Results
malaria
MALARIA
The four treatment arms were allocated in a randomised block design to 12 houses; hence there were three houses per treatment on any given night. The treatments were rotated through the houses so that each house received all four treatments to account for variations between houses. Mosquito sampling was done over four consecutive nights a week and treatments were rotated on a weekly basis. The study was implemented for 17 weeks during the rainy season between September and December 2018. In that time, a total of 33,290 primary malaria vectors in the The overall malaria vector density was similar indoors and outdoors, although the species composition was reversed in the two locations. Outdoors, using human landing catches (HLC), 13,640 Outdoor mosquito densities and protective efficacies under the different interventions. (Indoor mosquito densities and protective efficacies under the different interventions. (Almost all the All mosquito count data were analysed with a Bayesian negative binomial model, with separate inference for the outdoor (HLC), indoor (CDC light trap) and Suna trap catch data. Data points with missing information for one of the measurements, or missing information on the week and house of the measurement, were excluded from the analysis; these removed data points accounted for less than 5% of the data. A hierarchical, second level model structure (‘random effects’) was used to account for the dependence between data points collected from the same house or week. Thus, the model allowed for temporal and spatial heterogeneity in the intervention efficacy. Indoor and outdoor mosquito counts varied similarly across houses and weeks, but not proportionally to each other, as was shown by a less well-fitting alternative model that coupled indoor and outdoor bite counts per house or week (see For all estimates, uncertainty was quantified by Bayesian 95% highest density credible intervals (HDI), meaning values outside of the 95% HDI can be excluded with 95% probability. In order to ensure comparability with other studies, we follow the standard practice to quantify the uncertainty with respect to an average house and an average week. Hence, the reported interval estimates (HDI) are with respect to the first level model and do mask the variability of the outcome across different houses and weeks as modelled by the second level model. However, we believe that interval estimates should cover the full variability accounted for in the model that was used for the inference, ensuring a more robust intervention assessment. Thus, we report, in addition to the ‘average’ house-week analysis, interval estimates with respect to an ‘arbitrary’ house and an ‘arbitrary’ week in a separate section (‘arbitrary house-week analysis’), which contain all modelled variability.