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. 2020 Jul 13;19:244. doi: 10.1186/s12936-020-03316-3

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — Characterization of highly related infections. a Relationship between the proportion of highly related infections and distance between destination countries in Africa. b Relationship between the proportion of highly related infections and date difference in the reporting of malaria cases in Jiangsu, China. c Relationship between the proportion of highly related infections, distance and reporting time. Genetically related infections were usually imported from shared trips to a similar destination. d Relationship between the proportion of highly related infections and distance between residence district in Jiangsu, China. For panels A, B and D, black dots and blue lines indicate the observed and fitted data, respectively. The grey dots and red lines indicate a null distribution from a permutation test