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BackgroundLong lasting insecticide-treated bednets (LLINs) are the most widely used tool for preventing malaria. There has been a plateau in progress in the highest burden African countries since 2015, leading to questions about the effectiveness of LLINs. In this study, remote LLIN use monitors were deployed in a cohort in Eastern Uganda to explore how LLIN use interacts with mosquito exposure.MethodsThe SmartNet study included 20 households from May to October 2019. SmartNet devices recorded, every 15 min, whether an LLIN was unfurled or folded up. Unannounced visits were used to assess SmartNet accuracy. Risk factors associated with poor LLIN use were assessed using generalized linear equations. Female Anopheles exposure was estimated by combining hourly probabilities of exposure from human landing catches and measures of density from biweekly CDC light traps in participants rooms. Mosquito exposure averted by LLINs was quantified using SmartNet measurements and age-related differences were estimated using generalized linear equations, adjusting for relevant covariates and household clustering.Results96 individuals contributed 5,640 SmartNet observation nights. In 126 unannounced visits, SmartNet had an area under the curve of 0.869 in classifying whether the LLIN was up or down. The rate of non-use was 13.5% of nights (95% CI: 12.6–14.3%). Compared to children under 5, non-use was 1.8 times higher (95% CI: 1.6–2.1; p < 0.001) in children 5–15 years and 2.6 times higher (95% CI: 2.2–3.1; p < 0.001) in participants aged 15– 30 years. LLIN use averted 50.3% of female Anopheles mosquito exposure (95% CI: 40.0–60.0%), with decreasing point estimates of efficacy across age groups: from 61.7% (95% CI: 42.6–80.7%) in children under 5 years to 48.0% (95% CI: 29.1–66.8%) in adults over 30.ConclusionsObjective monitors are accurate and can feasibly be deployed to obtain data about LLIN use. LLINs provided protection from only 50% of female Anopheles mosquito exposure in this cohort and protection was dependent upon age. In assessing the role of LLINs in malaria prevention it is crucial to consider the dynamics between mosquito exposure and LLIN use behaviors.

Background The duration of trial follow-up affects the ability to detect recrudescent infections following anti-malarial treatment. The aim of this study was to explore the proportions of recrudescent parasitaemia as ascribed by genotyping captured at various follow-up time-points in treatment efficacy trials for uncomplicated Plasmodium falciparum malaria. Methods Individual patient data from 83 anti-malarial efficacy studies collated in the WorldWide Antimalarial Resistance Network (WWARN) repository with at least 28 days follow-up were available. The temporal and cumulative distributions of recrudescence were characterized using a Cox regression model with shared frailty on study-sites. Fractional polynomials were used to capture non-linear instantaneous hazard. The area under the density curve (AUC) of the constructed distribution was used to estimate the optimal follow-up period for capturing a P. falciparum malaria recrudescence. Simulation studies were conducted based on the constructed distributions to quantify the absolute overestimation in efficacy due to sub-optimal follow-up. Results Overall, 3703 recurrent infections were detected in 60 studies conducted in Africa (15,512 children aged 48 mg/kg total piperaquine (PIP) dose and 9% [95% CI 0–22%] in those treated with ≤ 48 mg/kg PIP dose. In absolute terms, the simulation study found that trials limited to 28 days follow-up following AL underestimated the risk of recrudescence by a median of 2.8 percentage points compared to day 63 estimates and those limited to 42 days following DP underestimated the risk of recrudescence by a median of 2.0 percentage points compared to day 42 estimates. The analysis was limited by few clinical trials following patients for longer than 42 days (9 out of 83 trials) and the imprecision of PCR genotyping which overcalls recrudescence in areas of higher transmission biasing the later distribution. Conclusions Restricting follow-up of clinical efficacy trials to day 28 for AL and day 42 for DP will miss a proportion of late recrudescent treatment failures but will have a modest impact in derived efficacy. The results highlight that as genotyping methods improve consideration should be given for trials with longer duration of follow-up to detect early indications of emerging drug resistance.

Additional file 2: List L1. List of included studies. Table S1. Summary of characteristics of included studies.

Additional file 3: Table S2. Baseline characteristics of patients in the analysis of haematological response. Table S3. Association between haemoglobin change and baseline value. Table S4. Bi-variable analysis of factors associated with drop in haemoglobin on Day 7. Table S5. Bi-variable analysis of factors associated with anaemia on Day 7. Table S6. Univariable analysis of recovery from anaemia. Table S7. Haemoglobin measurements in patients with severe anaemia at any time. Table S8. Baseline characteristics of patients included in the analysis of adverse events. Table S9. Listing of Serious Adverse Events reported in any study. Table S10. Baseline characteristics of patients included in the analysis of haemoglobinuria. Table S11. Univariable analysis for haemoglobinuria. Table S12. Multivariable model of change in haemoglobin on Day 7 in sub-Saharan Africa. Fig. S1. Map of study sites. Fig. S2. Haemoglobin in G6PD deficient patients not included in the Day 7 analysis. Fig. S3. Relationship between haemoglobin at baseline and Day 7 as absolute change. Fig. S4. Relationship between haemoglobin at baseline and Day 7 as fractional change Fig. S5. Relationship between baseline parasitaemia and haematological endpoints. Fig. S6. Dose response relationship for haematological endpoints. Fig. S7. Time to recovery by day when anaemia was first recorded. Fig. S8. Meta-analysis of odds of haemoglobinuria after PQ administration. Fig. S9. Distribution of parasitaemia (observed) used in the simulation study. Figure S10- Predicted risk of severe anaemia (Hb

Additional file 4: Table S13. Risk of bias assessment. Table S14. Individual participant data not available. Fig. S12. Sensitivity analysis for the main haematological results.

Anopheles balabacensis, the primary vector of Plasmodium knowlesi in Sabah, Malaysia, is both zoophilic and anthropophilic, feeding on macaques as well as humans. It is the dominant Anopheles species found in Kudat Division where it is responsible for all the cases of P. knowlesi. However there is a paucity of basic biological and ecological information on this vector. We investigated the genetic variation of this species using the sequences of cox1 (1,383 bp) and cox2 (685 bp) to gain an insight into the population genetics and inter-population gene flow in Sabah. A total of 71 An. balabacensis were collected from seven districts constituting 14 subpopulations. A total of 17, 10 and 25 haplotypes were detected in the subpopulations respectively using the cox1, cox2 and the combined sequence. Some of the haplotypes were common among the subpopulations due to gene flow occurring between them. AMOVA showed that the genetic variation was high within subpopulations as compared to between subpopulations. Mantel test results showed that the variation between subpopulations was not due to the geographical distance between them. Furthermore, Tajima's D and Fu's Fs tests showed that An. balabacensis in Sabah is experiencing population expansion and growth. High gene flow between the subpopulations was indicated by the low genetic distance and high gene diversity in the cox1, cox2 and the combined sequence. However the population at Lipasu Lama appeared to be isolated possibly due to its higher altitude at 873 m above sea level.

Artemisinin resistance was recently shown to have spread or emerged on the Thailand/Myanmar border. Evidence is accumulating that the parasite clearance time after artemisinin-based combination therapy (ACT) is increasing in settings in Asia and Africa. It is currently unknown if an extended parasite clearance time after ACTs has consequences for the individual patient or confers a higher malaria transmission potential. 298 children in Mbita, Western Kenya, with uncomplicated falciparum malaria were randomized to artemether-lumefantrine (AL, n = 153) ordihydroartemisinin-piperaquine (DP, n = 145). Parasite carriage post-treatment was determined by microscopy and qPCR, gametocyte carriage by quantitative nucleic acid sequence based amplication. Infectiousness to mosquitoes was determined by mosquito membrane feeding assays. Both drugs were efficacious as judged by standard trial outcomes. Sub-patent residual parasitaemia on day 3 was detected by qPCR in 36.11% (95% CI 25.11 - 48.29) of children treated with AL, and in 30.16% (95% CI 19.23 - 43.02) of children treated with DP. After adjustment for age, treatment arm and enrolment parasite density, children with an extended parasite clearance time were significantly more likely to have microscopically detected recurrent parasitaemia during follow-up (Odds Ratio: 19.51, 95% CI 5.24 - 72.71, p < 0.001). Children with an extended parasite clearance time were also more likely to be infectious to mosquitoes (Odds Ratio 2.76; 95% CI 1.14 - 6.67, p = 0.02) and gave rise to a higher oocyst load in mosquitoes (Incidence Rate Ratio 2.80, 95% CI 1.49 - 5.24, p = 0.001). Our findings indicate that an extended parasite clearance time after ACTs has consequences for the individual patient and for the population at large due to higher transmission potential. The high prevalence of residual subpatent parasitaemia after treatment may be due to novel parasite genotypes with reduced drug sensitivity, inadequate population-level immunity, or the higher sensitivity of qPCR for detection of persisting parasites.

Background Studies on Plasmodium falciparum gametocyte development and dynamics have almost exclusively focused on patients treated with antimalarial drugs, while the majority of parasite carriers in endemic areas are asymptomatic. This study identified factors that influence gametocytaemia in asymptomatic children in the absence and presence of pyrimethamine-sulphadoxine (SP) antimalarial treatment. Methods A cohort of 526 children (6 months – 16 years) from western Kenya was screened for asexual parasites and gametocytes and followed weekly up to four weeks. Children with an estimated parasitaemia of ≥1,000 parasites/μl were treated with SP according to national guidelines. Factors associated with gametocyte development and persistence were determined in untreated and SP-treated children with P. falciparum mono-infection. Results Gametocyte prevalence at enrolment was 33.8% in children below five years of age and decreased with age. In the absence of treatment 18.6% of the children developed gametocytaemia during follow-up; in SP-treated children this proportion was 29.8%. Age, high asexual parasite density and gametocyte presence at enrolment were predictive factors for gametocytaemia. The estimated mean duration of gametocytaemia for children below five, children from five to nine and children ten years and above was 9.4, 7.8 and 4.1 days, respectively. Conclusion This study shows that a large proportion of asymptomatic untreated children develop gametocytaemia. Gametocytaemia was particularly common in children below five years who harbor gametocytes for a longer period of time. The age-dependent duration of gametocytaemia has not been previously shown and could increase the importance of this age group for the infectious reservoir.

The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria (Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case–control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. In contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.

The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria (Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case–control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. In contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.