Journal:Blockchain and artificial intelligence technology for novel coronavirus disease 2019 self-testing

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Full article title Blockchain and artificial intelligence technology for novel coronavirus disease 2019 self-testing
Journal Diagnostics
Author(s) Mashamba-Thompson, Tivani P.; Crayton, Ellen D.
Author affiliation(s) University of Limpopo, Genesis Technology and Management Group
Primary contact Email: tivani dot mashamba at ul dot ac dot za
Year published 2020
Volume and issue 10(4)
Article # 198
DOI 10.3390/diagnostics10040198
ISSN 2075-4418
Distribution license Creative Commons Attribution 4.0 International
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The novel coronavirus disease 2019 (COVID-19) is rapidly spreading, with a rising death toll and transmission rate reported in high-income countries rather than in low-income countries. However, the overburdened healthcare systems and poor disease surveillance systems in resource-limited settings may struggle to cope with this COVID-19 outbreak, which calls for a tailored strategic response within these settings. Here, we recommend a low-cost blockchain and artificial intelligence-coupled self-testing and tracking system for COVID-19 and other emerging infectious diseases. Prompt deployment and appropriate implementation of the proposed system have the potential to curb the transmission of COVID-19 and its related mortalities, particularly in settings with poor access to laboratory infrastructure.

Keywords: self-testing, novel coronavirus disease-19, blockchain, artificial intelligence


The novel coronavirus disease 2019 (COVID-19) has now reached sub-Saharan Africa (SSA), with cases reported in more than 40 SSA countries. SSA health systems are already battling with poor health outcomes and high mortality rates linked to the unique quadruple (HIV, tuberculosis (TB), and non-communicable diseases) burden of disease.[1] In addition, SSA’s dense communities, informal settlements, and rural and resource-limited settings are at particular risk for being vulnerable to the COVID-19 outbreak. These populations are underserved in terms of health services and have the potential to become to new COVID-19 epicenters. The global COVID-19 statistics surprisingly show low transmission rates and fewer deaths in resource-limited countries, particularly countries in Sub-Saharan Africa (SSA). However, while SSA’s young population and warm climate may put SSA at an advantage for coping with the COVID-19 outbreak[2], there is growing concern about the impact of COVID-19 co-infections among the people living with other immune-system-weakening conditions such as HIV, TB, and diabetes, particularly given a struggling health care system in resource-limited settings such as SSA countries.[3][4]

There is a growing concern about a failure to find and report cases, especially given weak health systems, inadequate surveillance, insufficient laboratory capacity, and limited public health infrastructure in African countries.[5] Access to accurate diagnosis, monitoring, and reporting of health outbreaks requires a well-resourced healthcare system.[6] Evidence shows that most resource-limited countries lack an effective, rapid surveillance system.[7] These settings also have a limited availability of health technologies for the electronic surveillance of infectious diseases to facilitate the prevention and containment of emerging infectious diseases such as COVID-19.[7] Universal health coverage, as well as access to high-quality and timely pathology and laboratory medicine (PALM) services, is crucially needed to support healthcare systems that are tasked with achieving sustainable developmental goals.[8] This calls for the rapid development and deployment of health innovations for accurate diagnosis and electronic surveillance of COVID-19 in underserved populations.

Recent evidence shows that prompt development and deployment of point-of-care (POC) diagnostics for screening in response to the COVID-19 outbreak can help to curb the spread of the disease and to alleviate the burden on the healthcare system.[9][10] The impact of rapid testing on the COVID-19 death rate has been shown in Germany.[11] Emerging health innovations such as blockchain and artificial intelligence (AI) technology can be coupled with POC diagnostics to enable self-testing of patients in isolation as a result of exposure to COVID-19. Blockchain is a digital, public ledger that records online transactions. It involves the digital distribution of ledger and consensus algorithms and eliminates all the threats of intermediaries.[12][13] One of the commonly-known applications of blockchain is the cryptocurrency Bitcoin[14], which has been successfully used as a financial alternative in emerging economies including countries in SSA.[15] Blockchain technology has shown adaptability in recent years, leading to its incorporation in a wide range of applications, including biomedical and healthcare systems.[16][17][18] The use of blockchain and AI in healthcare has been demonstrated in the management of electronic medical records, drug and pharmaceutical supply chains, biomedical research, education, remote patient monitoring, and healthcare data analytics.[17]

Mobile connected point-of-care diagnostics and self-testing has been successfully implemented in resource-limited settings.[19][20][21] However, there is limited evidence on the use of blockchain and AI technology for disease diagnosis. Bearing in mind the era of COVID-19 and the evidence on the overburdened healthcare systems and poor disease surveillance systems in resource-limited settings, and taking advantage of the available mobile Health (mHealth) systems, we recommend a rapid development and deployment of low-cost blockchain and AI-coupled mHealth connected self-testing and tracking systems as one of the strategic response strategies for COVID-19 and other emerging infectious diseases (Figure 1).

Fig1 Mashamba-Thompson Diagnostics2020 10-4.png

Figure 1. Proposed community-based blockchain and artificial intelligence-coupled mobile-linked self-testing and tracking system for emerging infectious diseases.

The initial step for this system is through a mobile phone or tablet application (app) which could be adapted from existing self-testing apps.[22][23] The app requests a user’s personal identifier before opening pre-testing instructions. Following testing, the user then uploads results into the app. The blockchain and AI system enables the transfer of the test result to alert the outbreak surveillance authorities of all tests performed, as well as the number of positive and negative test results. This helps ensure that all positive cases are referred to a quarantine site for treatment and monitoring. The built-in geographic information system (GIS) in mobile devices enables the tracking of those who tested positive. This system could also be connected to local, national, and international databases to ensure appropriate surveillance and control of an outbreak.

The AI component of this technology enables powerful data collection (patient information, geographic location of the patient, and test results), security, analysis, and curation of disparate and clinical data sets from federated blockchain platforms to derive triangulated data at very high degrees of confidence and speed. With this well-architected integrative technology platform, public health researchers could ensure secure and immutable data sets that enable the collection of high-quality data and allow for deep insights to be made. Local development of these diagnostic tools can help overcome supply chain challenges[24] and related costs, which can limit accessibility of POC diagnostics in resource-limited settings. Additionally, this technology can be adapted for use in community-based case finding of other infectious diseases such as HIV, TB, and Malaria, which may be exacerbated by the current COVID-19 outbreak. However, relevant stakeholders’ involvement is crucial to ensure the efficient development and sustainable implementation of the proposed technology, particularly in underserved populations.



This research received no external funding.

Conflicts of interest

The authors declare no conflict of interest.


  1. Institute for Health Metrics and Evaluation (2018). "Findings from the Global Burden of Disease Study 2017" (PDF). Institute for Health Metrics and Evaluation. Retrieved 25 March 2020. 
  2. Chopera, D. (24 March 2020). "Can Africa Withstand COVID-19?". Project Syndicate. Retrieved 25 March 2020. 
  3. Wong, E. (24 March 2020). "TB, HIV and COVID-19: Urgent questions as three epidemics collide". The Conversation. Retrieved 25 March 2020. 
  4. Powell, A. (27 March 2020). "On-again, off-again looks to be best social-distancing option". The Harvard Gazette. Retrieved 28 March 2020. 
  5. Whiteside, A. (25 March 2020). "Covid-19 Watch: The World Wakes Up". Retrieved 25 March 2020. 
  6. Herida, M.; Dervaux, B.; Desenclos, J.C. (2016). "Economic Evaluations of Public Health Surveillance Systems: A Systematic Review". European Journal of Public Health 26 (4): 674–80. doi:10.1093/eurpub/ckv250. PMC PMC7108512. PMID 26850905. 
  7. 7.0 7.1 Rattanaumpawan, P.; Boonyasiri, A.; Vong, S. et al. (2018). "Systematic review of electronic surveillance of infectious diseases with emphasis on antimicrobial resistance surveillance in resource-limited settings". American Journal of Infection Control 46 (2): 139–46. doi:10.1016/j.ajic.2017.08.006. PMID 29029814. 
  8. United Nations (2019). "The Sustainable Development Goals Report 2019" (PDF). United Nations. ISBN 978-92-1-047887-8. Retrieved 19 March 2020. 
  9. Pang, J.; Wang, M.X.; Ang, I.Y.H. et al. (2020). "Potential Rapid Diagnostics, Vaccine and Therapeutics for 2019 Novel Coronavirus (2019-nCoV): A Systematic Review". Journal of Clinical Medicine 9 (3): E623. doi:10.3390/jcm9030623. PMC PMC7141113. PMID 32110875. 
  10. Wang, C.; Horby, P.W.; Hayden, F.G. et al. (2020). "A novel coronavirus outbreak of global health concern". Lancet 395 (10223): 470–73. doi:10.1016/S0140-6736(20)30185-9. PMC PMC7135038. PMID 31986257. 
  11. Jordans, F. (9 March 2020). "Experts: Rapid Testing Helps Explain Few German Virus Deaths". U.S. News & World Report. Retrieved 19 March 2020. 
  12. Yaqoob, S.; Khan, M.M.; Talib, R. et al. (2019). "Use of Blockchain in Healthcare: A Systematic Literature Review". International Journal of Advanced Computer Science and Applications 10 (5). doi:10.14569/IJACSA.2019.0100581. 
  13. Gomez, M.; Bustamante, P.; Weiss, M.B.H. et al. (2019). "Is Blockchain the Next Step in the Evolution Chain of [Market] Intermediaries?". SSRN: 20. doi:10.2139/ssrn.3427506. 
  14. Nakamoto, S.. "Bitcoin: A Peer-to-Peer Electronic Cash System" (PDF). Retrieved 25 March 2020. 
  15. Vincent, O. (2019). "Can cryptocurrency, mobile phones, and internet herald sustainable financial sector development in emerging markets?". Journal of Transnational Management 24 (3): 259–79. doi:10.1080/15475778.2019.1633170. 
  16. Mettler, M. (2016). "Blockchain technology in healthcare: The revolution starts here". Proceedings from the 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services: 1–3. doi:10.1109/HealthCom.2016.7749510. 
  17. 17.0 17.1 Agbo, C.C.; Mahmoud, Q.H.; Eklund, J.M. (2019). "Blockchain Technology in Healthcare: A Systematic Review". Healthcare 7 (2): E56. doi:10.3390/healthcare7020056. PMC PMC6627742. PMID 30987333. 
  18. Zhang, P.; Schmidt, D.C.; White, J.; Lenz, G. (2018). "Blockchain Technology Use Cases in Healthcare". Advanced in Computers 111: 1–41. doi:10.1016/bs.adcom.2018.03.006. 
  19. Makhudu, S.; Kuupiel, D.; Gwala, N. et al. (2019). "The Use of Patient Self-Testing in Low- and Middle-Income Countries: A Systematic Scoping Review". Point of Care 18 (1): 9–16. doi:10.1097/POC.0000000000000179. 
  20. Bervell, B.; Al-Samarraie, H. (2019). "A comparative review of mobile health and electronic health utilization in sub-Saharan African countries". Social Science & Medicine 232: 1–16. doi:10.1016/j.socscimed.2019.04.024. PMID 31035241. 
  21. Adeagbo, O.; Kim, H.Y.; Tanser, F. et al. (2020). "Acceptability of a tablet-based application to support early HIV testing among men in rural KwaZulu-Natal, South Africa: a mixed method study". AIDS Care: 1–8. doi:10.1080/09540121.2020.1742867. PMID 32172596. 
  22. Pant Pai, N.; Behlim, T.; Abrahams, L. et al. (2013). "Will an unsupervised self-testing strategy for HIV work in health care workers of South Africa? A cross sectional pilot feasibility study". PLoS One 8 (11): e79772. doi:10.1371/journal.pone.0079772. PMC PMC3842310. PMID 24312185. 
  23. Tay, I.; Garland, S.; Gorelik, A. et al. (2017). "Development and Testing of a Mobile Phone App for Self-Monitoring of Calcium Intake in Young Women". JMIR mHealth and uHealth 5 (3): e27. doi:10.2196/mhealth.5717. PMC PMC5360908. PMID 28270379. 
  24. Kuupiel, D.; Bawontuo, V.; Mashamba-Thompson, T.P. (2017). "Improving the Accessibility and Efficiency of Point-of-Care Diagnostics Services in Low- and Middle-Income Countries: Lean and Agile Supply Chain Management". Diagnostics 7 (4): e58. doi:10.3390/diagnostics7040058. PMC PMC5745394. PMID 29186013. 


This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.