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. 2020 Feb 28;10:3756. doi: 10.1038/s41598-020-60676-1

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© The Author(s) 2020

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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TDA network configuration of the parasite population diagnosed in Guapi between 2014-2017 and Malaria Transmission Unit. (A) Cases in the study area are represented using Mapper as a tool, using as preassigned filter functions genetic distances (p-distances) and time. The graph is the 1-skeleton of the TDA. Colours represent subpopulations identified through STRUCTURE (A: green; B: red; C: blue). Circle sizes represent the number of genotypes assigned to a given subpopulation over overlapping time periods. Note that the clustering algorithm of the TDA can assign genotypes to two different clusters while STRUCTURE assigns them to the same subpopulation, thus having separate clusters of the same colour over a single interval. (B) TDA map of network connections between cases diagnosed in the Guapi area between 2014–2017 and representation of Guapi Malaria Transmission Unit. Nodes are represented by a colour according to the identified parasite subpopulation (A: green; B: red; C: blue). This is the Point Intersection Graph representation of (A). Nodes are connected if they appear on different clusters of (B) (i.e. if there is an overlapping or “intersection” between clusters). In this graph, nodes point towards cases with the highest in-degree centrality. The size of the nodes correspond to the in-degree of the node (i.e the number of directed edges pointing towards it). (C) Geographical locations found after executing hierarchical clustering on the coordinates of the samples. The location of the point is computed using the mean of the coordinates and the size is proportional to the number of its elements. Notice that connectivity of case clusters does not necessarily occur in the geographical region with the highest diversity (Guapi), and that while cases of all genetically defined subpopulations are observed in urban Guapi, the connection between regions surrounding Guapi seems to play a fundamental role in the dissemination of genetic variants during epidemic outbreaks. Maps where created using the R package: ggmap⁷⁹.