ArticleJournal of theoretical biology2021
Optimal strategies for social distancing and testing to control COVID-19.
Article in Journal of theoretical biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 83 citations in OpenAlex.
- Economic Analyses of COVID-19 Interventions: A Narrative Review of Global Evidence.Healthcare (Basel, Switzerland) · 2025Review
- From node to network: weaving a global perspective on efficacy and costs of non-pharmaceutical interventions.Scientific reports · 2025Article
- Modeling and control of COVID-19 disease using deep reinforcement learning method.Medical & biological engineering & computing · 2024Article
- Computing optimal drug dosing regarding efficacy and safety: the enhanced OptiDose method in NONMEM.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- The optimal spatially-dependent control measures to effectively and economically eliminate emerging infectious diseases.PLoS computational biology · 2024Article
- Mathematical modeling of contact tracing and stability analysis to inform its impact on disease outbreaks; an application to COVID-19.Infectious Disease Modelling · 2024Article
- Optimal social distancing in epidemic control: cost prioritization, adherence and insights into preparedness principles.Scientific reports · 2024Article
- Self-Diagnosis of SARS-CoV-2 from Saliva Samples at Home: Isothermal Amplification Enabled by Do-It-Yourself Portable Incubators and Laminated Poly-ethyl Sulfonate Membranes.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- On fast simulation of dynamical system with neural vector enhanced numerical solver.Scientific reports · 2023Article
- Modelling COVID-19 pandemic control strategies in metropolitan and rural health districts in New South Wales, Australia.Scientific reports · 2023Article
- Epidemic Management via Imperfect Testing: A Multi-criterial Perspective.Bulletin of mathematical biology · 2023Article
- Computing optimal drug dosing with OptiDose: implementation in NONMEM.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Modeling social, economic, and health perspectives for optimal pandemic policy decision-making.Socio-economic planning sciences · 2023Article
- Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures.Health care management science · 2023Article
- Variation in leisure sport conflicts and coping strategies depending on participation type and proximity during the COVID-19 pandemic.Frontiers in public health · 2023Article
- Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations.European journal of operational research · 2023Article
- A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws.European journal of operational research · 2023Article
- Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature.Yonsei medical journal · 2023Review
- Voluntary vaccination may not stop monkeypox outbreak: A game-theoretic model.PLoS neglected tropical diseases · 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
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coronavirus disease (COVID-19) has infected more than 79 million individuals, with 1.7 million deaths worldwide. Several countries have implemented social distancing and testing policies with contact tracing as a measure to flatten the curve of the ongoing pandemic. Optimizing these control measures is urgent given the substantial societal and economic impacts associated with infection and interventions. To determine the optimal social distancing and testing strategies, we developed a mathematical model of COVID-19 transmission and applied optimal control theory, identifying the best approach to reduce the epidemiological burden of COVID-19 at a minimal cost. The results demonstrate that testing as a standalone optimal strategy does not have a significant effect on the final size of an epidemic, but it would delay the peak of the pandemic. If social distancing is the sole control strategy, it would be optimal to gradually increase the level of social distancing as the incidence curve of COVID-19 grows, and relax the measures after the curve has reached its peak. Compared with a single strategy, combined social distancing and testing strategies are demonstrated to be more efficient at reducing the disease burden, and they can delay the peak of the disease. To optimize these strategies, testing should be maintained at a maximum level in the early phases and after the peak of the epidemic, whereas social distancing should be intensified when the prevalence of the disease is greater than 15%. Accordingly, public health agencies should implement early testing and switch to social distancing when the incidence level begins to increase. After the peak of the pandemic, it would be optimal to gradually relax social distancing and switch back to testing.
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.