The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence

Spectroscopy-based techniques are emerging diagnostic and surveillance tools for mosquito-borne diseases. This review has consolidated and summarised recent research in the application of Raman and infrared spectroscopy techniques including near- and mid-infrared spectroscopy for malaria and arboviruses, identified knowledge gaps, and recommended future research directions. Full-length peer-reviewed journal articles related to the application of Raman and infrared (near- and mid-infrared) spectroscopy for malaria and arboviruses were systematically searched in PUBMED, MEDILINE, and Web of Science databases using the PRISMA guidelines. In text review of identified studies included the methodology of spectroscopy technique used, data analysis applied, wavelengths used, and key findings for diagnosis of malaria and arboviruses and surveillance of mosquito vectors. A total of 58 studies met the inclusion criteria for our systematic literature search. Although there was an increased application of Raman and infrared spectroscopy-based techniques in the last 10 years, our review indicates that Raman spectroscopy (RS) technique has been applied exclusively for the diagnosis of malaria and arboviruses. The mid-infrared spectroscopy (MIRS) technique has been assessed for the diagnosis of malaria parasites in human blood and as a surveillance tool for malaria vectors, whereas the near-infrared spectroscopy (NIRS) technique has almost exclusively been applied as a surveillance tool for malaria and arbovirus vectors. Conclusions/Significance The potential of RS as a surveillance tool for malaria and arbovirus vectors and MIRS for the diagnosis and surveillance of arboviruses is yet to be assessed. NIRS capacity as a surveillance tool for malaria and arbovirus vectors should be validated under field conditions, and its potential as a diagnostic tool for malaria and arboviruses needs to be evaluated. It is recommended that all 3 techniques evaluated simultaneously using multiple machine learning techniques in multiple epidemiological settings to determine the most accurate technique for each application. Prior to their field application, a standardised protocol for spectra collection and data analysis should be developed. This will harmonise their application in multiple field settings allowing easy and faster integration into existing disease control platforms. Ultimately, development of rapid and cost-effective point-of-care diagnostic tools for malaria and arboviruses based on spectroscopy techniques may help combat current and future outbreaks of these infectious diseases.