Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.
School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
Sci Rep. 2024 May 27;14(1):12100. doi: 10.1038/s41598-024-63082-z.
Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission intensities, computed as a product of human biting rates and prevalence of Plasmodium sporozoites in mosquitoes. Unfortunately, current methods for identifying infectious mosquitoes are laborious, time-consuming, and may require expensive reagents that are not always readily available. Here, we demonstrate the first field-application of mid-infrared spectroscopy and machine learning (MIRS-ML) to swiftly and accurately detect Plasmodium falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. We collected 7178 female An. funestus from rural Tanzanian households using CDC-light traps, then desiccated and scanned their heads and thoraces using an FT-IR spectrometer. The sporozoite infections were confirmed using enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), to establish references for training supervised algorithms. The XGBoost model was used to detect sporozoite-infectious specimen, accurately predicting ELISA and PCR outcomes with 92% and 93% accuracies respectively. These findings suggest that MIRS-ML can rapidly detect P. falciparum in field-collected mosquitoes, with potential for enhancing surveillance in malaria-endemic regions. The technique is both fast, scanning 60-100 mosquitoes per hour, and cost-efficient, requiring no biochemical reactions and therefore no reagents. Given its previously proven capability in monitoring key entomological indicators like mosquito age, human blood index, and identities of vector species, we conclude that MIRS-ML could constitute a low-cost multi-functional toolkit for monitoring malaria risk and evaluating interventions.
现场衍生指标对于有效控制疟疾至关重要，尤其是在撒哈拉以南非洲地区，那里每年有超过 50 万人因疟疾死亡。一个关键指标是昆虫接种率，这是衡量传播强度的直接指标，通过计算人类叮咬率和蚊子中疟原虫孢子虫的流行率来计算。不幸的是，目前识别感染蚊子的方法既繁琐又耗时，并且可能需要昂贵的试剂，而这些试剂并不总是容易获得。在这里，我们展示了中红外光谱和机器学习（MIRS-ML）在快速准确地检测野生捕获的恶性疟原虫孢子虫中的首次现场应用，而无需使用任何实验室试剂。我们使用 CDC 灯诱法从坦桑尼亚农村家庭中收集了 7178 只雌性恶性疟原虫，然后使用 FT-IR 光谱仪对其头部和胸部进行干燥和扫描。使用酶联免疫吸附试验（ELISA）和聚合酶链反应（PCR）确认孢子虫感染，为训练监督算法建立参考。使用 XGBoost 模型来检测孢子虫感染的样本，分别以 92%和 93%的准确率准确预测 ELISA 和 PCR 结果。这些发现表明，MIRS-ML 可以快速检测现场采集的蚊子中的恶性疟原虫，有可能增强疟疾流行地区的监测。该技术快速，每小时可扫描 60-100 只蚊子，成本效益高，不需要生化反应，因此也不需要试剂。鉴于它之前在监测关键昆虫学指标（如蚊子年龄、人类血液指数和媒介物种身份）方面的能力，我们得出结论，MIRS-ML 可以构成一种低成本的多功能工具包，用于监测疟疾风险和评估干预措施。
