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Open research questions in Data-Driven Disease Surveillance

71 unresolved questions extracted from the limitations and future-work sections of 463 Data-Driven Disease Surveillance papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The computational burden associated with large survey data. The need for a method to overcome administration challenges associated with the gold standard. The need for a method to construct a simpler diagnostic test that can accurately classify subjects into one of two categories.

    Optimal Cut-Points for Multiple Diagnostic Variables in Large and Complex Health Surveys · 2026 · DOI

  • The scarcity of data available for trajectory selection early in the season. The complexity of the season, particularly at the onset and peak. The need for efficient mechanisms to identify the most consistent trajectories with emerging observations.

    Adaptive multi-model ensembles for improved epidemic projections and decision support · 2026 · DOI

  • The complexity of infectious disease surveillance. The need for timely and accurate detection of infectious disease outbreaks.

    Pandemic preparedness: potential of routine general practice data for infectious disease early signal detection · 2026 · DOI

  • Although climatic factors are known to trigger flares, global real-time epidemiological data remain scarce.

    Atopic dermatitis web searches track anomalous atmospheric humidity rather than chronic dryness: a bihemispheric infodemiological analysis · 2026 · DOI

  • Many patients notice seasonal worsening, but whether this pattern is consistent globally had not been systematically studied.

    Atopic dermatitis web searches track anomalous atmospheric humidity rather than chronic dryness: a bihemispheric infodemiological analysis · 2026 · DOI

  • Integrating novel digital data streams (eg, internet searches and human mobility) with traditional surveillance can improve accuracy, but optimal modeling frameworks are underexplored.

    Influenza-Like Illness Forecasting Using Multisource Data: Comparative Deep Learning Study · 2026 · DOI

  • Background The disease burden of liver fibrosis-cirrhosis complicated with pneumonia-influenza has become a major public health concern in the world, yet the long-term trends, and core influencing factors remain unclear.

    Trends and factors associated with liver fibrosis-cirrhosis complicated with pneumonia-influenza mortality in U.S. Counties: a CDC WONDER and CHR database analysis · 2026 · DOI

  • Data on SARS-CoV-2 were obtained from the national Covid-19 surveillance system; data on influenza and RSV were limited to a sentinel hospital surveillance programme.

    Multi-pathogen situational assessment and forecasting of respiratory disease in Aotearoa New Zealand · 2026 · DOI

  • The satellite data products utilized in training have a native latency of approximately 24–48 hours relative to real-time, which constrains the operational refresh frequency of the alert system.

    Machine Learning-Based Early Warning System for Predicting Weather-Driven Disease Outbreaks and Public Health Risk Assessment using Climate Parameters · 2026 · DOI

  • Our ensemble COVID-19 forecasts were produced under contract with the Australian Government Department of Health and Aged Care, and participation was limited to a small number of institutes.

    Ensemble forecasts of COVID-19 activity to support Australia’s pandemic response: 2020–22 · 2026 · DOI

  • Exploring the use of deep learning methods for news classification and trend detection. Developing more efficient and effective algorithms for trend detection. Applying the system to other domains, such as social media and blogs.

    Daily News Classification and Trend Analysis System Using Machine Learning · 2026 · DOI

  • Few systems provide an integrated, validated pipeline that handles both news classification and trend detection. Systems designed for social media do not account for the long text, structured format, and editorial standards of professional news articles.

    Daily News Classification and Trend Analysis System Using Machine Learning · 2026 · DOI

  • There is a gap in the use of real-time data and advanced forecasting techniques to understand spatial and temporal trends in cancer incidence. The study identifies a need for more accurate projections of cancer incidence rates in India.

    Spatio temporal projection of cancer incidence in India using artificial neural networks · 2026 · DOI

  • While this study provides valuable insights, few limitations should be considered. The age wise CCIRs could not be computed and projected for the future since they were not given in the raw data. gender, cancer type, or urban/rural distinctions were not considered. The exclusion of island regions, such as Andaman & Nicobar and Lakshadweep, due to the absence of neighbouring regions for computing the spatial weights matrix, may limit the generalizability of the findings to these areas. Additionally, the analysis relied on secondary data from the OGD platform, which may be subject to underreporting. Moreover, the study’s temporal scope was limited to seven years (2018–2024), which may not fully capture long-term trends in cancer incidence and distribution.

    Spatio temporal projection of cancer incidence in India using artificial neural networks · 2026 · DOI

  • National summaries often conceal district-level risks. There is a need for more targeted subnational planning to address the uneven disruptions to routine immunization and outbreak control.

    Conflict-aware geospatial diagnostics to inform subnational immunization microplanning in Somalia from 2015 to 2023 · 2026 · DOI

  • The dataset has coverage gaps due to technical issues, resulting in approximately 14 days of missing data. Search volume is provided by Google in buckets rather than exact counts, limiting precision in quantitative analyses. Google's trend identification algorithm is proprietary, meaning the exact clustering of related queries is not transparent.

    GoogleTrendArchive: A Year-Long Archive of Real-Time Web Search Trends Worldwide · 2026 · DOI

  • Future research can use the GoogleTrendArchive dataset to study information diffusion patterns and collective attention dynamics. Future research can use the dataset to inform crisis response and emergency management. Future research can use the dataset to study cross-cultural attention dynamics and the temporal evolution of collective information-seeking at a global scale.

    GoogleTrendArchive: A Year-Long Archive of Real-Time Web Search Trends Worldwide · 2026 · DOI

  • Critical gaps persist in laboratory capacity, workforce training, and real-time data sharing. Data quality deficits remain the most persistent challenge. The incorporation of digital technologies and advanced data analytics is limited.

    EPIDEMIOLOGICAL SURVEILLANCE AS A TOOL FOR EFFECTIVE DISEASE CONTROL AND OUTBREAK RESPONSE · 2026 · DOI

  • infrastructure, technology, for www.ejpmr.com │ Vol 13, Issue 6, 2026. │ ISO 9001:2015 Certified Journal │ 408 Micheal et al. European Journal of Pharmaceutical and Medical Research Table 4: Domain-specific barriers to effective epidemiological surveillance and evidence-based recommendations for strengthening surveillance systems.

    EPIDEMIOLOGICAL SURVEILLANCE AS A TOOL FOR EFFECTIVE DISEASE CONTROL AND OUTBREAK RESPONSE · 2026 · DOI

  • There is a need to monitor the impact of public awareness activities on cystic fibrosis in Ireland. The study aims to address this gap by analyzing Google Trends data.

    Utilizing internet search activity to assess public awareness of cystic fibrosis in Ireland · 2026 · DOI

  • Current approaches to food security monitoring rely largely on structured survey data. There is a need for a multi-scale approach that can extract fine-grained spatial and temporal dynamics from unstructured textual sources. The proposed framework aims to address this gap.

    Spatio-Temporal Knowledge Graph from Unstructured Texts: A Multi-Scale Approach for Food Security Monitoring · 2026 · DOI

  • The absence of attribution evidence generated within Africa is a significant gap. The lack of quality observational data and skilled expertise are critical bottlenecks.

    Operationalizing extreme event attribution in data-scarce regions: why and how · 2026 · DOI

  • There was a need for a federated network for public health surveillance and research in the state of Minnesota. The existing health care systems in Minnesota lacked a collaborative approach to share data and inform policy and practice.

    Design and Implementation of a State‐Wide Network for Near Real‐Time Public Health Surveillance and Research: The Minnesota Electronic Health Record Consortium Experience · 2026 · DOI

  • Prior work has focused on optimizing sampling strategies, but there is a need for numerical evaluation of change point detection algorithms. There is a gap in the application of change point detection methods in the Agricultural Quarantine Inspection Monitoring program.

    A comparison of change point detection methods for pest outbreak detection · 2026 · DOI

  • The traditional approaches to financial markets assume that information is reflected in prices instantly, but this assumption has been criticized. There is a need for a more nuanced understanding of the relationship between investor interest and financial markets.

    Yatırımcı İlgisi ile BIST 100 Arasındaki İlişki: Google Trends Verileri Üzerinden Ampirik Analiz · 2026 · DOI

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71 open questions have been extracted from the limitations and future-work passages of 463 Data-Driven Disease Surveillance papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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