Emerging Microorganisms and Infectious Diseases: One Health Approach for Health Shared Vision

Abstract

Emerging infectious diseases (EIDs) are newly emerging and reemerging infectious diseases. The National Institute of Allergy and Infectious Diseases identifies the following as emerging infectious diseases: SARS, MERS, COVID-19, influenza, fungal diseases, plague, schistosomiasis, smallpox, tick-borne diseases, and West Nile fever. The factors that should be taken into consideration are the genetic adaptation of microbial agents and the characteristics of the human host or environment. The new approach to identifying new possible pathogens will have to go through the One Health approach and omics integration data, which are capable of identifying high-priority microorganisms in a short period of time. New bioinformatics technologies enable global integration and sharing of surveillance data for rapid public health decision-making to detect and prevent epidemics and pandemics, ensuring timely response and effective prevention measures. Machine learning tools are being more frequently utilized in the realm of infectious diseases to predict sepsis in patients, diagnose infectious diseases early, and forecast the effectiveness of treatment or the appropriate choice of antibiotic regimen based on clinical data. We will discuss emerging microorganisms, omics techniques applied to infectious diseases, new computational solutions to evaluate biomarkers, and innovative tools that are useful for integrating omics data and electronic medical records data for the clinical management of emerging infectious diseases.

Keywords: artificial intelligence; emerging infectious disease (EID); emerging microorganisms; newly identified pathogens; one health.

Figure 1

Stages of emergence. The stages of emergency include a phase of adaptation of the organism (such as bacteria, viruses, fungi, and parasites) to the new host, followed by an epidemic/pathogenic phase, where there is an increase in the number of cases; then, there is the endemic phase, where the infectious disease is still present but limited to a particular region, and, finally, the adaptation phase, where the organism can become non-pathogenic and potentially beneficial (e.g., the human microbiota) or be stably integrated into the host genome (e.g., endogenous retroviruses) (created with Canva.com).

Figure 2

Categories of microorganisms. Category A: agents that pose the highest risk to public and security health, can be easily transmitted from person to person, have high mortality rates, and have an impact on public health; Category B: agents with moderate morbidity and low mortality rates, are moderately easy to spread, require a specific increase in diagnostic capacity, and increase disease surveillance; Category C: agents with high morbidity and mortality rates and ease of dissemination, ease of production, and potential for high morbidity and mortality rates and major health impacts (created with Canva.com).

Figure 3

Distribution of transmission of infectious diseases in humans. In human infectious diseases, transmission through zoonosis has a higher impact (61%) and includes coronaviruses, influenza viruses, West Nile virus, Yersinia pestis, and Sporothrix brasiliensis. On the other hand, non-zoonotic transmissions have a lower impact rate of 39%, including Variola virus, Staphylococcus aureus, Clostridium difficile, Candida auris, Blastomyces, Emergomyces species, Rasamsonia argillacea, Trichophyton indotinae, Aspergillus fumigatus, and Schistosoma species (created with Canva.com).

Figure 4

PRISMA flowchart depicting the study design and selection process.

Figure 5

The One Health approach and omics techniques (created with BioRender.com). An integrative approach could be a new strategy for in-depth profiling in infectious diseases, combining data from the One Health approach and analyzing these data with different platforms: genome sequencing (next-generation sequencing—NGS), proteomics, metabolomics through liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry (LC–MS or GC–MS), or even from metabolomic data obtained from nuclear magnetic resonance (NMR) experiments. With this amount of data, there is a need for an analysis of individual data first and then proceeding to an integrated analysis, which can allow the creation of models, network analysis, and the use of AI.