Moumita Das

Dengue remains a significant public health challenge in Bangladesh, with considerable variation in disease burden and mortality. This study aimed to assess how dengue case fatality rates (CFRs) had varied across months, seasons, and geographic regions of Bangladesh. A retrospective observational study was conducted from January 2021 to December 2025 using surveillance data obtained from the Directorate General of Health Services, Bangladesh (DGHS). Descriptive statistical analyses were performed to estimate dengue CFR. Univariate and multivariable logistic regression analyses were conducted to evaluate the association between the studied variables and dengue CFR. Seasonal and trend components of the data were examined through LOESS‐based decomposition to understand underlying time‐series patterns. Over the past 5 years, a total of 6,15,991 dengue cases and 3,079 deaths, with an overall CFR of 0.49%, were recorded. The year‐wise highest CFR was recorded in 2024 (0.56%; 95% CI: 0.52–0.62). The month‐wise and season‐wise mean CFRs were recorded highest in February (1.21%) and winter (0.74%), respectively, while the division‐wise highest mean CFR was recorded in Dhaka (0.60%). In addition, the month of February (AOR = 3.09; 95% CI 1.59−5.38; p < 0.001) and the winter season (OR = 1.61; 95% CI 1.41−1.82; p < 0.001) had significantly higher odds for dengue CFR. Compared to Dhaka, most of the other divisions had significantly ( p < 0.001) lower odds for dengue CFR. The year 2024 had significantly ( p < 0.001) higher odds for dengue CFR compared to most other years under study. This present study identified the critical time and high‐risk areas in Bangladesh, underscoring the need for public health interventions and enhanced surveillance systems.

Free-roaming dogs pose a significant public health concern due to their role in disease transmission. Rabies has been endemic in Kenya for over a century, yet a sustainable and standardized method for estimating dog populations remains unestablished. To address this gap, we applied kriging and co-kriging spatial interpolation techniques to predict the distribution of free-roaming domestic dogs across different Kenyan counties. To improve the model’s accuracy, we incorporated environmental and demographic predictors such as daily temperature, the Normalized Difference Vegetation Index (NDVI), and human density. Dog population data at the village level were collected from 34 counties through an online survey of veterinary professionals in both the public and private sectors. A spherical model was used to construct the semivariogram, integrating temperature, NDVI, and human density to refine spatial predictions. Kenya’s total free-roaming dog population was estimated to be 7.46 million. The density of dogs per square kilometer varied across counties, corresponding to a median national density of 12.13 dogs per square kilometer. Compared to models incorporating no or only single or multiple covariates, the co-kriging model incorporating human density provided the best fit, with the minimum estimated difference between observed and predicted values. The spatial distribution map highlights arid and sparsely populated pastoral counties having lower dog densities, whereas peri-urban, densely populated, and agricultural counties exhibit higher dog numbers. This study provides a spatial framework for estimating free-roaming dog populations, which can inform the design and implementation of rabies control programs and public health interventions in Kenya and other infected countries.

Biosecurity measures are the main defense against infectious disease spread in swine farms. Farmers often acknowledge the need for enhanced biosecurity, but a gap remains between claiming measures and practicing them on the farm. This study looks at barriers to implementing biosecurity, key control points, and successes in Midwest U.S. swine operations. Semi‐structured interviews were held with industry experts knowledgeable about swine farms and responsible for biosecurity compliance. The interview transcripts were analyzed using qualitative text mining methods like word frequency, n‐gram analysis, topic modeling (latent Dirichlet allocation [LDA]), and thematic analysis. Seven major themes that converged with the results of content analysis emerged, which demonstrated major barriers, potential critical control points, and success stories in biosecurity compliance. Biosecurity practices differed significantly between operation types, with stricter practices in sow and boar studs compared to grow‐finish farm operations. Human behavior was identified as both the primary vulnerability and the greatest opportunity for improvement. Compliance was influenced by communication strategies, training, and employee workload levels. Limitations on barn infrastructure design, financial constraints, and risk perception were identified as further challenges to biosecurity implementation. Critical control points, such as transportation (trucks and trailers), equipment sharing, manure handling, and interactions with packing plants and cull markets, appear to be high‐risk nodes for disease transmission. Despite these challenges, success stories such as long‐term PRRS prevention through air filtration, strategic application of basic biosecurity measures, and the use of dedicated biosecurity managers have proven successful in reducing disease risks. Implementing biosecurity in swine farms is a complex system‐level challenge influenced by farm design, human, economic, and external interface factors. Thus, progress requires a comprehensive approach that combines infrastructure upgrades, risk‐based decision making, tailored training, and ongoing communication among stakeholders. This study identifies vulnerabilities and proven successes and offers practical insights to inform a more resilient biosecurity system.