ArticleIEEE internet of things journal2021
Disaster and Pandemic Management Using Machine Learning: A Survey.
Article in IEEE internet of things journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI applications in disaster governance with health approach: A scoping review.Archives of public health = Archives belges de sante publique · 2025Pooled it
- Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases-A Systematic Literature Review.International journal of environmental research and public health · 2021Pooled it
- AI in Disaster Medicine: Scoping Review of Methods, Validation, and System Integration.JMIR AI · 2026Review
- Support Systems of Clinical Decisions in the Triage of the Emergency Department Using Artificial Intelligence: The Efficiency to Support Triage.Acta medica Lituanic · 2023Article
- Bounding pandemic spread by heat spread.Journal of engineering mathematics · 2023Article
- A machine learning and blockchain based secure and cost-effective framework for minor medical consultations.Sustainable computing : informatics and systems · 2022Article
- Toward smart diagnosis of pandemic infectious diseases using wastewater-based epidemiology.Trends in analytical chemistry : TRAC · 2022Review
- Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses.Interdisciplinary sciences, computational life sciences · 2022Article
- Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management.Neural computing & applications · 2022Article
- Smart healthcare IoT applications based on fog computing: architecture, applications and challenges.Complex & intelligent systems · 2022Article
- Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction.Journal of personalized medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.
Indexed as
Identifiers
What OpenQuestion holds
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.