ArticleInfection and drug resistance2025
An Approach to, and Tool for, Predicting the Time Course of an Infectious Disease Outbreak as a Function of Behavioral Interventions.
Article in Infection and drug resistance, 2025. 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: In the early twenty-first century, humanity faced life-threatening infectious diseases, primarily caused by zoonotic pathogens, such as Ebola, Influenza, Middle East Respiratory Syndrome Coronavirus and Severe Acute Respiratory Syndrome Coronavirus. Early warnings based on epidemic models are crucial for preventing and controlling outbreaks. However, most decisions rely on expert opinions rather than robust epidemic model outputs. Implementing epidemic models can be challenging without a strong background in statistical modeling. Methods: This paper presents a simple, user-friendly tool in MATLAB to predict the time course of an infectious disease outbreak using various modified epidemic models. These models incorporate key non-pharmaceutical interventions to limit transmission within communities. Additionally, we introduce improved epidemiological model structures for outbreak control and prevention. To demonstrate the application of our interactive program and discuss user decision-making, we provide an example using key parameters from recent studies. The simulation model was run with varying scenarios, adjusting the effectiveness and coverage of social distancing, hand washing, and face mask usage. Results: Without any intervention, 99.6% of the population could be infected within 100 days. Combining social distancing with 80% coverage of face masks and hand washing reduced transmission and death by 99.96% and 99.98%, respectively, compared to no preventive measures. Conclusion: This interactive computer program aids epidemiologists, public health experts, and decision-makers in understanding and predicting infectious transmission. It is also valuable for generating rapid reports on infectious diseases and outbreak responses where time is a critical. Furthermore, it enhances collaboration between public health stakeholders and modeling professionals, aiming to optimize disease prevention and control strategies during outbreaks or epidemics.
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.