Detecting Interactions Among Pathogens Using Likelihood-Based Inference
Author Information
Author(s): Shrestha Sourya, King Aaron A., Rohani Pejman
Primary Institution: University of Michigan
Hypothesis
Can a likelihood-based inference framework accurately detect and quantify the presence and nature of pathogen interactions from epidemiological data?
Conclusion
The study demonstrates that likelihood-based inference can effectively identify and quantify interactions between pathogens, even in the presence of noise and stochasticity.
Supporting Evidence
- Likelihood-based inference can accurately detect pathogen interactions even with noisy data.
- Stronger and longer-lasting interactions are more easily quantified.
- Phase relationships alone are unreliable indicators of pathogen interactions.
Takeaway
This study shows that scientists can use data from sick people to figure out how different germs interact with each other, which helps in understanding diseases better.
Methodology
The study used a likelihood-based inference framework to analyze simulated epidemiological data for detecting pathogen interactions.
Potential Biases
Potential biases from under-reporting and aggregation of data were considered, but the method showed robustness against these issues.
Limitations
The approach may be limited by assumptions of known epidemiological parameters and the quality of the data used.
Digital Object Identifier (DOI)
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