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We computed a fractal network as the maximum cluster of a cubic lattice at critical site percolation probability (ben-Avraham and Havlin, 2005).
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The K-function analysis identified the maximum clustering of cases at a distance of 680 m (Additional file 3: Fig. S1).
Clusters were identified as a maximum cluster size of 50% of case data and a maximum temporal window of 50% of the study period (Technical Appendix, Geospatial Analyses).
We analysed elliptical clusters with a maximum cluster size of 15% of the total population and with the option "No Cluster Centers in Other Clusters".
To identify community-level HCA spatial clustering, we used the Poisson model in SaTScan (7 ) with a maximum cluster size of 25% of the population at risk.
Applying a maximum cluster size of 15% of the population at risk was considered the most informative alternative when presenting results.
In SaTScan we used the Bernoulli model, a circular scan window and allowed a maximum cluster size of 25%% of the population at risk.
It was tested against the distribution of the maximum cluster sums of t-values under the null hypothesis.
In a network with J BSs and a maximum cluster size of JMAX, the number of possible BS clusters that can be constructed to serve a given UE is ∑ j = 1 J MAX J j, (11).
An example of this for a maximum cluster size of three is shown in Fig. 2 where clustered TPs are connected with red edges.
In this study, Poisson models applying both different cluster shapes (circular and elliptic) and sizes (maximum cluster sizes of 50, 20 and 10% of the total population at risk) were built to identify both high-risk and low-risk clusters.
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