key: cord-0812191-k8kwlnqp authors: Fu, Shihua; Wang, Bo; Zhou, Ji; Xu, Xiaocheng; Liu, Jiangtao; Ma, Yueling; Li, Lanyu; He, Xiaotao; Li, Sheng; Niu, Jingping; Luo, Bin; zhang, Kai title: Meteorological Factors, Governmental Responses and COVID-19: Evidence from Four European countries date: 2020-12-09 journal: Environ Res DOI: 10.1016/j.envres.2020.110596 sha: 4792a6ee033d488253dd3516b27b170e44ed8761 doc_id: 812191 cord_uid: k8kwlnqp With the global lockdown, meteorological factors are highly discussed for COVID-19 transmission. In this study, national-specific and region-specific data sets from Germany, Italy, Spain and the United Kingdom were used to explore the effect of temperature, absolute humidity and diurnal temperature range (DTR) on COVID-19 transmission. From February 1(st) to November 1(st), a 7-day COVID-19 case doubling time (Td), meteorological factors with cumulative 14-day-lagged, government response index and other factors were fitted in the distributed lag nonlinear models. The overall relative risk (RR) of the 10th and the 25th percentiles temperature compared to the median were 0.0074 (95% CI: 0.0023, 0.0237) and 0.1220 (95% CI: 0.0667, 0.2232), respectively. The pooled RR of lower (10th, 25th) and extremely high (90th) absolute humidity were 0.3266 (95% CI: 0.1379, 0.7734), 0.6018 (95% CI: 0.4693, 0.7718) and 0.3438 (95% CI: 0.2254, 0.5242), respectively. While the DTR did not have a significant effect on Td. The total cumulative effect of temperature (10th) and absolute humidity (10th, 90th) on Td increased with the change of lag days. Similarly, a decline in temperature and absolute humidity at cumulative 14-day-lagged corresponded to the lower RR on Td in pooled region-specific effects. In summary, the government responses are important factors in alleviating the spread of COVID-19. After controlling that, our results indicate that both the cold and the dry environment also likely facilitate the COVID-19 transmission. Where represents the cumulative number of diagnosed on the day of the study, and 101 represents the cumulative number of diagnosed after an interval of 7 days. 102 The daily meteorological data came from the "Wheat-A" data system 104 (http://www.xiaomaiya.cc/). Based on longitude and latitude, the meteorological data of 326 105 weather stations were matched with regions. Daily meteorological data included 106 average/minimum/maximum temperature, dew-point temperature and average wind velocity. 107 Absolute humidity was calculated indirectly through vapor pressure, using the Clausius-108 Where t is the observation date; W refers to the regions; 3(5 6 ) is the expected value of 144 the Td observed in region W on day t; : is the intercept; < is the regression coefficient; 145 ∑ ?@() represents the two-dimensional matrix of meteorological factors and lag days and the 146 natural cubic spline function with 3 degrees of freedom was used; We defined 14 days as the 147 maximum lag days; &D() denotes the smoother based on natural regression splines; ,, 148 EF G.0 , LM and N%& are the three-day moving average of temperature (df=6), PM 2.5 (df=3), 149 absolute humidity (df=3) and wind velocity (df=3), respectively; log(5 6O= ) is the COVID-19 150 count of logarithmic conversion on t-1 to control potential sequential autocorrelations; DOP 151 denotes the number of people living on land per unit area; Q R B C14 is the GRI at single 152 14-day-lagged; $" means the day of week was controlled as a categorical variable; 153 T C%$& 7 indicates region fixed effects to control for any observable and unobservable 154 characteristics over time that could confound results, such as differences in social, economic 155 and cultural activities, etc. After establishing the DLNM model, we used the random effects model of meta-analysis 157 to pool the national-specific effects of the meteorological factors. We examined the 158 cumulative lag effects of meteorological factors at 10th and 90th percentiles on Td under 159 different lag exposure (lag 03, lag 05, lag 07, lag 09, lag 011 and lag 014). The meta-analysis 160 was based on R software "meta" package. 161 As our design of including multiple regions, we further investigated the region-specific 162 effect estimates of meteorological factors on Td in 42 regions and applied a multivariate 163 meta-analysis to combine the overall effect estimates obtained from the region-specific effect 164 estimates. In short, we used a two-stage analysis. Firstly, DLNM models were applied to each 165 region's data to obtain region-specific effect estimates. Secondly, we applied a multivariate 166 meta-regression model to combine the overall effect estimates (Gasparrini and Armstrong, 167 2013). The multivariate meta-regression analysis was fitted with R software using the 168 "mvmeta" package. 169 Besides, we conducted a sensitivity analysis by changing the interval of the exponential 170 model (eq1) to assess the effect of meteorological factors on Td at different time intervals (5 171 and 9 days) in the DLNM models (eq4-eq6). 172 in these four countries corresponded to shorter Td, while the second increasing wave observed 177 much stronger since August compared with the first one (with a rapid increase since early July 178 in Spain). With the latest data, we could find that the confirmed COVID-19 cases were still on 179 the rising trend, which have not reached the peak so far. However, the correlations between diurnal temperature range (DTR) and Td were weak in 187 Germany, Italy and Spain (r Germany =0.07, r Italy =0.10, r Spain =0.12, respectively), while the correlation 188 in the UK was not statistically significant (r UK =-0.01, P >0.05). There was a negative correlation 189 between population density and Td in four countries, but the correlation was higher in the UK 190 (Table. 1 and Fig.S4 ). In linear regression analysis, the GRI at single 14-day-lagged was highly 191 positively correlated with Td (except for Italy) ( Table.1 The overall estimates from the region-specific effects are similar to the country-specific 218 effect (Fig.6) , indicating that low temperature and low absolute humidity may be a factor 219 leading to shorter Td in four European countries, resulting in a faster spread of the virus. In 220 the sensitivity analysis, the associations between meteorological factors and the Td of 221 different time intervals (5 and 9 days) were robust. The low temperature and extreme absolute 222 humidity have a greater impact on the COVID-19 Td (Table.S1 Previous studies were usually under a linear regression framework, showing that there 261 was a significant negative correlation between humidity and COVID-19 cases (Sarkodie and 262 Owusu, 2020), which need to be confirmed with more precise statistic model. Islam et al. 263 regarded humidity as a driver of SARS℃CoV℃2 transmission, and a higher COVID-19 264 transmission rate was reported in specific humidity ranged from 6 to 9 g/kg (Islam et al., 2020; 265 Runkle et al., 2020b). In line with these studies, we found that extreme (high and low) 266 absolute humidity have a greater impact on the COVID-19 Td, which were still robust in 267 pooled analysis for the four European countries. When the humidity in the air is low, the virus 268 forms small aerosol particles, which increases the risk of viral transmission and reduces 269 immunity (Sarkodie and Owusu, 2020). It's reported that up to 3 hours are needed for transmission. Therefore, our research results need to be further discussed. Thirdly, we only 338 have analyzed the data from four countries covering nine months, which may not be enough 339 to study the COVID-19 change trend at global level. Even so, the conclusion based on the 340 present study at least provides new clues for understanding the relationship between the 341 spread of COVID-19 and temperature and humidity. 342 In summary, the government responses are important factors in alleviating the spread of 344 COVID-19. Our results indicate that both the cold and the dry environment also likely facilitate 345 the COVID-19 transmission after controlling the bias from population density, government 346 response policies, air pollutants and other factors in long study periods covering two increasing 347 waves of COVID-19 in four European countries. This study used data from February 1 st to 348 November 1 st , which provide strong scientific evidence for the importance of stressing the cold 349 weather effect on COVID-19 transmission with the arriving colder season. In particular, we 350 observed that the confirmed case of COVID-19 are still madly increasing in the Northern 351 Hemisphere, so we strongly suggest to provide more public health resources and governmental 352 interventions on the controlling of COVID-19 in this cold season. Besides, studies covering the 353 entire earth in a longer period are urgently needed to quantify the combined effects of 354 meteorological factors and policy interventions on the spread of COVID-19. By doing that, we 355 hope to find the most effective intervention in controlling the COVID-19, particularly before the 356 vaccinating of an effective vaccine against this tricky virus. 357 All data is public, there is no patient contact, and no PIN is required. Therefore, the study 359 does not require ethical approval. 360 Not applicable. 362 All COVID-19 daily confirmed cases were collected from the official websites of 364 national or regional health departments from February 1 st through November 1 st , 2020, which 365 are publicly available. The meteorological datasets used and/or analyzed during the current 366 study are not publicly available but are available from the corresponding author on reasonable 367 request. 368 Infectious 421 Diseases of Poverty Emerging coronaviruses: first SARS, 423 second MERS and third SARS-CoV-2: epidemiological updates of COVID-19 SEIR modeling of the COVID-19 and its dynamics Overview: The history 428 and pediatric perspectives of severe acute respiratory syndromes: Novel or just like SARS Yin LH and others Clinical features of patients infected with 2019 novel coronavirus in Wuhan Time series regression model for 437 infectious disease and weather COVID-19 and climatic factors: A global analysis The sensitivity and specificity analyses of 443 ambient temperature and population size on the transmission rate of the novel coronavirus (COVID-19) 444 in different provinces of Iran Effects of humidity and other factors on 447 the generation and sampling of a coronavirus aerosol Future liasing of the lockdown during COVID-19 pandemic: The dawn is 451 expected at hand from the darkest hour The most eagerly awaited summer of the Anthropocene: A 454 perspective of SARS-CoV-2 decay and seasonal change A chronicle of SARS-CoV-2: Seasonality, 458 environmental fate, transport, inactivation, and antiviral drug resistance diagnosis, prognosis, transmission and treatment Effect of meteorological parameters on spread of COVID-19 in India and air 464 quality during lockdown Environmental factors on the SARS epidemic: air 467 temperature, passage of time and multiplicative effect of hospital infection Impact of 470 meteorological factors on the COVID-19 transmission: A multi-city study in China Impact of 473 probable interaction of low temperature and ambient fine particulate matter on the function of rats 474 alveolar macrophages Effects of temperature 477 variation and humidity on the death of COVID-19 in Wuhan Mechanistic insights into the effect of 480 humidity on airborne influenza virus survival, transmission and incidence The calendar of epidemics: Seasonal cycles of infectious diseases Biomedical application, 485 drug delivery and metabolic pathway of antiviral nanotherapeutics for combating viral pandemic: A 486 review Severe 488 Acute Respiratory Syndrome Coronavirus 2 Transmission Potential, Iran, 2020. Emerging infectious 489 diseases Warmer 491 weather unlikely to reduce the COVID-19 transmission: An ecological study in 202 locations in 8 492 countries Temperature significantly changes COVID-19 495 transmission in (sub) tropical cities of Brazil SARS-CoV-2, SARS-CoV, and MERS-COV: A 499 comparative overview Short-term effects of 501 specific humidity and temperature on COVID-19 morbidity in select US cities Impact of weather on COVID-19 pandemic in Turkey. Science of The Total 504 Environment Impact of meteorological factors on COVID-19 pandemic: 506 Evidence from top 20 countries with confirmed cases Seasonality and selective trends in viral acute respiratory tract infections Impact of temperature on the 511 dynamics of the COVID-19 outbreak in China Is temperature reducing the transmission of COVID-19 ? Correlation 519 between weather and Covid-19 pandemic in Jakarta, Indonesia. Science of The Total Environment Rapid response to the COVID-19 pandemic: Vietnam 522 government's experience and preliminary success Aerosol and Surface Stability of SARS-CoV-2 526 as Compared with SARS-CoV-1 Stability of Middle East respiratory syndrome 529 coronavirus (MERS-CoV) under different environmental conditions Quasi-Poisson vs. negative binomial regression: how should 532 we model overdispersed count data? Using a partial differential equation with Google Mobility data to 534 predict COVID-19 in Arizona Impact of hydrological factors on the 537 dynamic of COVID-19 epidemic: A multi-region study in China Unique epidemiological and clinical features of the 540 emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures World Health Organization (WHO). 2020a. Clinical Characteristics of Covid-19 in China World Health Organization (WHO). 2020b. Director-General's opening remarks at the media 546 briefing on COVID-19 -11th World Health Organization (WHO). 2020c. Coronavirus Disease (COVID-19) Dashboard | WHO 550 Coronavirus Disease (COVID-19) Dashboard Characteristics of and Important Lessons From the Coronavirus 552 Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese 553 No association of COVID-19 559 transmission with temperature or UV radiation in Chinese cities Measures for diagnosing and treating infections by a 562 novel coronavirus responsible for a pneumonia outbreak originating in Wuhan Humidity May Modify the Relationship between Temperature and Cardiovascular Mortality in 566 Zhejiang Province, China COVID-19 seeding time and doubling time 569 model: an early epidemic risk assessment tool COVID-19): A Perspective from China We thank the other participants of the study for their valuable contributions. The authors 370 would also like to thank the investigators and the staff of the public data for making the study 371