ReviewJournal of the Indian Institute of Science2020
Data-Driven Modeling for Different Stages of Pandemic Response.
Review in Journal of the Indian Institute of Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Spatio-temporal forecasting of COVID-19 cases in the Netherlands for source and contact tracing.International journal of population data science · 2025Article
- An effective drift-diffusion model for pandemic propagation and uncertainty prediction.Biophysical reports · 2024Article
- Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation.Infectious Disease Modelling · 2024Article
- Real-Time Analysis of Predictors of COVID-19 Infection Spread in Countries in the European Union Through a New Tool.International journal of public health · 2022Article
- Rigorous Policy-Making Amid COVID-19 and Beyond: Literature Review and Critical Insights.International journal of environmental research and public health · 2021Review
- The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces.International journal of environmental research and public health · 2021Article
- A Whirlwind Tour of Complex Systems.Journal of the Indian Institute of Science · 2021Review
- Mass Testing and Proactiveness Affect Epidemic Spreading.Journal of the Indian Institute of Science · 2021Review
Corrections and comments
- Update of
Authors and funding
6 authors.
Funding
Abstract
Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who are at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision-making. As different countries and regions go through phases of the pandemic, the questions and data availability also change. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.
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.