Editorial: Applications of STEM (Science, Technology, Engineering and Mathematics) Tools in Microbiology of Infectious Diseases

Epidemiology is a discipline intended to systematically investigate, and ideally quantify, disease dynamics in populations (Perez, 2015). Epidemiological assessments may be divided into four large areas, namely, (a) identification and characterization of a pathogen, (b) development of systems for detection of cases, (c) descriptive epidemiology and quantification of disease patterns, and (d) advanced analytical methods to design intervention strategies. Briefly, there is an initial need for understanding the pathogeny of a disease and condition, which may also include experimental studies and development of new models of infection and proliferation under different conditions. Subsequently, such knowledge may be applied to support the identification of cases, which typically includes the design, evaluation, and validation of diagnostic tests. Disease may then be quantified in a population, leading to the identification of patterns and application of molecular characterization techniques to understand disease spread, and ultimately to identify factors preventing or promoting disease. Finally, those factors may be incorporated into advanced quantitative methods and epidemiological models, which are used to design and evaluate strategies aimed at preventing, controlling, or eliminating disease in the population. Recent years have seen a dramatic increase in the application of science, technology, engineering, and mathematical (STEM) tools and approaches intended to enhance such analytical epidemiology process, with the ultimate goal of supporting disease prevention, control, and eradication. The research topic here provides an update of the current state-of-the-art scientific knowledge of the application of STEM tools to the microbiology of infectious diseases, at the various components of an integral epidemiological approach, divided into the four large components of (a) experimental studies, (b) novel diagnostic techniques, (c) epidemiological characterization, and (d) population modeling and intervention.


INTRODUCTION
Epidemiology is a discipline intended to systematically investigate, and ideally quantify, disease dynamics in populations (Perez, 2015). Epidemiological assessments may be divided into four large areas, namely, (a) identification and characterization of a pathogen, (b) development of systems for detection of cases, (c) descriptive epidemiology and quantification of disease patterns, and (d) advanced analytical methods to design intervention strategies. Briefly, there is an initial need for understanding the pathogeny of a disease and condition, which may also include experimental studies and development of new models of infection and proliferation under different conditions. Subsequently, such knowledge may be applied to support the identification of cases, which typically includes the design, evaluation, and validation of diagnostic tests. Disease may then be quantified in a population, leading to the identification of patterns and application of molecular characterization techniques to understand disease spread, and ultimately to identify factors preventing or promoting disease. Finally, those factors may be incorporated into advanced quantitative methods and epidemiological models, which are used to design and evaluate strategies aimed at preventing, controlling, or eliminating disease in the population.
Recent years have seen a dramatic increase in the application of science, technology, engineering, and mathematical (STEM) tools and approaches intended to enhance such analytical epidemiology process, with the ultimate goal of supporting disease prevention, control, and eradication. The research topic here provides an update of the current state-of-the-art scientific knowledge of the application of STEM tools to the microbiology of infectious diseases, at the various components of an integral epidemiological approach, divided into the four large components of (a) experimental studies, (b) novel diagnostic techniques, (c) epidemiological characterization, and (d) population modeling and intervention.

EXPERIMENTAL STUDIES
The increased knowledge gained in the last decades on the importance of host-pathogen and environment-pathogen interactions has provided a better understanding on the complexities associated with the design of effective therapeutic and preventive strategies against infectious agents, but also raised new questions on how to evaluate those potential new approaches in a capturing their complexity. Six papers looking into the use of STEM tools for the design of experimental models for replicating this environment and host-pathogen interactions and ultimately the assessment of alternative strategies to prevent pathogen replication were selected here. Those papers were focused on emerging and/or neglected pathogens whose control is impaired due, at least in part, to its fastidious nature (Mycobacterium tuberculosis)

EPIDEMIOLOGICAL CHARACTERIZATION
Once the tools for detection and characterization of the pathogen of interest have been developed the challenge shifts to the interpretation of the results obtained in different populations. Four papers focusing on three bacterial pathogens (Escherichia coli, Vibrio parahaemolyticus, and V. cholerae) that exemplify these challenges and include the application of molecular biology methods were selected (Han et al.; Cabal et al.; Ghosh et al.; Han et al.). Use of molecular characterization techniques allow identification of the pathogen beyond the species level, thus offering a much higher degree of refinement that can help to understand better the epidemiology of a disease (Muellner et al., 2011) and clarify the biological relationship between different strains. In addition, when used directly on clinical samples, it may help to improve the sensitivity of the diagnostic approach used eliminating the need for isolation of the pathogen, what can be particularly useful when its concentration is low and there are other microorganisms that could outgrow it. For example, a set of specific real-time (RT) PCRs aiming at a panel of virulence genes characteristic of different E. coli pathotypes was used to assess their distribution and, when detected, quantify the number of copies present, in samples from clinical human cases and healthy volunteers in Spain and thus provide qualitative and quantitative information on the significance of their detection (Cabal et al.). Detection of virulence genes, coupled with other molecular (DNA sequencing, ribotyping, restriction fragment length polymorphism-RFLP-and pulsed field gel electrophoresis-PFGE) and conventional (serotyping, antimicrobial susceptibility determination) was also used for a detailed characterization of a collection of V. cholerae strains recovered in a 5-year period in India, revealing a considerable diversity and thus a dynamic evolution of the pathogen that must be taken into account when evaluating individual isolates (Ghosh et al.). Finally, a well-established molecular characterization technique, multi-locus sequence typing (MLST), was used to assess the genetic diversity of V. parahaemolyticum at the national (China) (Han et al.) and, for pandemic strains (those harboring a given set of virulence markers), global levels (Han et al.) by analyzing two collections of clinical and environmental isolates. While the first study demonstrated a high genetic diversity among strains recovered from the same country, comparable to what had been described at a global scale (Han et al.), the second evidenced the persistence of certain pandemic strains in different countries and their occasional detection in environmental samples (Han et al.).

POPULATION MODELING AND INTERVENTION
Population modeling for the characterization of spatial risk is prerequisite for the design and implementation of prevention and control interventions in a region. Three techniques (Bayesian analysis, ecological niche modeling, and phylogeography tools) have been demonstrated in this research topic (Martínez-López et al.; Alkhamis et al.; Escobar and Craft). Bayesian techniques have the potential to support the characterization of disease risk in a region because of their flexibility to adapt the coding to different data structure and variables distribution. A Bayesian multivariable logistic regression mixed model was used to assess the relation between hypothesized risk factors and African swine fever virus (ASFV) distribution in Sardinia (Italy) after the beginning of the eradication program in 1993 (Martínez-López et al.). Additionally, one of the challenges epidemiologists often face is the absence of control or population data. Ecological niche modeling is a tool that allows quantification of spatial risk using presence-only (case) data. An overview of the background, history, and conceptual framework of ecological niche modeling applied to epidemiology and public health was presented (Escobar and Craft). Finally, the use of Bayesian phylodinamic models was demonstrated using a large volume of porcine reproductive and respiratory syndrome virus (PRRSV) collected in the United States (Alkhamis et al.).

FINAL REMARKS
The acknowledgment that health of humans, animals, and the environment is inter-connected has resulted in the establishment of the One Health approach (Perez, 2015). Cross-cutting a myriad of pathogens, host species, and settings, and under the One Health umbrella, novel STEM tools and methods have a tremendous potential to increase our ability to understand pathogens dynamics, improve effectiveness of detection, characterize the epidemiological setting in which disease spread, and, ultimately, develop effective strategies to mitigate or eliminate the impact of disease.

AUTHOR CONTRIBUTIONS
JA and AP co-edited the Research Topic and wrote this editorial.

ACKNOWLEDGMENTS
The authors would like to thank the authors that submitted their work to this Research Topic, the reviewers that critically evaluated these and the Frontiers Editorial Office for their help producing it. The authors would also like to thank the crew of the University of Minnesota Science Technology Engineering and Mathematics for Minnesota Advancement (STEMMA) laboratory for the support in the organization of this editorial and research topic.