Estimating intervention effectiveness in trials of malaria interventions with contamination

Background In cluster randomized trials (CRTs) or stepped wedge cluster randomized trials (SWCRTs) of malaria interventions, mosquito movement leads to contamination between trial arms unless buffer zones separate the clusters. Contamination can be accounted for in the analysis, yielding an estimate of the contamination range, the distance over which contamination measurably biases the effectiveness. Methods A previously described analysis for CRTs is extended to SWCRTs and estimates of effectiveness are provided as a function of intervention coverage. The methods are applied to two SWCRTs of malaria interventions, the SolarMal trial on the impact of mass trapping of mosquitoes with odor-baited traps and the AvecNet trial on the effect of adding pyriproxyfen to long-lasting insecticidal nets. Results For the SolarMal trial, the contamination range was estimated to be 146 m ($$95\%$$ 95 % credible interval $$[0.052,\,0.923]$$ [ 0.052 , 0.923 ] km), together with a $$31.9\%$$ 31.9 % ($$95\%$$ 95 % credible interval $$[15.3,\,45.8]\%$$ [ 15.3 , 45.8 ] % ) reduction of Plasmodium infection, compared to the $$30.0\%$$ 30.0 % reduction estimated without accounting for contamination. The estimated effectiveness had an approximately linear relationship with coverage. For the AvecNet trial, estimated contamination effects were minimal, with insufficient data from the cluster boundary regions to estimate the effectiveness as a function of coverage. Conclusions The contamination range in these trials of malaria interventions is much less than the distances Anopheles mosquitoes can fly. An appropriate analysis makes buffer zones unnecessary, enabling the design of more cost-efficient trials. Estimation of the contamination range requires information from the cluster boundary regions and trials should be designed to collect this.

Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling

This paper creatively proposes a cluster boundary sampling method based on density clustering to solve the problem of resampling in IDS classification and verify its effectiveness experimentally. We use the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of resampling more scientifically and accurately. Then, we adopt the penalty factor to regulate the data imbalance effect on SVM classification algorithm. The achievements and scientific significance of this paper do not propose the best classifier or solution of imbalanced data set and just verify the validity and stability of proposed IDS resampling method. Experiments show that our method acquires obvious promotion effect in various imbalanced data sets.

Research on SOM-Based Query Reformulation in Emergency Decision

The efficiency and accuracy of professional retrieval are closely related to semantic representation model in professional field. Representing retrieval requirement accurately is very important to guaranteeing the accuracy of retrieval. In case of professional emergency field, a query reformulation model used to clearing and improving user retrieval requirement is researched. Constructing a latent semantic word space of emergency field, semantic features are clustered by using SOM, and CI index using to determining the optimal cluster boundary is proposed. On this basis, a new query reformulation method is designed. The effectiveness of model is verified by experiment.