Fig 1.
Scheme of the possible infection rules according to RhD blood types and ABO ones.
Full line connection indicated the possibility of infection, Wij = 1, while dotted connection its impossibility. In the RhD infection system, individuals of the same group can infect each other, people with the RhD- group can transmit the infection to people with RhD+ one, but not viceversa. The scenario of the ABO system is similar although richer. For instance, the O type can infect the A type, while the opposite is not possible.
Table 1.
Data used to determine the quantities plotted in Figs 2 and 3: ln(di/fi) and , with di being the fractions of infected having blood group i, fi is the fraction of population with blood group i and
represent the susceptibility of the population to become infected.
Table 2.
Percentages of blood groups (fi), susceptibility, , and inverse characteristic time, m, of the exponential phase of the infection for the analyzed countries as derived for the fit to the observed data.
Fig 2.
a) Ratio between frequencies of infected people for each ABO group, di over frequency of the group on the whole population, fi as measured in four hospitals of the Wuhan/Shenzhen region versus its theoretical prediction, obtained from Eq (8) using the ABO set of rules and the blood type frequencies in Table 1. The black line represents the best solution of a linear fit. b) Ratio between frequencies of infected people for each ABO groups, di over frequency of the group on the whole population, fi as measured in Denmark versus its theoretical prediction,
obtained from Eq (8) using the ABO set of rules and the blood type frequencies in Table 1. The black line represents the best solution of a linear fit. The error bars on ln(di/fi) are one standard deviation calculated by assuming a Poissonian distribution for the number of cases, thus
being ni the number of infected people with blood type i.
Fig 3.
Ratio between frequencies of infected people for each ABO, di over frequency of the group on the whole population, fi versus its theoretical prediction, obtained from Eq (8) using the ABO set of rules and all the blood type frequencies in Table 1.
Black line represents the best solution of a linear fit. The error bars on ln(di/fi) are one standard deviation calculated by assuming a Poissonian distribution for the number of cases, thus being ni the number of infected people with blood type i.
Fig 4.
a) Inverse characteristic time of the epidemic exponential phase extracted from cumulative infection curves, m, vs theoretical prediction, obtained from Eq (11) using the ABO set of rules and the blood type frequencies in Table 2. Each dot corresponds to one of the 29 analyzed countries in the European region named according to the 2-letter ISO code and reported in Table 2. The black line represents the best solution of a linear fit performed with the York method and the grey shaded area is the ± one standard deviation confidence band. The uncertainty on
are obtained as
where Δf = 10−3. b) Map representation of European countries. Each country is colored according to its
susceptibility value obtained from Eq (11) using the ABO set of rules and the blood type frequencies reported in Table 2.
values increase going from blue to red. Gray countries have not been considered due to a lack of either blood or infection information. The map shows large variability in the susceptibility
(that ranges from 0.56 to 0.68) and a clear east-to-west gradient. The increase of susceptibility going west is a direct consequence of the tendency of increase of the O blood type in this direction: the more one blood type dominates, the higher is the susceptibility. The map has been realized using the python ‘Cartopy’ library [32] with Natural Earth data.
Table 3.
Main results for the different countries aggregation.
Fig 5.
Inverse characteristic times of the epidemic exponential phase extracted from cumulative infection curves, m vs theoretical prediction, obtained from Eq (11) using the ABO sets of rules and using the blood type frequencies in Table 2.
Each dot corresponds to one of the 21 analyzed countries in the Asiatic region named according to the 2-letter ISO code and reported in Table 2. The black line represents the best solution of a linear fit. The uncertainty on are obtained as
where Δf = 10−3.
Table 4.
Pearson correlations between m and for different continent aggregations.