ArticlePopulation health metrics2025
Modeling COVID-19 response in Cuba: a hybrid approach combining agent-based modeling and time series analysis.
Article in Population health metrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
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Who cites it
2 citing papers in PubMed.
- Revealing institutional vulnerabilities in Nordic health systems through multidimensional scaling of COVID-19 mortality data.Scientific reports · 2026Article
- A hybrid STL-LightGBM framework with probabilistic forecasting for Influenza A incidence in the post-pandemic Saudi Arabia.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has disproportionately impacted vulnerable populations, such as low-income households, exacerbating existing health and economic challenges. In Cuba, the crisis exposed the effects of long-standing economic difficulties, worsened by sanctions, but the country's robust public health system and independent vaccine development enabled an effective response. This study addresses the gap in understanding how socio-economic factors and individual behaviors interact to influence disease spread. It proposes a hybrid, efficient, and parsimonious model combining ABM (Agent-Based Modeling) and ARIMAX (AutoRegressive Integrated Moving Average with eXogenous variables) time series analysis to forecast COVID-19 cases, offering valuable insights for policymakers to tailor interventions and enhance crisis management.
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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.