TY - JOUR
T1 - COVID-19 and the difficulty of inferring epidemiological parameters from clinical data
AU - Wood, Simon
AU - Wit, Ernst
AU - Fasiolo, Matteo
AU - Green, Peter J
PY - 2020/3/28
Y1 - 2020/3/28
N2 - Knowing the infection fatality ratio (IFR) is crucial for epidemic management: for immediate planning, for balancing the life-years saved against those lost to the consequences of management, and for considering the ethics of paying substantially more to save a life-year from the epidemic than from other diseases. Impressively, Robert Verity and colleagues1 rapidly assembled case data and used statistical modelling to infer the IFR for COVID-19. We have attempted an in-depth statistical review of their paper, eschewing statistical nit-picking, but attempting to identify the extent to which the (necessarily compromised) data are more informative about the IFR than are the modelling assumptions.
AB - Knowing the infection fatality ratio (IFR) is crucial for epidemic management: for immediate planning, for balancing the life-years saved against those lost to the consequences of management, and for considering the ethics of paying substantially more to save a life-year from the epidemic than from other diseases. Impressively, Robert Verity and colleagues1 rapidly assembled case data and used statistical modelling to infer the IFR for COVID-19. We have attempted an in-depth statistical review of their paper, eschewing statistical nit-picking, but attempting to identify the extent to which the (necessarily compromised) data are more informative about the IFR than are the modelling assumptions.
U2 - 10.1016/S1473-3099(20)30437-0
DO - 10.1016/S1473-3099(20)30437-0
M3 - Article (Academic Journal)
C2 - 32473661
SN - 1473-3099
VL - 21
SP - 27
JO - The Lancet Infectious Diseases
JF - The Lancet Infectious Diseases
IS - 1
ER -