The data analysis and validation engine: an application of artificial intelligence in the improvement of COVID-19 data management


  • Golden Owhonda Department of Public Health & Disease Control, Rivers State Ministry of Health, Port Harcourt, Rivers State, Nigeria
  • Anwuri Luke Department of Community Medicine, College of Medicine, Rivers State University, Nkpolu-Oroworukwo, Port Harcourt, Rivers State, Nigeria
  • Japheth Russell Inyele Department of Public Health & Disease Control, Rivers State Ministry of Health, Port Harcourt, Rivers State, Nigeria
  • Chidinma Eze-Emiri Department of Public Health & Disease Control, Rivers State Ministry of Health, Port Harcourt, Rivers State, Nigeria



Data analysis and validation engine, Artificial intelligence, COVID-19, Data management


Background: The proper management of healthcare data is fundamental to the health system processes; artificial intelligence has proven its value in these processes. Artificial intelligence can simplify the management of information, improve data security, and automate data flow. It is also useful in the analysis and interpretation of big data. Hence, it has the possibility of screening and diagnosing diseases, categorizing disease severity, detecting therapeutic agents, and forecasting outbreak spots.

Methods: A data analysis and validation engine was developed to perform data quality control checks, classify addresses, and generate epidemiology numbers using the index and parse command on the command-line interface of DAVE.

Results: DAVE correctly formatted data and created a local copy of the datastore and the index. It also returned previous EPID numbers to each entry and assigned a new EPID number to missed entries. DAVE imported the entries into the data template of the existing data management tool and generated a sample manifest that is then sent to the Laboratory. The data flow from the point of collection to storage and reporting was assessed as 100% accurate without errors and in real-time; there was also the ability to roll back if any error occurred.

Conclusions: DAVE is a semi-autonomous system that operates with minimal human intervention; it is automatically faster as it leverages computing power to parse, store, and retrieve data while practically eliminating the need for manual data quality assessment. The DAVE functionality can be extended to incorporate additional features like forecasting outbreaks of emerging/re-emerging diseases, categorizing the severity of diseases and analysis of data in our setting.


Chen J, See KC. Artificial intelligence for COVID-19: Rapid Review. J Med Internet Res. 2020;22(10):12.

Horgan D, Hackett J, Westphalen CB, Kalra D, Richer E, Romao M, et al. Digitalization and COVID-19: The Perfect Storm. Biomed Hub. 2020;5(3):1-23.

Nguyen T, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry: current clinical trends and research advances. J Can Dent Assoc. 2021;87(17): 8.

Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial Intelligence with Multi-Functional Machine Learning Platform Development for Better Healthcare and Precision Medicine. Database J Biol Databases Curation. 2020;2020(10):35.

Randhawa GS, Soltysiak MPM, El Roz H, de Souza CPE, Hill KA, et al. Machine Learning Using Intrinsic Genomic Signatures for Rapid Classification of Novel Pathogens: COVID-19 Case Study. PLoS ONE. 2020;15(4):24.

Albahri AS, Hamid RA, Alwan JK, Al-qays ZT, Zaidan AA, Zaidan BB, et al. Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review. J Med Syst. 2020; 44(122):11.

Srinivasa Rao ASR, Vazquez JA. Identification of COVID-19 can be Quicker Through Artificial Intelligence Framework Using a Mobile Phone-Based Survey When Cities and Towns are Under Quarantine. Infect Control Hosp Epidemiol. 2020;41(7):826-30.

Bochenek B, Ustrnul Z. Machine Learning in weather prediction and climate analyses: applications and perspectives. Atmosphere J. 2022;13(180):16.

Pley C, Evans M, Lowe R, Montgomery H, Yacoub S. Digital and Technological Innovation in Vector-Borne Disease Surveillance to Predict, Detect, and Control Climate-Driven Outbreaks. Lancet Planet Health. 2021;5(10):739-45.

Owoyemi A, Owoyemi J, Osiyemi A, Boyd A. Artificial Intelligence for Healthcare in Africa. Front Digit Health. 2020;2(6):5.

Morgenstern JD, Rosella LC, Daley MJ, Goel V, Schünemann HJ, Piggott T. “AI’s Gonna Have an Impact on Everything in Society, So It Has to Have an Impact on Public Health”: A Fundamental Qualitative Descriptive Study of the Implications of Artificial Intelligence for Public Health. BMC Public Health. 2021;21(40):14.

Davenport T, Kalakota R. The potential for Artificial Intelligence in Healthcare. Future Health J. 2019; 6(2):94.

Vinuesa R, Azizpour H, Leite I, Balaam M, Dignum V, Domisch S, et al. The Role of Artificial Intelligence in Achieving the Sustainable Development Goals. Nat Commun. 2020;11(233):10.

Marks M, Lal S, Brindle H, Gsell P-S, MacGregor M, Stott C, et al. Electronic Data Management for Vaccine Trials in Low Resource Settings: Upgrades, Scalability, and Impact of ODK. Front Public Health. 2021;9(665584):11.

Maduka O, Akpan G, Maleghemi S. Using Android and Open Data Kit Technology in Data Management for Research in Resource-Limited Settings in the Niger Delta Region of Nigeria: Cross-Sectional Household Survey. JMIR. 2017;5(11):8.

Bokonda PL, Ouazzani-Touhami, K, Souissi N. Open Data Kit: Mobile Data Collection Framework for Developing Countries. Int J Innov Technol Explor Eng. 2019;8(12):4749-54.

Nampa IW, Mudita IW, Riwu Kaho NPLB, Widinugraheni S, Lasarus Natonis R. The KoBoCollect for Research Data Collection and Management: An Experience in Researching the Socio-Economic Impact of Blood Disease in Banana. SOCA J Sos Ekon Pertan. 2020;14(3):545-56.

Abu-Dalbouh HM, Alateyah SA. Predictive Data Mining Rule-Based Classifiers Model for Novel Coronavirus (COVID-19) Infected Patients’ Recovery in the Kingdom of Saudi Arabia. J Theor Appl Inf Technol. 2021;99(8):19.

AlMoammar A, AlHenaki L, Kurdi H. Selecting Accurate Classifier Models for a MERS-CoV Dataset. AISC. 2019;2018(868):1070-84.

National Centre for Disease Prevention and Control. National Interim Guidelines for Clinical Management of COVID-19. Abuja, Nigeria: National Centre for Disease Prevention and Control; 2020:44. Available at: Accessed on 25 February 2022.

Managing Epidemics: Key Facts about Major Deadly Diseases. Geneva, Switzerland; 2018:257. Available at: 10665/272442. Accessed on 25 February 2022.

Biden JR. National Strategy for the COVID-19 Response and Pandemic Preparedness. Washington DC, United States of America; 2021:200. Available at: Accessed on 17 March 2022.

Watters KE, Kirkpatrick J, Palmer MJ, Koblentz GD. The CRISPR Revolution and its Potential Impact on Global Health Security. Pathog Glob Health. 2019;115(2):80-92.

Malik YS, Sircar S, Bhat S, Ansari MI, Pande T, Kumar P, et al. How Artificial Intelligence May Help the Covid‐19 pandemic: Pitfalls and Lessons for the Future. Rev Med Virol. 2020;2020(e2205):11.

Konwarh R. Can CRISPR/Cas Technology Be a Felicitous Stratagem Against the COVID-19 Fiasco? Prospects and Hitches. Front Mol Biosci. 2020;7 (557377):8.

Ding R, Long J, Yuan M, Jin Y, Yang H, Chen M, et al. CRISPR/Cas System: A Potential Technology for the Prevention and Control of COVID-19 and Emerging Infectious Diseases. Front Cell Infect Microbiol. 2021;11(639108):10.

Agrebi S, Larbi A. Use of artificial intelligence in infectious diseases. In: Artificial Intelligence in Precision Health. Artificial Intelligence in Precision Health; 2020;23:415-38.

Tom-Aba D, Silenou BC, Doerrbecker J, Fourie C, Leitner C, Wahnschaffe M, et al. The Surveillance Outbreak Response Management and Analysis System (SORMAS): Digital Health Global Goods Maturity Assessment. JMIR Public Health Surveill. 2020;6(2):9.

Silenou BC, Tom-Aba D, Adeoye O, Arinze CC, Oyiri F, Suleman AK, et al. Use of surveillance outbreak response management and analysis system for human monkeypox Outbreak, Nigeria, 2017-2019. Emerg Infect Dis. 2020;26(2):345-9.

Adeoye O, Tom-Aba D, Ameh C, Ojo O, Ilori E, Gidado S, et al. Implementing Surveillance and Outbreak Response Management and Analysis System (SORMAS) for Public Health in West Africa- Lessons Learnt and Future Direction. Int J Trop Dis Health. 2017;22(2):1-17.

Silenou BC, Nyirenda JLZ, Zaghloul A, Lange B, Doerrbecker J, Schenkel K, et al. Availability and Suitability of Digital Health Tools in Africa for Pandemic Control: Scoping Review and Cluster Analysis. JMIR Public Health Surveill. 2021;7(12):16.

Nguyen G, Dlugolinsky S, Bobák M, Tran V, López García Á, Heredia I, et al. Machine Learning and Deep Learning Frameworks and Libraries for Large-scale Data Mining: A Survey. Artif Intell Rev. 2019; 52(1): 77-124.

Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions. J Big Data. 2021; 8(53):74.

Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):47.




How to Cite

Owhonda, G., Luke, A., Inyele, J. R., & Eze-Emiri, C. (2022). The data analysis and validation engine: an application of artificial intelligence in the improvement of COVID-19 data management. International Journal Of Community Medicine And Public Health, 9(6), 2437–2441.



Original Research Articles