As we can see, only the links (4, 5) and (5, 9) are correctly estimated. The rest of the estimated links are all wrong, partly because of the algorithm, and partly because of the censorship. To improve the accuracy of our estimated network, all tentative links are introduced with a weight of w = 1.00. Thereafter, the weight of all links decay by Dw = 20.01 every time step. Tentative links are removed when their weights fall to zero. In the above example, the links (5, 9) , (4, 5) , (5, 11) , (5, 13) , (4, 8) , (8, 11) , (8, 13) are introduced at t = 1, 2, 2, 2, 3, 3, 3, and so when the epidemics end at t = 4, their weights would be 0.97, 0.98, 0.98, 0.98, 0.99, 0.99, 0.99 respectively. We then repeat the estimation over more SIR epidemics. In subsequent epidemics, if an existing estimated link is reactivated, it is reinforced by adding Dw = 1.00 to its weight. We expect the algorithm to produce mostly wrong links at first. However, as the weights are decaying with time, most of the wrongly-estimated links will disappear at the end, because they will not consistently appear in the estimation process. On the other hand, correctly-estimated links tend to get constantly reinforced over multiple epidemics. Hence, we expect the population of correctly-estimated links to increase over time. More importantly, we note that existing estimation can always be further refined by incorporating new incidence data as and when they become available.