ArticleEpidemics2018
Using data-driven agent-based models for forecasting emerging infectious diseases.
Article in Epidemics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 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
58 citing papers in PubMed.
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Heterogeneous estimations of non-pharmaceutical mitigation behavior during the COVID-19 pandemic.Scientific data · 2026Article
- A multi-method study evaluating the inference of compartmental model parameters from a generative agent-based model.Infectious Disease Modelling · 2026Article
- Improving outbreak forecasts through model augmentation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Impact of excessive social media use on adolescent depression and its consequences in France: An individual-based microsimulation model.PLoS medicine · 2025Article
- Impact of unequal testing on vaccine effectiveness estimates across two study designs: a simulation study.Nature communications · 2025Article
- Behavior-driven forecasts of neighborhood-level COVID-19 spread in New York City.PLoS computational biology · 2025Article
- Model development to assess the impact of a preventive treatment with sarolaner and moxidectin on Dirofilaria immitis infection dynamics in dogs.Parasites & vectors · 2025Article
- Modeling COVID-19 spread using multi-agent simulation with small-world network approach.BMC public health · 2024Article
- Generating synthetic population for simulating the spatiotemporal dynamics of epidemics.PLoS computational biology · 2024Article
- Article
- Infection prediction in swine populations with machine learning.Scientific reports · 2023Article
- Mobility constraints in segregation models.Scientific reports · 2023Article
- On learning agent-based models from data.Scientific reports · 2023Article
- Human-mediated dispersal drives the spread of the spotted lanternfly (Lycorma delicatula).Scientific reports · 2023Article
- Local protection bubbles: an interpretation of the slowdown in the spread of coronavirus in the city of São Paulo, Brazil, in July 2020.Cadernos de saude publica · 2023Article
- Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario.International journal of environmental research and public health · 2022Article
- The stochasticCommunications in nonlinear science & numerical simulation · 2022Article
- Multiplexed rapid antigen tests developed using multicolored nanoparticles and cross-reactive antibody pairs: Implications for pandemic preparedness.Nano today · 2022Article
- Explicit Calculation of Structural Commutation Relations for Stochastic and Dynamical Graph Grammar Rule Operators in Biological Morphodynamics.Frontiers in systems biology · 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
6 authors.
Funding
Abstract
Producing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models provide a comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper, we describe one such agent-based model framework developed for forecasting the 2014-2015 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refined and adapted for future epidemics, and share the lessons learned over the course of the challenge.
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.