DHIRAJ JHA - Academia.edu (original) (raw)
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Graduate Institute of International and Development Studies (IHEID), Geneva
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Papers by DHIRAJ JHA
BMJ Global Health
Low-income and middle-income countries are struggling with a growing epidemic of non-communicable... more Low-income and middle-income countries are struggling with a growing epidemic of non-communicable diseases. To achieve the Sustainable Development Goals, their healthcare systems need to be strengthened and redesigned. The Starfield 4Cs of primary care—first-contact access, care coordination, comprehensiveness and continuity—offer practical, high-quality design options for non-communicable disease care in low-income and middle-income countries. We describe an integrated non-communicable disease intervention in rural Nepal using the 4C principles. We present 18 months of retrospective assessment of implementation for patients with type II diabetes, hypertension and chronic obstructive pulmonary disease. We assessed feasibility using facility and community follow-up as proxy measures, and assessed effectiveness using singular ‘at-goal’ metrics for each condition. The median follow-up for diabetes, hypertension and chronic obstructive pulmonary disease was 6, 6 and 7 facility visits, a...
International Journal of Computer Applications, Dec 18, 2012
Increasingly, for many application areas, it is becoming important to include elements of nonline... more Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. The problem of identifying nonlinear system models arise in various applications in control and signal processing. In this context, one of the most successful and popular stastical identification approaches is Particle Filtering, otherwise known as Sequential Monte Carlo (SMC) methods. As compared to Extended Kalman Filter and Gaussian Sum Filter, this approach is computationally reliable for identification of highly nonlinear systems in terms of accuracy, and, at the same time chance of failure in difficult circumstances decreases. The numerical integration techniques, on the other hand, are only feasible in lowdimensional state-spaces. In this paper the particle filtering approach has been attempted for non-linear system identification. The particles and their associated importance weights in particle filtering approach evolve randomly in time according to a simulation-based rule. This is equivalent to a dynamic grid approximation of the target distributions, where the regions of higher probability are allocated proportionally more grid positions. Using these particles Monte Carlo estimates of the quantities of interest may be obtained, with the accuracy of these estimates being independent of the dimension of the state space. The envisioned method is easier to implement than classical numerical methods and allows complex nonlinear and non-Gaussian estimation problems to be solved efficiently in an online manner. The experimental results on comparison with Kalman filtering show the efficacy of the proposed method through illustrative examples.
BMJ Global Health
Low-income and middle-income countries are struggling with a growing epidemic of non-communicable... more Low-income and middle-income countries are struggling with a growing epidemic of non-communicable diseases. To achieve the Sustainable Development Goals, their healthcare systems need to be strengthened and redesigned. The Starfield 4Cs of primary care—first-contact access, care coordination, comprehensiveness and continuity—offer practical, high-quality design options for non-communicable disease care in low-income and middle-income countries. We describe an integrated non-communicable disease intervention in rural Nepal using the 4C principles. We present 18 months of retrospective assessment of implementation for patients with type II diabetes, hypertension and chronic obstructive pulmonary disease. We assessed feasibility using facility and community follow-up as proxy measures, and assessed effectiveness using singular ‘at-goal’ metrics for each condition. The median follow-up for diabetes, hypertension and chronic obstructive pulmonary disease was 6, 6 and 7 facility visits, a...
International Journal of Computer Applications, Dec 18, 2012
Increasingly, for many application areas, it is becoming important to include elements of nonline... more Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. The problem of identifying nonlinear system models arise in various applications in control and signal processing. In this context, one of the most successful and popular stastical identification approaches is Particle Filtering, otherwise known as Sequential Monte Carlo (SMC) methods. As compared to Extended Kalman Filter and Gaussian Sum Filter, this approach is computationally reliable for identification of highly nonlinear systems in terms of accuracy, and, at the same time chance of failure in difficult circumstances decreases. The numerical integration techniques, on the other hand, are only feasible in lowdimensional state-spaces. In this paper the particle filtering approach has been attempted for non-linear system identification. The particles and their associated importance weights in particle filtering approach evolve randomly in time according to a simulation-based rule. This is equivalent to a dynamic grid approximation of the target distributions, where the regions of higher probability are allocated proportionally more grid positions. Using these particles Monte Carlo estimates of the quantities of interest may be obtained, with the accuracy of these estimates being independent of the dimension of the state space. The envisioned method is easier to implement than classical numerical methods and allows complex nonlinear and non-Gaussian estimation problems to be solved efficiently in an online manner. The experimental results on comparison with Kalman filtering show the efficacy of the proposed method through illustrative examples.