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the claim

Absolute risk of death from infectious diseases is defined by specific epidemiological parameters

the verdict
SUPPORTED
the evidence backs this
Recorded sources
5 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that absolute risk of death from infectious diseases is defined by specific epidemiological parameters?

Yes. Multiple studies demonstrate that the absolute risk of death from infectious diseases is determined and predicted by specific epidemiological parameters and clinical risk factors.

Verdict: SUPPORTED · 5 sources for, 0 against · Last checked

The analysis

The retrieved papers consistently utilize epidemiological parameters (such as transmission rates, mortality rates, incubation periods, viral loads, and patient-level clinical risk factors) to define and predict the absolute risk of death across various infectious diseases, directly supporting the claim.

Evidence for · 5
COVID-19 Mortality Prediction among Patients using Epidemiological parameters: An Ensemble Machine Learning Approach
2021
Demonstrates that epidemiological parameters and patient features predict COVID-19 mortality risk.
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Chadaga, K., Prabhu, S., Umakanth, S., K, V., Sampathila, N., P., R., & K, K. (2021). COVID-19 Mortality Prediction among Patients using Epidemiological parameters: An Ensemble Machine Learning Approach. doi.org. https://doi.org/10.30919/es8d579

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More for · 4
Exact Bayesian inference of epidemiological parameters from mortality data: application to African swine fever virus
2022
Shows how mortality data is used to infer key epidemiological parameters for disease outbreaks.
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Ewing, D., Pooley, C., Gamado, K. M., Porphyre, T., & Marion, G. (2022). Exact Bayesian inference of epidemiological parameters from mortality data: application to African swine fever virus. doi.org. https://doi.org/10.1098/rsif.2022.0013

Actuarial Analysis of an Infectious Disease Insurance Based on an SEIARD Epidemiological Model
2025
Integrates disease-induced mortality and epidemiological parameters into actuarial risk models.
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Zinihi, A., Ehrhardt, M., & Ammi, M. R. S. (2025). Actuarial Analysis of an Infectious Disease Insurance Based on an SEIARD Epidemiological Model. doi.org. https://doi.org/10.1080/10920277.2026.2664588

Retrospective Analysis of Severe Fever With Thrombocytopenia Syndrome and Construction of a Nomogram Prediction Model for Mortality Risk Factors
2025
Identifies specific independent mortality risk factors and parameters in infectious disease outcomes.
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Chen, G., Du, Y., Ma, X., Liang, Y., Chen, A., Wei, J., Wu, J., Qian, W., Xie, S., Yan, Y., Hu, Z., Zheng, Y., Tian, M., & Yi, C. (2025). Retrospective Analysis of Severe Fever With Thrombocytopenia Syndrome and Construction of a Nomogram Prediction Model for Mortality Risk Factors. doi.org. https://doi.org/10.1093/ofid/ofaf318

Determinants of variation in epidemiological parameters for African Swine Fever virus
2025
Compiles and analyzes epidemiological parameters like mortality rate and incubation period for infectious diseases.
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Talbert, M., Feuka, A. B., Miller, R. S., & Pepin, K. M. (2025). Determinants of variation in epidemiological parameters for African Swine Fever virus. doi.org. https://doi.org/10.1101/2025.07.21.665936

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What does the evidence say?

The retrieved papers consistently utilize epidemiological parameters (such as transmission rates, mortality rates, incubation periods, viral loads, and patient-level clinical risk factors) to define and predict the absolute risk of death across various infectious diseases, directly supporting the claim.

Receipt History
first checked04 Aug 2026
judged → SUPPORTED · 8404 Aug 2026
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