ReviewAnnual reviews in control2021
Data-driven methods for present and future pandemics: Monitoring, modelling and managing.
Review in Annual reviews in control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Performance of Routine Disease Surveillance in Sub-Saharan Africa (2010-2024): A Systematic Review and Meta-Analysis of Core Reporting and Detection Indicators.Health science reports · 2026Review
- Rapid Epidemiological Data Collection on Social Media for COVID-19: Comparative Study Between Online Surveys and Conventional Cohorts.Journal of medical Internet research · 2026Observational
- Exploring novel kinetics of automated HMicrobiology spectrum · 2026Article
- A dual-branch deep learning framework for tiered early warning of COVID-19 utilizing wastewater data.Journal of water and health · 2026Article
- Dynamical behavior analysis of 2-control strategies on tuberculosis model.PLOS global public health · 2026Article
- A laboratory-focussed desk review of health systems in Uganda, Kenya, and the UK to respond to current and future pandemics.Journal of global health · 2025Review
- Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models.Healthcare (Basel, Switzerland) · 2023Article
- COVID-19 multiwaves as multiphase percolation: a general N-sigmoidal equation to model the spread.European physical journal plus · 2023Article
- Climate Change Influences the Spread of African Swine Fever Virus.Veterinary sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.