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      Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China

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          Abstract

          Meteorological parameters are the important factors influencing the infectious diseases such as severe acute respiratory syndrome (SARS) and influenza. This study aims to explore the association between Corona Virus Disease 2019 (COVID-19) deaths and weather parameters. In this study, we collected the daily death numbers of COVID-19, meteorological parameters and air pollutant data from 20 January 2020 to 29 February 2020 in Wuhan, China. Generalized additive model was applied to explore the effect of temperature, humidity and diurnal temperature range on the daily death counts of COVID-19. There were 2299 COVID-19 death counts in Wuhan during the study period. A positive association with COVID-19 daily death counts was observed for diurnal temperature range (r = 0.44), but negative association for relative humidity (r = −0.32). In addition, one unit increase in diurnal temperature range was only associated with a 2.92% (95% CI: 0.61%, 5.28%) increase in COVID-19 deaths in lag 3. However, both 1 unit increase of temperature and absolute humidity were related to the decreased COVID-19 death in lag 3 and lag 5, with the greatest decrease both in lag 3 [−7.50% (95% CI: −10.99%, −3.88%) and −11.41% (95% CI: −19.68%, −2.29%)]. In summary, this study suggests the temperature variation and humidity may also be important factors affecting the COVID-19 mortality.

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          Highlights

          • First study to explore the effects of meteorological factors on COVID-19 mortality

          • A positive association is found between daily death counts of COVID-19 and DTR.

          • Absolute humidity is negatively associated with daily death counts of COVID-19.

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          A new coronavirus associated with human respiratory disease in China

          Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health 1–3 . Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing 4 of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China 5 . This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans.
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            A novel coronavirus outbreak of global health concern

            In December, 2019, Wuhan, Hubei province, China, became the centre of an outbreak of pneumonia of unknown cause, which raised intense attention not only within China but internationally. Chinese health authorities did an immediate investigation to characterise and control the disease, including isolation of people suspected to have the disease, close monitoring of contacts, epidemiological and clinical data collection from patients, and development of diagnostic and treatment procedures. By Jan 7, 2020, Chinese scientists had isolated a novel coronavirus (CoV) from patients in Wuhan. The genetic sequence of the 2019 novel coronavirus (2019-nCoV) enabled the rapid development of point-of-care real-time RT-PCR diagnostic tests specific for 2019-nCoV (based on full genome sequence data on the Global Initiative on Sharing All Influenza Data [GISAID] platform). Cases of 2019-nCoV are no longer limited to Wuhan. Nine exported cases of 2019-nCoV infection have been reported in Thailand, Japan, Korea, the USA, Vietnam, and Singapore to date, and further dissemination through air travel is likely.1, 2, 3, 4, 5 As of Jan 23, 2020, confirmed cases were consecutively reported in 32 provinces, municipalities, and special administrative regions in China, including Hong Kong, Macau, and Taiwan. 3 These cases detected outside Wuhan, together with the detection of infection in at least one household cluster—reported by Jasper Fuk-Woo Chan and colleagues 6 in The Lancet—and the recently documented infections in health-care workers caring for patients with 2019-nCoV indicate human-to-human transmission and thus the risk of much wider spread of the disease. As of Jan 23, 2020, a total of 835 cases with laboratory-confirmed 2019-nCoV infection have been detected in China, of whom 25 have died and 93% remain in hospital (figure ). 3 Figure Timeline of early stages of 2019-nCoV outbreak 2019-nCoV=2019 novel coronavirus. In The Lancet, Chaolin Huang and colleagues 7 report clinical features of the first 41 patients admitted to the designated hospital in Wuhan who were confirmed to be infected with 2019-nCoV by Jan 2, 2020. The study findings provide first-hand data about severity of the emerging 2019-nCoV infection. Symptoms resulting from 2019-nCoV infection at the prodromal phase, including fever, dry cough, and malaise, are non-specific. Unlike human coronavirus infections, upper respiratory symptoms are notably infrequent. Intestinal presentations observed with SARS also appear to be uncommon, although two of six cases reported by Chan and colleagues had diarrhoea. 6 Common laboratory findings on admission to hospital include lymphopenia and bilateral ground-glass opacity or consolidation in chest CT scans. These clinical presentations confounded early detection of infected cases, especially against a background of ongoing influenza and circulation of other respiratory viruses. Exposure history to the Huanan Seafood Wholesale market served as an important clue at the early stage, yet its value has decreased as more secondary and tertiary cases have appeared. Of the 41 patients in this cohort, 22 (55%) developed severe dyspnoea and 13 (32%) required admission to an intensive care unit, and six died. 7 Hence, the case-fatality proportion in this cohort is approximately 14·6%, and the overall case fatality proportion appears to be closer to 3% (table ). However, both of these estimates should be treated with great caution because not all patients have concluded their illness (ie, recovered or died) and the true number of infections and full disease spectrum are unknown. Importantly, in emerging viral infection outbreaks the case-fatality ratio is often overestimated in the early stages because case detection is highly biased towards the more severe cases. As further data on the spectrum of mild or asymptomatic infection becomes available, one case of which was documented by Chan and colleagues, 6 the case-fatality ratio is likely to decrease. Nevertheless, the 1918 influenza pandemic is estimated to have had a case-fatality ratio of less than 5% 13 but had an enormous impact due to widespread transmission, so there is no room for complacency. Table Characteristics of patients who have been infected with 2019-nCoV, MERS-CoV, and SARS-CoV7, 8, 10, 11, 12 2019-nCoV * MERS-CoV SARS-CoV Demographic Date December, 2019 June, 2012 November, 2002 Location of first detection Wuhan, China Jeddah, Saudi Arabia Guangdong, China Age, years (range) 49 (21–76) 56 (14–94) 39·9 (1–91) Male:female sex ratio 2·7:1 3·3:1 1:1·25 Confirmed cases 835† 2494 8096 Mortality 25† (2·9%) 858 (37%) 744 (10%) Health-care workers 16‡ 9·8% 23·1% Symptoms Fever 40 (98%) 98% 99–100% Dry cough 31 (76%) 47% 29–75% Dyspnoea 22 (55%) 72% 40–42% Diarrhoea 1 (3%) 26% 20–25% Sore throat 0 21% 13–25% Ventilatory support 9·8% 80% 14–20% Data are n, age (range), or n (%) unless otherwise stated. 2019-nCoV=2019 novel coronavirus. MERS-CoV=Middle East respiratory syndrome coronavirus. SARS-CoV=severe acute respiratory syndrome coronavirus. * Demographics and symptoms for 2019-nCoV infection are based on data from the first 41 patients reported by Chaolin Huang and colleagues (admitted before Jan 2, 2020). 8 Case numbers and mortalities are updated up to Jan 21, 2020) as disclosed by the Chinese Health Commission. † Data as of Jan 23, 2020. ‡ Data as of Jan 21, 2020. 9 As an RNA virus, 2019-nCoV still has the inherent feature of a high mutation rate, although like other coronaviruses the mutation rate might be somewhat lower than other RNA viruses because of its genome-encoded exonuclease. This aspect provides the possibility for this newly introduced zoonotic viral pathogen to adapt to become more efficiently transmitted from person to person and possibly become more virulent. Two previous coronavirus outbreaks had been reported in the 21st century. The clinical features of 2019-nCoV, in comparison with SARS-CoV and Middle East respiratory syndrome (MERS)-CoV, are summarised in the table. The ongoing 2019-nCoV outbreak has undoubtedly caused the memories of the SARS-CoV outbreak starting 17 years ago to resurface in many people. In November, 2002, clusters of pneumonia of unknown cause were reported in Guangdong province, China, now known as the SARS-CoV outbreak. The number of cases of SARS increased substantially in the next year in China and later spread globally, 14 infecting at least 8096 people and causing 774 deaths. 12 The international spread of SARS-CoV in 2003 was attributed to its strong transmission ability under specific circumstances and the insufficient preparedness and implementation of infection control practices. Chinese public health and scientific capabilities have been greatly transformed since 2003. An efficient system is ready for monitoring and responding to infectious disease outbreaks and the 2019-nCoV pneumonia has been quickly added to the Notifiable Communicable Disease List and given the highest priority by Chinese health authorities. The increasing number of cases and widening geographical spread of the disease raise grave concerns about the future trajectory of the outbreak, especially with the Chinese Lunar New Year quickly approaching. Under normal circumstances, an estimated 3 billion trips would be made in the Spring Festival travel rush this year, with 15 million trips happening in Wuhan. The virus might further spread to other places during this festival period and cause epidemics, especially if it has acquired the ability to efficiently transmit from person to person. Consequently, the 2019-nCoV outbreak has led to implementation of extraordinary public health measures to reduce further spread of the virus within China and elsewhere. Although WHO has not recommended any international travelling restrictions so far, 15 the local government in Wuhan announced on Jan 23, 2020, the suspension of public transportation, with closure of airports, railway stations, and highways in the city, to prevent further disease transmission. 16 Further efforts in travel restriction might follow. Active surveillance for new cases and close monitoring of their contacts are being implemented. To improve detection efficiency, front-line clinics, apart from local centres for disease control and prevention, should be armed with validated point-of-care diagnostic kits. Rapid information disclosure is a top priority for disease control and prevention. A daily press release system has been established in China to ensure effective and efficient disclosure of epidemic information. Education campaigns should be launched to promote precautions for travellers, including frequent hand-washing, cough etiquette, and use of personal protection equipment (eg, masks) when visiting public places. Also, the general public should be motivated to report fever and other risk factors for coronavirus infection, including travel history to affected area and close contacts with confirmed or suspected cases. Considering that substantial numbers of patients with SARS and MERS were infected in health-care settings, precautions need to be taken to prevent nosocomial spread of the virus. Unfortunately, 16 health-care workers, some of whom were working in the same ward, have been confirmed to be infected with 2019-nCoV to date, although the routes of transmission and the possible role of so-called super-spreaders remain to be clarified. 9 Epidemiological studies need to be done to assess risk factors for infection in health-care personnel and quantify potential subclinical or asymptomatic infections. Notably, the transmission of SARS-CoV was eventually halted by public health measures including elimination of nosocomial infections. We need to be wary of the current outbreak turning into a sustained epidemic or even a pandemic. The availability of the virus' genetic sequence and initial data on the epidemiology and clinical consequences of the 2019-nCoV infections are only the first steps to understanding the threat posed by this pathogen. Many important questions remain unanswered, including its origin, extent, and duration of transmission in humans, ability to infect other animal hosts, and the spectrum and pathogenesis of human infections. Characterising viral isolates from successive generations of human infections will be key to updating diagnostics and assessing viral evolution. Beyond supportive care, 17 no specific coronavirus antivirals or vaccines of proven efficacy in humans exist, although clinical trials of both are ongoing for MERS-CoV and one controlled trial of ritonavir-boosted lopinavir monotherapy has been launched for 2019-nCoV (ChiCTR2000029308). Future animal model and clinical studies should focus on assessing the effectiveness and safety of promising antiviral drugs, monoclonal and polyclonal neutralising antibody products, and therapeutics directed against immunopathologic host responses. We have to be aware of the challenge and concerns brought by 2019-nCoV to our community. Every effort should be given to understand and control the disease, and the time to act is now. This online publication has been corrected. The corrected version first appeared at thelancet.com on January 29, 2020
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              Influenza Virus Transmission Is Dependent on Relative Humidity and Temperature

              Introduction Influenza A virus, of the family Orthomyxoviridae, carries an RNA genome consisting of eight segments of negative-stranded RNA. This genome encodes one or two non-structural proteins and nine structural proteins, which, together with a host cell–derived lipid envelope, comprise the influenza virus particle. Influenza virus causes widespread morbidity and mortality among human populations worldwide: in the United States alone, an average of 41,400 deaths and 1.68 million hospitalizations [1] are attributed to influenza each year. In temperate regions like the United States, this impact is felt predominantly during the winter months; that is, epidemics recur with a highly predictable seasonal pattern. In northern latitudes, influenza viruses circulate from November to March, while in the southern hemisphere influenza occurs primarily from May to September [2]. Tropical regions, by contrast, experience influenza throughout the year, although increased incidence has been correlated with rainy seasons [2,3]. Despite extensive documentation of the seasonal cycles of influenza and curiosity as to their causes, little concrete data is available to indicate why influenza virus infections peak in the wintertime. Theories to explain the seasonal variation of influenza have therefore proliferated over the years (reviewed in [4]). Current hypotheses include fluctuations in host immune competence mediated by seasonal factors such as melatonin [5] and vitamin D [6] levels; seasonal changes in host behavior, such as school attendance, air travel [7], and indoor crowding during cold or rainy weather; and environmental factors, including temperature [8], relative humidity (RH), and the direction of air movement in the upper atmosphere [9]. In early studies using mouse-adapted strains of influenza virus, experiments performed in the winter months yielded a transmission rate of 58.2%; in contrast, a rate of only 34.1% was observed in the summer months [10]. While these data suggested that the seasonal influences acting on humans also affect laboratory mice, no mechanism to explain the observations was identified. Herein, we directly tested the hypotheses that ambient air temperature and RH impact the efficiency with which influenza virus is spread. As a mammalian animal model we used Hartley strain guinea pigs, which we have recently shown to be highly susceptible to infection with human influenza viruses [11]. Importantly, we also found that naïve guinea pigs readily become infected when exposed to inoculated guinea pigs, unlike mice, which do not efficiently transmit influenza virus [11]. Thus, by housing infected and naïve guinea pigs together in an environmental chamber, we were able to assess the efficiency of transmission under conditions of controlled RH and temperature. Our data show that both RH and temperature do indeed affect the frequency of influenza virus transmission among guinea pigs, although via apparently differing mechanisms. Results Twenty replicate experiments were performed in which all factors remained constant except for the RH and/or temperature inside the environmental chamber. Each experiment involved eight guinea pigs, and transmission under each set of conditions was assessed in duplicate. The arrangement of animals in the environmental chamber is illustrated in Figure 1. Virus contained in nasal wash samples collected on alternating days post-inoculation (p.i.) was titrated by plaque assay to determine the infection status of each animal. Serum samples were collected from each animal prior to infection and on day 17 p.i., and seroconversion was assessed by hemagglutination inhibition assay (results in Table S1). Figure 1 Arrangement of Infected and Exposed Guinea Pigs in Environmental Chamber In each experiment, eight animals were housed in a Caron 6030 environmental chamber. Each guinea pig was placed in its own cage, and two cages were positioned on each shelf. Naïve animals were placed behind infected animals, such that the direction of airflow was toward the naïve animals. The cages used were open to airflow through the top and one side, both of which were covered by wire mesh. Although infected and exposed guinea pigs were placed in pairs, air flowed freely between shelves, allowing transmission to occur from any infected to any naïve animal. In general, the behavior (level of activity, food and water consumption, symptoms of infection) of guinea pigs was not observed to change with the ambient relative humidity. Likewise, animals housed at 5 °C behaved in a similar manner to those housed at 20 °C. Guinea pigs kept at 30 °C consumed more water than those housed under cooler conditions, and appeared lethargic. Consistent with our previous observations [11], influenza virus–infected guinea pigs did not display detectable symptoms of disease (e.g., weight loss, fever, sneezing, coughing) during the experiments described. Transmission Efficiency Is Dependent on Relative Humidity The results of transmission experiments performed at 20 °C and five different RHs (20%, 35%, 50%, 65%, and 80%) indicated that the efficiency of aerosol spread of influenza virus varied with RH. Transmission was highly efficient (occurred to three or four of four exposed guinea pigs) at low RH values of 20% or 35%. At an intermediate RH of 50%, however, only one of four naïve animals contracted infection. Three of four exposed guinea pigs were infected at 65% RH, while no transmission was observed at a high RH of 80% (Figure 2). Where transmission was observed, the kinetics with which infection was detected in each exposed animal varied between and within experiments. To an extent, we believe this variation is due to the stochastic nature of infection. However, while most infection events were the product of primary transmission from an inoculated animal, others could be the result of secondary transmission from a previously infected, exposed guinea pig. With the exception of the lack of transmission at 80% RH, the observed relationship between transmission and RH is similar to that between influenza virus stability in an aerosol and RH [12], suggesting that at 20 °C the sensitivity of transmission to humidity is due largely to virus stability. Figure 2 Transmission of Influenza Virus from Guinea Pig to Guinea Pig Is Dependent on Relative Humidity Titers of influenza virus in nasal wash samples are plotted as a function of day p.i. Overall transmission rate and the RH and temperature conditions of each experiment are stated underneath the graph. Titers from intranasally inoculated guinea pigs are represented as dashed lines; titers from exposed guinea pigs are shown with solid lines. Virus titrations were performed by plaque assay on Madin Darby canine kidney cells. Transmission Efficiency Is Inversely Correlated with Temperature To test whether cold temperatures would increase transmission, the ambient temperature in the chamber was lowered to 5 °C and experiments were performed at 35%–80% RH. Overall, transmission was more efficient at 5 °C: 75%–100% transmission occurred at 35% and 50% RH, and 50% transmission was observed at 65% and 80% RH (Figure 3A–3H). The statistical significance of differences in transmission rates at 5 °C compared to 20 °C was assessed using the Fisher's exact test. While at 35% and 65% RH the difference was not found to be significant, at both 50% and 80% RH, transmissibility at 5 °C was found to be greater than that at 20 °C (p 20 °C) and either intermediate (50%) or high (80%) RHs. Materials and Methods Virus. Influenza A/Panama/2007/99 virus (Pan/99; H3N2) was kindly supplied by Adolfo García-Sastre and was propagated in Madin Darby canine kidney cells. Animals. Female Hartley strain guinea pigs weighing 300–350 g were obtained from Charles River Laboratories. Animals were allowed free access to food and water and kept on a 12-h light/dark cycle. Guinea pigs were anesthetized for the collection of blood and of nasal wash samples, using a mixture of ketamine (30 mg/kg) and xylazine (2 mg/kg), administered intramuscularly. All procedures were performed in accordance with the Institutional Animal Care and Used Committee guidelines. During guinea pig transmission experiments, strict measures were followed to prevent aberrant cross-contamination between cages: sentinel animals were handled before inoculated animals, gloves were changed between cages, and work surfaces were sanitized between guinea pigs. Transmission experiments. The term “aerosol” is used herein to describe respiratory droplets of all sizes. The term “droplet nuclei” is used to refer to droplets that remain airborne (typically less than 5 μm in diameter). Each transmission experiment involved eight guinea pigs. On day 0, four of the eight guinea pigs were inoculated intranasally with 103 PFU of influenza A/Panama/2007/99 virus (150 μl per nostril in phosphate buffered saline [PBS] supplemented with 0.3% bovine serum albumin [BSA]) and housed in a separate room from the remaining animals. At 24 h p.i., each of the eight guinea pigs was placed in a “transmission cage”, a standard rat cage (Ancare R20 series) with an open wire top, which has been modified by replacing one side panel with a wire grid. The transmission cages were then placed into the environmental chamber (Caron model 6030) with two cages per shelf, such that the wire grids opposed each other (Figure 1). In this arrangement, the guinea pigs cannot come into physical contact with each other. Each infected animal was paired on a shelf with a naïve animal. The guinea pigs were housed in this way for 7 d, after which they were removed from the chamber and separated. On day 2 p.i. (day 1 post-exposure) and every second day thereafter up to day 12 p.i., nasal wash samples were collected from anesthetized guinea pigs by instilling 1 ml of PBS-BSA into the nostrils and collecting the wash in a Petri dish. Titers in nasal wash samples were determined by plaque assay of 10-fold serial dilutions on Madin Darby canine kidney cells. Serum samples were collected from each animal prior to infection and on day 17 post-infection, and seroconversion was assessed by hemagglutination inhibition assay. All transmission experiments reported herein were performed between September 2006 and April 2007. Analysis of expression levels of mediators of innate immunity. Guinea pigs were inoculated with 103 PFU of Pan/99 virus intranasally and immediately housed under the appropriate conditions (5 °C or 20 °C and 35% RH). At days 1, 2, 3, 5, and 7 post-infection, three guinea pigs were killed and their nasal turbinates removed. Tissues were placed immediately in RNAlater reagent (Qiagen), and stored at 4 °C for 1 to 5 d. RNA was extracted from equivalent masses of tissue using the RNAeasy Protect Mini kit (Qiagen) and subjected to DNAse treatment (Qiagen). One microgram of RNA was subjected to reverse transcription using MMLV reverse transcriptase (Roche). One microlitre of the resultant product was used as the template in a SYBR green (Invitrogen) real-time PCR assay (Roche Light Cycler 480) with Ampli-taq Gold polymerase (Perkin-Elmer). Primers used were as follows: β-actin f AAACTGGAACGGTGAAGGTG; β-actin r CTTCCTCTGTGGAGGAGTGG; Mx1 f CATCCCYTTGrTCATCCAGT; Mx1 r CATCCCyTTGRTCATCCAGT; MDA-5 f GAGCCAGAGCTGATGARAGC; MDA-5 r TCTTATGWGCATACTCCTCTGG; IL-1β f GAAGAAGAGCCCATCGTCTG; IL-1β r CATGGGTCAGACAACACCAG; RANTES f GCAATGCTAGCAGCTTCTCC; RANTES r TTGCCTTGAAAGATGTGCTG; TLR3 f TAACCACGCACTCTGTTTGC; TLR3 r ACAGTATTGCGGGATCCAAG; TNFα f TTCCGGGCAGATCTACTTTG; TNFα r TGAACCAGGAGAAGGTGAGG; MCP-1 f ATTGCCAAACTGGACCAGAG; MCP-1 r CTACGGTTCTTGGGGTCTTG; MCP-3 f TCATTGCAGTCCTTCTGTGC; MCP-3 r TAGTCTCTGCACCCGAATCC; IFNγ f GACCTGAGCAAGACCCTGAG; IFNγ r TGGCTCAGAATGCAGAGATG; STAT1 f AAGGGGCCATCACATTCAC; STAT1 r GCTTCCTTTGGCCTGGAG; TBK1 f CAAGAAACTyTGCCwCAGAAA; TBK1 r AGGCCACCATCCAykGTTA; IRF5 f CAAACCCCGaGAGAAGAAG; IRF5 r CTGCTGGGACtGCCAGA; IRF7 f TGCAAGGTGTACTGGGAGGT; IRF7 r TCACCAGGATCAGGGTCTTC (where R = A or G, Y = C or T, W = A or T, K = T or G). Primer sequences were based either on guinea pig mRNA sequences available in GenBank (MCP1, MCP3, IL-1b, IFNγ, RANTES, TLR3, TNFα, and β-actin), or on the consensus sequence of all species available in GenBank (Mx1, MDA-5, IRF5, IRF7, STAT1, and TBK1). Sequencing of each PCR product indicated that all primer pairs were specific for the expected transcript. Reactions were performed in duplicate and normalized by dividing the mean value of the cycle threshold (Ct) of β-actin expressed as an exponent of 2 (2Ct) by the mean value of 2Ct for the target gene. The fold-induction over the mock-infected was then calculated by dividing the normalized value by the normalized mock value. Data is represented in Figure 5 as the mean of three like samples (nasal turbinates harvested on the same day p.i. from three guinea pigs) ± standard deviation. Statistical analyses. Statistical analyses were performed using GraphPad Prism 5 software. Supporting Information Table S1 Seroconversion of Inoculated and Exposed Guinea Pigs Results of hemagglutination inhibition tests for each transmission experiment are shown. (58 KB DOC) Click here for additional data file. Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/Genbank/index.html) accession numbers of guinea pig genes used for primer design are as follows: β-actin (AF508792.1); IFNγ (AY151287.1); IL-1β (AF119622); MCP-1 (L04985); MCP-3 (AB014340); RANTES (CPU77037); TLR3 (DQ415679.1); and TNFα (CPU77036).
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                Author and article information

                Contributors
                Journal
                Sci Total Environ
                Sci. Total Environ
                The Science of the Total Environment
                Elsevier B.V.
                0048-9697
                1879-1026
                26 March 2020
                1 July 2020
                26 March 2020
                : 724
                : 138226
                Affiliations
                [a ]Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, People's Republic of China
                [b ]Sexually Transmitted Disease and Acquired Immune Deficiency Syndrome Prevention Branch, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, Gansu 730000, People's Republic of China
                [c ]Department of General Surgery, Lanzhou University First Affiliated Hospital, Lanzhou, Gansu 730000, People's Republic of China
                [d ]Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, People's Republic of China
                [e ]Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Bureau, Shanghai 200030, People's Republic of China
                [f ]Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, People's Republic of China
                Author notes
                [* ]Corresponding author at: Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, People's Republic of China. luob@ 123456lzu.edu.cn
                [1]

                These authors contributed equally to this work.

                Article
                S0048-9697(20)31739-3 138226
                10.1016/j.scitotenv.2020.138226
                7142681
                32408453
                262b7951-3db6-4ed4-bc49-6be2adc8538d
                © 2020 Elsevier B.V. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 20 March 2020
                : 24 March 2020
                : 25 March 2020
                Categories
                Article

                General environmental science
                covid-19,diurnal temperature range,temperature,humidity,generalized additive model

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