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      Association between Weather Data and COVID-19 Pandemic Predicting Mortality Rate: Machine Learning Approaches

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          Abstract

          Nowadays, a significant number of infectious diseases such as human coronavirus disease (COVID-19) are threatening the world by spreading at an alarming rate. Some of the literatures pointed out that the pandemic is exhibiting seasonal patterns in its spread, incidence and nature of the distribution. In connection to the spread and distribution of the infection, scientific analysis that answers the questions whether the next summer can save people from COVID-19 is required. Many researchers have been exclusively asked whether high temperature during summer can slow down the spread of the COVID-19 as it has with other seasonal flues. Since there are a lot of questions that are unanswered right now, and many mysteries aspects about the COVID-19 that is still unknown to us, in-depth study and analysis of associated weather features are required. Moreover, understanding the nature of COVID-19 and forecasting the spread of COVID-19 request more investigation of the real effect of weather variables on the transmission of the COVID-19 among people. In this work, various regressor machine learning models are proposed to extract the relationship between different factors and the spreading rate of COVID-19. The machine learning algorithms employed in this work estimate the impact of weather variables such as temperature and humidity on the transmission of COVID-19 by extracting the relationship between the number of confirmed cases and the weather variables on certain regions. To validate the proposed method, we have collected the required datasets related to weather and census features and necessary prepossessing is carried out. From the experimental results, it is shown that the weather variables are more relevant in predicting the mortality rate when compared to the other census variables such as population, age, and urbanization. Thus, from this result, we can conclude that temperature and humidity are important features for predicting COVID-19 mortality rate. Moreover, it is indicated that the higher the value of temperature the lower number of infection cases.

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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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            Is Open Access

            Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

            Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great potential to predict pillar stability. This study aims to predict hard rock pillar stability using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms. First, 236 cases with five indicators were collected from seven hard rock mines. Afterwards, the hyperparameters of each model were tuned using a five-fold cross validation (CV) approach. Based on the optimal hyperparameters configuration, prediction models were constructed using training set (70% of the data). Finally, the test set (30% of the data) was adopted to evaluate the performance of each model. The precision, recall, and F1 indexes were utilized to analyze prediction results of each level, and the accuracy and their macro average values were used to assess the overall prediction performance. Based on the sensitivity analysis of indicators, the relative importance of each indicator was obtained. In addition, the safety factor approach and other ML algorithms were adopted as comparisons. The results showed that GBDT, XGBoost, and LightGBM algorithms achieved a better comprehensive performance, and their prediction accuracies were 0.8310, 0.8310, and 0.8169, respectively. The average pillar stress and ratio of pillar width to pillar height had the most important influences on prediction results. The proposed methodology can provide a reliable reference for pillar design and stability risk management.
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              Probabilistic model of influenza virus transmissibility at various temperature and humidity conditions

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                Author and article information

                Contributors
                Journal
                Chaos Solitons Fractals
                Chaos Solitons Fractals
                Chaos, Solitons, and Fractals
                Published by Elsevier Ltd.
                0960-0779
                0960-0779
                17 July 2020
                17 July 2020
                : 110137
                Affiliations
                [a ]College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
                [b ]Faculty of Science, Tanta University, Egypt
                [c ]Faculty of Computers and Artificial Intelligence, Cairo University, Egypt
                [d ]Department of Computer Science, Institute of Technology, Dire Dawa University, Ethiopia
                [e ]Mansoura University, Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura, Egypt
                Author notes
                Article
                S0960-0779(20)30533-6 110137
                10.1016/j.chaos.2020.110137
                7367008
                32834583
                2d514e4e-c0c2-42f0-b1cf-6b3b0f27c30c
                © 2020 Published by Elsevier Ltd.

                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
                : 15 June 2020
                : 14 July 2020
                : 15 July 2020
                Categories
                Article

                covid-19,ols,temperature,humidity,machine learning,prediction
                covid-19, ols, temperature, humidity, machine learning, prediction

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