Version 1
: Received: 26 July 2022 / Approved: 29 July 2022 / Online: 29 July 2022 (11:25:46 CEST)
How to cite:
Mukamakuza, C. P.; Tuyishimire, E.; Mbituyumuremyi, A.; Brown, T. X.; Iradukunda, D.; Phuti, O.; Happiness, R. M. A Dependable Digital System Model for Malaria Monitoring. Preprints2022, 2022070461. https://doi.org/10.20944/preprints202207.0461.v1
Mukamakuza, C. P.; Tuyishimire, E.; Mbituyumuremyi, A.; Brown, T. X.; Iradukunda, D.; Phuti, O.; Happiness, R. M. A Dependable Digital System Model for Malaria Monitoring. Preprints 2022, 2022070461. https://doi.org/10.20944/preprints202207.0461.v1
Mukamakuza, C. P.; Tuyishimire, E.; Mbituyumuremyi, A.; Brown, T. X.; Iradukunda, D.; Phuti, O.; Happiness, R. M. A Dependable Digital System Model for Malaria Monitoring. Preprints2022, 2022070461. https://doi.org/10.20944/preprints202207.0461.v1
APA Style
Mukamakuza, C. P., Tuyishimire, E., Mbituyumuremyi, A., Brown, T. X., Iradukunda, D., Phuti, O., & Happiness, R. M. (2022). A Dependable Digital System Model for Malaria Monitoring. Preprints. https://doi.org/10.20944/preprints202207.0461.v1
Chicago/Turabian Style
Mukamakuza, C. P., Ofentse Phuti and Rose Mary Happiness. 2022 "A Dependable Digital System Model for Malaria Monitoring" Preprints. https://doi.org/10.20944/preprints202207.0461.v1
Abstract
Malaria is a long-standing disease and one of the top life-threatening diseases, yet its treatment has not changed, while the world has already embraced the Fourth Industrial Revolution (4IR). A wave of research on digitizing monitoring mechanisms of such a deadly disease has surfaced. Automated malaria screening is one of the detection processes which are gaining popularity in the research domain. However, the process needs to be coupled with other processes aiming a nationally or regionally contextualised malaria monitoring system. This paper proposes a digital malaria monitoring system in the context of an African country or region. One advantage of such a digital system is that is enables a novel disease spread forecasting model based on the dynamics of different malaria types. The architecture of the diagnosis system is described, and the disease spread model is mathematically modelled in terms of a SPITR (Susceptible- Protected- Infected-Treated- Recovered) epidemic model which is further analysed. The forecasting model is expressed and analysed whereas experiments are conducted using a Monte Carlo simulation method. The design of the monitoring system has inspired how predictions can be made in the complex cases such as mixed infections. Results show that reinforcing the model parameter makes a significant improvement on the disease prediction.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
