Spatiotemporal distribution and bionomics of Anopheles stephensi in different eco-epidemiological settings in Ethiopia

Background: Malaria is a major public health concern in Ethiopia, and its incidence could worsen with the spread of the invasive mosquito species Anopheles stephensi in the country. This study aimed to provide updates on the distribution of An. stephensi and likely household exposure in Ethiopia. Methods: Entomological surveillance was performed in 26 urban settings in Ethiopia from 2021 to 2023. A kilometer-by-kilometer quadrant was established per town, and approximately 20 structures per quadrant were surveyed every 3 months. Additional extensive sampling was conducted in 50 randomly selected structures in four urban centers in 2022 and 2023 to assess households’ exposure to An. stephensi. Prokopack aspirators and CDC light traps were used to collect adult mosquitoes, and standard dippers were used to collect immature stages. The collected mosquitoes were identified to species level by morphological keys and molecular methods. PCR assays were used to assess Plasmodium infection and mosquito blood meal source. Results: Catches of adult An. stephensi were generally low (mean: 0.15 per trap), with eight positive sites among the 26 surveyed. This mosquito species was reported for the first time in Assosa, western Ethiopia. Anopheles stephensi was the predominant species in four of the eight positive sites, accounting for 75–100% relative abundance of the adult Anopheles catches. Household-level exposure, defined as the percentage of households with a peridomestic presence of An. stephensi, ranged from 18% in Metehara to 30% in Danan. Anopheles arabiensis was the predominant species in 20 of the 26 sites, accounting for 42.9–100% of the Anopheles catches. Bovine blood index, ovine blood index and human blood index values were 69.2%, 32.3% and 24.6%, respectively, for An. stephensi, and 65.4%, 46.7% and 35.8%, respectively, for An. arabiensis. None of the 197 An. stephensi mosquitoes assayed tested positive for Plasmodium sporozoite, while of the 1434 An. arabiensis mosquitoes assayed, 62 were positive for Plasmodium (10 for P. falciparum and 52 for P. vivax). Conclusions: This study shows that the geographical range of An. stephensi has expanded to western Ethiopia. Strongly zoophagic behavior coupled with low adult catches might explain the absence of Plasmodium infection. The level of household exposure to An. stephensi in this study varied across positive sites. Further research is needed to better understand the bionomics and contribution of An. stephensi to malaria transmission. Graphical Abstract

Interactive Image Segmentation Using Constrained Dominant Sets

We propose a new approach to interactive image segmentation based on some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. In particular, we show that by properly controlling a regularization parameter which determines the structure and the scale of the underlying problem, we are in a position to extract groups of dominant-set clusters which are constrained to contain user-selected elements. The resulting algorithm can deal naturally with any type of input modality, including scribbles, sloppy contours, and bounding boxes, and is able to robustly handle noisy annotations on the part of the user. Experiments on standard benchmark datasets show the effectiveness of our approach as compared to state-of-the-art algorithms on a variety of natural images under several input conditions

Path-Based Dominant-Set Clustering

Although off-the-shelf clustering algorithms, such as those based on spectral graph theory, do a pretty good job at finding clusters of arbitrary shape and structure, they are inherently unable to satisfactorily deal with situations involving the presence of cluttered backgrounds. On the other hand, dominant sets, a generalization of the notion of maximal clique to edge-weighted graphs, exhibit a complementary nature: they are remarkably effective in dealing with background noise but tend to favor compact groups. In order to take the best of the two approaches, in this paper we propose to combine path-based similarity measures, which exploit connectedness information of the elements to be clustered, with the dominant-set approach. The resulting algorithm is shown to consistently outperform standard clustering methods over a variety of datasets under severe noise conditions

Constrained Dominant Sets for Retrieval

Learning new global relations based on an initial affinity of the database objects has shown significant improvements in similarity retrievals. Locally constrained diffusion process is one of the recent effective tools in learning the intrinsic manifold structure of a given data. Existing methods, which constrain the diffusion process locally, have problems - manual choice of optimal local neighborhood size, do not allow for intrinsic relation among the neighbors, fix initialization vector to extract dense neighbor - which negatively affect the affinity propagation. We propose a new approach, which alleviate these issues, based on some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. In particular, we show that by properly controlling a regularization parameter which determines the structure and the scale of the underlying problem, we are in a position to extract dominant set cluster which is constrained to contain user-provided query. Experimental results on standard benchmark datasets show the effectiveness of the proposed approach

Dominant-set clustering using multiple affinity matrices

Pairwise (or graph-based) clustering algorithms typically assume the existence of a single affinity matrix, which describes the similarity between the objects to be clustered. In many practical applications, however, several similarity relations might be envisaged and the problem arises as to how properly select or combine them. In this paper we offer a solution to this problem for the case of dominant sets, a well-known formalization of the notion of a cluster, which generalizes the notion of maximal clique to edge-weighted graphs and has intriguing connections to evolutionary game theory. Specifically, it has been shown that dominant sets can be bijectively related to Evolutionary Stable Strategies (ESS) - a classic notion of equilibrium in the evolutionary game theory field - of a so-called “clustering game”. The clustering game is a non-cooperative game between two-players, where the objects to cluster form the set of strategies, while the affinity matrix provides the players’ payoffs. The proposed approach generalizes dominant sets to multiple affinities by extending the clustering game, which has a single payoff, to a multi-payoff game. Accordingly, dominant sets in the multi-affinity setting become equivalent to ESSs of a corresponding multi-payoff clustering game, which can be found by means of so-called Biased Replicator Dynamics. Experiments conducted over standard benchmark datasets consistently show that the proposed combination scheme allows one to substantially improve the performance of dominant-set clustering over its single-affinity counterpart

Maternal satisfaction on quality of childhood vaccination services and its associated factors at public health centers in Addis Ababa, Ethiopia

Abstract Background Vaccination is one of the most important public health interventions to reduce child mortality and morbidity. In Ethiopia, about 472,000 children die each year by vaccine-preventable diseases. A satisfied mother is assumed to use the services and complies with the service provider for better health care outcomes. However, there was no adequate evidence regarding maternal satisfaction with quality of childhood vaccination services. This study aimed to assess maternal satisfaction on quality of childhood vaccination services and its associated factors at public health centers in Addis Ababa, Ethiopia. Methods A facility-based cross-sectional study was conducted from 12 July to 12 August 2021 at public health centers in Addis Ababa, Ethiopia. A total of 366 mothers (caretakers) of under one-year-old children participated in the study. A systematic sampling technique with an interviewer-administered questionnaire and inventory checklist were used to collect the data. A binary logistic regression model was fitted. Adjusted Odds Ratio (AOR) with 95% confidence interval (CI) and p-value < 0.05 were used to identify the factors associated with the outcome. Results Nearly two-thirds (61.2%) of mothers (caretakers) were satisfied with the quality of childhood vaccination services. Service providers’ greeting [AOR = 1.60; 95%CI: 1.37–1.99] and information about the types of vaccines [AOR = 1.54; 95%CI: 1.32–1.89] were positively associated with maternal satisfaction. On the contrary, long waiting time of mothers (caretakers) to receive services [AOR = 0.29; 95%CI: 0.14–0.62] was negatively associated with services. Conclusion The overall maternal satisfaction towards the quality of childhood vaccination services in this study was found to be low. Minimizing waiting time at the health facility, enhancing greetings and providing adequate information regarding childhood vaccination for mothers (caretakers) improved their satisfaction with the services

Simultaneous Clustering and Outlier Detection using Dominant sets

We present a unified approach for simultaneous clustering and outlier detection in data. We utilize some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. Unlike most (all) of the previous techniques, in our framework the number of clusters arises intuitively and outliers are obliterated automatically. The resulting algorithm discovers both parameters from the data. Experiments on real and on large scale synthetic dataset demonstrate the effectiveness of our approach and the utility of carrying out both clustering and outlier detection in a concurrent manner

Multi-Target Tracking in Multiple Non-Overlapping Cameras using Fast-Constrained Dominant Sets

In this paper, a unified three-layer hierarchical approach for solving tracking problems in a multiple non-overlapping cameras setting is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by associating tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, we propose Fast-Constrained Dominant Set Clustering (FCDSC), a novel method that is an order of magnitude faster than constrained dominant sets clustering technique. FCDSC is employed to solve both within- and across-camera tracking tasks. We first build a graph where nodes of the graph represent short-tracklets, tracklets and tracks in the first, second and third layer of the framework, respectively. The edge weight depicts the similarity between nodes. FCDSC takes as an input a constraint set, a subset of nodes from the graph which one wants the extracted cluster to include. Given a constraint set, FCDSC generates compact cluster selecting nodes from the graph which are highly similar to each other and with elements in the constraint set. The approach is based on a parametrized family of quadratic programs that generalizes the standard quadratic optimization problem. In addition to having a unified framework that simultaneously solves within- and across-camera tracking, the third layer helps to link broken tracks of the same person occurring during within-camera tracking. We have tested this approach on a very large and challenging dataset (namely, MOTchallenge DukeMTMC) and show that the proposed framework outperforms the current state of the art. Even though the main focus of this paper is on multi-target tracking in non-overlapping cameras, proposed approach can also be applied to solve video-based person re-identification problem. We show that when the re-identification problem is formulated as a clustering problem, FCDSC can be used in conjunction with state-of-the-art video-based re-identification algorithms, to increase their already good performances. Our experiments demonstrate the general applicability of the proposed framework for non-overlapping across-camera tracking and person re-identification tasks

Treatment Outcome of Tuberculosis Patients under Directly Observed Treatment Short Course and Factors Affecting Outcome in Southern Ethiopia: A Five-Year Retrospective Study.

Tuberculosis (TB) is one of the major public health and socio-economic issues in the 21st century globally. Assessment of TB treatment outcomes, and monitoring and evaluation of its risk factors in Directly Observed Treatment Short Course (DOTS) are among the major indicators of the performance of a national TB control program. Hence, this institution-based retrospective study was conducted to determine the treatment outcome of TB patients and investigate factors associated with unsuccessful outcome at Dilla University Referral Hospital, southern Ethiopia. Five years (2008 to 2013) TB record of TB clinic of the hospital was reviewed. A total 1537 registered TB patients with complete information were included. Of these, 942 (61.3%) were male, 1015 (66%) were from rural areas, 544 (35.4%) were smear positive pulmonary TB (PTB+), 816 (53.1%) were smear negative pulmonary TB (PTB-) and 177(11.5%) were extra pulmonary TB (EPTB) patients. Records of the 1537 TB patients showed that 181 (11.8%) were cured, 1129(73.5%) completed treatment, 171 (11.1%) defaulted, 52 (3.4%) died and 4 (0.3%) had treatment failure. The overall mean treatment success rate of the TB patients was 85.2%. The treatment success rate of the TB patients increased from 80.5% in September 2008-August 2009 to 84.8% in September 2012-May 2013. Tuberculosis type, age, residence and year of treatment were significantly associated with unsuccessful treatment outcome. The risk of unsuccessful outcome was significantly higher among TB patients from rural areas (AOR = 1.63, 95% CI: 1.21-2.20) compared to their urban counterparts. Unsuccessful treatment outcome was also observed in PTB- patients (AOR = 1.77, 95% CI: 1.26-2.50) and EPTB (AOR = 2.07, 95% CI: 1.28-3.37) compared to the PTB+ patients. In conclusion, it appears that DOTS have improved treatment success in the hospital during five years. Regular follow-up of patients with poor treatment outcome and provision of health information on TB treatment to patients from rural area is recommended