ArticleInfectious diseases and therapy2026
Calculating the Probability that a Previously Susceptible Individual is Infected as a Function of Time Following Exposure to SARS-CoV-2.
Article in Infectious diseases and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
introductionReliable assessment of disease state probabilities for an individual following a specific exposure event, such as an occupational exposure, is critical for managing isolation and quarantine and reducing onward transmission to susceptible individuals. Such assessments are particularly important for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), where infection, testing, and infectiousness vary substantially across individuals and time since exposure.
methodsWe present a method, accompanying software programs, and a publicly available website for calculating the probability that an individual is in each disease state immediately following an exposure event that may or may not have resulted in transmission of SARS-CoV-2. The framework integrates timing of exposure, test type and timing, and symptom status to estimate probabilities of latent infection, infectiousness, recovery, or no infection.
resultsWe illustrate the utility of this approach by calculating: (i) the time at which an exposed individual's risk of being infectious falls below an acceptable threshold; (ii) the benefit of a second test for asymptomatic individuals with an initial negative test; (iii) the value of polymerase chain reaction (PCR) and antigen testing for case counting; and (iv) the time at which the risk that an infected individual remains infectious becomes comparable to background population risk. The results demonstrate that test interpretation should not be done naively: a negative test may reflect absence of transmission, a false-negative result, rapid resolution of infection, or an unusually prolonged latent period, each with distinct implications for risk management.
conclusionsAccurate differentiation among possible disease states following exposure is essential for informed public health decision-making. Our software provides a rigorous, transparent means to assess and clearly communicate state probabilities, enabling more nuanced interpretation of test results and better-supported decisions regarding isolation, quarantine, and testing strategies.
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.