New Ways to Manage Pandemics: Using Technologies in the Era of COVID-19, a Narrative Review
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Abstract
Objective: Health care systems and professionals worldwide are relying on technology as an essential partner to manage the COVID-19 epidemic. This paper explains how digital technologies can benefit the public, medical workers, and health care systems.
Method: This nonsystematic literature review was conducted on different technologies and their impact and applications in the COVID-19 epidemic using proper search keywords on the PubMed, Google Scholar, and Science Direct databases.
Results: We found various helpful technologies, which can help us to appropriately contain and manage the COVID-19 pandemic through broad areas of clinical care, logistics, maintenance of socioeconomic activities, and inspection. However, main challenges still need to be addressed for obtaining the full capacities of the technologies to support health care systems.
Conclusion: Technologies can offer many innovative ideas and solutions against global and local emergencies. In this time of great vagueness and danger, we require all the resources we can collect to rescue ourselves and our patients. Barriers and challenges, such as lack of technology proficiency, confidentiality requirements, and reimbursement matters, need to be recognized and resolved rapidly, accurately, and compassionately.
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8. Coiera E. Guide to health informatics: CRC press; 2015.
9. Prem K, Liu Y, Russell TW, Kucharski AJ, Eggo RM, Davies N, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. Lancet Public Health. 2020;5(5):e261-e70.
10. Zhou C, Su F, Pei T, Zhang A, Du Y, Luo B, et al. COVID-19: Challenges to GIS with big data. Geography and Sustainability. 2020.
11. Oliver N, Letouzé E, Sterly H, Delataille S, De Nadai M, Lepri B, et al. Mobile phone data and COVID-19: Missing an opportunity? arXiv preprint arXiv:2003.12347. 2020.
12. Pepe E, Bajardi P, Gauvin L, Privitera F, Lake B, Cattuto C, Tizzoni M. COVID-19 outbreak response: a first assessment of mobility changes in Italy following national lockdown. medRxiv. 2020 Jan 1.
13. Cho H, Ippolito D, Yu YW. Contact tracing mobile apps for COVID-19: Privacy considerations and related trade-offs. arXiv preprint arXiv:2003.11511. 2020.
14. Martín-Calvo D, Aleta A, Pentland A, Moreno Y, Moro E. Effectiveness of social distancing strategies for protecting a community from a pandemic with a data driven contact network based on census and real-world mobility data. Technical Report2020.
15. Park S, Choi GJ, Ko H. Information Technology-Based Tracing Strategy in Response to COVID-19 in South Korea-Privacy Controversies. Jama. 2020.
16. Wang CJ, Ng CY, Brook RH. Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing. Jama. 2020.
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18. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6(2):66-73.
19. Khaleghi A, Sheikhani A, Mohammadi MR, Nasrabadi AM, Vand SR, Zarafshan H, et al. EEG classification of adolescents with type I and type II of bipolar disorder. Australas Phys Eng Sci Med. 2015;38(4):551-9.
20. Zarafshan H, Khaleghi A, Mohammadi MR, Moeini M, Malmir N. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task. J Clin Exp Neuropsychol. 2016;38(3):361-9.
21. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(4):337-9.
22. Wang Y, Hu M, Li Q, Zhang X-P, Zhai G, Yao N. Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. arXiv preprint arXiv:2002.05534. 2020.
23. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv:2003.05037. 2020.
24. Barstugan M, Ozkaya U, Ozturk S. Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424. 2020.
25. Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43(2):635-40.
26. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology. 2020:200905.
27. Salman FM, Abu-Naser SS, Alajrami E, Abu-Nasser BS, Alashqar BA. Covid-19 detection using artificial intelligence. 2020.
28. Wang L, Wong A. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871. 2020.
29. Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849. 2020.
30. Yan L, Zhang H-T, Xiao Y, Wang M, Sun C, Liang J, et al. Prediction of survival for severe Covid-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in Wuhan. medRxiv. 2020.
31. Ge Y, Tian T, Huang S, Wan F, Li J, Li S, et al. A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19. bioRxiv. 2020.
32. Zhavoronkov A, Aladinskiy V, Zhebrak A, Zagribelnyy B, Terentiev V, Bezrukov DS, Polykovskiy D, Shayakhmetov R, Filimonov A, Orekhov P, Yan Y. Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches. ChemRxiv. 2020.
33. Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. bioRxiv. 2020.
34. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577(7792):706-10.
35. Randhawa GS, Soltysiak MPM, El Roz H, de Souza CPE, Hill KA, Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One. 2020;15(4):e0232391.
36. Priya S. Singapore government launches COVID-19 chatbot. 2020.
37. Hollander JE, Carr BG. Virtually Perfect? Telemedicine for Covid-19. N Engl J Med. 2020;382(18):1679-81.
38. Leite H, Hodgkinson IR, Gruber T. New development:‘Healing at a distance’—telemedicine and COVID-19. Public Money & Management. 2020 Apr 10:1-3.
39. Greenhalgh T, Wherton J, Shaw S, Morrison C. Video consultations for covid-19. Bmj. 2020;368:m998.
40. Greenhalgh T, Koh GCH, Car J. Covid-19: a remote assessment in primary care. Bmj. 2020;368:m1182.
41. Armfield NR, Bradford M, Bradford NK. The clinical use of Skype--For which patients, with which problems and in which settings? A snapshot review of the literature. Int J Med Inform. 2015;84(10):737-42.
42. Backhaus A, Agha Z, Maglione ML, Repp A, Ross B, Zuest D, et al. Videoconferencing psychotherapy: a systematic review. Psychol Serv. 2012;9(2):111-31.
43. Ignatowicz A, Atherton H, Bernstein CJ, Bryce C, Court R, Sturt J, et al. Internet videoconferencing for patient-clinician consultations in long-term conditions: A review of reviews and applications in line with guidelines and recommendations. Digit Health. 2019;5:2055207619845831.
44. Abimbola S, Keelan S, Everett M, Casburn K, Mitchell M, Burchfield K, et al. The medium, the message and the measure: a theory-driven review on the value of telehealth as a patient-facing digital health innovation. Health Econ Rev. 2019;9(1):21.
45. Liu S, Yang L, Zhang C, Xiang YT, Liu Z, Hu S, et al. Online mental health services in China during the COVID-19 outbreak. Lancet Psychiatry. 2020;7(4):e17-e8.
46. Mohammadi MR, Zarafshan H, Bashi SK, Mohammadi F, Khaleghi A. The Role of Public Trust and Media in the Psychological and Behavioral Responses to the Pandemics of COVID-19. Preprints with The Lancet, Available at SSRN 3586701. 2020.
47. Kang L, Li Y, Hu S, Chen M, Yang C, Yang BX, et al. The mental health of medical workers in Wuhan, China dealing with the 2019 novel coronavirus. Lancet Psychiatry. 2020;7(3):e14.
48. Xiang YT, Yang Y, Li W, Zhang L, Zhang Q, Cheung T, et al. Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed. Lancet Psychiatry. 2020;7(3):228-9.
49. Wright JH, Caudill R. Remote Treatment Delivery in Response to the COVID-19 Pandemic. Psychother Psychosom. 2020;89(3):130-2.
50. Murphy R, Calugi S, Cooper Z, Dalle Grave R. Challenges and Opportunities for enhanced cognitive behaviour therapy (CBT-E) in light of COVID-19. the Cognitive Behaviour Therapist. 2020:13:1-31.
51. Ho CS, Chee CY, Ho RC. Mental Health Strategies to Combat the Psychological Impact of COVID-19 Beyond Paranoia and Panic. Ann Acad Med Singapore. 2020;49(3):155-60.
52. Leipheimer JM, Balter ML, Chen AI, Pantin EJ, Davidovich AE, Labazzo KS, et al. First-in-human evaluation of a hand-held automated venipuncture device for rapid venous blood draws. Technology (Singap World Sci). 2019;7(3-4):98-107.
53. Kovach CR, Taneli Y, Neiman T, Dyer EM, Arzaga AJ, Kelber ST. Evaluation of an ultraviolet room disinfection protocol to decrease nursing home microbial burden, infection and hospitalization rates. BMC Infect Dis. 2017;17(1):186.
54. Centers for Disease Control and Prevention. Interim guidelines for collecting, handling, and testing clinical specimens from persons under investigation (PUIs) for coronavirus disease 2019 (COVID-19). COVID-19. 2020 Mar 9.
55. Keesara S, Jonas A, Schulman K. Covid-19 and Health Care's Digital Revolution. N Engl J Med. 2020;382(23):e82.
56. Smith AC, Thomas E, Snoswell CL, Haydon H, Mehrotra A, Clemensen J, et al. Telehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19). J Telemed Telecare. 2020;26(5):309-13.
57. Ahlqvist E. Digital inclusion in Sweden done in the “Digidel way”. 2015.
2. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020;395(10225):689-97.
3. Bengtsson L, Gaudart J, Lu X, Moore S, Wetter E, Sallah K, et al. Using mobile phone data to predict the spatial spread of cholera. Sci Rep. 2015;5:8923.
4. Bates M. Tracking Disease: Digital Epidemiology Offers New Promise in Predicting Outbreaks. IEEE Pulse. 2017;8(1):18-22.
5. Khaleghi A, Mohammadi MR, Zandifar A, Ahmadi N, Alavi SS, Ahmadi A, et al. Epidemiology of psychiatric disorders in children and adolescents; in Tehran, 2017. Asian J Psychiatr. 2018;37:146-53.
6. Mohammadi MR, Ahmadi N, Khaleghi A, Mostafavi SA, Kamali K, Rahgozar M, et al. Prevalence and Correlates of Psychiatric Disorders in a National Survey of Iranian Children and Adolescents. Iran J Psychiatry. 2019;14(1):1-15.
7. Talepasand S, Mohammadi MR, Alavi SS, Khaleghi A, Sajedi Z, Akbari P, et al. Psychiatric disorders in children and adolescents: Prevalence and sociodemographic correlates in Semnan Province in Iran. Asian J Psychiatr. 2019;40:9-14.
8. Coiera E. Guide to health informatics: CRC press; 2015.
9. Prem K, Liu Y, Russell TW, Kucharski AJ, Eggo RM, Davies N, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. Lancet Public Health. 2020;5(5):e261-e70.
10. Zhou C, Su F, Pei T, Zhang A, Du Y, Luo B, et al. COVID-19: Challenges to GIS with big data. Geography and Sustainability. 2020.
11. Oliver N, Letouzé E, Sterly H, Delataille S, De Nadai M, Lepri B, et al. Mobile phone data and COVID-19: Missing an opportunity? arXiv preprint arXiv:2003.12347. 2020.
12. Pepe E, Bajardi P, Gauvin L, Privitera F, Lake B, Cattuto C, Tizzoni M. COVID-19 outbreak response: a first assessment of mobility changes in Italy following national lockdown. medRxiv. 2020 Jan 1.
13. Cho H, Ippolito D, Yu YW. Contact tracing mobile apps for COVID-19: Privacy considerations and related trade-offs. arXiv preprint arXiv:2003.11511. 2020.
14. Martín-Calvo D, Aleta A, Pentland A, Moreno Y, Moro E. Effectiveness of social distancing strategies for protecting a community from a pandemic with a data driven contact network based on census and real-world mobility data. Technical Report2020.
15. Park S, Choi GJ, Ko H. Information Technology-Based Tracing Strategy in Response to COVID-19 in South Korea-Privacy Controversies. Jama. 2020.
16. Wang CJ, Ng CY, Brook RH. Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing. Jama. 2020.
17. Bai L, Yang D, Wang X, Tong L, Zhu X, Bai C, et al. Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019. Clinical eHealth. 2020.
18. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6(2):66-73.
19. Khaleghi A, Sheikhani A, Mohammadi MR, Nasrabadi AM, Vand SR, Zarafshan H, et al. EEG classification of adolescents with type I and type II of bipolar disorder. Australas Phys Eng Sci Med. 2015;38(4):551-9.
20. Zarafshan H, Khaleghi A, Mohammadi MR, Moeini M, Malmir N. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task. J Clin Exp Neuropsychol. 2016;38(3):361-9.
21. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(4):337-9.
22. Wang Y, Hu M, Li Q, Zhang X-P, Zhai G, Yao N. Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. arXiv preprint arXiv:2002.05534. 2020.
23. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv:2003.05037. 2020.
24. Barstugan M, Ozkaya U, Ozturk S. Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424. 2020.
25. Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43(2):635-40.
26. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology. 2020:200905.
27. Salman FM, Abu-Naser SS, Alajrami E, Abu-Nasser BS, Alashqar BA. Covid-19 detection using artificial intelligence. 2020.
28. Wang L, Wong A. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871. 2020.
29. Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849. 2020.
30. Yan L, Zhang H-T, Xiao Y, Wang M, Sun C, Liang J, et al. Prediction of survival for severe Covid-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in Wuhan. medRxiv. 2020.
31. Ge Y, Tian T, Huang S, Wan F, Li J, Li S, et al. A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19. bioRxiv. 2020.
32. Zhavoronkov A, Aladinskiy V, Zhebrak A, Zagribelnyy B, Terentiev V, Bezrukov DS, Polykovskiy D, Shayakhmetov R, Filimonov A, Orekhov P, Yan Y. Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches. ChemRxiv. 2020.
33. Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. bioRxiv. 2020.
34. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577(7792):706-10.
35. Randhawa GS, Soltysiak MPM, El Roz H, de Souza CPE, Hill KA, Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One. 2020;15(4):e0232391.
36. Priya S. Singapore government launches COVID-19 chatbot. 2020.
37. Hollander JE, Carr BG. Virtually Perfect? Telemedicine for Covid-19. N Engl J Med. 2020;382(18):1679-81.
38. Leite H, Hodgkinson IR, Gruber T. New development:‘Healing at a distance’—telemedicine and COVID-19. Public Money & Management. 2020 Apr 10:1-3.
39. Greenhalgh T, Wherton J, Shaw S, Morrison C. Video consultations for covid-19. Bmj. 2020;368:m998.
40. Greenhalgh T, Koh GCH, Car J. Covid-19: a remote assessment in primary care. Bmj. 2020;368:m1182.
41. Armfield NR, Bradford M, Bradford NK. The clinical use of Skype--For which patients, with which problems and in which settings? A snapshot review of the literature. Int J Med Inform. 2015;84(10):737-42.
42. Backhaus A, Agha Z, Maglione ML, Repp A, Ross B, Zuest D, et al. Videoconferencing psychotherapy: a systematic review. Psychol Serv. 2012;9(2):111-31.
43. Ignatowicz A, Atherton H, Bernstein CJ, Bryce C, Court R, Sturt J, et al. Internet videoconferencing for patient-clinician consultations in long-term conditions: A review of reviews and applications in line with guidelines and recommendations. Digit Health. 2019;5:2055207619845831.
44. Abimbola S, Keelan S, Everett M, Casburn K, Mitchell M, Burchfield K, et al. The medium, the message and the measure: a theory-driven review on the value of telehealth as a patient-facing digital health innovation. Health Econ Rev. 2019;9(1):21.
45. Liu S, Yang L, Zhang C, Xiang YT, Liu Z, Hu S, et al. Online mental health services in China during the COVID-19 outbreak. Lancet Psychiatry. 2020;7(4):e17-e8.
46. Mohammadi MR, Zarafshan H, Bashi SK, Mohammadi F, Khaleghi A. The Role of Public Trust and Media in the Psychological and Behavioral Responses to the Pandemics of COVID-19. Preprints with The Lancet, Available at SSRN 3586701. 2020.
47. Kang L, Li Y, Hu S, Chen M, Yang C, Yang BX, et al. The mental health of medical workers in Wuhan, China dealing with the 2019 novel coronavirus. Lancet Psychiatry. 2020;7(3):e14.
48. Xiang YT, Yang Y, Li W, Zhang L, Zhang Q, Cheung T, et al. Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed. Lancet Psychiatry. 2020;7(3):228-9.
49. Wright JH, Caudill R. Remote Treatment Delivery in Response to the COVID-19 Pandemic. Psychother Psychosom. 2020;89(3):130-2.
50. Murphy R, Calugi S, Cooper Z, Dalle Grave R. Challenges and Opportunities for enhanced cognitive behaviour therapy (CBT-E) in light of COVID-19. the Cognitive Behaviour Therapist. 2020:13:1-31.
51. Ho CS, Chee CY, Ho RC. Mental Health Strategies to Combat the Psychological Impact of COVID-19 Beyond Paranoia and Panic. Ann Acad Med Singapore. 2020;49(3):155-60.
52. Leipheimer JM, Balter ML, Chen AI, Pantin EJ, Davidovich AE, Labazzo KS, et al. First-in-human evaluation of a hand-held automated venipuncture device for rapid venous blood draws. Technology (Singap World Sci). 2019;7(3-4):98-107.
53. Kovach CR, Taneli Y, Neiman T, Dyer EM, Arzaga AJ, Kelber ST. Evaluation of an ultraviolet room disinfection protocol to decrease nursing home microbial burden, infection and hospitalization rates. BMC Infect Dis. 2017;17(1):186.
54. Centers for Disease Control and Prevention. Interim guidelines for collecting, handling, and testing clinical specimens from persons under investigation (PUIs) for coronavirus disease 2019 (COVID-19). COVID-19. 2020 Mar 9.
55. Keesara S, Jonas A, Schulman K. Covid-19 and Health Care's Digital Revolution. N Engl J Med. 2020;382(23):e82.
56. Smith AC, Thomas E, Snoswell CL, Haydon H, Mehrotra A, Clemensen J, et al. Telehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19). J Telemed Telecare. 2020;26(5):309-13.
57. Ahlqvist E. Digital inclusion in Sweden done in the “Digidel way”. 2015.
| Files | ||
| Issue | Vol 15 No 3 (2020) | |
| Section | Review Article(s) | |
| DOI | https://doi.org/10.18502/ijps.v15i3.3816 | |
| PMCID | PMC7603586 | |
| PMID | 33193772 | |
| Keywords | ||
| COVID-19 Health Care Informatics Robotics Technology Telehealth | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Khaleghi A, Mohammadi MR, Pirzad Jahromi G, Zarafshan H. New Ways to Manage Pandemics: Using Technologies in the Era of COVID-19, a Narrative Review. Iran J Psychiatry. 2020;15(3):236-242.
