Browsing by Author "Okello, Tom"

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    Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers
    (International Conference on Autonomous Agents and Multiagent Systems, 2018) Gholami, Shahrzad; Mc Carthy, Sara; Dilkina, Bistra; Plumptre, Andrew; Tambe, Milind; Driciru, Margaret; Wanyama, Fred; Rwetsiba, Aggrey; Nsubaga, Mustapha; Mabonga, Joshua; Okello, Tom; Enyel, Eric
    Poachers are engaged in extinction level wholesale slaughter, so it is critical to harness historical data for predicting poachers’ behavior. However, in these domains, data collected about adversarial actions are remarkably imperfect, where reported negative instances of crime may be mislabeled or uncertain. Unfortunately, past attempts to develop predictive and prescriptive models to address this problem suffer from shortcomings from a modeling perspective as well as in the implementability of their techniques. Most notably these models i) neglect the uncertainty in crime data, leading to inaccurate and biased predictions of adversary behavior, ii) use coarse-grained crime analysis and iii) do not provide a convincing evaluation as they only look at a single protected area. Additionally, they iv) proposed time-consuming techniques which cannot be directly integrated into low resource outposts. In this innovative application paper, we (I) introduce iWare-E a novel imperfect-observation aWare Ensemble (iWare-E) technique, which is designed to handle the uncertainty in crime information efficiently. This approach leads to superior accuracy and efficiency for adversary behavior prediction compared to the previous stateof- the-art. We also demonstrate the country-wide efficiency of the models and are the first to (II) evaluate our adversary behavioral model across different protected areas in Uganda, i.e., Murchison Fall and Queen Elizabeth National Park, (totaling about 7500 km2) as well as (III) on fine-grained temporal resolutions. Lastly, (IV) we provide a scalable planning algorithm to design fine-grained patrol routes for the rangers, which achieves up to 150% improvement in number of predicted attacks detected.
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    Prevalence and Factors Associated with transfusion-transmissible infections among blood donors in Arua regional blood bank, Uganda
    (Springer Nature B.V, 2024-09) Cwinyaai, Norman; Opio, Denis; Kajumbula, Henry; Zalwango, Jane F; Akunzirwe, Rebecca; Okello, Tom; Francis, Anguzu
    Background -Blood transfusion services play a very key role in modern health care service delivery. About 118.5 million blood donations were collected globally in 2022. However, about 1.6 million units of blood are destroyed annually due to transfusion-transmissible infections (TTIs). There is a very high risk of TTIs through donated blood to recipients if safe transfusion practices are not observed. This study determined the prevalence and factors associated with TTIs among blood donors in Arua regional blood bank, Uganda. Methods -This study was a retrospective cross-sectional design that involved a review of a random sample of 1370 blood donors registered between January 1st, 2018 and December 31st, 2019 at Arua regional blood bank, Uganda. Descriptive statistics were used to describe the characteristics of the blood donors. The binary logistic regression was used to determine the factors associated with TTIs. Results -The majority of the blood donors were male (80.1%), and the median donor age was 23 years (IQR=8 years). The overall prevalence of TTIs was found to be 13.8% (95%CI: 12.0-15.6%), with specific prevalences of 1.9% for HIV, 4.1% for HBV, 6.6% for HCV and 2.8% for treponema pallidum. Male sex (AOR=2.10, 95%CI: 1.32–3.36, p-value=0.002) and lapsed donor type compared to new donor type (AOR=0.34, 95%CI: 0.13–0.87, p-value=0.025) were found to be associated with TTIs. Conclusion -The prevalence of TTIs among blood donors of West Nile region, Uganda was found to be significantly high, which implies a high burden of TTIs in the general population. Hence, there is need to implement a more stringent donor screening process to ensure selection of risk-free donors, with extra emphasis on male and new blood donors. Additionally, sensitization of blood donors on risky behaviors and self-deferral will reduce the risk of donating infected blood to the recipients. Keywords -Blood donor, Voluntary donor, Transfusion-transmissible infections (TTIs), Seropositive blood donor
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    Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations
    (IEEE, 2019) Xu, Lily; Gholami, Shahrzad; Mc Carthy, Sara; Dilkina, Bistra; Plumptre, Andrew; Tambe, Milind; Singh, Rohit; Nsubuga, Mustapha; Mabonga, Joshua; Driciru, Margaret; Wanyama, Fred; Rwetsiba, Aggrey; Okello, Tom; Enyel, Eric
    Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an end-to-end approach to the data-to-deployment pipeline for anti-poaching. In doing so, we address challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS, and we apply our methodology to three national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to improve robustness of our prescribed patrols and increase detection of snares by an average of 30%. We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 800 parks around the world through integration with SMART conservation software.