Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence
Abstract
:1. Introduction
- In connection with the COVID-19 epidemic, a variety of research projects were carried out. However, limited research has paid attention to the prediction of a pandemic. Therefore, in this paper, we used predictive analysis to determine the trend of the growing epidemic curve.
- This study conducted a sentiment analysis using Twitter in addition to EDA, which was a previously unexplored area in terms of lockdown circumstances. While EDA explores the hidden functionality of the data, sentiment analysis assists to comprehend the emotional state of the behavioral pattern.
- Additionally, this study validated the usefulness of social media data for examining health-related communications, as well as for determining the emotional condition of the public during a healthcare crisis.
2. Related Works
3. Materials and Methods
3.1. Epidemiological Data
3.1.1. Time-Series Data Analysis
3.1.2. Data Preprocessing
3.1.3. Support Vector Machine (SVM) Model for Prediction
3.2. Twitter Data
Model Building
4. Results
4.1. Exploratory Data Analysis
4.2. Predictive Analysis Using Support Vector Machine (SVM)
4.3. Comparison of Visual Data from Multiple Reliable Web Sources
4.4. Results of Tweets to Predict Emotions (Anxiety and Stress)
5. Discussion
6. Implications and Limitations
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Li, Q.; Guan, X.; Wu, P.; Wang, X.; Zhou, L.; Tong, Y.; Ren, R.; Leung, K.S.M.; Lau, E.H.Y.; Wong, J.Y.; et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. N. Engl. J. Med. 2020, 382, 1199–1207. [Google Scholar] [CrossRef]
- Jung, S.-M.; Akhmetzhanov, A.R.; Hayashi, K.; Linton, N.M.; Yang, Y.; Yuan, B.; Kobayashi, T.; Kinoshita, R.; Nishiura, H. Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases. J. Clin. Med. 2020, 9, 523. [Google Scholar] [CrossRef] [Green Version]
- Novel, C.P.E.R.E. The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) in China. Zhonghua Liuxingbingxue Zazhi 2020, 41, 145. [Google Scholar]
- Liu, Y.; Gayle, A.A.; Wilder-Smith, A.; Rocklöv, J. The Reproductive Number of COVID-19 Is Higher Compared to SARS Coronavirus. J. Travel Med. 2020, 27, 1–4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Heymann, D.L.; Shindo, N. COVID-19: What Is Next for Public Health? Lancet 2020, 395, 542–545. [Google Scholar] [CrossRef] [Green Version]
- Xu, Z.; Shi, L.; Wang, Y.; Zhang, J.; Huang, L.; Zhang, C.; Liu, S.; Zhao, P.; Liu, H.; Zhu, L.; et al. Pathological Findings of COVID-19 Associated With Acute Respiratory Distress Syndrome. Lancet Respir. Med. 2020, 8, 420–422. [Google Scholar] [CrossRef]
- Giwa, A.; Desai, A. Novel Coronavirus COVID-19: An Overview for Emergency Clinicians. Emerg. Med. Pract. 2020, 22, 1–21. [Google Scholar] [PubMed]
- Hasan, N. A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model. Internet Things 2020, 11, 100228. [Google Scholar] [CrossRef]
- Hasan, N.; Bao, Y.; Shawon, A.; Huang, Y. DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image. SN Comput. Sci. 2021, 2, 1–11. [Google Scholar] [CrossRef]
- Dhama, K.; Sharun, K.; Tiwari, R.; Sircar, S.; Bhat, S.; Malik, Y.S.; Singh, K.P.; Chaicumpa, W.; Bonilla-Aldana, D.K.; Rodriguez-Morales, A.J. Coronavirus Disease 2019–COVID-19. Clin. Microbiol. Rev. 2020, 33, e00028-20. [Google Scholar] [CrossRef]
- Dsouza, J.; Velan, Senthil S. Using Exploratory Data Analysis for Generating Inferences on the Correlation of COVID-19 Cases. In Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagapur, India, 1–3 July 2020; pp. 1–6. [Google Scholar] [CrossRef]
- Dey, S.K.; Rahman, M.; Siddiqi, U.R.; Howlader, A. Analyzing the Epidemiological Outbreak of COVID-19: A Visual Exploratory Data Analysis Approach. J. Med. Virol. 2020, 92, 632–638. [Google Scholar] [CrossRef] [PubMed]
- Zu, Z.Y.; Di Jiang, M.; Xu, P.P.; Chen, W.; Ni, Q.Q.; Lu, G.M.; Zhang, L.J. Coronavirus Disease 2019 (COVID-19): A Perspective from China. Radiology 2020, 296, E15–E25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Johns Hopkins University. COVID-19 Dashboard. Available online: https://coronavirus.jhu.edu/map.html (accessed on 25 January 2020).
- Centers for Disease Control and Prevention. Coronavirus Disease 2019 (COVID-19). Available online: https://www.cdc.gov/coronavirus/2019-ncov/index.html (accessed on 25 January 2020).
- WHO. WHO Coronavirus (COVID-19) Dashboard. 2020. Available online: https://covid19.who.int/ (accessed on 28 January 2020).
- Thompson, R.; Gilligan, C.; Cunniffe, N. When Does a Minor Outbreak Become a Major Epidemic? Linking the Risk from Invading Pathogens to Practical Definitions of a Major Epidemic. J. R. Soc. Interface 2019. [Google Scholar] [CrossRef] [Green Version]
- Bastos, S.B.; Cajueiro, D.O. Modeling and forecasting the early evolution of the Covid-19 pandemic in Brazil. Sci. Rep. 2020, 10, 19457. [Google Scholar] [CrossRef] [PubMed]
- Shahi, G.K.; Dirkson, A.; Majchrzak, T.A. An Exploratory Study of COVID-19 Misinformation on Twitter. Online Soc. Networks Media 2021, 22, 100104. [Google Scholar] [CrossRef] [PubMed]
- Latif, A.; Sajid, I. Pakistan Stays under Lockdown Amid Coronavirus Outbreak. 2020. Available online: https://www.aa.com.tr/en/asia-pacific/pakistan-stays-under-lockdown-amid-coronavirus-outbreak/1777394 (accessed on 1 July 2021).
- Nation, T. Pakistan Continues to Observe Partial Lockdown Across Country. 2020. Available online: https://nation.com.pk/26-Mar-2020/partial-lockdown-continues-across-pakistan (accessed on 2 March 2020).
- Abd-Alrazaq, A.; Alhuwail, D.; Househ, M.; Hamdi, M.; Shah, Z. Top Concerns of Tweeters during the COVID-19 Pandemic: Infoveillance Study. J. Med. Internet Res. 2020, 22, e19016. [Google Scholar] [CrossRef] [Green Version]
- Jordan, S.E.; Hovet, S.E.; Fung, I.C.-H.; Liang, H.; Fu, K.-W.; Tse, Z.T.H. Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response. Data 2019, 4, 6. [Google Scholar] [CrossRef] [Green Version]
- Naseer, J.Q.K.; Qazi, A.; Avuglah, B.K.; Tahir, R.; Rasheed, R.A.; Khan, S.K.; Khan, B.A.; Zeeshan, M.; Humayun, M.A.; Naseem, U. Travel behaviour prediction amid covid-19 underlaying situational awareness theory and health belief model. Behav. Inf. Technol. 2021. [Google Scholar] [CrossRef]
- Abo, M.E.M.; Raj, R.G.; Qazi, A. A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges. IEEE Access 2019, 7, 162008–162024. [Google Scholar] [CrossRef]
- Qazi, A.; Bhowmik, C.; Hussain, F.; Yang, S.; Naseem, U.; Adebayo, A.-A.; Gumaei, A.; Al-Rakhami, M. Analyzing the Public Opinion As a Guide for Renewable-Energy Status in Malaysia: A Case Study. IEEE Trans. Eng. Manag. 2021, 1–15. [Google Scholar] [CrossRef]
- Zhou, Y.; Yang, S.; Li, Y.; Chen, Y.; Yao, J.; Qazi, A. Does the Review Deserve More Helpfulness When Its Title Resembles the Content? Locating Helpful Reviews by Text Mining. Inf. Process. Manag. 2020, 57, 102179. [Google Scholar] [CrossRef]
- Abo, M.E.M.; Raj, R.G.; Qazi, A.; Zakari, A. Sentiment Analysis for Arabic in Social Media Network: A Systematic Mapping Study. Comput. Sci. 2019. Available online: https://arxiv.org/abs/1911.05483 (accessed on 1 October 2021).
- Qazi, A.; Hussain, F.; Rahim, N.A.; Hardaker, G.; Alghazzawi, D.; Shaban, K.; Haruna, K. Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions. IEEE Access 2019, 7, 63837–63851. [Google Scholar] [CrossRef]
- Abo, M.E.M.; Idris, N.; Mahmud, R.; Qazi, A.; Hashem, I.A.T.; Maitama, J.Z.; Naseem, U.; Khan, S.K.; Yang, S. A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection. Sustainability 2021, 13, 10018. [Google Scholar] [CrossRef]
- Naseem, U.; Khushi, M.; Khan, S.K.; Waheed, N.; Mir, A.; Qazi, A.; Alshammari, B.; Poon, S.K. Diabetic Retinopathy Detection Using Multi-Layer Neural Networks and Split Attention With Focal Loss. In Transactions on Petri Nets and Other Models of Concurrency XV; Springer Science and Business Media LLC: Berlin/Heidelberg, Germany, 2020; pp. 26–37. [Google Scholar] [CrossRef]
- Angulo, C.; Ruiz, F.J.; González, L.; Ortega, J.A. Multi-Classification by Using Tri-Class SVM. Neural Process. Lett. 2006, 23, 89–101. [Google Scholar] [CrossRef]
- Phillips, T.; Abdulla, W. Developing a New Ensemble Approach With Multi-Class SVMs for Manuka Honey Quality Classification. Appl. Soft Comput. 2021, 111, 107710. [Google Scholar] [CrossRef]
- Khan, M.; Reza, Q.; Salhan, A.K.; Sirdeshmukh, S.P. Classification of Oils by ECOC Based Multi-Class SVM Using Spectral Analysis of Acoustic Signals. Appl. Acoust. 2021, 183, 108273. [Google Scholar] [CrossRef]
- Adeniyi, M.O.; Ekum, M.I.; Iluno, C.; Ogunsanya, A.S.; Akinyemi, J.A.; Oke, S.I.; Matadi, M.B. Dynamic model of COVID-19 disease with exploratory data analysis. Sci. Afr. 2020, 9, e00477. [Google Scholar] [CrossRef]
- Dey, S.K.; Rahman, M.; Siddiqi, U.R.; Howlader, A. Exploring Epidemiological Behavior of Novel Coronavirus (COVID-19) Outbreak in Bangladesh. SN Compr. Clin. Med. 2020, 2, 1724–1732. [Google Scholar] [CrossRef]
- Zandifar, A.; Badrfam, R.; Yazdani, S.; Arzaghi, S.M.; Rahimi, F.; Ghasemi, S.; Khamisabadi, S.; Khonsari, N.M.; Qorbani, M. Prevalence and Severity of Depression, Anxiety, Stress and Perceived Stress in Hospitalized Patients With COVID-19. J. Diabetes Metab. Disord. 2020, 19, 1431–1438. [Google Scholar] [CrossRef]
- Hasan, N.; Bao, Y. Impact of “e-Learning Crack-up” Perception on Psychological Distress among College Students during COVID-19 Pandemic: A Mediating Role of “fear of Academic Year loss”. Child. Youth Serv. Rev. 2020, 118, 105355. [Google Scholar] [CrossRef] [PubMed]
- Munawar, H.; Khan, S.; Qadir, Z.; Kouzani, A.; Mahmud, M. Insight into the Impact of COVID-19 on Australian Transportation Sector: An Economic and Community-Based Perspective. Sustainability 2021, 13, 1276. [Google Scholar] [CrossRef]
- Qazi, A.; Qazi, J.; Naseer, K.; Zeeshan, M.; Hardaker, G.; Maitama, J.Z.; Haruna, K. Analyzing Situational Awareness through Public Opinion to Predict Adoption of Social Distancing Amid Pandemic COVID-19. J. Med. Virol. 2020, 92, 849–855. [Google Scholar] [CrossRef]
- Qazi, A.; Qazi, J.; Naseer, K.; Zeeshan, M.; Qazi, S.; Abayomi-Alli, O.; Ahmad, I.S.; Darwich, M.; Talpur, B.A.; Hardaker, G.; et al. Adaption of Distance Learning to Continue the Academic Year Amid COVID-19 Lockdown. Child. Youth Serv. Rev. 2021, 126, 106038. [Google Scholar] [CrossRef]
- Qazi, A.; Naseer, K.; Qazi, J.; AlSalman, H.; Naseem, U.; Yang, S.; Hardaker, G.; Gumaei, A. Conventional to Online Education During COVID-19 Pandemic: Do Develop and Underdeveloped Nations Cope Alike. Child. Youth Serv. Rev. 2020, 119, 105582. [Google Scholar] [CrossRef]
- Sifat, R.I. COVID-19 Pandemic: Mental Stress, Depression, Anxiety among the University Students in Bangladesh. Int. J. Soc. Psychiatry 2020, 1, 2. [Google Scholar] [CrossRef]
- Qazi, A.; Hardaker, G.; Ahmad, I.S.; Darwich, M.; Maitama, J.Z.; Dayani, A. The Role of Information & Communication Technology in Elearning Environments: A Systematic Review. IEEE Access 2021, 9, 45539–45551. [Google Scholar] [CrossRef]
- Al-Dmour, H.; Masa’Deh, R.; Salman, A.; Abuhashesh, M.; Al-Dmour, R. Influence of Social Media Platforms on Public Health Protection Against the COVID-19 Pandemic via the Mediating Effects of Public Health Awareness and Behavioral Changes: Integrated Model. J. Med. Internet Res. 2020, 22, e19996. [Google Scholar] [CrossRef]
- De Rosis, S.; Lopreite, M.; Puliga, M.; Vainieri, M. The Early Weeks of the Italian Covid-19 Outbreak: Sentiment Insights from a Twitter Analysis. Health Policy 2021, 125, 987–994. [Google Scholar] [CrossRef] [PubMed]
- Petersen, K.; Gerken, J.M. #Covid-19: An Exploratory Investigation of Hashtag Usage on Twitter. Health Policy 2021, 125, 541–547. [Google Scholar] [CrossRef] [PubMed]
- Singh, P.; Singh, S.; Sohal, M.; Dwivedi, Y.K.; Kahlon, K.S.; Sawhney, R.S. Psychological Fear and Anxiety Caused by COVID-19: Insights from Twitter Analytics. Asian J. Psychiatry 2020, 54, 102280. [Google Scholar] [CrossRef] [PubMed]
- Bentivegna, S.; Artieri, G.B. Rethinking Public Agenda in a Time of High-Choice Media Environment. Media Commun. 2020, 8, 6–15. [Google Scholar] [CrossRef]
- Tukey, J.W. Exploratory Data Analysis; Addison-Wesley Pub. Co.: Reading, MA, USA, 1977; Volume 2, pp. 5–23. Available online: http://theta.edu.pl/wp-content/uploads/2012/10/exploratorydataanalysis_tukey.pdf (accessed on 2 March 2020).
- Qazi, A.; Raj, R.G.; Tahir, M.; Waheed, M.; Khan, S.U.R.; Abraham, A. A Preliminary Investigation of User Perception and Behavioral Intention for Different Review Types: Customers and Designers Perspective. Sci. World J. 2014. [Google Scholar] [CrossRef] [PubMed]
- Jindal, N.; Liu, B. Mining Comparative Sentences and Relations. In Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, Boston, MA, USA, 16–20 July 2006; pp. 1331–1336. [Google Scholar]
- Qazi, A.; Fayaz, H.; Wadi, A.; Raj, R.G.; Rahim, N.; Khan, W.A. The Artificial Neural Network for Solar Radiation Prediction and Designing Solar Systems: A Systematic Literature Review. J. Clean. Prod. 2015, 104, 1–12. [Google Scholar] [CrossRef]
- Qazi, A.; Raj, R.G.; Hardaker, G.; Standing, C. A Systematic Literature Review on Opinion Types and Sentiment Analysis Techniques. Internet Res. 2017, 27, 608–630. [Google Scholar] [CrossRef]
- Salim, N.A.M.; Wah, Y.B.; Reeves, C.; Smith, M.; Yaacob, W.F.W.; Mudin, R.N.; Dapari, R.; Sapri, N.N.F.F.; Haque, U. Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques. Sci. Rep. 2021, 11, 939. [Google Scholar] [CrossRef]
- Li, H.; Sun, F. Comparative Studies of Alignment, Alignment-Free and SVM Based Approaches for Predicting the Hosts of Viruses Based on Viral Sequences. Sci. Rep. 2018, 8, 10032. [Google Scholar] [CrossRef] [Green Version]
- Bendfeldt, K.; Taschler, B.; Gaetano, L.; Madoerin, P.; Kuster, P.; Mueller-Lenke, N.; Amann, M.; Vrenken, H.; Wottschel, V.; Barkhof, F.; et al. MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry. Brain Imaging Behav. 2019, 13, 1361–1374. [Google Scholar] [CrossRef]
- Leong, W.; Kelani, R.; Ahmad, Z. Prediction of Air Pollution Index (API) Using Support Vector Machine (SVM). J. Environ. Chem. Eng. 2020, 8, 103208. [Google Scholar] [CrossRef]
- Manavalan, B.; Shin, T.H.; Lee, G. PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine. Front. Microbiol. 2018, 9, 476. [Google Scholar] [CrossRef]
- Jee, Y. WHO International Health Regulations Emergency Committee for the COVID-19 Outbreak. Epidemiol. Health 2020, 42, e2020013. [Google Scholar] [CrossRef] [Green Version]
- Reuters. FACTBOX-Airlines Suspend China Flights over Coronavirus. 2020. Available online: https://www.reuters.com/article/us-china-health-airlines-factbox-idUSKBN1ZT1RZ (accessed on 1 February 2020).
- Carlisle, S.M.A.M. Coronavirus Grounds Flights to China From 3 Continents. Here’s What Travelers Should Know. 2020. Available online: https://time.com/5774906/airlines-ground-flights-coronavirus/ (accessed on 28 January 2020).
- Reid, D. More Airlines Suspend Flights to and from China Amid Coronavirus Fears. 2020. Available online: https://www.cnbc.com/2020/01/31/coronavirus-more-airlines-suspend-flights-to-and-from-china.html (accessed on 24 February 2020).
- Ghani, A.C.; Donnelly, C.; Cox, D.R.; Griffin, J.T.; Fraser, C.; Lam, T.H.; Ho, L.M.; Chan, W.-S.; Anderson, R.; Hedley, A.J.; et al. Methods for Estimating the Case Fatality Ratio for a Novel, Emerging Infectious Disease. Am. J. Epidemiol. 2005, 162, 479–486. [Google Scholar] [CrossRef] [PubMed]
- Nishiura, H.; Klinkenberg, D.; Roberts, M.; Heesterbeek, J.A.P. Early Epidemiological Assessment of the Virulence of Emerging Infectious Diseases: A Case Study of an Influenza Pandemic. PLoS ONE 2009, 4, e6852. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- A Donnelly, C.; Ghani, A.; Leung, G.; Hedley, A.J.; Fraser, C.; Riley, S.; Abu-Raddad, L.; Ho, L.-M.; Thach, T.Q.; Chau, P.; et al. Epidemiological Determinants of Spread of Causal Agent of Severe Acute Respiratory Syndrome in Hong Kong. Lancet 2003, 361, 1761–1766. [Google Scholar] [CrossRef] [Green Version]
- Mizumoto, K.; Saitoh, M.; Chowell, G.; Miyamatsu, Y.; Nishiura, H. Estimating the Risk of Middle East Respiratory Syndrome (MERS) Death During the Course of the Outbreak in the Republic of Korea, 2015. Int. J. Infect. Dis. 2015, 39, 7–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yang, Y.; Peng, F.; Wang, R.; Guan, K.; Jiang, T.; Xu, G.; Sun, J.; Chang, C. The Deadly Coronaviruses: The 2003 SARS Pandemic and the 2020 Novel Coronavirus Epidemic in China. J. Autoimmun. 2020, 109, 102434. [Google Scholar] [CrossRef]
- Powers, D.M. Evaluation: From precision, recall and f-measure to roc, informedness, markedness and correlation. J. Mach. Learn. Technol. 2011, 2, 37–63. [Google Scholar]
- Mukhtar, S. Preparedness and Proactive Infection Control Measures of Pakistan During COVID-19 Pandemic Outbreak. Res. Soc. Adm. Pharm. 2021, 17, 2052. [Google Scholar] [CrossRef] [PubMed]
- Chohan, U.W. Forecasting the Economic Impact of Coronavirus on Developing Countries: Case of Pakistan. SSRN Electron. J. 2020. [Google Scholar] [CrossRef]
- Hyder, A. eBook Short Notes on the Economy during the COVID-19 Crisis; Institute of Business Administration: Karachi, Pakistan, 2020; Volume 2. [Google Scholar]
- Breland, J.Y.; Quintiliani, L.M.; Schneider, K.L.; May, C.N.; Pagoto, S. Social Media As a Tool to Increase the Impact of Public Health Research. Am. J. Public Health 2017, 107, 1890–1891. [Google Scholar] [CrossRef] [PubMed]
- Dashtipour, K.; Poria, S.; Hussain, A.; Cambria, E.; Hawalah, A.Y.A.; Gelbukh, A.; Zhou, Q. Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques. Cogn. Comput. 2016, 8, 757–771. [Google Scholar] [CrossRef] [PubMed] [Green Version]
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Alghazzawi, D.; Qazi, A.; Qazi, J.; Naseer, K.; Zeeshan, M.; Abo, M.E.M.; Hasan, N.; Qazi, S.; Naz, K.; Dey, S.K.; et al. Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence. Sustainability 2021, 13, 11339. https://doi.org/10.3390/su132011339
Alghazzawi D, Qazi A, Qazi J, Naseer K, Zeeshan M, Abo MEM, Hasan N, Qazi S, Naz K, Dey SK, et al. Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence. Sustainability. 2021; 13(20):11339. https://doi.org/10.3390/su132011339
Chicago/Turabian StyleAlghazzawi, Daniyal, Atika Qazi, Javaria Qazi, Khulla Naseer, Muhammad Zeeshan, Mohamed Elhag Mohamed Abo, Najmul Hasan, Shiza Qazi, Kiran Naz, Samrat Kumar Dey, and et al. 2021. "Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence" Sustainability 13, no. 20: 11339. https://doi.org/10.3390/su132011339