eng International Academy of Ecology and Environmental Sciences Network Biology 2220-8879 2019-9-1 9 3 42 57 1 article Bita Shams 1 2 Mohammad Khansari 1 2 Faculty of New Sciences and Technologies, University of Tehran, Amir Abad, North Kargar Street, Tehran 14399-57131, Iran It is a fundamental issue to find a small subset of individuals in a complex network such that their immunization (i.e. removal) minimizes epidemic growth in the network. Though some network topological metrics have been proposed to estimate the effect of individual immunization or epidemic growth of the network, none of them considered the severity of the current epidemic. This paper proposes a new metric, called average reachability (AR) to estimate epidemic growth in a network. AR incorporates infection rate of epidemics to make a trade-off between network local connectivity and global reachability. Moreover, we intend to generalize stochastic hill-climbing immunization (SHCI) algorithm to minimize network epidemic growth regarding all estimation criteria. SIR simulation on immunized networks shows that the combination of AR and SHCI results in minimal epidemic growth compared to immunization algorithms that minimize density or sum of square partitions. http://www.iaees.org/publications/journals/nb/articles/2019-9(3)/average-reachability-A-new-metric-to-estimate-epidemic-growth.pdf complex network epidemic spreading immunization stochastic hill-climbing algorithm average reachability