the systematic review by Bai et al. (2019), we used the self-designed risk assessment item list (Table S2) to assess the qualities of the included ecological studies. The risks of bias in the included ecological studies were evaluated with twelve risk-biased items that were divided into external validity (items 1 to 3) and internal validity (items 4 to 12), which assessed the domain of selection and the domain of measurement bias and interpretation or extrapolation bias, respectively. For each item, the study was classified as “Yes” or “No”, which indicates “Low risk” or “High risk,” respectively. Two investigators (ZHL and GZL) negotiated with the help of the principal investigator (WW) and completed the quality assessment.

The resulting interpretation of the risk assessment, which was similar to the previously established standards (Zhang et al. 2019), was as follows: studies with a “No” score ≤30% (1–3) were classified as being low risk, studies with a “No” score 30–60% (4–7) were classified as being moderate risk, and studies with a “No” score >60% (8–12) were classified as being high risk.

Certainty of evidence

The included studies were given a narrative GRADE related to the outcomes and effects of climate variables on the transmission of COVID-19, which was evaluated in this review according to the GRADE guidelines (Balshem et al. 2011). The guidelines consider five aspects for rating the following levels of evidence: design, risk of bias, consistency, directness, and precision of the studies. The levels of evidence were classified as being high, moderate, low, or very low. The outcomes that were evaluated were “association between weather (solar radiation, temperature, humidity, and other climate factors) and transmission of COVID-19.”

Results

Study selection

The initial searches identified 346 articles: 102 articles from Web of Science, 235 articles from PubMed, and 9 articles from CNKI. A total of 215 articles that were related to the objective and published online between Dec 2019 and Feb 1, 2021, were identified, including 206 publications in English and 9 publications in Chinese. After reading the titles, abstracts, and full-texts of these articles, only 62 publications (61 in English and 1 in Chinese) were ultimately included in this systematic review and selected for qualitative assessments of bias risk. The literature selection process is shown in Fig. 1.

Fig. 1
figure 1

Flow diagram of study selection.

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Characteristics of the included studies

The characteristics of the included studies are presented in Table 1. All of the studies were retrospective observational studies analyzing the association between climate variables with the transmission of COVID-19.

Table 1 Characteristics of the included studies, Dec 2019–Feb 1, 2021
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Of these studies, seven studies were global analyses of weather variables—three studies assessed the global distributions at the continent, country, or region level, two studies evaluated the associations in 65 countries and 67 countries, one study selected analyses of 47 affected countries on six continents, and the remaining study analyzed 127 “Belt and Road” countries (not including China). The remaining fifty-five studies were at the province, city, site, county, or community level, including six continents (except for Antarctica). Thirty-three studies focused on the correlation between weather and COVID-19 transmission in Asia: thirteen studies for selected cities or provinces of China but only eight studies from China, nine studies for India, two studies for Pakistan, two studies for Bangladesh, and seven studies for Jordan, Korea, Japan, Singapore, Jakarta of Indonesia, 9 Asian cities, and 4 South Asian countries, respectively. Five studies focused on the correlation in North America, with two studies for the USA and three studies for Ontario of Canada, Canadian, and Victoria of Mexico regions. Seven studies focused on the association in Europe for 4 European countries, Spanish, the original EU-15 countries, the Russian Federation in Europe, 10 European countries, Oslo of Norway, and Italy. Three studies were based in Africa, including Ghana, Lagos of Nigeria, and 16 countries of Africa. Only one study was based in South America (São Paulo in Brazil). Finally, there are six remaining studies: one study that intentionally selected 11 of the most infected cities worldwide and 3 countries, one comparative study concerning China, England, Germany, and Japan, one study selecting the 10 hottest and 10 coldest countries, one study for the top 20 countries with confirmed cases, one study analyzing 9 locations in four continents, and the final study selecting 428 Chinese cities and districts, 18 Italian provinces, and 13 other countries.

All of the included studies focused on many weather factors, including temperature, dew point, temperature range, solar radiation, sunshine duration, humidity, pressure, evaporation, precipitation, wind, and visibility. Lagged effects were considered in 22 studies. The incubation period was considered in 6 studies. Moreover, only one study provided a positive control and a negative control. To avoid potential differences in the absolute number of medical records among the districts (due to different criteria and regulations), a normalization test was conducted (Rashed et al. 2020). Additionally, time series data or COVID-19 data were smoothed by using a sliding window in some studies.

Correlations between climate variables and the transmission of COVID-19

In the 62 included studies, the correlations between major climate variables and the transmission of COVID-19 are presented in Table 2.

Table 2 Correlations between major climate variables and the transmission of COVID-19
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Temperature, humidity, and wind were the most popular factors to study. Although the effects of climate variables on COVID-19 activity varied among different country populations, time units, and analytical methods, there were also similar results in the included studies, and especially most of the literature showed that higher temperatures may have largely influenced the spread of coronavirus and suppressed the pandemic. Among the included studies, fifty-eight studies explored the relationship between temperature and COVID-19 transmission, but only one study investigated the heat index, which was found to be positively correlated with the daily basic reproductive number (R0), growth rate, and doubling time (Adnan et al. 2021). Additionally, another study adopted the mean equivalent temperature and found it to be a noninfluential factor on COVID-19 activity (Jamshidi et al. 2020).

In thirty-seven studies that analyzed relative humidity, fourteen studies did not observe any significant correlations, which was a result that we could not ignore. In addition, another paper included the specific humidity and indicated the existence of a low positive correlation between confirmed cases of COVID-19 and specific humidity. Among twenty-eight studies that analyzed wind speed, only two studies included wind direction, with one study showing that wind direction affected the number of COVID-19 cases (based on wind rose analysis), and the other study indicating that wind direction and wind speed produced minimal effects on the number of confirmed cases in 37.9% and 27.5% of the provinces, respectively, in China. When regarding humidity and wind, we cannot provide a specific conclusion through Table 1.

Similarly, it is not clear how sunlight, pressure, and precipitation were related to COVID-19 activity. In fact, precipitation includes rainfall and snowfall. Only one paper included rainfall, snowfall, and snow depth with rainfall and snow depth imparting minimal effects on the number of confirmed cases in 6% and 24.1% of the provinces, respectively, but no correlation being observed between snowfall rate and the number of confirmed cases in all of the Chinese provinces (Al-Rousan and Al-Najjar 2020). Many coronaviruses are sensitive to ultraviolet light under laboratory conditions, but the effect of ultraviolet light on COVID-19 was undefined at the macrolevel based on the results that 28.5% of the studies about sunlight found no correlation, and the remaining studies were also not relatively consistent. Furthermore, evaporation is not presented in Table 2. Only two studies analyzed evaporation, and both studies showed a positive relationship between confirmed cases and water vapor.

Synthesis of results

We did not perform a meta-analysis because of the heterogeneity of the modeling methods, locations, meteorological indicators, and data processing. Additionally, differing policies, abilities in resisting the disease, test standards, test ranges, and units of measure did not support meaningful comparisons. Hence, only simple and descriptive comparisons and summaries were conducted, beyond the risk of bias and the narrative GRADE of evidence of the results.

Results of risk assessment and certainty of evidence

The PRISMA checklist is provided in Table S1, and the risk bias and assessment results are provided in Table S2. The questions that received more “No” answers indicated the existence of study limitations. Among all of the included studies, 32 studies had a low risk of study bias, and 30 studies had a moderate risk of study bias. For the question of “Were potential confounding factors identified?”, only four studies provided “Yes” answers (Islam et al. 2021; Sarkodie and Owusu 2020; Shao et al. 2021; Xie and Zhu 2020). For the question of “Were strategies to deal with confounding factors stated?”, only three studies provided “Yes” answers (Islam et al. 2021; Shao et al. 2021; Xie and Zhu 2020).

The evaluation of the certainty of the evidence according to GRADE is described in Table 3. The level of certainty of the evaluated outcomes (“Association between weather variables and transmission of COVID-19”) was classified as “low” in this systematic review.

Table 3 Narrative GRADE evidence profile table
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Discussion

All of the included studies varied in times, countries, populations, data sources, data processing methods, models, controlling methods, independent variables, and dependent variables, thus leading to different results. This review did not consider COVID-19 mortality, recovery rate, and hospitalization rate, among other factors. The factors influencing these indicators can be more complex, and these indicators cannot clearly describe the prevalence of COVID-19 on a macrolevel. In addition, the included study periods must be longer than 2 months in order to observe a substantial change in the variables (to some extent).

Variable selection

For confirmed cases, little distinction was made between local and imported cases in all of the included studies, but Meyer A used daily local cases of COVID-19 (Meyer et al. 2020). When regarding the choice of outcome variables, the forty-eight selected studies only focused on the incidence rate, the number of new cases, or their proportions, but a small number of studies focused on the case growth rate, the changing rate, or infectivity of the novel coronavirus. Only three studies analyzed the effective reproductive numbers, four studies analyzed the basic reproductive numbers, six studies researched the growth rates, and three studies focused on the doubling times. In addition, two studies focused on the spread duration and decay duration, which could also describe the acceleration of the epidemic.

Influencing factors

There were many possible non-meteorological factors, such as governmental interventions, social contact, population mobility, and coverage rate of COVID-19, that could influence the correlation analysis between meteorological factors and COVID-19 spread. He et al. considered city level and public health measures as being controlling factors in the linear regression (He et al. 2021). Moreover, Zhang et al. included a lockdown variable to explain government intervention in local and cross-regional COVID-19 transmission (Zhang et al. 2021). Panel data models with fixed effects were used to identify the links between daily mean temperature, human mobility, and transmission rate s(Shao et al. 2021). Additionally, Ladha et al. added the number of COVID-19 tests into the linear regression (Ladha et al. 2020). Fu et al. entered the government response index and other factors into the distributed lag nonlinear models as independent variables (Fu et al. 2021). However, only four studies identified potential confounding factors and conducted strategies to address the stated confounding factors, such as incorporations into the models and inclusions of dew point, cloud cover, precipitation, relative humidity, air pressure, or wind speed for the same period. The hypothesized associations between climatic variables and COVID-19 may change or not be maintained when a range of potential confounding variables are taken into account. It is strange that many studies regarded public opinion, gene mutation, social isolation, universal masking, and other factors as being confounding factors, but this systematic review does not agree with this perception, and we believe that only those factors meeting the definition of confounders are confounding factors (Valente et al. 2017).

Due to the confinement and reduction of socioeconomic activities caused by the pandemic, the air quality in Victoria, Mexico, has improved. Moreover, temperature was moderately to very strongly negatively correlated with all of the air pollution variables, and PM10 and PM2.5 possessed significant correlations with the cases (Tello-Leal and Macías-Hernández 2020). Hence, we speculated that air quality factors may be confounding factors. Geographic factors, such as elevation, are highly associated with the weather type and can indirectly affect air pressure (Zhang et al. 2021). Furthermore, the sunlight UV radiation dose varies with latitude and season (Tang et al. 2021), but no statistically significant association was found between any geographic characteristic and the R0 in India (Kulkarni et al. 2021). Therefore, the question as to whether latitude and longitude are confounding factors requires further study.

Another important issue is the direct link between meteorological variables and collinearity problems. Fan et al. performed a multicollinearity test to verify the degree to which weather variables were related to each other and found that multicollinearity was not a primary issue (Fan et al. 2021). Instead of using average daily temperature, Byass adopted the averages of maximum and minimum daily temperatures as a single measure of temperature to avoid collinearity between maximum and minimum temperatures and solar radiation when constructing a multivariable regression model (Byass 2020). To solve the problem that temperature and UV index were highly correlated, two separate models were used to fit for the temperature and the UV index, respectively, and all of the other variables were kept identical (Islam et al. 2021). Some studies were concerned about the relationships between meteorological factors and provided the correlation coefficients. For example, Tello-Leal et al. demonstrated the Pearson correlation coefficient matrix for main variables by using a dataset of the last 4 weeks of the partial lockdown (Tello-Leal and Macías-Hernández 2020). Moreover, Rendana et al. provided the Spearman correlation coefficients between wind speed and other meteorological factors and found that wind speed was positively associated with rainfall and temperature, as well as the fact that the correlation may be influenced by seasonal characteristics (Rendana 2020; Thangariyal et al. 2020).

Interpretation and understanding of the main results

When considering the existing scientific evidence, higher temperatures could slow the progression of the COVID-19 epidemic to a certain extent because high temperatures may reduce the viability, survival, activation, and infectivity of the virus. Fifteen studies believed that low temperature was related to higher morbidity. The possible reason for this effect is that the activity of the crowd is more indoors and windows are usually closed, which may increase the frequency of contact between people when it is cold or windy outside. For other climate variables, their correlations with the epidemic can vary, and there is not a relatively consistent view, due to a small amount of literature. Hence, more studies are needed.

There is an issue that cannot be ignored—variable contributions. There are several studies considering this issue. Diao et al. used the threshold value of the VIF to differentiate between low and high contributions and found a higher population density resulted in longer spread and decay durations, whereas meteorological factors had little effect on the durations (Diao et al. 2021). Malki et al. ranked feature importance through a random forest feature selector algorithm and found that temperature and hours of sunlight were important features for infected cases of COVID-19 cases, and climate factors were more important than demographics, such as population, age, and urban percentage, when inspecting mortality (Malki et al. 2020). Kulkarni et al. estimated the proportional reduction in error by using an established approach to quantify the relative contribution of each covariate with the time-dependent R0 and found that the contributions of air temperature and wind speed to dampening the R0 estimate were 3–4 times weaker than that in the countrywide lockdown phases 2–4 (Kulkarni et al. 2021). Hence, governments should take necessary human mobility restrictions and precautionary measures and regard prevention and control of the epidemic as regular.

Strengths and limitations

The greatest strength of our systematic review was that all of the meteorological variables appearing in the included studies were contained, including temperature, humidity, wind, dew point, temperature range, solar radiation, sunshine duration, pressure, evaporation, precipitation, and visibility. In addition, we have provided reference information for global epidemic control and proposed new breakthrough directions for future research. However, we only searched three databases, which could result in biases. The identification of confounding variables, the control of collinearity problems, and the consideration of influential factors were also important limitations of this systematic review. Moreover, there was no consideration of detailed information of cases such as age, weight, personal health status, and other factors. The relatively longer study periods could avoid the bias caused by various ecological factors over time, and more proper processing methods should be explored. Lastly, days of lagging effects and incubation periods need to be distinguished, especially if we want to consider both factors. More investigation is required to address the stated limitations.

Recommendations for future research

In future research, it is better to solve the previously stated limitations as much as possible and to pay more attention to the weathers and not to popular demand. In addition, large-scale multicenter studies may be able to avoid many biases and obtain more concrete results. The optimization of existing models and the addition of prediction models or spatial-temporal models at a more specific and proper level are also good choices. Another essential consideration for future research is vaccine popularization and patient personal information when exploring the correlation between climate factors and the transmission of COVID-19.

Conclusion

In summary, based on a low level of evidence and limited studies, these climate variables alone could not explain most of the variability in disease transmission, but higher temperatures could slow the progression of the COVID-19 epidemic (to a certain extent). It is certain that weather factors, especially temperature, humidity, wind speed, and ultraviolet light, could play an important role in the epidemic, but the contribution of meteorological factors is relatively small compared to factors like lockdown, social interaction, herd immunity, migration patterns, population density, personal hygiene, defense mechanisms, obedience of individuals to policies, and socio-economic level. Therefore, countries should focus more on health policies and vaccines while taking into account the influence of weather on outbreaks.