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

Mosquito (Diptera: Culicidae) diversity and arbovirus detection across an urban and agricultural landscape

Fragmented landscapes in Mexico, characterized by a mix of agricultural, urban, and native vegetation cover, presents unique ecological characteristics that shape the mosquito community composition and mosquito-borne diseases. The extent to which landscape influences mosquito populations and mosquito-borne diseases is still poorly understood. This work assessed the effect of landscape metrics -agriculture, urban, and native vegetation cover- on mosquito diversity and arbovirus presence in fragmented tropical deciduous forests in Central Mexico during 2021. Among the 21 mosquito species across six genera we identified, Culex quinquefasciatus was the most prevalent species, followed by Aedes aegypti, Ae. albopictus, and Ae. epactius. Notably, areas with denser native vegetation cover displayed higher mosquito species richness, which could have an impact on phenomena such as the dilution effect. Zika and dengue virus were detected in 85% of captured species, with first reports of DENV in several Aedes species and ZIKV in multiple Aedes and Culex species. These findings underscore the necessity of expanding arbovirus surveillance beyond Ae. aegypti and advocate for a deeper understanding of vector ecology in fragmented landscapes to adequately address public health strategies.

Risk of a vector-borne endemic zoonosis for wildlife: Hosts, large-scale geography, and diversity of vector-host interactions for Trypanosoma cruzi

Drivers for wildlife infection are multiple and complex, particularly for vector-borne diseases. Here, we studied the role of host competence, geographic area provenance, and diversity of vector-host interactions as drivers of wild mammal infection risk to Trypanosoma cruzi, the aetiological agent of Chagas disease. We performed a systematic sampling of wild mammals in 11 states of Mexico, from 2017 to 2018. We tested the positivity of T. cruzi with the Tc24 marker in tissues samples for 61 wild mammal species (524 specimens sampled). 26 mammal species were positive for T. cruzi, of which 11 are new hosts recorded in Mexico 75 specimens were positive and 449 were negative for T. cruzi infection, yielding an overall prevalence of 14.3%. The standardized infection risk of T. cruzi of our examined specimens was similar, no matter the host species or their geographic origins. Additionally, we used published data of mammal positives for T. cruzi to complement records of T. cruzi infection in wild mammals and inferred a trophic network of Triatoma spp. (vectors) and wild mammal species in Mexico, using spatial data-mining modelling. Infection with T. cruzi was not homogeneously distributed in the inferred trophic network. This information allowed us to develop a predictive model for T. cruzi infection risk for wild mammals in Mexico, considering risk as a function of the diversity of vector-host spatial associations in a large-scale geographic context, finding that the addition of competent vectors to a multi-host parasite system amplifies host infection risk. The diversity of vector-host interactions per se constitutes a relevant driver of infection risk because hosts and vectors are not isolated from each other.

Network Analysis of Hosts and Vectors in the Multiple Transmissions of Flavivirus

Background: It is well established that infection patterns in nature can be driven by host, vector, and symbiont communities. One of the first stages in understanding how these complex systems have influenced the incidence of vector-borne diseases is to recognize what are the major vertebrate (i.e., hosts) and invertebrate (i.e., vectors) host species that propagate those microbes. Such identification opens the possibility to identify such essential species to develop targeted preventive efforts. Methods: The goal of this study, which relies on a compilation of a global database based on published literature, is to identify relevant host species in the global transmission of mosquito-borne flaviviruses, such as West Nile virus, St. Louis virus, Dengue virus, and Zika virus, which pose a concern to animal and public health. Results: The analysis of the resulting database involving 1174 vertebrate host species and 46 reported vector species allowed us to establish association networks between these species. Three host species (Mus musculus, Sapajus flavius, Sapajus libidinosus, etc.) have a much larger centrality values, suggesting that they play a key role in flavivirus community interactions. Conclusion: The methods used and the species detected as relevant in the network provide new knowledge and consistency that could aid health officials in rethinking prevention and control strategies with a focus on viral communities and their interactions. Other infectious diseases that harm animal and human health could benefit from such network techniques.

Disentangling the contributions of biotic and abiotic predictors in the niche and the species distribution model of Trypanosoma cruzi, etiological agent of Chagas disease

The potential benefits of incorporating biotic, as well as abiotic, predictors in niche and species distribution models (SDMs), as well as how to achieve this, is still debated, with their interpretability and explanatory potential being particularly questioned. It is therefore important to stress test modelling methodologies that include biotic factors against use cases where there is ample knowledge of the potential biotic component of the niche. Relatively well studied and important vector-borne diseases offer just such an opportunity, where knowledge of the agents involved in the transmission cycle -vectors and hosts- can serve to calibrate and test the niche model and corresponding SDM. Here, we study the contributions of biotic -14 vectors, 459 potential hosts- and abiotic -258 climatic categories- predictors to the explanatory and predictive features of the niche and corresponding SDM for the etiological agent of Chagas disease, Trypanosoma cruzi, in Mexico. Using an established spatial data mining technique, we generate biotic, abiotic and biotic+abiotic niche and SDM models. We test our models by comparing predictions of the most important probable hosts of Chagas disease with a previously published list of confirmed hosts. We quantify, compare, and contrast the individual and total contributions of predictors to the niche and distribution of Chagas disease in Mexico. We assess the relative predictive potential of these variables to model performance, showing that models that include relevant biotic niche variables lead to more predictive, more ecologically realistic SDMs. Our research illustrates a useful general procedure for identifying and ranking potential biotic interactions and for assessing the relative importance of biotic and abiotic predictors. We conclude that the inclusion of both abiotic and biotic predictors in SDMs not only provides more predictive and accurate models but also models that are more understandable and explainable from an ecological niche perspective.