Applications of sensitivity analysis in epidemiology

Sensitivity analysis studies the relation between the uncertainty in a model-based the inference and the uncertainties in the model assumptions.[1][2] Sensitivity analysis can play an important role in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study.[3] It is also important in all mathematical modelling studies of epidemics.[4]

Sensitivity analysis can be used in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study.[3][5] The use of sensitivity analysis in mathematical modelling of infectious disease is suggested in [4] on the Coronavirus disease 2019 outbreak. Given the significant uncertainty at play, the use of sensitivity analysis to apportion the output uncertainty into input parameters is crucial in the context of Decision-making. Examples of applications of sensitivity analysis to modelling of COVID-19 are [6] and.[7] in particular, the time of intervention time in containing the pandemic spread is identified as a key parameter.

References
  1. Saltelli, A. (2002). "Sensitivity Analysis for Importance Assessment". Risk Analysis. 22 (3): 1–12. CiteSeerX 10.1.1.194.7359. doi:10.1111/0272-4332.00040. PMID 12088235. S2CID 62191596.
  2. Saltelli, A.; Ratto, M.; Andres, T.; Campolongo, F.; Cariboni, J.; Gatelli, D.; Saisana, M.; Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons.
  3. Ding, Peng; VanderWeele, Tyler J., 2016, Sensitivity Analysis Without Assumptions, Epidemiology, Volume 27 - Issue 3 - p 368-377
  4. Saltelli, A.; Bammer, G.; Bruno, I.; Charters, E.; Di Fiore, M.; Didier, E.; Espeland Nelson, W.; Kay, J.; Lo Piano, S.; Mayo, D.; Pielke Jr, R.; Portaluri, T.; Porter, T.M.; Puy, A.; Rafols, I.; Ravetz, J.R.; Reinert, E.; Sarewitz, D.; Stark, P.B.; Stirling, A.; van der Sluijs, J.; Vineis, P. (2020). "Five ways to ensure that models serve society: a manifesto". Nature. 582 (7813): 482–484. Bibcode:2020Natur.582..482S. doi:10.1038/d41586-020-01812-9. hdl:1885/219031. PMID 32581374.
  5. Joseph AC Delaney, John D Seeger, 2013, Chapter 11, Sensitivity Analysis, in Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide, Velentgas P, Dreyer NA, Nourjah P, et al., editors, Agency for Healthcare Research and Quality (US); Publication No.: 12(13)-EHC099.
  6. Arino, J.; Portet, S. (2020). "A simple model for COVID-19". Infectious Disease Modelling. 5: 309–315. doi:10.1016/j.idm.2020.04.002. PMC 7186130. PMID 32346663.
  7. Borgonovo, E.; Lu, X. (2020). "Is Time to Intervention in the COVID-19 Outbreak Really Important? A Global Sensitivity Analysis Approach". arXiv:2005.01833 [stat.AP].
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