ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2018
Optimal treatment allocations in space and time for on-line control of an emerging infectious disease.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 34 citations in OpenAlex.
- Spatial aggregation with respect to a population distribution: Impact on inference.Spatial statistics · 2022Article
- A spatiotemporal recommendation engine for malaria control.Biostatistics (Oxford, England) · 2022Article
- Value of Information Analysis in Models to Inform Health Policy.Annual review of statistics and its application · 2022Article
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.International statistical review = Revue internationale de statistique · 2021Article
- Estimating optimal dynamic treatment strategies under resource constraints using dynamic marginal structural models.Statistics in medicine · 2021Article
- Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.Statistics in medicine · 2020Article
- Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).Psychological methods · 2020Article
- Precision Medicine.Annual review of statistics and its application · 2019Article
- Comment on Laber et al.Journal of the Royal Statistical Society. Series C, Applied statistics · 2018Article
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 at 2 institutions in 1 country.
Funding
Abstract
A key component in controlling the spread of an epidemic is deciding where, when and to whom to apply an intervention. We develop a framework for using data to inform these decisions in realtime. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up-to-date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g. the number of uninfected locations, the geographic footprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategy for an emerging infectious disease is challenging because spatial proximity induces interference between locations, the number of possible allocations is exponential in the number of locations, and because disease dynamics and intervention effectiveness are unknown at out-break. We derive a Bayesian on-line estimator of the optimal allocation strategy that combines simulation-optimization with Thompson sampling. The estimator proposed performs favourably in simulation experiments. This work is motivated by and illustrated using data on the spread of white nose syndrome, which is a highly fatal infectious disease devastating bat populations in North America.
Indexed as
Identifiers
30662097PMC6334759W2883353135What 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.