ArticleAdvances in statistical analysis : AStA : a journal of the German Statistical Society2022
On the role of data, statistics and decisions in a pandemic.
Article in Advances in statistical analysis : AStA : a journal of the German Statistical Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
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.
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Who cites it
10 citing papers in PubMed.
- Artificial Intelligence in Infectious Disease Care: Selected Applications in Tuberculosis, Sepsis, and Antimicrobial Stewardship.Diagnostics (Basel, Switzerland) · 2026Review
- Multi-model approach to understand and predict past and future dengue epidemic dynamics.Royal Society open science · 2025Article
- Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a European survey-based modellers' perspective, 2020 to 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Article
- A unified framework for diagnostic test development and evaluation during outbreaks of emerging infections.Communications medicine · 2024Review
- Compartmental modeling for pandemic data analysis: The gap between statistics and models.Heliyon · 2024Article
- Legislative incapacity and underreporting of COVID-19 mortality.Preventive medicine reports · 2024Article
- COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020-2021.BMC public health · 2023Article
- The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.PLOS global public health · 2023Review
- Editorial: Big data and machine learning in sociology.Frontiers in sociology · 2023Article
- Discussion onAdvances in statistical analysis : AStA : a journal of the German Statistical Society · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.
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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.