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A comprehensive approach to integrated one health surveillance and responseSurveillance data plays a crucial role in understanding and responding to emerging infectious diseases; here, we learn why adopting a One Health surveillance approach to EIDs can help to protect human, animal, and environmental health. Over 75% of emerging infectious diseases (EIDs) affecting humans are zoonotic diseases with animal hosts, which can be transmitted by waterborne, foodborne, vector-borne, or air-borne pathways. (7) Early detection is important and allows for a rapid response through preventive and control measures. However, early detection of EIDs is hindered by several obstacles, such as climate change, which can alter habitats, leading to shifts in the distribution of disease- carrying vectors like mosquitoes and ticks. This can result in diseases such as malaria, dengue fever, and Lyme disease becoming more common in areas with established transmission or spreading to new areas entirely. (4) Environmental changes such as deforestation and urbanization disrupt ecosystems, increasing the likelihood of zoonotic disease spillover from wildlife to humans. In addition to working at the interface of these changes, detection and tracking of EIDs also requires sharing and standardization of complex data and integrating processes across different regions and health systems. 

Revolutionising disease detection: The emergence of non-invasive VOC breathomicsBreathomics marks a revolutionary approach to disease detection by analyzing the chemical composition of exhaled breath. As the world recovers from the recent global health crises, the detection and management of pandemic diseases like COVID-19, RSV, and flu have come to the forefront. The COVID-19 pandemic alone has affected over 96 million people in the US, with a devastating count of more than a million fatalities. Similarly, respiratory syncytial virus (RSV) and influenza (flu) collectively burden the healthcare system with millions of cases annually, leading to hundreds of thousands of hospitalizations and tens of thousands of deaths. These staggering statistics underscore an urgent need for diagnostic methods that are not only swift and accurate but also non- invasive to facilitate rapid, widespread testing. Enter Breathomics—a revolutionary approach that analyzes the chemical composition of exhaled breath to detect diseases. 

Applying data science advances in disease surveillance and control Dr. David S. Ebert and Dr. Aaron Wendelboe explain how a cohesive, multidisciplinary, and multi-tiered approach can support a more predictive model in disease surveillance and control. Public health disease surveillance is being conducted in countless settings, including healthcare, vertebrate and invertebrate animals, wastewater, air quality, transportation, and commercial activities, but, attaining the goal of early disease detection has been somewhat elusive. For instance, one of the few key shortcoming of public health preparedness efforts is the insufficient collaboration between multidisciplinary experts, such as data scientists, computer engineers, anthropologists, social scientists, and systems engineers. To address these gaps in knowledge and preparedness, we are responding in a multi-tiered approach with a One Health perspective that will be economically feasible and sustainable. The authors have also engaged a broad set of stakeholders, created broad multidisciplinary teams, are combining relevant data sources in innovative ways that will serve as early indicators, are using advanced technologies for early diagnosis, and advancing analytic methods to maintain high specificity for true event identification. 

Online sampling-supported visual analytics is increasingly important, as it allows users to explore large datasets with acceptable approximate answers at interactive rates. However, existing online spatiotemporal sampling techniques are often biased, as most researchers have primarily focused on reducing computational latency. Biased sampling approaches select data with unequal probabilities and produce results that do not match the exact data distribution, leading end users to incorrect interpretations. In this paper, we propose a novel approach to perform unbiased online sampling of large spatiotemporal data. The proposed approach ensures the same probability of selection to every point that qualifies the specifications of a user's multidimensional query. To achieve unbiased sampling for accurate representative interactive visualizations, we design a novel data index and an associated sample retrieval plan. Our proposed sampling approach is suitable for a wide variety of visual analytics tasks, e.g., tasks that run aggregate queries of spatiotemporal data. Extensive experiments confirm the superiority of our approach over a state-of-the-art spatial online sampling technique, demonstrating that within the same computational time, data samples generated in our approach are at least 50% more accurate in representing the actual spatial distribution of the data and enable approximate visualizations to present closer visual appearances to the exact ones.