Background Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.
Climate change is expected to increase waterborne diseases especially in developing countries.However, we lack understanding of how different types of water sources (both improved and unimproved) are affected by climate change, and thus, where to prioritize future investments and improvements to maximize health outcomes. This is due to limited knowledge of the relationships between source water quality and the observed variability in climate conditions. To address this gap, a 20-month observational study was conducted in Tanzania, aiming to understand how water quality changes at various types of sources due to short-term climate variability. Nine rounds of microbiological water quality sampling were conducted for Escherichia coli and total coliforms, at three study sites within different climatic regions. Each round included approximately 233 samples from water sources and 632 samples from households. To identify relationships between water quality and short-term climate variability, Bayesian hierarchical modeling was adopted, allowing these relationships to vary with source types and sampling regions to account for potentially different physical processes. Across water sources, increases in E. coli/total coliform levels were most closely related to increases in recent heavy rainfall. Our key recommendations to future longitudinal studies are (a) demonstrated value of high sampling frequency and temporal coverage (a minimum of 3 years) especially during wet seasons; (b) utility of the Bayesian hierarchical models to pool data from multiple sites while allowing for variations across space and water sources; and (c) importance of a multidisciplinary team approach with consistent commitment and sharing of knowledge.Plain Language Summary It is vital to understand how different types of water sources (both improved and unimproved) are influenced by changing climate conditions. This is needed to appreciate the reliability of these water sources in the future. A 20-month observational study was carried out in Tanzania to explore these relationships. Nine rounds of sampling were conducted across three study sites within different climatic regions, with fecal pathogen levels sampled at both water sources and households. A novel statistical model was developed to link water quality change with types of water quality and climate variability. We found that across different source types, the increases in fecal pathogen levels are most closely related to increases in recent heavy rainfall. We recommend that future studies to include at least 3 years of data collection. The successful study design here shows the value of multidisciplinary teams to ensure that appropriate statistical modeling structure can be used to analyze the data and provide new information for climate change adaptation. In the long term, such studies will provide evidence for decision-makers to prioritize future water investments and improvements to maximize public health outcomes. Key Points:• We present a longitudinal study in a developing country on wa...
Background: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study shows that mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods: Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in twelve wards in south-eastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range, 4000 cm-1 to 500 cm-1. The spectra were cleaned to compensate for atmospheric water vapor and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria-positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results: Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and P. ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion: These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in dried human blood spots. The approach could have potential for rapid and high-throughput screening of Plasmodium infections in both non-clinical settings (e.g. field surveys) and clinical settings (diagnosis to aid case management). However, full utility will require further advances in classification algorithms, field validation of this technology in other study sites and an in-depth evaluation of the biological basis of the observed test results. Training the models on larger datasets could also improve specificity and sensitivity of the technique. The MIR-ML spectroscopy system is robust, low-cost, and requires minimum maintenance.
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