Author: Lloyd A. C. Chapman; Simon E. F. Spencer; Timothy M. Pollington; Chris P. Jewell; Dinesh Mondal; Jorge Alvar; T. Deirdre Hollingsworth; Mary M. Cameron; Caryn Bern; Graham F. Medley
Title: Inferring transmission trees to guide targeting of interventions against visceral leishmaniasis and post-kala-azar dermal leishmaniasis Document date: 2020_2_25
ID: nqn1qzcu_98
Snippet: The corresponding autocorrelation plots are shown in Figure S7 . The high degree of autocorrelation evident for all the 589 parameters is due to strong correlation between the transmission parameters and the missing data, in particular between the 590 spatial transmission rate constantand the asymptomatic infection times. Figure S8 shows thatis strongly negatively 591 correlated with the mean asymptomatic infection timeĀ. This is expected since .....
Document: The corresponding autocorrelation plots are shown in Figure S7 . The high degree of autocorrelation evident for all the 589 parameters is due to strong correlation between the transmission parameters and the missing data, in particular between the 590 spatial transmission rate constantand the asymptomatic infection times. Figure S8 shows thatis strongly negatively 591 correlated with the mean asymptomatic infection timeĀ. This is expected since a higher overall transmission rate leads to show that there is some negative correlation betweenand ', -and ', and " and p. These correlations are not surprising: the 603 more transmission that is explained by proximity to infectious individuals (the higher -), the less needs to be explained by the 604 background transmission (the lower '); the flatter the spatial kernel (the larger -), the fewer infections need to be explained by 605 the background transmission; and the more infections are accounted for by transmission within the same household (the higher 606 "), the longer the incubation period (the lower p) needs to be (due to long times between onsets of cases in the same household).
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