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1.
Journal of Pharmaceutical Research International ; 33(57B):78-88, 2021.
Article in English | Web of Science | ID: covidwho-1614274

ABSTRACT

Telemedicine, also known as telehealth, has been around for decades, but despite its many perceived benefits, its adoption has remained low. The objective our study was to know how consumers felt about telemedicine service during COVID -19 and to find out factors influencing consumers' perceptions of telemedicine services, a survey was done using a questionnaire. Social media and e-mail were used to inform people about the research due to onset of pandemic. An e online survey was done from the period of April 1st to June 30th, 2021 in India's capital Delhi and adjoining areas, 122 service users were sampled for the survey. A 10-item scale was used to assess telemedicine satisfaction, revealing that all participants were satisfied with their telemedicine experience(s) in general. The elements of perception were studied using factor analysis. The results of the analysis revealed that an individual's intention to utilize a system or technology may be influenced not only by factors affecting the user's direct encounter with the system or technology but also by factors affecting the service provider. Patients place a high value on these qualities, thus service providers can design their interface, appointment procedure, and consultation process around them.

2.
Nguyen, T.; Qureshi, M.; Martins, S.; Yamagami, H.; Qiu, Z.; Mansour, O.; Czlonkowska, A.; Abdalkader, M.; Sathya, A.; de Sousa, D. A.; Demeestere, J.; Mikulik, R.; Vanacker, P.; Siegler, J.; Korv, J.; Biller, J.; Liang, C.; Sangha, N.; Zha, A.; Czap, A.; Holmstedt, C.; Turan, T.; Grant, C.; Ntaios, G.; Malhotra, K.; Tayal, A.; Loochtan, A.; Mistry, E.; Alexandrov, A.; Huang, D.; Yaghi, S.; Raz, E.; Sheth, S.; Frankel, M.; Lamou, E. G. B.; Aref, H.; Elbassiouny, A.; Hassan, F.; Mustafa, W.; Menecie, T.; Shokri, H.; Roushdy, T.; Sarfo, F. S.; Alabi, T.; Arabambi, B.; Nwazor, E.; Sunmonu, T. A.; Wahab, K. W.; Mohammed, H. H.; Adebayo, P. B.; Riahi, A.; Ben Sassi, S.; Gwaunza, L.; Rahman, A.; Ai, Z. B.; Bai, F. H.; Duan, Z. H.; Hao, Y. G.; Huang, W. G.; Li, G. W.; Li, W.; Liu, G. Z.; Luo, J.; Shang, X. J.; Sui, Y.; Tian, L.; Wen, H. B.; Wu, B.; Yan, Y. Y.; Yuan, Z. Z.; Zhang, H.; Zhang, J.; Zhao, W. L.; Zi, W. J.; Leung, T. K.; Sahakyan, D.; Chugh, C.; Huded, V.; Menon, B.; Pandian, J.; Sylaja, P. N.; Usman, F. S.; Farhoudi, M.; Sadeghi-Hokmabadi, E.; Reznik, A.; Sivan-Hoffman, R.; Horev, A.; Ohara, N.; Sakai, N.; Watanabe, D.; Yamamoto, R.; Doijiri, R.; Tokuda, N.; Yamada, T.; Terasaki, T.; Yazawa, Y.; Uwatoko, T.; Dembo, T.; Shimizu, H.; Sugiura, Y.; Miyashita, F.; Fukuda, H.; Miyake, K.; Shimbo, J.; Sugimura, Y.; Yagita, Y.; Takenobu, Y.; Matsumaru, Y.; Yamada, S.; Kono, R.; Kanamaru, T.; Yamazaki, H.; Sakaguchi, M.; Todo, K.; Yamamoto, N.; Sonodda, K.; Yoshida, T.; Hashimoto, H.; Nakahara, I.; Faizullina, K.; Kamenova, S.; Kondybayeva, A.; Zhanuzakov, M.; Baek, J. H.; Hwang, Y.; Lee, S. B.; Moon, J.; Park, H.; Seo, J. H.; Seo, K. D.; Young, C. J.; Ahdab, R.; Aziz, Z. A.; Zaidi, W. A. W.; Bin Basri, H.; Chung, L. W.; Husin, M.; Ibrahim, A. B.; Ibrahim, K. A.; Looi, I.; Tan, W. Y.; Yahya, Wnnw, Groppa, S.; Leahu, P.; Al Hashmi, A.; Imam, Y. Z.; Akhtar, N.; Oliver, C.; Kandyba, D.; Alhazzani, A.; Al-Jehani, H.; Tham, C. H.; Mamauag, M. J.; Narayanaswamy, R.; Chen, C. H.; Tang, S. C.; Churojana, A.; Aykac, O.; Ozdemir, A. O.; Hussain, S. I.; John, S.; Vu, H. L.; Tran, A. D.; Nguyen, H. H.; Thong, P. N.; Nguyen, T.; Nguyen, T.; Gattringer, T.; Enzinger, C.; Killer-Oberpfalzer, M.; Bellante, F.; De Blauwe, S.; Van Hooren, G.; De Raedt, S.; Dusart, A.; Ligot, N.; Rutgers, M.; Yperzeele, L.; Alexiev, F.; Sakelarova, T.; Bedekovic, M. R.; Budincevic, H.; Cindric, I.; Hucika, Z.; Ozretic, D.; Saric, M. S.; Pfeifer, F.; Karpowicz, I.; Cernik, D.; Sramek, M.; Skoda, M.; Hlavacova, H.; Klecka, L.; Koutny, M.; Vaclavik, D.; Skoda, O.; Fiksa, J.; Hanelova, K.; Nevsimalova, M.; Rezek, R.; Prochazka, P.; Krejstova, G.; Neumann, J.; Vachova, M.; Brzezanski, H.; Hlinovsky, D.; Tenora, D.; Jura, R.; Jurak, L.; Novak, J.; Novak, A.; Topinka, Z.; Fibrich, P.; Sobolova, H.; Volny, O.; Christensen, H. K.; Drenck, N.; Iversen, H.; Simonsen, C.; Truelsen, T.; Wienecke, T.; Vibo, R.; Gross-Paju, K.; Toomsoo, T.; Antsov, K.; Caparros, F.; Cordonnier, C.; Dan, M.; Faucheux, J. M.; Mechtouff, L.; Eker, O.; Lesaine, E.; Ondze, B.; Pico, F.; Pop, R.; Rouanet, F.; Gubeladze, T.; Khinikadze, M.; Lobjanidze, N.; Tsiskaridze, A.; Nagel, S.; Ringleb, P. A.; Rosenkranz, M.; Schmidt, H.; Sedghi, A.; Siepmann, T.; Szabo, K.; Thomalla, G.; Palaiodimou, L.; Sagris, D.; Kargiotis, O.; Kaliaev, A.; Liebeskind, D.; Hassan, A.; Ranta, A.; Devlin, T.; Zaidat, O.; Castonguay, A.; Jovin, T.; Tsivgoulis, G.; Malik, A.; Ma, A.; Campbell, B.; Kleinig, T.; Wu, T.; Gongora, F.; Lavados, P.; Olavarria, V.; Lereis, V. P.; Corredor, A.; Barbosa, D. M.; Bayona, H.; Barrientos, J. D.; Patino, M.; Thijs, V.; Pirson, A.; Kristoffersen, E. S.; Patrik, M.; Fischer, U.; Bernava, G.; Renieri, L.; Strambo, D.; Ayo-Martin, O.; Montaner, J.; Karlinski, M.; Cruz-Culebras, A.; Luchowski, P.; Krastev, G.; Arenillas, J.; Gralla, J.; Mangiafico, S.; Blasco, J.; Fonseca, L.; Silva, M. L.; Kwan, J.; Banerjee, S.; Sangalli, D.; Frisullo, G.; Yavagal, D.; Uyttenboogaart, M.; Bandini, F.; Adami, A.; de Lecina, M. A.; Arribas, M. A. T.; Ferreira, P.; Cruz, V. T.; Nunes, A. P.; Marto, J. P.; Rodrigues, M.; Melo, T.; Saposnik, G.; Scott, C. A.; Shuaib, A.; Khosravani, H.; Fields, T.; Shoamanesh, A.; Catanese, L.; Mackey, A.; Hill, M.; Etherton, M.; Rost, N.; Lutsep, H.; Lee, V.; Mehta, B.; Pikula, A.; Simmons, M.; Macdougall, L.; Silver, B.; Khandelwal, P.; Morris, J.; Novakovic-White, R.; Ramakrishnan, P.; Shah, R.; Altschul, D.; Almufti, F.; Amaya, P.; Ordonez, C. E. R.; Lara, O.; Kadota, L. R.; Rivera, L. I. P.; Novarro, N.; Escobar, L. D.; Melgarejo, D.; Cardozo, A.; Blanco, A.; Zelaya, J. A.; Luraschi, A.; Gonzalez, V. H. N.; Almeida, J.; Conforto, A.; Almeida, M. S.; Silva, L. D.; Cuervo, D. L. M.; Zetola, V. F.; Martins, R. T.; Valler, L.; Giacomini, L. V.; Cardoso, F. B.; Sahathevan, R.; Hair, C.; Hankey, G.; Salazar, D.; Lima, F. O.; Mont'Alverne, F.; Moises, D.; Iman, B.; Magalhaes, P.; Longo, A.; Rebello, L.; Falup-Pecurariu, C.; Mazya, M.; Wisniewska, A.; Fryze, W.; Kazmierski, R.; Wisniewska, M.; Horoch, E.; Sienkiewicz-Jarosz, H.; Fudala, M.; Rogoziewicz, M.; Brola, W.; Sobolewski, P.; Kaczorowski, R.; Stepien, A.; Klivenyi, P.; Szapary, L.; van den Wijngaard, I.; Demchuk, A.; Abraham, M.; Alvarado-Ortiz, T.; Kaushal, R.; Ortega-Gutierrez, S.; Farooqui, M.; Bach, I.; Badruddin, A.; Barazangi, N.; Nguyen, C.; Brereton, C.; Choi, J. H.; Dharmadhikari, S.; Desai, K.; Doss, V.; Edgell, R.; Linares, G.; Frei, D.; Chaturvedi, S.; Gandhi, D.; Chaudhry, S.; Choe, H.; Grigoryan, M.; Gupta, R.; Helenius, J.; Voetsch, B.; Khwaja, A.; Khoury, N.; Kim, B. S.; Kleindorfer, D.; McDermott, M.; Koyfman, F.; Leung, L.; Linfante, I.; Male, S.; Masoud, H.; Min, J. Y.; Mittal, M.; Multani, S.; Nahab, F.; Nalleballe, K.; Rahangdale, R.; Rafael, J.; Rothstein, A.; Ruland, S.; Sharma, M.; Singh, A.; Starosciak, A.; Strasser, S.; Szeder, V.; Teleb, M.; Tsai, J.; Mohammaden, M.; Pineda-Franks, C.; Asyraf, W.; Nguyen, T. Q.; Tarkanyi, G.; Horev, A.; Haussen, D.; Balaguera, O.; Vasquez, A. R.; Nogueira, R..
Neurology ; 96(15):42, 2021.
Article in English | Web of Science | ID: covidwho-1576349
3.
Nguyen, T.; Qureshi, M.; Martins, S.; Yamagami, H.; Qiu, Z.; Mansour, O.; Czlonkowska, A.; Abdalkader, M.; Sathya, A.; Sousa, D. A.; Demeester, J.; Mikulik, R.; Vanacker, P.; Siegler, J.; Korv, J.; Biller, J.; Liang, C.; Sangha, N.; Zha, A.; Czap, A.; Holmstedt, C.; Turan, T.; Grant, C.; Ntaios, G.; Malhotra, K.; Tayal, A.; Loochtan, A.; Mistry, E.; Alexandrov, A.; Huang, D.; Yaghi, S.; Raz, E.; Sheth, S.; Frankel, M.; Lamou, E. G. B.; Aref, H.; Elbassiouny, A.; Hassan, F.; Mustafa, W.; Menecie, T.; Shokri, H.; Roushdy, T.; Sarfo, F. S.; Alabi, T.; Arabambi, B.; Nwazor, E.; Sunmonu, T. A.; Wahab, K. W.; Mohammed, H. H.; Adebayo, P. B.; Riahi, A.; Sassi, S. B.; Gwaunza, L.; Rahman, A.; Ai, Z.; Bai, F.; Duan, Z.; Hao, Y.; Huang, W.; Li, G.; Li, W.; Liu, G.; Luo, J.; Shang, X.; Sui, Y.; Tian, L.; Wen, H.; Wu, B.; Yan, Y.; Yuan, Z.; Zhang, H.; Zhang, J.; Zhao, W.; Zi, W.; Leung, T. K.; Sahakyan, D.; Chugh, C.; Huded, V.; Menon, B.; Pandian, J.; Sylaja, P. N.; Usman, F. S.; Farhoudi, M.; Sadeghi-Hokmabadi, E.; Reznik, A.; Sivan-Hoffman, R.; Horev, A.; Ohara, N.; Sakai, N.; Watanabe, D.; Yamamoto, R.; Doijiri, R.; Kuda, N.; Yamada, T.; Terasaki, T.; Yazawa, Y.; Uwatoko, T.; Dembo, T.; Shimizu, H.; Sugiura, Y.; Miyashita, F.; Fukuda, H.; Miyake, K.; Shimbo, J.; Sugimura, Y.; Yagita, Y.; Takenobu, Y.; Matsumaru, Y.; Yamada, S.; Kono, R.; Kanamaru, T.; Yamazaki, H.; Sakaguchi, M.; Todo, K.; Yamamoto, N.; Sonodda, K.; Yoshida, T.; Hashimoto, H.; Nakahara, I.; Faizullina, K.; Kamenova, S.; Kondybayev, A.; Zhanuzakov, M.; Baek, J. H.; Hwang, Y.; Lee, S. B.; Moon, J.; Park, H.; Seo, J. H.; Seo, K. D.; Young, C. J.; Ahdab, R.; Aziz, Z. A.; Zaidi, W. A. W.; Basr, H. B.; Chung, L. W.; Husin, M.; Ibrahim, A. B.; Ibrahim, K. A.; Looi, I.; Tan, W. Y.; Yahya, W. N. W.; Groppa, S.; Leahu, P.; Hashmi, A. A.; Imam, Y. Z.; Akhtar, N.; Oliver, C.; Kandyba, D.; Alhazzani, A.; Al-Jehani, H.; Tham, C. H.; Mamauag, M. J.; Narayanaswamy, R.; Chen, C. H.; Tang, S. C.; Churojana, A.; Aykaç, O.; Özdemir, A.; Hussain, S. I.; John, S.; Vu, H. L.; Tran, A. D.; Nguyen, H. H.; Thong, P. N.; Nguyen, T.; Nguyen, T.; Gattringer, T.; Enzinger, C.; Killer-Oberpfalzer, M.; Bellante, F.; Deblauwe, S.; Hooren, G. V.; Raedt, S. D.; Dusart, A.; Ligot, N.; Rutgers, M.; Yperzeele, L.; Alexiev, F.; Sakelarova, T.; Bedekovic, M.; Budincevic, H.; Cindric, I.; Hucika, Z.; Ozretic, D.; Saric, M. S.; Pfeifer, F.; Karpowicz, I.; Cernik, D.; Sramek, M.; Skoda, M.; Hlavacova, H.; Klecka, L.; Koutny, M.; Skoda, O.; Fiksa, J.; Hanelova, K.; Nevsimalova, M.; Rezek, R.; Prochazka, P.; Krejstova, G.; Neumann, J.; Vachova, M.; Brzezanski, H.; Hlinovsky, D.; Tenora, D.; Jura, R.; Jurak, L.; Novak, J.; Novak, A.; Topinka, Z.; Fibrich, P.; Sobolova, H.; Volny, O.; Christensen, H. K.; Drenck, N.; Iversen, H.; Simonsen, C.; Truelsen, T.; Wienecke, T.; Vibo, R.; Gross-Paju, K.; Toomsoo, T.; Antsov, K.; Caparros, F.; Cordonnier, C.; Dan, M.; Faucheux, J. M.; Mechtouff, L.; Eker, O.; Lesaine, E.; Pico, F.; Pop, R.; Rouanet, F.; Gubeladze, T.; Khinikadze, M.; Lobjanidze, N.; Tsiskaridze, A.; Nagel, S.; Arthurringleb, P.; Rosenkranz, M.; Schmidt, H.; Sedghi, A.; Siepmann, T.; Szabo, K.; Thomalla, G.; Palaiodimou, L.; Sagris, D.; Kargiotis, O.; Kaliaev, A.; Liebeskind, D.; Hassan, A.; Ranta, A.; Devlin, T.; Zaidat, O.; Castonguay, A.; Jovin, T.; Tsivgoulis, G.; Malik, A.; Ma, A.; Campbel, B.; Kleinig, T.; Wu, T.; Gongora, F.; Lavados, P.; Olavarria, V.; Lereis, V. P.; Corredor, A.; Barbosa, D. M.; Bayona, H.; Barrientos, J. D.; Patino, M.; Thijs, V.; Pirson, A.; Kristoffersen, E. S.; Patrik, M.; Fischer, U.; Bernava, G.; Renieri, L.; Strambo, D.; Ayo-Martin, O.; Montaner, J.; Karlinski, M.; Cruz-Culebras, A.; Luchowski, P.; Krastev, G.; Arenillas, J.; Gralla, J.; Mangiafico, S.; Blasco, J.; Fonseca, L.; Silva, M. L.; Kwan, J.; Banerjee, S.; Sangalli, D.; Frisullo, G.; Yavagal, D.; Uyttenboogaart, M.; Bandini, F.; Adami, A.; Lecina, M. A. D.; Arribas, M. A. T.; Ferreira, P.; Cruz, V. T.; Nunes, A. P.; Marto, J. P.; Rodrigues, M.; Melo, T.; Saposnik, G.; Scott, C. A.; Shuaib, A.; Khosravani, H.; Fields, T.; Shoamanesh, A.; Catanese, L.; MacKey, A.; Hill, M.; Etherton, M.; Rost, N.; Lutsep, H.; Lee, V.; Mehta, B.; Pikula, A.; Simmons, M.; MacDougall, L.; Silver, B.; Khandelwal, P.; Morris, J.; Novakovic-White, R.; Shah, R.; Altschul, D.; Almufti, F.; Amaya, P.; Ordonez, C. E. R.; Lara, O.; Kadota, L. R.; Rivera, L. I.; Novarro, N.; Escobar, L. D.; Melgarejo, D.; Cardozo, A.; Blanco, A.; Zelaya, J. A.; Luraschi, A.; Gonzalez, V. H.; Almeida, J.; Conforto, A.; Almeida, M. S.; Silva, L. D. D.; Cuervo, D. L. M.; Zetola, V. F.; Martins, R. T.; Valler, L.; Giacomini, L. V.; Buchdidcardoso, F.; Sahathevan, R.; Hair, C.; Hankey, G.; Salazar, D.; Lima, F. O.; Mont'alverne, F.; Iman, D. M. B.; Longo, A.; Rebello, L.; Falup-Pecurariu, C.; Mazya, M.; Wisniewska, A.; Fryze, W.; Kazmierski, R.; Wisniewska, M.; Horoch, E.; Sienkiewicz-Jarosz, H.; Fudala, M.; Goziewicz, M.; Brola, W.; Sobolewski, P.; Kaczorowski, R.; Stepien, A.; Klivenyi, P.; Szapary, L.; Wijngaard, I. V. D.; Demchuk, A.; Abraham, M.; Alvarado-Ortiz, T.; Kaushal, R.; Ortega-Gutierrez, S.; Farooqui, M.; Bach, I.; Badruddin, A.; Barazangi, N.; Nguyen, C.; Brereton, C.; Choi, J. H.; Dharmadhikari, S.; Desai, K.; Doss, V.; Edgell, R.; Linares, G.; Frei, D.; Chaturvedi, S.; Gandhi, D.; Chaudhry, S.; Choe, H.; Grigoryan, M.; Gupta, R.; Helenius, J.; Voetsch, B.; Khwaja, A.; Khoury, N.; Kim, B. S.; Kleindorfer, D.; McDermott, M.; Koyfman, F.; Leung, L.; Linfante, I.; Male, S.; Masoud, H.; Min, J.; Mittal, M.; Multani, S.; Nahab, F.; Nalleballe, K.; Rahangdale, R.; Rafael, J.; Rothstein, A.; Ruland, S.; Sharma, M.; Singh, A.; Starosciak, A.; Strasser, S.; Szeder, V.; Teleb, M.; Tsai, J.; Mohammaden, M.; Pineda-Franks, C.; Asyraf, W.; Nguyen, T. Q.; Tarkanyi, A.; Haussen, D.; Balaguera, O.; Rodriguezvasquez, A.; Nogueira, R..
Neurology ; 96(15 SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1407898

ABSTRACT

Objective: The objectives of this study were to measure the global impact of the pandemic on the volumes for intravenous thrombolysis (IVT), IVT transfers, and stroke hospitalizations over 4 months at the height of the pandemic (March 1 to June 30, 2020) compared with two control 4-month periods. Background: The COVID-19 pandemic led to widespread repercussions on the delivery of health care worldwide. Design/Methods: We conducted a cross-sectional, observational, retrospective study across 6 continents, 70 countries, and 457 stroke centers. Diagnoses were identified by ICD-10 codes and/or classifications in stroke center databases. Results: There were 91,373 stroke admissions in the 4 months immediately before compared to 80,894 admissions during the pandemic months, representing an 11.5% (95%CI,-11.7 to-11.3, p<0.0001) decline. There were 13,334 IVT therapies in the 4 months preceding compared to 11,570 procedures during the pandemic, representing a 13.2% (95%CI,-13.8 to-12.7, p<0.0001) drop. Interfacility IVT transfers decreased from 1,337 to 1,178, or an 11.9% decrease (95%CI,-13.7 to-10.3, p=0.001). There were greater declines in primary compared to comprehensive stroke centers (CSC) for stroke hospitalizations (-17.3% vs-10.3%, p<0.0001) and IVT (-15.5% vs-12.6%, p=0.0001). Recovery of stroke hospitalization volume (9.5%, 95%CI 9.2-9.8, p<0.0001) was noted over the two later (May, June) versus the two earlier (March, April) months of the pandemic, with greater recovery in hospitals with lower COVID-19 hospitalization volume, high volume stroke center, and CSC. There was a 1.48% stroke rate across 119,967 COVID-19 hospitalizations. SARS-CoV-2 infection was noted in 3.3% (1,722/52,026) of all stroke admissions. Conclusions: The COVID-19 pandemic was associated with a global decline in the volume of stroke hospitalizations, IVT, and interfacility IVT transfers. Primary stroke centers and centers with higher COVID19 inpatient volumes experienced steeper declines. Recovery of stroke hospitalization was noted in the later pandemic months, with greater recovery in hospitals with lower COVID-19 hospitalizations, high volume stroke centers, and CSCs.

4.
Indian Pediatrics ; 58(6):525-531, 2021.
Article in English | Scopus | ID: covidwho-1303381

ABSTRACT

Background: Limited evidence exists on perinatal transmission and outcomes of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in neonates. Objective: To describe clinical outcomes and risk factors for transmission in neonates born to mothers with perinatal SARS-CoV-2 infection. Design: Prospective cohort of suspected and confirmed SARS-CoV-2 infected neonates entered in National Neonatology Forum (NNF) of India registry. Subjects: Neonates born to women with SARS-CoV-2 infection within two weeks before or two days after birth and neonates with SARS-CoV-2 infection. Outcomes: Incidence and risk factors of perinatal transmission. Results: Among 1713 neonates, SARS-CoV-2 infection status was available for 1330 intramural and 104 extramural neonates. SARS-CoV-2 positivity was reported in 144 intramural and 39 extramural neonates. Perinatal transmission occurred in 106 (8%) and horizontal transmission in 21 (1.5%) intramural neonates. Neonates roomed-in with mother had higher transmission risk (RR1.16, 95% CI 1.1 to 2.4;P=0.01). No association was noted with the mode of delivery or type of feeding. The majority of neonates positive for SARS-CoV2 were asymptomatic. Intramural SARS-CoV-2 positive neonates were more likely to be symptomatic (RR 5, 95%CI 3.3 to 7.7;P<0.0001) and need resuscitation (RR 2, 95%CI 1.0 to 3.9;P=0.05) compared to SARS-CoV-2 negative neonates. Amongst symptomatic neonates, most morbidities were related to prematurity and perinatal events. Conclusion: Data from a large cohort suggests perinatal transmission of SARS-CoV-2 infection and increased morbidity in infected infants. © 2021, Indian Academy of Pediatrics.

5.
Proceedings of the 2020 5th International Conference on Computing, Communication and Security ; 2020.
Article in English | Web of Science | ID: covidwho-1271166

ABSTRACT

Worldwide governments have decided to temporarily closures of educational institutions in an attempt to minimize the spread of the COVID-19 Pandemic, which has forged significant challenges for the education community. The present study is from the digital education scenario during the COVID-19 lockdown to find out the factors affecting online learning. This study is exploratory from 1218 students who have been collected based on a structured questionnaire having a 5-point linear scale. Jamovl software has been used for data analysis and results demonstrate that there are three major factors like affordability, infrastructural, and training that affect online learning during the COVID-19. Besides, correlation analysis between these factors highlights the relationship among them. Linear regression has applied to know the impact of affordability and infrastructure on the training factor. Outcomes suggested that infrastructure has a negative impact but affordability has a positive impact on the training factor. In the present scenario, this study highlighted the importance of social distancing and digital education tools that should he adopted by schools and colleges.

6.
Mathematical Engineering ; : 253-272, 2021.
Article in English | Scopus | ID: covidwho-1184633

ABSTRACT

Epidemic diseases are well known to be fatal and cause great loss worldwide—economically, socially and mentally. Even after around nine months, since the Coronavirus Disease 2019 (COVID-19) began to spread, people are getting infected all over the world. This is one of the areas where human medical advancements fail because by the time the disease is identified and its treatment is figured out, most of the population is already exposed to it. In such cases, it becomes easier to take steps if the dynamics of the disease and its sensitivity to various factors is known. This chapter deals with developing a mathematical model for the spread of Coronavirus disease, by employing a number of parameters that affect its spread. A compartmental modelling approach using ordinary differential equation has been used to formulate the set of equations that describe the model. We have used the next generation matrix method to find the basic reproduction number of the system and proved that the system is locally asymptotically stable at the disease-free equilibrium for R0&lt;1. Stability and existence of endemic equilibrium have been discussed, followed by sensitivity of infective classes to parameters like proportion of vaccinated individuals and precautionary measures like social distancing. It is expected that after the vaccine is developed and is available to use, as the proportion of vaccinated individuals will increase, the infection will decrease in the population which can gradually lead to herd immunity. Since, the vaccine is still under development, non-intervention measures play a major role in coping with the disease. The disease generally transmits when the water droplets from an infected individuals’ mouth or nose are inhaled by a healthy individual. The best measures that should be adopted are social distancing, washing one’s hands frequently, and covering one’s mouth with mask, quarantine and lockdowns. Thus, as more and more precautionary measures are taken, it would gradually reduce the infection which has also been proved numerically by the sensitivity analysis of ‘w’ in our dynamical analysis. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

7.
Mathematical Engineering ; : 77-100, 2021.
Article in English | Scopus | ID: covidwho-1184625

ABSTRACT

The treatment of corona virus disease is not possible without any vaccine. However, spreading of the deadly virus can be controlled by various measures being imposed by Government like lockdown, quarantine, isolation, contact tracing, social distancing and putting face mask on mandatory basis. As per information from the Department of Medical Health and Family Welfare of Rajasthan on 19 September 2020, corona virus COVID-19 severely affected the state of Rajasthan, resulting in cumulative positive cases 113,124, cumulative recovered 93,805 and cumulative deaths 1322. Without any appropriate treatment, it may further spread globally as it is highly communicable and because potentially affecting the human body respiratory system, which could be fatal to mankind. Therefore, to reduce the spread of infection, authors are motivated to construct a predictive mathematical model with sustainable conditions as per the ongoing scenario in the state of Rajasthan. Mathematica software has been used for numerical evaluation and graphical representation for variation of infection, recovery, exposed, susceptibles and mortality versus time. Moreover, comparative analysis of results obtained by predictive mathematical model has been done with the exact data plotting by curve fitting as obtained from Rajasthan government website. As a part of analysis and result, it is noted that due to the variation of transmission rate from person to person corresponding rate of infection goes on increasing monthly and mortality rate found high as shown and discussed numerically. Further, we can predict that the situation will become worse in the winter months especially in month of December due to unavailability of proper vaccine. This model may become more efficient when the researchers, experts from medical sciences and technologist work together. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

8.
Journal of Content, Community and Communication ; 12:198-209, 2020.
Article in English | Scopus | ID: covidwho-1061301

ABSTRACT

This study highlights the role of utilitarian shopping, hedonic shopping, and online advertisement on cognitive dissonance. Impulse Buying plays a role as a mediator in this research. The objective of this research is to investigate how utilitarian shopping value, hedonic shopping value, and online advertising influence the cognitive dissonance of customers. 338 response data have been collected from consumers involved in fashion apparel;respondents are majorly from central zone of India. Partial least square (PLS) - Structural equation modelling (SEM) is implemented using Smart PLS 3.0. The simulation result shows that utilitarian shopping value, hedonic shopping value, and online ads are found to be important in predicting cognitive dissonance and impulse buying, whereas impulse buying is impeccable in terms of predicting positive relationships with cognitive dissonance. Moreover, Impulse buying is playing as positive mediating effect in relation with constructs. Hence, this research suggests that a complex representation which may better understanding about consumer shopping behaviour. Conclusively, this research’s major contribution towards authors’ knowledge, and help the marketing expert to focus on important parameter of consumer buying behaviour. © 2020. All Rights Reserved.

9.
Proc. Int. Conf. Comput., Commun. Secur., ICCCS ; 2020.
Article in English | Scopus | ID: covidwho-1020400

ABSTRACT

Worldwide governments have decided to temporarily closures of educational institutions in an attempt to minimize the spread of the COVID-19 Pandemic, which has forged significant challenges for the education community. The present study is from the digital education scenario during the COVID-19 lockdown to find out the factors affecting online learning. This study is exploratory from 1218 students who have been collected based on a structured questionnaire having a 5-point linear scale. Jamovi software has been used for data analysis and results demonstrate that there are three major factors like affordability, infrastructural, and training that affect online learning during the COVID-19. Besides, correlation analysis between these factors highlights the relationship among them. Linear regression has applied to know the impact of affordability and infrastructure on the training factor. Outcomes suggested that infrastructure has a negative impact but affordability has a positive impact on the training factor. In the present scenario, this study highlighted the importance of social distancing and digital education tools that should be adopted by schools and colleges. © 2020 IEEE.

10.
EAI Endorsed Transactions on Pervasive Health and Technology ; 6(22):1-9, 2020.
Article in English | Scopus | ID: covidwho-823568

ABSTRACT

INTRODUCTION: SARS-CoV-2 is the latest virus responsible for an outburst of a unique respiratory infection identified as COVID-19. The virus popularly known as Corona Virus has spread quickly in recent days from China to several other countries around the world. Health is always of prime concern for mankind. Computing is playing an important role in improving the current state of the healthcare industry. OBJECTIVES: This paper focuses on summarizing the happenings about the coronavirus and the disease spread. This review study concentrates on the history of the virus, its technical details, the disease caused by the virus, its symptoms and precautions. The study also tries to develop an understanding of the role of technology in dealing with the outbreak, its impact in diverse fields, and the current state of the pandemic. METHODS: This work is an attempt towards presenting a perspective of computing and technology in fighting against the COVID-19 pandemic. RESULTS: This work presents a perspective showing technology in healthcare as a rescuer in such situations. In this survey, we simply discuss SARS-COV2 and COVID-19 from different perspectives in order to serve as a quick reference for the readers and to achieve a better insight into the fast-evolving pandemic. CONCLUSION: Social distancing, staying home and lockdowns are some known solutions to combat the pandemic in the absence of the vaccine, and technology can play a significant role in combating the pandemic. © 2020 Sunil Chawla et al., licensed to EAI.

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