Emmanuel Mwanga

ORCID iDhttps://orcid.org/0000-0003-1799-3830

Research title: Using machine learning and infrared spectroscopy for rapid assessment of key entomological and parasitological indicators of malaria transmission

Research Summary

Research Interests

  • Vector surveillance
  • Application of machine learning in parasite surveillance
  • Malaria genomic data analysis
  • Public health

Research Summary

  • My research aims to use machine learning and infrared spectroscopy for rapid assessment of key entomological and parasitological indicators of malaria transmission. The focus of this work is to  validate infrared-based approach to rapidly and accurately identify field collected mosquitoes that have bitten humans or are malaria-infected. I also aim to study the mechanism of the Mid-infrared spectroscopy (MIR) in detecting Plasmodium parasite, by examining the unique parasite signals in MIR spectra from packed red blood cells.

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022
Number of items: 7.

2024

Mshani, I. H. et al. (2024) Screening of malaria infections in human blood samples with varying parasite densities and anaemic conditions using AI-Powered mid-infrared spectroscopy. Malaria Journal, 23(1), 188. (doi: 10.1186/s12936-024-05011-z) (PMID:38880870) (PMCID:PMC11181574)

Mwanga, E. P. et al. (2024) Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning. Scientific Reports, 14, 12100. (doi: 10.1038/s41598-024-63082-z)

Mwanga, E. P., Siria, D. J., Mshani, I. H., Mwinyi, S. H., Abbas, S., Gonzalez Jimenez, M. , Wynne, K. , Baldini, F. , Babayan, S. A. and Okumu, F. O. (2024) Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus. Parasites and Vectors, 17, 143. (doi: 10.1186/s13071-024-06209-5) (PMID:38500231)

Mwanga, E. P. et al. (2024) Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning. Malaria Journal, 23(1), 86. (doi: 10.1186/s12936-024-04915-0) (PMID:38532415) (PMCID:PMC10964711)

2023

Mshani, I. H. et al. (2023) Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis. Malaria Journal, 22(1), 346. (doi: 10.1186/s12936-023-04780-3) (PMID:37950315) (PMCID:PMC10638832)

Mwanga, E. P., Siria, D. J., Mitton, J., Mshani, I. H., González-Jiménez, M. , Selvarajah, P., Wynne, K. , Baldini, F. , Okumu, F. O. and Babayan, S. A. (2023) Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra. BMC Bioinformatics, 24, 11. (doi: 10.1186/s12859-022-05128-5) (PMID:36624386) (PMCID:PMC9830685)

2022

Siria, D. J. et al. (2022) Rapid age-grading and species identification of natural mosquitoes for malaria surveillance. Nature Communications, 13, 1501. (doi: 10.1038/s41467-022-28980-8) (PMID:35314683) (PMCID:PMC8938457)

This list was generated on Thu Nov 21 09:30:31 2024 GMT.
Jump to: Articles
Number of items: 7.

Articles

Mshani, I. H. et al. (2024) Screening of malaria infections in human blood samples with varying parasite densities and anaemic conditions using AI-Powered mid-infrared spectroscopy. Malaria Journal, 23(1), 188. (doi: 10.1186/s12936-024-05011-z) (PMID:38880870) (PMCID:PMC11181574)

Mwanga, E. P. et al. (2024) Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning. Scientific Reports, 14, 12100. (doi: 10.1038/s41598-024-63082-z)

Mwanga, E. P., Siria, D. J., Mshani, I. H., Mwinyi, S. H., Abbas, S., Gonzalez Jimenez, M. , Wynne, K. , Baldini, F. , Babayan, S. A. and Okumu, F. O. (2024) Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus. Parasites and Vectors, 17, 143. (doi: 10.1186/s13071-024-06209-5) (PMID:38500231)

Mwanga, E. P. et al. (2024) Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning. Malaria Journal, 23(1), 86. (doi: 10.1186/s12936-024-04915-0) (PMID:38532415) (PMCID:PMC10964711)

Mshani, I. H. et al. (2023) Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis. Malaria Journal, 22(1), 346. (doi: 10.1186/s12936-023-04780-3) (PMID:37950315) (PMCID:PMC10638832)

Mwanga, E. P., Siria, D. J., Mitton, J., Mshani, I. H., González-Jiménez, M. , Selvarajah, P., Wynne, K. , Baldini, F. , Okumu, F. O. and Babayan, S. A. (2023) Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra. BMC Bioinformatics, 24, 11. (doi: 10.1186/s12859-022-05128-5) (PMID:36624386) (PMCID:PMC9830685)

Siria, D. J. et al. (2022) Rapid age-grading and species identification of natural mosquitoes for malaria surveillance. Nature Communications, 13, 1501. (doi: 10.1038/s41467-022-28980-8) (PMID:35314683) (PMCID:PMC8938457)

This list was generated on Thu Nov 21 09:30:31 2024 GMT.

Supervisors

External supervisors

Grants

  • December 2021 – December 2024: Local PI – “Infrared & AI to Diagnose and Quantify Onchocerca volvulus in Blackflies” project, Sub-award from University of Glasgow
  • June 2019 – December 2021: Principal Investigator (PI) - "Using machine learning and midinfrared spectroscopy for rapid assessment of blood-feeding histories and parasite infection rates in field-collected malaria mosquitoes" (MIRS – ML Project), Wellcome Trust International master’s fellowship in public health (30 months)

Conference

  • Emmanuel P. Mwanga, Halfan Ngowo, Salum Mapua, Arnold Mmbando, Hamisi Kifungo, and Fredros Okumu “Evaluation of light trap (Mosclean IW1) for sampling Anopheles arabiensis and Culex Mosquito species in South-eastern Tanzania”. 17-LB-4228-ASTMH, Presented at the American Society of Tropical Medicine and Hygiene (ASTMH). For 66th Annual Meeting November 5-9, 2017, DOI: https://doi.org/10.4269/ajtmh.abstract2017. Volume 97, Issue 5_Suppl, Nov 2017, p. 1 - 674.
  • Emmanuel P. Mwanga, Joshua Mitton, Doreen J. Siria, Francesco Baldini, Fredros O. Okumu, and Simon A. Babayan. “Using transfer learning and dimensionality reduction to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra”. 0317-ASTMH, Presented at the American Society of Tropical Medicine and Hygiene (ASTMH). For 70th Annual Meeting November 17-21, 2021, DOI: https://www.astmh.org/getmedia/59a95de8-1a06-49ca-9fd0-286454cc241a/ASTMH-2021- Annual-Meeting-Abstract-Book.pdf

Research datasets

Jump to: 2022
Number of items: 1.

2022

Siria, D., Sanou, R., Mitton, J., Mwanga, E., Niang, A., Saré, I., Johnson, P. , Wynne, K. , Murray-Smith, R. , Ferguson, H. , Gonzalez Jimenez, M. , Babayan, S. , Diabaté, A., Okumu, F. and Baldini, F. (2022) Rapid age-grading and species identification of natural mosquitoes for malaria surveillance. [Data Collection]

This list was generated on Thu Nov 21 06:29:45 2024 GMT.