arXiv subjects

Nuzhat Nuari Khan Rivu

Publications and source records attributed to Nuzhat Nuari Khan Rivu.

3 recordsLinked to original sources

arXiv2026

Measles Resurgence in Bangladesh, 2026: A Situational Analysis for Urgent Public Health Response

\textbf{Background} Measles has resurged globally in the post-pandemic period as routine immunisation recovery remains below the two-dose threshold required to interrupt transmission. Bangladesh, previously nearing measles--rubella elimination, entered 2026 with widening coverage gaps, depleted vaccine stocks, and increasing numbers of missed children. We conducted a situation analysis to assess the scale, concentration, and programmatic implications of the outbreak. \textbf{Methods} We performed a rapid mixed-evidence review from 1--15 April 2026 using data from WHO, UNICEF, DGHS bulletins, PubMed/MEDLINE, ReliefWeb, SEARO updates, and Bangla-language media. Of 46 records screened, 19 were included. Analysis was based on aggregated, publicly available surveillance and programme data. \textbf{Findings} By 15 April 2026, Bangladesh reported 19{,}161 suspected cases, 2{,}973 confirmed cases, 166 suspected deaths, and 32 confirmed deaths across 58 districts since 15 March 2026. The outbreak was spatially concentrated: the top two divisions accounted for 56.5\% of cases (HHI = 0.217). Children under five comprised 81\% of cases, including 34\% infants under nine months. Vaccination status showed 72\% zero-dose and 16\% partially vaccinated cases. Coverage declined from 88.6\% to 86\% for MR1 and from 89\% to 80.7\% for MR2 (2019--2024), leaving about 20 million children vulnerable. \textbf{Interpretation} The resurgence reflects accumulated immunity gaps rather than vaccine failure, driven by subnational inequities and programme disruption. Urgent priorities include targeted vaccination campaigns, restoration of vitamin A supplementation, strengthening paediatric care capacity, and integrating real-time surveillance into outbreak response.

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arXiv2025

HIV/AIDS Suppression in North America: Intervention Plans and Cost-Effectiveness of UNAIDS 90-90-90 and 95-95-95 Targets

This study utilizes mathematical models to assess progress toward achieving the UNAIDS 90-90-90 and 95-95-95 targets aimed at managing and eradicating HIV/AIDS. It contrasts stochastic and deterministic models, focusing on their utility in optimizing public health strategies. Stochastic models account for real-world unpredictability, offering more realistic insights compared to deterministic approaches. The 95-95-95 targets aim for 95\% of people living with HIV to know their status, 95\% of those diagnosed to receive antiretroviral therapy (ART), and 95\% of those on ART to achieve viral suppression. These benchmarks are critical for reducing transmission and improving health outcomes. This analysis establishes the basic reproduction number ($R_0$) to guide interventions and examines the stability of disease-free and endemic equilibria, providing a foundation for applying optimal control strategies to minimize HIV prevalence effectively and cost-efficiently. Moreover, the data for this study was sourced from the official UNAIDS website, focusing on North America. An innovative feature of this study is the application of the Stochastic method, which enhances model accuracy and operational efficiency in simulating HIV transmission under various interventions. This research offers actionable insights for policymakers and contributes to global efforts to achieve the 95-95-95 targets by 2030, advancing the fight against HIV/AIDS.

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arXiv2025

Comparative Analysis of Stochastic and Predictable Models in the HIV Epidemic Across Genders

This study conducts a comparative analysis of stochastic and deterministic models to better understand the dynamics of the HIV epidemic across genders. By incorporating gender-specific transmission probabilities and treatment uptake rates, the research addresses gaps in existing models that often overlook these critical factors. The introduction of gender-specific treatment, where only one gender receives treatment, allows for a detailed examination of its effects on both male and female populations. Two compartmental models, divided by gender, are analyzed in parallel to identify the parameters that most significantly impact the control of infected populations and the number of treated females. Stochastic methods, including the Euler, Runge-Kutta, and Non-Standard Finite Difference (SNSFD) approaches, demonstrate that stochastic models provide a more accurate and realistic portrayal of HIV transmission and progression compared to deterministic models. Key findings reveal that the stochastic Runge-Kutta method is particularly effective in capturing the epidemic's complex dynamics, such as subtle fluctuations in transmission and population changes. The study also emphasizes the crucial role of transmission probabilities and treatment rates in shaping the epidemic's trajectory, highlighting their importance for optimizing public health interventions. The research concludes that advanced stochastic modeling is essential for improving public health policies and responses, especially in resource-constrained settings.

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