ReviewExtreme Mechanics Letters2020
Data-driven modeling of COVID-19-Lessons learned.
Review in Extreme Mechanics Letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 72 citations in OpenAlex.
- The Risk from Reopening International Travel During a Disease X Pandemic: A Case Study of SARS-CoV-2.Risk analysis : an official publication of the Society for Risk Analysis · 2026Article
- Combinatorial (bio-geo-temporal) and non-combinatorial analysis of the COVID-19 dissemination that affected Georgia (the country) in 2021.Frontiers in public health · 2026Article
- From Knowledge to Habits: Changes in COVID-19 Health Attitudes, Practices, and Sources of Information (HAPS) at Qatar University Post-Pandemic.Health science reports · 2025Article
- A rubric for assessing conformance to the Ten Rules for credible practice of modeling and simulation in healthcare.PloS one · 2025Article
- Compartmental modeling for pandemic data analysis: The gap between statistics and models.Heliyon · 2024Article
- Analysis of the yearly transition function in measles disease modeling.Statistics in medicine · 2024Article
- Exploring the dynamics of monkeypox transmission with data-driven methods and a deterministic model.Frontiers in epidemiology · 2024Article
- A two-phase fluid model for epidemic flow.Infectious Disease Modelling · 2023Article
- Epidemic dynamics in census-calibrated modular contact network.Network modeling and analysis in health informatics and bioinformatics · 2023Article
- Model-Based Analysis of SARS-CoV-2 Infections, Hospitalization and Outcome in Germany, the Federal States and Districts.Viruses · 2022Article
- Building Process-Oriented Data Science Solutions for Real-World Healthcare.International journal of environmental research and public health · 2022Article
- Simulating the effects of mobility restrictions in the spread of SARS-CoV-2 in metropolitan areas in Portugal.PloS one · 2022Article
- Mathematical modeling of the outbreak of COVID-19.Network modeling and analysis in health informatics and bioinformatics · 2022Article
- A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility.Nonlinear dynamics · 2022Article
- Mathematical epidemiologic and simulation modelling of first wave COVID-19 in Malaysia.Scientific reports · 2021Article
- A simple method to describe the COVID-19 trajectory and dynamics in any country based on Johnson cumulative density function fitting.Scientific reports · 2021Article
- Global and local mobility as a barometer for COVID-19 dynamics.Biomechanics and modeling in mechanobiology · 2021Article
- Effects of B.1.1.7 and B.1.351 on COVID-19 Dynamics: A Campus Reopening Study.Archives of computational methods in engineering : state of the art reviews · 2021Article
- Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics.PloS one · 2021Article
- COVID-19 dynamics considering the influence of hospital infrastructure: an investigation into Brazilian scenarios.Nonlinear dynamics · 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
1 author at 1 institution in 1 country.
Funding
Abstract
Understanding the outbreak dynamics of COVID-19 through the lens of mathematical models is an elusive but significant goal. Within only half a year, the COVID-19 pandemic has resulted in more than 19 million reported cases across 188 countries with more than 700,000 deaths worldwide. Unlike any other disease in history, COVID-19 has generated an unprecedented volume of data, well documented, continuously updated, and broadly available to the general public. Yet, the precise role of mathematical modeling in providing quantitative insight into the COVID-19 pandemic remains a topic of ongoing debate. Here we discuss the lessons learned from six month of modeling COVID-19. We highlight the early success of classical models for infectious diseases and show why these models fail to predict the current outbreak dynamics of COVID-19. We illustrate how data-driven modeling can integrate classical epidemiology modeling and machine learning to infer critical disease parameters-in real time-from reported case data to make informed predictions and guide political decision making. We critically discuss questions that these models can and cannot answer and showcase controversial decisions around the early outbreak dynamics, outbreak control, and exit strategies. We anticipate that this summary will stimulate discussion within the modeling community and help provide guidelines for robust mathematical models to understand and manage the COVID-19 pandemic. EML webinar speakers, videos, and overviews are updated at https://imechanica.org/node/24098.
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.