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.

Dengue virus (DENV) is the world’s most common arboviral infection, with an estimated 3.9 million people at risk of the infection, 100 million symptomatic cases and 10,000 deaths per year. Current diagnosis for DENV includes the use of molecular methods, such as polymerase chain reaction, which can be costly for routine use. The near-infrared spectroscopy (NIR) technique is a high throughput technique that involves shining a beam of infrared light on a biological sample, collecting a reflectance spectrum, and using machine learning algorithms to develop predictive algorithms. Here, we used NIR to detect DENV1 artificially introduced into whole blood, plasma, and serum collected from human donors. Machine learning algorithms were developed using artificial neural networks (ANN) and the resultant models were used to predict independent samples. DENV in plasma samples was detected with an overall accuracy, sensitivity, and specificity of 90% (N = 56), 88.5% (N = 28) and 92.3% (N = 28), respectively. However, a predictive sensitivity of 33.3% (N = 16) and 80% (N = 10) and specificity of 46.7% (N = 16) and 32% (N = 10) was achieved for detecting DENV1 in whole blood and serum samples, respectively. DENV1 peaks observed at 812 nm and 819 nm represent C-H stretch, peaks at 1130–1142 nm are related to methyl group and peaks at 2127 nm are related to saturated fatty groups. Our findings indicate the potential of NIR as a diagnostic tool for DENV, however, further work is recommended to assess its sensitivity for detecting DENV in people naturally infected with the virus and to determine its capacity to differentiate DENV serotypes and other arboviruses.

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