Using Data Mining and Network Analysis to Infer Arboviral Dynamics: The Case of Mosquito-Borne Flaviviruses Reported in Mexico.

Abstract

Simple SummaryGiven the significant impact on both human and animal health of mosquito-borne flaviviruses, a better understanding of their transmission cycles, viewed as a complex multi pathogen-vector-host system is urgently required. Here, we use a spatial datamining framework, based on co-occurrence data that includes biotic niche variables to create models for Dengue, Yellow fever, West Nile Virus and St. Louis encephalitis in Mexico that predict: (i) which mosquito species are likely to be the most important vectors for a given pathogen; (ii) which species are most likely to be important from a multi-pathogenic viewpoint; and (iii) which mosquito and/or mammal assemblages are most likely to play an important role in the transmission cycles. Our predictions are consistent with known information about the dynamics of these mosquito-borne flaviviruses and predict new potential vectors. Our approach can improve disease surveillance efforts and generate useful information regarding public health and biodiversity conservation.Given the significant impact of mosquito-borne flaviviruses (MBFVs) on both human and animal health, predicting their dynamics and understanding their transmission cycle is of the utmost importance. Usually, predictions about the distribution of priority pathogens, such as Dengue, Yellow fever, West Nile Virus and St. Louis encephalitis, relate abiotic elements to simple biotic components, such as a single causal agent. Furthermore, focusing on single pathogens neglects the possibility of interactions and the existence of common elements in the transmission cycles of multiple pathogens. A necessary, but not sufficient, condition that a mosquito be a vector of a MBFV is that it co-occurs with hosts of the pathogen. We therefore use a recently developed modeling framework, based on co-occurrence data, to infer potential biotic interactions between those mosquito and mammal species which have previously been identified as vectors or confirmed positives of at least one of the considered MBFVs. We thus create models for predicting the relative importance of mosquito species as potential vectors for each pathogen, and also for all pathogens together, using the known vectors to validate the models. We infer that various mosquito species are likely to be significant vectors, even though they have not currently been identified as such, and are likely to harbor multiple pathogens, again validating the predictions with known results. Besides the above “niche-based” viewpoint we also consider an assemblage-based analysis, wherein we use a community-identification algorithm to identify those mosquito and/or mammal species that form assemblages by dint of their significant degree of co-occurrence. The most cohesive assemblage includes important primary vectors, such as A. aegypti, A. albopictus, C. quinquefasciatus, C. pipiens and mammals with abundant populations that are well-adapted to human environments, such as the white-tailed deer (Odocoileus virginianus), peccary (Tayassu pecari), opossum (Didelphis marsupialis) and bats (Artibeus lituratus and Sturnira lilium). Our results suggest that this assemblage has an important role in the transmission dynamics of this viral group viewed as a complex multi-pathogen-vector-host system. By including biotic risk factors our approach also modifies the geographical risk profiles of the spatial distribution of MBFVs in Mexico relative to a consideration of only abiotic niche variables.