## 3 Host-Pathogen Interactions
### Predicting Sepsis  
Sepsis is a systemic inflammatory response syndrome, because of the uncontrolled high level of inflammation, usually triggered to fight an infection, which can dysregulate host response, and consequently, organ dysfunction, multi-organ failed, and finally death. Certainly, sepsis is a complex disorder resulting from a host over-responding to a threat, which also modulates host immune system. According to World Health Organization, in 2015, more than 50% of deaths of neonates and children aged under 5 years were due to sepsis (A70/13 document), and according to U.S. Centers of Disease Control and prevention, one in every three patients admitted at American hospital suffer sepsis. The current protocol to treat sepsis is mainly focused on offering vital support, amines, antibiotics of wide spectrum, and time expecting the patient to positively respond; and unfortunately, there are no specific drugs capable to control the prognosis of this disease. Therefore, and after decades of consensus and continues up-grades on the guidelines, to manage septicaemia, computational engineering has begun to offer models that allow for the investigation of the disease, while aiming to control it. In this section, we comment on the most relevant models focused on understanding the mechanism of infection and sepsis. Dynamic agent-based models (ABM) have been used to identify host-pathogen interactions involved in sepsis [81; 82]. [PERSON] et al. developed a virtual environment that mimics the interface of a host _Pseudomonas aeruginosa_ in the gut milieu ABM (GM-ABM). The authors, in a meticulous study, used ABM to model spatially diverse, dynamic, and multi-factorial systems, allowing them to further include rules that dictate the interactions within the local environment and other agents. Before integration into the GM-ABM, the experimental references were validated and integrated in vivo into GM-ABM, which was used like a virtual animal model [83]. [PERSON] et al. [84; 85], using a _Salmonella_ murine model, integrated the adaptive immunity results obtained in vivo, and used mathematical models that allowed them to define three different patterns, healing, persistent infection, and organ dysfunction. The model quantified the levels of neutrophils and monocytes, too. Based on the results, they hypothesised that the outcome of the septicaemia would improve if patients were given anti-inflammatory drugs in the first 3-6 h post-infection [84]. Other studies used _Klebsiella pneumoniae_ and _Acinetobacter baumannii_ in a five-compartment kinetic model to explore the pathogenesis and kinetics of a dialysis-like device. Based on the extra-corporeal strategy to intensify the bacterial clearance, the results revealed a faster bacterial clearance using a nanomagnetic device [86]. [PERSON] and [PERSON] proposed a different approach using ABM as an Innate Immune Response (IIR), or an IIR-ABM. They represented a more realistic 2-D model of the human endothelial-blood interface using five representative parameters (the damage and resilience from host, and virulence from bacteria) [52] (Table A1). Currently, machine learning and AI have gained greater significance in the infectious disease research field. Using big databases for the extraction of different variables enables us to obtain patterns of sepsis and the progression of the disease [87; 88; 89]. Although the machine learning approach appears to be better than lineal regression models [90], it also has limitations. A general challenge is to determinate the number of training examples necessary to fit the model. Another big challenge is to improve the learning curves according to the greater complexity introduced by additional features or more complex model architectures, such as clinical notes or vital sign data. The major limitation is to address missing data (non-diagnosed infections) and variables (non-identified bacteria), which induce a bias in the relationship between variables [91].