KRAUS, Andrea a VM PANARETOS. Frequentist estimation of an epidemic's spreading potential when observations are scarce. Biometrika. Oxford: Oxford Univ Press, roč. 101, č. 1, s. 141-154. ISSN 0006-3444. doi:10.1093/biomet/ast049. 2014.
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Základní údaje
Originální název Frequentist estimation of an epidemic's spreading potential when observations are scarce
Autoři KRAUS, Andrea a VM PANARETOS.
Vydání Biometrika, Oxford, Oxford Univ Press, 2014, 0006-3444.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Utajení není předmětem státního či obchodního tajemství
Impakt faktor Impact factor: 1.418
Doi http://dx.doi.org/10.1093/biomet/ast049
UT WoS 000332328400009
Klíčová slova anglicky Birth and death process; Explosion; Malthusian parameter; Markov process; Marked point process; Martingale; Partial observation; Quasilikelihood
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Mgr. Andrea Kraus, M.Sc., Ph.D., učo 238225. Změněno: 12. 1. 2016 23:13.
Anotace
We consider the problem of inferring the potential of an epidemic for escalating into a pandemic on the basis of limited observations in its initial stages. Classical results of Becker & Hasofer (J. R. Statist. Soc. B, 59, 415-29) illustrate that frequentist estimation of the complete set of parameters of an epidemic modelled as a birth and death process remains feasible even when one is able to observe only the deaths and the total number of births. These assumptions on the observation mechanism, however, are too strong to be met in practice. We consider a more realistic scenario where only temporally aggregated random proportions of the deaths are observed over time. We demonstrate that the frequentist estimation of the Malthusian parameter governing the growth of the epidemic is still feasible in this context. We construct explicit straightforwardly calculable estimators motivated heuristically by the martingale dynamics of the process, and show that they admit a rigorous quasilikelihood interpretation. We establish the consistency and asymptotic normality of these estimators, allowing for the construction of approximate confidence intervals that can be used to infer the spreading potential of the epidemic. A simulation study and an application to the initial outbreak data of the 2009 H1N1 influenza pandemic illustrate that the method can be expected to give reasonable results in practice.
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