To accelerate malaria elimination in areas where core interventions such as insecticide-treated nets (ITNs) are already widely used, it is crucial to consider additional factors associated with persistent transmission. Qualitative data on human behaviours and perceptions regarding malaria risk was triangulated with quantitative data on Anopheles mosquito bites occurring indoors and outdoors in south-eastern Tanzania communities where ITNS are already used but lower level malaria transmission persists. Each night (18:00h-07:00h), trained residents recorded human activities indoors, in peri-domestic outdoor areas, and in communal gatherings. Host-seeking mosquitoes were repeatedly collected indoors and outdoors hourly, using miniaturized exposure-free double net traps (DN-Mini) occupied by volunteers. In-depth interviews were conducted with household representatives to explore perceptions on persistent malaria and its control. Higher proportions of people stayed outdoors than indoors in early-evening and early-morning hours, resulting in higher exposures outdoors than indoors during these times. However, exposure during late-night hours (22:00h–05:00h) occurred mostly indoors. Some of the popular activities that kept people outdoors included cooking, eating, relaxing and playing. All households had at least one bed net, and 83.9% of people had access to ITNs. Average ITN use was 96.3%, preventing most indoor exposure. Participants recorgnized the importance of ITNs but also noted that the nets were not perfect. No complementary interventions were reported being used widely. Most people believed transmission happens after midnight. We conclude that insecticide-treated nets, where properly used, can still prevent most indoor exposures, but significant risk continues unabated before bedtime, outdoors and at communal gatherings. Such exposure is greatest for rural and low-income households. There is therefore an urgent need for complementary interventions, particularly those targeting outdoor-biting and are applicable for all people including the marginalised populations such as migratory farmers and fishermen. Besides, the differences in community understanding of ongoing transmission, and feedback on imperfections of ITNs should be considered when updating malaria-related communication and interventions.

BackgroundA number of mosquito vectors bite and rest outdoors, which contributes to sustained residual malaria transmission in endemic areas. Spatial repellents are thought to create a protective “bubble” within which mosquito bites are reduced and may be ideal for outdoor use. This study builds on previous studies that proved efficacy of transfluthrin-treated hessian strips against outdoor biting mosquitoes. The goal of this study was to modify strips into practical, attractive and acceptable transfluthrin treated sisal and hessian emanators that confer protection against potential infectious bites before people use bed nets especially in the early evening and outdoors. This study was conducted in Kilombero Valley, Ulanga District, south-eastern Tanzania.ResultsThe protective efficacy of hand-crafted transfluthrin-treated sisal decorative baskets and hessian wall decorations against early evening outdoor biting malaria vectors was measured by human landing catches (HLC) in outdoor bars during peak outdoor mosquito biting activity (19:00 to 23:00 h). Treated baskets and wall decorations reduced bites of Anopheles arabiensis mosquitoes by 89% (Relative Rate, RR = 0.11, 95% confidence interval, CI: 0.09–0.15, P < 0.001) and 86% (RR = 0.14, 95% CI: 0.11–0.18, P < 0.001), respectively. In addition, they significantly reduced exposure to outdoor bites of Culex spp. by 66% (RR = 0.34, 95% CI: 0.22–0.52, P < 0.001) and 56% (RR = 0.44, 95% CI: 0.29–0.66, P < 0.001), respectively.ConclusionLocally hand-crafted transfluthrin-treated sisal decorative baskets and hessian wall decorations are readily acceptable and confer protection against outdoor biting malaria vectors in the early evening and outdoors: when people are resting on the verandas, porches or in outdoor social places such as bars and restaurants. Additional research can help support the use of such items as complementary interventions to expand protection to communities currently experiencing outdoor transmission of mosquito-borne pathogens.

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.

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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