PendingISSN (Online)
Volume 1 , Issue 2 , PP: 01-11, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
El-Sayed M. El-kenawy 1 * , Marwa M. Eid 2 , Laith Abualigah 3
Doi: https://doi.org/10.54216/MOR.010201
The Zika virus is a severe public health threat all across the world, owing to its spreading mechanism through Aedes mosquitoes and its ability to result in extreme neurological diseases, which include the congenital Zika syndrome and the Guillain-Barré syndrome, amongst others. Conventional monitoring techniques often fail because many asymptomatic cases render early diagnosis challenging. Machine learning (ML) techniques can be seen as a constructive development in addressing this challenge, which entails predicting and tracking the spread of diseases such as Zika through extensive and complex datasets. Data analytic ML systems also enhance early warning systems and situational uplift by using data from social media, climate history, and genetics. This helps reasonably to predict the mosquito population biologically and the environmental factors that favor the spread of the virus for a more practical approach from the public health sector. Over and above, some issues are still pending, especially regarding the quality of data, understanding the models and how to apply such models within the current health systems. These factors must be solved to implement ML successfully in surveillance practice. This review provides an overview of the issue, stating the potential of machine learning applications in the development of public health, whose actions focus on Zika and other diseases transmitted by vectors.
Zika virus , Machine learning , Disease outbreaks , Public health monitoring , Vector-borne diseases , Data analytics , Epidemiology , Predictive modeling
[1] R. K. Singh et al., “Advances in diagnosis, surveillance, and monitoring of zika virus: An update,” Front Microbiol, vol. 8, no. JAN, p. 304633, Jan. 2018, doi: 10.3389/FMICB.2017.02677/BIBTEX.
[2] A. Abdulrahman, .. Z. M., A. M. Zaki, F. H. H.Rizk, M. M. Eid, and E.-S. M. EL EL-Kenawy, “Exploring Optimization Algorithms: A Review of Methods and Applications,” Journal of Artificial Intelligence and Metaheuristics, vol. 7, no. 2, pp. 08–17, 2024, doi: 10.54216/JAIM.070201.
[3] N. Jonkmans, V. D’Acremont, and A. Flahault, “Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseases,” BMJ Glob Health, vol. 6, no. 9, p. e006623, Sep. 2021, doi: 10.1136/BMJGH-2021-006623.
[4] D. Sharifrazi, N. Javed, R. Alizadehsani, P. N. Paradkar, U. R. Acharya, and A. Bhatti, “Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis,” Dec. 2023, Accessed: Oct. 16, 2024. [Online]. Available: https://arxiv.org/abs/2312.08654v1
[5] S. S. Kazmi, W. Ali, N. Bibi, and F. Nouroz, “A review on Zika virus outbreak, epidemiology, transmission and infection dynamics,” Journal of Biological Research (Greece), vol. 27, no. 1, pp. 1–11, Mar. 2020, doi: 10.1186/S40709-020-00115-4/FIGURES/7.
[6] D. Sharifrazi, N. Javed, R. Alizadehsani, P. N. Paradkar, U. R. Acharya, and A. Bhatti, “Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis,” Dec. 2023, Accessed: Oct. 16, 2024. [Online]. Available: https://arxiv.org/abs/2312.08654v1
[7] D. Sharifrazi, N. Javed, R. Alizadehsani, P. N. Paradkar, U. R. Acharya, and A. Bhatti, “Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis,” Dec. 2023, Accessed: Oct. 16, 2024. [Online]. Available: https://arxiv.org/abs/2312.08654v1
[8] R. R. Selvaraju, A. Das, R. Vedantam, M. Cogswell, D. Parikh, and D. Batra, “Grad-CAM: Why did you say that?,” Nov. 2016, Accessed: Oct. 16, 2024. [Online]. Available: http://arxiv.org/abs/1611.07450
[9] T. Alafif, A. M. Tehame, S. Bajaba, A. Barnawi, and S. Zia, “Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions,” International Journal of Environmental Research and Public Health 2021, Vol. 18, Page 1117, vol. 18, no. 3, p. 1117, Jan. 2021, doi: 10.3390/IJERPH18031117.
[10] A. Stachel, K. Daniel, D. Ding, F. Francois, M. Phillips, and J. Lighter, “Development and validation of a machine learning model to predict mortality risk in patients with COVID-19,” BMJ Health Care Inform, vol. 28, no. 1, p. e100235, May 2021, doi: 10.1136/BMJHCI-2020-100235.
[11] S. B. Choi and I. Ahn, “Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina,” PLoS One, vol. 15, no. 7, p. e0233855, Jul. 2020, doi: 10.1371/JOURNAL.PONE.0233855.
[12] I. Dieng et al., “Re-Emergence of Dengue Serotype 3 in the Context of a Large Religious Gathering Event in Touba, Senegal,” Int J Environ Res Public Health, vol. 19, no. 24, Dec. 2022, doi: 10.3390/IJERPH192416912.
[13] M. Mathoho, D. van der Haar, and H. Vadapalli, “Innovations in Mosquito Identification: Integrating Deep Learning with Citizen Science,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14976 LNCS, pp. 189–202, 2024, doi: 10.1007/978-3-031-67285-9_14/FIGURES/3.
[14] A. Esteva et al., “A guide to deep learning in healthcare,” Nature Medicine 2019 25:1, vol. 25, no. 1, pp. 24–29, Jan. 2019, doi: 10.1038/s41591-018-0316-z.
[15] O. Anikeeva et al., “The impact of increasing temperatures due to climate change on infectious diseases,” BMJ, vol. 387, p. e079343, Oct. 2024, doi: 10.1136/BMJ-2024-079343.
[16] G. Habarugira, W. W. Suen, J. Hobson-Peters, R. A. Hall, and H. Bielefeldt-Ohmann, “West Nile Virus: An Update on Pathobiology, Epidemiology, Diagnostics, Control and ‘One Health’ Implications,” Pathogens 2020, Vol. 9, Page 589, vol. 9, no. 7, p. 589, Jul. 2020, doi: 10.3390/PATHOGENS9070589.
[17] S. M. Zina, G. Hoarau, M. Labetoulle, M. Khairallah, and A. Rousseau, “Ocular Manifestations of Flavivirus Infections,” Pathogens 2023, Vol. 12, Page 1457, vol. 12, no. 12, p. 1457, Dec. 2023, doi: 10.3390/PATHOGENS12121457.
[18] D. zhong Zhao et al., “A Swin Transformer-based model for mosquito species identification,” Scientific Reports 2022 12:1, vol. 12, no. 1, pp. 1–13, Nov. 2022, doi: 10.1038/s41598-022-21017-6.
[19] M. A. Geraldes, M. V. Cunha, C. Godinho, R. F. de Lima, M. Giovanetti, and J. Lourenço, “The historical ecological background of West Nile virus in Portugal indicates One Health opportunities,” Science of The Total Environment, vol. 944, p. 173875, Sep. 2024, doi: 10.1016/J.SCITOTENV.2024.173875.
[20] S. G. Jaybhaye, R. L. Chavhan, V. R. Hinge, A. S. Deshmukh, and U. S. Kadam, “CRISPR-Cas assisted diagnostics of plant viruses and challenges,” Virology, vol. 597, p. 110160, Sep. 2024, doi: 10.1016/J.VIROL.2024.110160.
[21] C. Meseko, M. Sanicas, K. Asha, L. Sulaiman, and B. Kumar, “Antiviral options and therapeutics against influenza: history, latest developments and future prospects,” Front Cell Infect Microbiol, vol. 13, p. 1269344, Nov. 2023, doi: 10.3389/FCIMB.2023.1269344/BIBTEX.
[22] J. L. Oei et al., “Global Pandemics, the Mother and Her Infant: Learning from the Past to Help the Future,” 2020, doi: 10.1007/978-3-319-18159-2_294-1.
[23] M. H. D. Ndione et al., “Re-Introduction of West Nile Virus Lineage 1 in Senegal from Europe and Subsequent Circulation in Human and Mosquito Populations between 2012 and 2021,” Viruses 2022, Vol. 14, Page 2720, vol. 14, no. 12, p. 2720, Dec. 2022, doi: 10.3390/V14122720.
[24] A. L. L. Windah et al., “Immunoinformatics-Driven Strategies for Advancing Epitope-Based Vaccine Design for West Nile Virus,” J Pharm Sci, vol. 113, no. 4, pp. 906–917, Apr. 2024, doi: 10.1016/J.XPHS.2023.11.025.
[25] S. Kobayashi et al., “Development of recombinant West Nile virus expressing mCherry reporter protein,” J Virol Methods, vol. 317, p. 114744, Jul. 2023, doi: 10.1016/J.JVIROMET.2023.114744.
[26] G. T. Norris, J. M. Ames, S. F. Ziegler, and A. Oberst, “Oligodendrocyte-derived IL-33 functions as a microglial survival factor during neuroinvasive flavivirus infection,” PLoS Pathog, vol. 19, no. 11, p. e1011350, Nov. 2023, doi: 10.1371/JOURNAL.PPAT.1011350.
[27] O. Anikeeva et al., “The impact of increasing temperatures due to climate change on infectious diseases,” BMJ, vol. 387, p. e079343, Oct. 2024, doi: 10.1136/BMJ-2024-079343.
[28] M. Tariq Bhatti, J. R. Long, and A. R. Carey, “Denial,” Surv Ophthalmol, vol. 0, no. 0, 2024, doi: 10.1016/j.survophthal.2024.05.003.
[29] K. Jannat et al., “Nanotechnology Applications of Flavonoids for Viral Diseases,” Pharmaceutics 2021, Vol. 13, Page 1895, vol. 13, no. 11, p. 1895, Nov. 2021, doi: 10.3390/PHARMACEUTICS13111895.
