Open research questions in Data-Driven Disease Surveillance
115 gap statements mined from Data-Driven Disease Surveillance papers in our 4.5M-paper local library, which holds 759 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.
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What the literature leaves open
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 · DOIWhile 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.
Spatio temporal projection of cancer incidence in India using artificial neural networks · 2026 · DOIThe 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.
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.
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 · DOIThere 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 · DOIPrior 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.
The complexity of internal processes in machine learning models can make them unclear to users. The lack of interpretability in machine learning models can be a challenge. The need to combine regression modelling and neural networks to uncover causal pathways underlying societal behaviour.
Public announcements and mask use during COVID-19 bridging regression and machine learning · 2026 · DOIThere is a lack of direct, visual evidence that mask-related policies led to appropriate mask-wearing behaviour. There is a need to understand how mask-related policies impact mask-wearing behaviour. Prior work has not addressed the impact of public announcements on mask-wearing behaviour.
Public announcements and mask use during COVID-19 bridging regression and machine learning · 2026 · DOIThe 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 · DOIProvincial-level spatial clustering patterns have not been comprehensively characterized over extended time series. There is a need for spatially targeted surveillance and intervention strategies to reduce the burden of food poisoning in Thailand.
Spatial Autocorrelation and High-Risk Area Identification of Food Poisoning in Thailand, 2003–2022 · 2026 · DOIThe lack of real-time intelligence infrastructure for epidemic events in urban centers. The absence of health and epidemic intelligence in current smart city frameworks.
EpiCity: An AI-Enabled Epidemic-Aware Smart City Health Intelligence Framework for Sustainable Urban Planning · 2026 · DOIDespite the global importance of HIV-1 subtype C, a global-scale GIS characterization of its geographic clustering and the temporal persistence of hotspots is lacking, and systematic cross-method comparisons are scarce.
Comparative Spatiotemporal Analysis of Global HIV-1 Subtype C Hotspots: Applying Bayesian Hierarchical Modeling, SaTScan, and Getis-Ord Gi* Statistics · 2026 · DOIThe diagnosis of Cryptosporidium infection is challenging in resource-limited settings. The development of clinical prediction models requires large datasets and advanced machine learning techniques. The models must be validated in other populations to ensure generalizability.
Derivation and validation of clinical prediction models for Cryptosporidium -attributed acute diarrhea in African children · 2026 · DOIThere is a lack of clinical prediction models for Cryptosporidium-attributed acute diarrhea. There is a need for models that can identify high-risk populations and target interventions.
Derivation and validation of clinical prediction models for Cryptosporidium -attributed acute diarrhea in African children · 2026 · DOIThe lack of understanding of the temporal behavior of bacterial blight severity in pomegranate cultivation. The need for effective disease surveillance and management strategies. The limited accuracy of existing forecasting models for bacterial blight severity.
Machine learning and time-series approaches for forecasting bacterial blight of pomegranate · 2026 · DOIThe lack of robust policy frameworks and cross-sector collaboration to support the use of non-traditional urban data sources. The need for standardized protocols and reliable long-term investment in infrastructure. The limitations of traditional surveillance methods in meeting the demands of contemporary urban environments.
Leveraging Non-Traditional Urban Data Sources for Public Health Surveillance: A Review of Emerging Data Streams, Analytical Approaches, and Implementation Challenges · 2026 · DOIThe reason for this has not been established, but we hypothesise that mobility trends on longer time scales are sufficient to describe the transmission dynamics, and at the same time it might be easier to train the model when the mobility changes less frequently. 4) suggesting that the bias introduced by the Swedish data is limited.
Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data · 2026 · DOIThe 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 · DOIOur 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. Forecasts performed well in periods of stable behaviour, but struggled at longer horizons around inflection points, and individual models varied widely in their ability to account for new COVID-19 variants.
Ensemble forecasts of COVID-19 activity to support Australia’s pandemic response: 2020–22 · 2026 · DOIFew 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.
The study was limited to data from 4 out of 6 general practices. The study did not assess external completeness, external validity, sensitivity, specificity, and other surveillance attributes.
Pandemic preparedness: potential of routine general practice data for infectious disease early signal detection · 2026 · DOIExploring 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.
To apply the adaptive ensemble approach to other infectious diseases and public health threats. To further evaluate the performance of the adaptive ensemble approach in different scenarios and contexts.
The development of national interoperability protocols. The establishment of clear standards for data integration that balance innovation with equity. The exploration of new non-traditional urban data sources and analytical approaches.
Leveraging Non-Traditional Urban Data Sources for Public Health Surveillance: A Review of Emerging Data Streams, Analytical Approaches, and Implementation Challenges · 2026 · DOI
Most-cited papers in Data-Driven Disease Surveillance
- A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker) · Nature Human Behaviour · 2021 · 3,523 citations
- Detecting influenza epidemics using search engine query data · Nature · 2008 · 3,366 citations
- The Parable of Google Flu: Traps in Big Data Analysis · Science · 2014 · 1,915 citations
- Spatial Point Pattern Analysis and Its Application in Geographical Epidemiology · Transactions of the Institute of British Geographers · 1996 · 565 citations
- Spatial Epidemiology: Current Approaches and Future Challenges · Environmental Health Perspectives · 2004 · 540 citations
- Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico. · American Journal of Public Health · 1998 · 524 citations
- On Academics : OpenEpi: A Web-Based Epidemiologic and Statistical Calculator for Public Health · Public Health Reports · 2009 · 518 citations
- Invited Commentary: "Race," Racism, and the Practice of Epidemiology · American Journal of Epidemiology · 2001 · 433 citations
- Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics · Nature Human Behaviour · 2020 · 431 citations
- Forecasting private consumption: survey-based indicators vs. Google trends · Journal of Forecasting · 2011 · 417 citations
Most recent work
- Biological data governance in an age of AI · Science · 2026
- Unexplained Pauses in Centers for Disease Control and Prevention Surveillance: Erosion of the Public Evidence Base for Health Policy · Annals of Internal Medicine · 2026
- Global approaches to infectious disease surveillance and modeling · Nature Medicine · 2026
- serocalculator, an R package for estimating seroincidence from cross-sectional serological data · medRxiv · 2026
- Trends in asthma and pneumonia-related mortality in the United States: a CDC wonder database analysis (1999–2023) · Frontiers in Medicine · 2026
- Restoring the forecasting power of Google Trends with statistical preprocessing · International Journal of Forecasting · 2026
- Tampering With Evidence: Selective Silencing of Timely Public Health Data · Annals of Internal Medicine · 2026
- Information exposure and economic losses during food safety outbreaks: Evidence from onion markets and Google Trends · Food Policy · 2026
- Analytical Derivations of the Neighborhood Effect Averaging Problem ( NEAP ) and Its Two Variants: From Representative Samples to Unbiased Health Effect Sizes and Critical Insights Into Health Geography · Geographical Analysis · 2026
- AI Agents and Epidemic Intelligence on Respiratory Infectious Diseases: Toward a Conceptual Framework Integrating Decision Support · Journal of Medical Internet Research · 2026
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