Pith. sign in

REVIEW 3 major objections 4 minor 40 references

An AI-Based Public Health Data Monitoring System

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that replacing threshold alerts with a ranked anomaly list, plus context displays, lets a small team monitor millions of health data points daily and detect more events, including a 54x reviewer speedup in a deployed…

desk verdict A real deployed system and a good design story, but the 54x efficiency claim is measured against the wrong baseline and needs a major revision before the numbers can be used. read the letter →

arxiv 2506.04429 v1 pith:UY7CNAED submitted 2025-06-04 cs.AI

Ananya Joshi , Nolan Gormley , Richa Gadgil , Tina Townes , Roni Rosenfeld , Bryan Wilder This is my paper
classification cs.AI
keywords publichealthmonitoringanomalyrankingalertthresholdhuman-centeredAIoutbreakdetectiondataqualitylongitudinalevaluationsituationalawareness
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Public health data monitoring traditionally works by setting thresholds that fire alerts, but the authors argue this paradigm cannot scale to modern high-volume, noisy, nonstationary data: thresholds need constant manual retuning, alert volumes become unusable, and reviewers lose situational awareness. The paper's proposed fix is a ranking-based monitoring paradigm: an AI method scores each data point by its deviation from expectation, and reviewers work down a ranked list instead of wading through binary alerts. In a three-month pre-registered longitudinal deployment at a national organization, the system monitored up to 5,000,000 data points daily, and reviewers recorded events 54x faster on average than with the prior manual-exploratory workflow (described in the abstract as a 54x speedup over alert-based methods) while finding more events and higher-level 'meta-events.' If the result holds, it gives public health agencies and other high-volume monitoring domains a concrete model for combining AI anomaly scores with human triage.

What carries the argument

The central object is the ranking-based monitoring paradigm: instead of emitting a binary alert when a threshold is crossed, an anomaly-detection method assigns every data point a score measuring deviation from expectation, and the system presents the top-scoring streams as a ranked list. The underlying method is an unsupervised multiple-univariate outlier-ranking approach validated for human-in-the-loop use, and the interface adds three interaction modalities that carry the argument: data-point filters for segmentation, situational-awareness panels (a county-level choropleth and indicator bar charts built from score aggregations), and rolling-mean heatmaps with variance tags that show how event scores change as data are revised. Because scores, unlike raw values, can be compared across geographic tiers and indicators, the ranked list makes it possible to aggregate context without the correlation errors that plague raw-value fusion.

What would settle it

A crossover deployment in which the same reviewers alternate between the ranked-list interface and a threshold-alert interface that emits only a manageable number of top alerts per day would settle the claim: if the speed advantage over that alert interface is small or zero, the 54x result is specific to the exploratory baseline, not to alerting as such.

Watch

Extended reading notes

Core claim

The paper's central claim is that replacing threshold-based alerts with a ranked list of anomalies, presented with contextual displays, transforms a failing public health monitoring workflow into one that works at national scale. The deployed system, built through an 18-month collaboration among data reviewers, engineers, and computer scientists, uses an unsupervised human-in-the-loop outlier-ranking method that scores data points by how far they deviate from reviewer-defined expectations, and the interface lets reviewers expand each ranked row to see sibling, parent, and child streams, toggle geographic context, and record structured events and meta-events. The longitudinal evaluation showed reviewers spent more time per row, recorded up to 49 events per session versus 1-2 in the prior baseline, and identified meta-events that were re-analyzed and matched notable public health events. The authors report that reviewers were 54x faster on average than with the previous exploratory manual system and faster than with the AI method presented alone; the abstract summarizes this as a 54x increase in reviewer speed efficiency compared with traditional alert-based methods.

Load-bearing premise

The result assumes the measured speedup comes from the ranking paradigm and interface design rather than from reviewer learning, motivation, or the specific baseline used, since Baseline 1 was a manual exploratory tool, not a functioning alerting system.

Editorial extensions

If this is right

  • A small reviewer team can handle up to 5,000,000 data points per day, a workload that would overwhelm a manual or threshold-alert workflow.
  • Reviewers record more real events per session and can identify meta-events, combined anomalies that signal higher-level phenomena, which were absent or rare in the baselines.
  • The system cuts the need for per-stream threshold tuning; reviewers instead adjust data-expectation inputs roughly monthly as data dynamics change.
  • The pre-registered longitudinal evaluation strategy, with sequential interface changes and multi-week adaptation periods, provides a reusable pattern for assessing human-AI monitoring systems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the 54x figure is best read as a comparison to an exploratory manual-review workflow, not to a functioning threshold-alerting system; a fair comparison against a modest-volume alert list would likely show a much smaller speedup, though the ranking approach still removes the threshold-tuning burden.
  • Beyond the paper: the same ranking-plus-context design should transfer to other high-volume monitoring domains, such as environmental sensors, financial transactions, and agricultural reports, where the failure modes it targets appear in the same form.
  • Beyond the paper: a testable extension is whether the benefit persists with less expert reviewers or a different anomaly-scoring method, since the current evaluation involves three domain experts who helped design the system.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper describes a deployed public-health data monitoring system that replaces threshold-based alerting with an AI-based ranking paradigm, integrated into a human-in-the-loop interface for data reviewers at the Delphi group. The authors report a multi-year co-design process with reviewers, engineers, and computer scientists; a three-month longitudinal deployment; and a headline claim of a 54x increase in reviewer speed efficiency compared with traditional alert-based methods. They also report increased event and meta-event recording, positive reviewer feedback, and design guidelines for human-centered AI monitoring systems.

Significance. If the core claims hold, the paper would be a valuable real-world case study of a human-centered AI monitoring system deployed at national scale, with an unusual longitudinal evaluation and a concrete alternative to alert-fatigue-prone surveillance. The authors give appropriate emphasis to stakeholder-engaged design and to the practical failure modes of alerting systems. However, the quantitative headline claim is not currently supported by the evidence as presented, and the evaluation lacks the raw data and preregistration needed to verify the reported multipliers. The qualitative findings and design lessons are nonetheless likely to be useful to the HCI and public-health informatics communities.

major comments (3)
  1. [Abstract; Sec. 8; Sec. 4.1] The abstract claims "54x increase in reviewer speed efficiency compared to traditional alert-based methods," but Sec. 8 measures the 54x against "the exploratory system in Baseline 1." Sec. 4.1 defines Baseline 1 as the deployed alerting system, yet immediately states that "reviewers reverted to manual data inspection using exploratory tools." Thus the comparison denominator is the manual fallback, not alert-based monitoring, and no quantitative alerting-phase data enter the ratio. The headline should be revised to compare like with like, or actual alerting-phase speed should be measured and reported.
  2. [Sec. 8; Fig. 6] The efficiency claim is based on "recorded events per day" (Fig. 6B), which is an event count, not a speed, unless total review time is held constant. Fig. 6A shows that time spent per data row increased with each modification, so the reported 54x speedup cannot be separated from time investment without session-level duration and workload data. Please report raw event counts, session durations, per-reviewer logs, and the exact rate formula used to compute the multiplier.
  3. [Sec. 8; Sec. 9] The paper states that reviewers were 5x faster than Baseline 2 in Sec. 8 and 6x faster in Sec. 9, with no reconciliation. The paper also states that the evaluation strategy was preregistered on OSF, but no OSF link, preregistration document, or data release is provided. These issues, together with the small uncontrolled sequential design (no control condition; reviewer learning over three months is a plausible confound), limit confidence in the quantitative multipliers. Please reconcile the discrepancy and provide the preregistration and supporting data, or explicitly restrict claims to qualitative trends.
minor comments (4)
  1. [Throughout] The manuscript contains typos, including "effeciency" (Abstract and Sec. 8), "appporpriate" (Sec. 4.3), "Reviwer Actions" (Sec. 1), and "Perscriptive Alerting Fallacy" (Sec. 4.2). Please proofread the text carefully.
  2. [Sec. 1 and Sec. 8] The paper says "We preregistered the Github commits and evaluation strategy on OSF before experiments began" but provides no link or identifier. Please add the OSF record, or remove the preregistration claim if the record is not public.
  3. [Fig. 6 caption] The caption says "Baselines had no comparable metrics," which is contradicted by the numeric 54x and 5x claims in the text. Please clarify what baseline metrics were and were not available.
  4. [Sec. 4.1] The label "Baseline 1: Deployed Alerting System (120 weeks)" is misleading because the description immediately shifts to a manual exploratory fallback. Consider renaming or restructuring this subsection to distinguish the alerting phase from the manual-inspection phase.

Circularity Check

0 steps flagged · score 0.0 of 10

No construction-level circularity: the 54x speedup is an empirically measured deployment outcome, and self-citations are not load-bearing.

full rationale

This paper does not present a derivation in which a prediction is equivalent to its inputs. The central quantitative claim, a 54x improvement in reviewer speed, is an externally measured behavioral outcome (recorded events per day, Sec. 8, Fig. 6B) comparing the deployed ranking-based system against prior baselines; it is not computed from the AI method's parameters or from any defining equation. The core AI ranking method is adopted from the authors' prior work [Joshi et al., 2024] in Sec. 4.3, and a few background claims cite the same authors, but these self-citations are not load-bearing for the evaluation's outcome: the speed and efficacy figures are measured under deployment conditions rather than entailed by the method's definition. The abstract's phrase 'traditional alert-based methods' versus Sec. 8's actual comparison to the 'exploratory system in Baseline 1' is a potential baseline-mischaracterization or validity concern, but it is not circularity as defined here. No self-definitional, fitted-input-called-prediction, uniqueness-imported-from-authors, or ansatz-smuggled-in-via-citation pattern is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 1 invented entities

The paper's quantitative claims do not depend on any fitted parameters introduced here; the underlying ranking method and its parameters come from prior work [Joshi et al., 2024]. The display formulas in M2 include normalization constants (e.g., w) that are under-specified but peripheral to the main efficiency claim. The main assumptions are domain-level: that the cited ranking method is fit for purpose, that scores are cross-dimensionally comparable, and that reviewer-recorded events are a valid measure of monitoring effectiveness.

assumptions (3)
  • domain assumption The multiple-univariate outlier ranking method from [Joshi et al., 2024] is appropriate and validated for this setting.
    The paper relies on this method without re-deriving or independently evaluating it in this paper; invoked in Sec. 4.3 as the basis for the system.
  • domain assumption Event scores from the ranking method are comparable across geographic tiers and indicators, enabling the M2 aggregation.
    Sec. 7 M2 states 'This is only possible because event scores can be compared across these dimensions.' This comparability is asserted, not demonstrated in the paper.
  • domain assumption Reviewer-recorded event counts and interaction times are valid proxies for monitoring effectiveness.
    Sec. 8 defines KPIs from reviewer workflows, but there is no independent validation against ground-truth outbreak events or external quality criteria.
invented entities (1)
  • Meta-event
    purpose: A reviewer-created record that combines multiple individual events into a higher-level phenomenon, used as an efficacy metric.
    The paper introduces meta-events and reports that a quality assurance check corresponded with notable public health events, but no external data or fixed criteria are provided, so the construct has no independently falsifiable handle in this paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of An AI-Based Public Health Data Monitoring System." pith.science (2026). https://pith.science/paper/UY7CNAED

@misc{pith2026250604429,
  author       = {Pith},
  title        = {Pith review of: An AI-Based Public Health Data Monitoring System},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UY7CNAED}},
  note         = {Machine review of arXiv:2506.04429}
}
read the original abstract

Public health experts need scalable approaches to monitor large volumes of health data (e.g., cases, hospitalizations, deaths) for outbreaks or data quality issues. Traditional alert-based monitoring systems struggle with modern public health data monitoring systems for several reasons, including that alerting thresholds need to be constantly reset and the data volumes may cause application lag. Instead, we propose a ranking-based monitoring paradigm that leverages new AI anomaly detection methods. Through a multi-year interdisciplinary collaboration, the resulting system has been deployed at a national organization to monitor up to 5,000,000 data points daily. A three-month longitudinal deployed evaluation revealed a significant improvement in monitoring objectives, with a 54x increase in reviewer speed efficiency compared to traditional alert-based methods. This work highlights the potential of human-centered AI to transform public health decision-making.

Figures

Figures reproduced from arXiv: 2506.04429 by the authors.

Figure 1
Figure 1. Data monitoring catches the data quality changes in case [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Data reviewers triage notable events from public health [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (3 more)
Figure 6
Figure 6. Figure 6: A. Reviewer engagement, displayed here with 95% CI bars, increased with each added modification. Baselines had no comparable metrics. B. Reviewers recorded significantly more events on average than the prior baselines. C. More meta-events were identified after situatio…
Figure 5
Figure 5. Figure 5: For mechanism M3, we added a tag with the rolling vari￾ance and a 1D heat-map of the rolling mean event scores so that reviewers have an intuition for how event severity changes over data revisions and time. (on a scale of 0 to 1) is calculated using ∀r ∈ R, c(r) = P e…
Figure 7
Figure 7. Figure 7: Recorded events per session (up to 49) are far greater than [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

40 extracted references · 38 canonical work pages

  1. [1]

    Results from the second year of a collaborative effort to forecast influenza seasons in the united states

    Matthew Biggerstaff, Michael Johansson, David Alper, Logan C Brooks, Prithwish Chakraborty, David C Farrow, Sangwon Hyun, Sasikiran Kandula, Craig McGowan, Naren Ramakrishnan, et al. Results from the second year of a collaborative effort to forecast influenza seasons in the united states. Epidemics , 24:26--33, 2018

    work page 2018

  2. [2]

    A review on outlier/anomaly detection in time series data

    Ane Bl \'a zquez-Garc \' a, Angel Conde, Usue Mori, and Jose A Lozano. A review on outlier/anomaly detection in time series data. ACM Computing Surveys (CSUR) , 54(3):1--33, 2021

    work page 2021

  3. [3]

    Outbreak detection through automated surveillance: a review of the determinants of detection

    David L Buckeridge. Outbreak detection through automated surveillance: a review of the determinants of detection. Journal of biomedical informatics , 40(4):370--379, 2007

    work page 2007

  4. [4]

    Evolution of public health surveillance: status and recommendations, 2017

    Howard S Burkom. Evolution of public health surveillance: status and recommendations, 2017

    work page 2017

  5. [5]

    Voila: Visual anomaly detection and monitoring with streaming spatiotemporal data

    Nan Cao, Chaoguang Lin, Qiuhan Zhu, Yu-Ru Lin, Xian Teng, and Xidao Wen. Voila: Visual anomaly detection and monitoring with streaming spatiotemporal data. IEEE transactions on visualization and computer graphics , 24(1):23--33, 2017

    work page 2017

  6. [6]

    Visualization and analytics tools for infectious disease epidemiology: a systematic review

    Lauren N Carroll, Alan P Au, Landon Todd Detwiler, Tsung-chieh Fu, Ian S Painter, and Neil F Abernethy. Visualization and analytics tools for infectious disease epidemiology: a systematic review. Journal of biomedical informatics , 51:287--298, 2014

    work page 2014

  7. [7]

    Infectious disease informatics: syndromic surveillance for public health and biodefense , volume 21

    Hsinchun Chen, Daniel Zeng, and Ping Yan. Infectious disease informatics: syndromic surveillance for public health and biodefense , volume 21. Springer, 2010

    work page 2010

  8. [8]

    Data visualization, information dissemination, and alerting

    Hsinchun Chen, Daniel Zeng, Ping Yan, Hsinchun Chen, Daniel Zeng, and Ping Yan. Data visualization, information dissemination, and alerting. Infectious Disease Informatics: Syndromic Surveillance for Public Health and BioDefense , pages 73--87, 2010

    work page 2010

Show all 40 references
  1. [9]

    What can you really do with 35,000 statistical alerts a week anyways? Online Journal of Public Health Informatics , 11(1), 2019

    Michael Coletta and Hong Zhou. What can you really do with 35,000 statistical alerts a week anyways? Online Journal of Public Health Informatics , 11(1), 2019

    2019

  2. [10]

    A survey of urban visual analytics: Advances and future directions

    Zikun Deng, Di Weng, Shuhan Liu, Yuan Tian, Mingliang Xu, and Yingcai Wu. A survey of urban visual analytics: Advances and future directions. Computational Visual Media , 9(1):3--39, 2023

    2023

  3. [11]

    From explanation to action: An end-to-end human-in-the-loop framework for anomaly reasoning and management

    Xueying Ding, Nikita Seleznev, Senthil Kumar, C Bayan Bruss, and Leman Akoglu. From explanation to action: An end-to-end human-in-the-loop framework for anomaly reasoning and management. arXiv preprint arXiv:2304.03368 , 2023

    2023 arXiv

  4. [12]

    The johns hopkins university center for systems science and engineering covid-19 dashboard: data collection process, challenges faced, and lessons learned

    Ensheng Dong, Jeremy Ratcliff, Tamara D Goyea, Aaron Katz, Ryan Lau, Timothy K Ng, Beatrice Garcia, Evan Bolt, Sarah Prata, David Zhang, et al. The johns hopkins university center for systems science and engineering covid-19 dashboard: data collection process, challenges faced...

    2022

  5. [13]

    Challenges of big data analysis

    Jianqing Fan, Fang Han, and Han Liu. Challenges of big data analysis. National science review , 1(2):293--314, 2014

    2014

  6. [14]

    National syndromic surveillance program (nssp) new users

    Centers for Disease Control and Prevention. National syndromic surveillance program (nssp) new users. https://www.cdc.gov/nssp/new-users.html, 2023

    2023

  7. [15]

    High output management

    Andrew S Grove. High output management . Vintage, 2015

    2015

  8. [16]

    A review on the development of big data analytics and effective data visualization techniques in the context of massive and multidimensional data

    J Jabanjalin Hilda, C Srimathi, and Bhulakshmi Bonthu. A review on the development of big data analytics and effective data visualization techniques in the context of massive and multidimensional data. Indian Journal of Science and Technology , 9(27):1--13, 2016

    2016

  9. [17]

    A practitioner-driven research agenda for syndromic surveillance

    Richard S Hopkins, Catherine C Tong, Howard S Burkom, Judy E Akkina, John Berezowski, Mika Shigematsu, Patrick D Finley, Ian Painter, Roland Gamache, Victor J Del Rio Vilas, et al. A practitioner-driven research agenda for syndromic surveillance. Public Health Reports , 132(1\...

    2017

  10. [18]

    Syndromic surveillance on the epidemiologist’s desktop: making sense of much data

    Kathy J Hurt-Mullen and J Coberly. Syndromic surveillance on the epidemiologist’s desktop: making sense of much data. MMWR Morb Mortal Wkly Rep , 54(Suppl):141--6, 2005

    2005

  11. [19]

    The effectiveness of syndromic surveillance for the early detection of waterborne outbreaks: a systematic review

    Susanne Hyllestad, Ettore Amato, Karin Nyg rd, Line Vold, and Preben Aavitsland. The effectiveness of syndromic surveillance for the early detection of waterborne outbreaks: a systematic review. BMC Infectious Diseases , 21:1--12, 2021

    2021

  12. [20]

    Effective dashboard design

    Andrea Janes, Alberto Sillitti, and Giancarlo Succi. Effective dashboard design. Cutter IT Journal , 26(1):17--24, 2013

    2013

  13. [21]

    Computationally assisted quality control for public health data streams

    Ananya Joshi, Kathryn Mazaitis, Roni Rosenfeld, and Bryan Wilder. Computationally assisted quality control for public health data streams. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence , pages 6004--6012, 2023

    2023

  14. [22]

    Outlier ranking in large-scale public health streams

    Ananya Joshi, Tina Townes, Nolan Gormley, Luke Neureiter, Roni Rosenfeld, and Bryan Wilder. Outlier ranking in large-scale public health streams. Proceedings of the AAAI Conference , 2024

    2024

  15. [23]

    A visual analytics framework for reviewing streaming performance data

    Suraj P Kesavan, Takanori Fujiwara, Jianping Kelvin Li, Caitlin Ross, Misbah Mubarak, Christopher D Carothers, Robert B Ross, and Kwan-Liu Ma. A visual analytics framework for reviewing streaming performance data. In 2020 IEEE Pacific Visualization Symposium (PacificVis) , pag...

    2020

  16. [24]

    Identifying changing variant behavior during a pandemic: An exploratory analysis

    Tara Lakdawala and Ananya Joshi. Identifying changing variant behavior during a pandemic: An exploratory analysis. https://delphi.cmu.edu/blog, Dec 2023

    2023

  17. [25]

    Avoiding drill-down fallacies with vispilot: Assisted exploration of data subsets

    Doris Jung-Lin Lee, Himel Dev, Huizi Hu, Hazem Elmeleegy, and Aditya Parameswaran. Avoiding drill-down fallacies with vispilot: Assisted exploration of data subsets. In Proceedings of the 24th International Conference on Intelligent User Interfaces, \ IUI \ 2019, Marina del Ra...

    2019

  18. [26]

    A visual analytics approach to understanding spatiotemporal hotspots

    Ross Maciejewski, Stephen Rudolph, Ryan Hafen, Ahmad Abusalah, Mohamed Yakout, Mourad Ouzzani, William S Cleveland, Shaun J Grannis, and David S Ebert. A visual analytics approach to understanding spatiotemporal hotspots. IEEE Transactions on Visualization and Computer Graphic...

    2009

  19. [27]

    Pixal: Anomaly reasoning with visual analytics

    Brian Montambault, Camelia D Brumar, Michael Behrisch, and Remco Chang. Pixal: Anomaly reasoning with visual analytics. arXiv preprint arXiv:2205.11004 , 2022

    2022 arXiv

  20. [28]

    Explaining artificial intelligence with visual analytics in healthcare

    Jeroen Ooge, Gregor Stiglic, and Katrien Verbert. Explaining artificial intelligence with visual analytics in healthcare. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 12(1):e1427, 2022

    2022

  21. [29]

    fast data

    Miguel Angel L \'o pez Pe \ n a, Carlos Area Rua, and Sergio Segovia Lozoya. A “fast data” architecture: Dashboard for anomalous traffic analysis in data networks. In 2016 Eleventh International Conference on Digital Information Management (ICDIM) , pages 37--42. IEEE, 2016

    2016

  22. [30]

    A survey of visual analytics for public health

    Bernhard Preim and Kai Lawonn. A survey of visual analytics for public health. In Computer Graphics Forum , volume 39, pages 543--580. Wiley Online Library, 2020

    2020

  23. [31]

    An open repository of real-time covid-19 indicators

    Alex Reinhart, Logan Brooks, Maria Jahja, Aaron Rumack, Jingjing Tang, Sumit Agrawal, Wael Al Saeed, Taylor Arnold, Amartya Basu, Jacob Bien, et al. An open repository of real-time covid-19 indicators. Proceedings of the National Academy of Sciences , 118(51):e2111452118, 2021

    2021

  24. [32]

    Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs

    Henry Rolka, Howard Burkom, Gregory F Cooper, Martin Kulldorff, David Madigan, and Weng-Keen Wong. Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs. Statistics in Medicine , 26(8):1834--1856, 2007

    2007

  25. [33]

    What do we talk about when we talk about dashboards? IEEE transactions on visualization and computer graphics , 25(1):682--692, 2018

    Alper Sarikaya, Michael Correll, Lyn Bartram, Melanie Tory, and Danyel Fisher. What do we talk about when we talk about dashboards? IEEE transactions on visualization and computer graphics , 25(1):682--692, 2018

    2018

  26. [34]

    Statistical challenges facing early outbreak detection in biosurveillance

    Galit Shmueli and Howard Burkom. Statistical challenges facing early outbreak detection in biosurveillance. Technometrics , 52(1):39--51, 2010

    2010

  27. [35]

    A framework for visual analytics of spatio-temporal sensor observations from data streams

    Bolelang H Sibolla, Serena Coetzee, and Terence L Van Zyl. A framework for visual analytics of spatio-temporal sensor observations from data streams. ISPRS International Journal of Geo-Information , 7(12):475, 2018

    2018

  28. [36]

    Anomaly detection in dynamic networks using multi-view time-series hypersphere learning

    Xian Teng, Yu-Ru Lin, and Xidao Wen. Anomaly detection in dynamic networks using multi-view time-series hypersphere learning. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , pages 827--836, 2017

    2017

  29. [37]

    Trafficvis: visualizing organized activity and spatio-temporal patterns for detecting and labeling human trafficking

    Catalina Vajiac, Duen Horng Chau, Andreas Olligschlaeger, Rebecca Mackenzie, Pratheeksha Nair, Meng-Chieh Lee, Yifei Li, Namyong Park, Reihaneh Rabbany, and Christos Faloutsos. Trafficvis: visualizing organized activity and spatio-temporal patterns for detecting and labeling h...

    2022

  30. [38]

    Note on a method for calculating corrected sums of squares and products

    BP Welford. Note on a method for calculating corrected sums of squares and products. Technometrics , 4(3):419--420, 1962

    1962

  31. [39]

    Who hub for pandemic and epidemic intelligence

    WHO. Who hub for pandemic and epidemic intelligence. https://pandemichub.who.int/publications/m/item/the-who-hub-for-pandemic-and-epidemic-intelligence-strategy-paper , Dec. 2022. Accessed: 2023-06-05

    2022

  32. [40]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence '...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.