A machine learning approach to map the potential agroecological complexity in an indigenous community of Colombia.

Agroecological systems are potential solutions to the environmental challenges of intensive agriculture. Indigenous communities, such as the Kamëntšá Biyá and Kamëntšá Inga from the Sibundoy Valley (SV) in Colombia, have their own ancient agroecological systems called chagras. However, they are threatened by population growth and expansion of intensive agriculture.

Modelling the transmission of dengue, zika and chikungunya: a scoping review protocol.

Introduction: mosquitoes are the primary vectors for the spread of viruses like dengue (DENV), zika (ZIKV) and chikungunya (CHIKV), all of which affect humans. Those diseases contribute to global public health issues because of their great dispersion in rural and urban areas. Mathematical and statistical models have become helpful in understanding these diseases' epidemiological dynamics.

Identification of the elements of models of antimicrobial resistance of bacteria for assessing their usefulness and usability in One Health decision making: a protocol for scoping review.

Introduction: Antimicrobial resistance (AMR) is a complex problem that requires the One Health approach, that is, a collaboration among various disciplines working in different sectors (animal, human and environment) to resolve it. Mathematical and statistical models have been used to understand AMR development, emergence, dissemination, prediction and forecasting. A review of the published models of AMR will help consolidate our knowledge of the dynamics of AMR and will also facilitate decision-makers and researchers in evaluating the credibility, generalisability and interpretation of the results and aspects of AMR models.

Optimal control and Bayes inference applied to complex microbial communities.

Interactions between species are essential in ecosystems, but sometimes competition dominates over mutualism. The transition between mutualism-competition can have several implications and consequences, and it has hardly been studied in experimental settings. This work studies the mutualism between cross-feeding bacteria in strains that supply an essential amino acid for their mutualistic partner when both strains are exposed to antimicrobials.

Contribution of Governance and Socioeconomic Factors to the MDR in Europe.

This work aims to explain the behavior of the multi-drug resistance (MDR) percentage of in Europe, through multivariate statistical analysis and machine learning validation, using data from the European Antimicrobial Resistance Surveillance System, the World Health Organization, and the World Bank. We ran a multidimensional data panel regression analysis and used machine learning techniques to validate a pooling panel data case. The results of our analysis showed that the most important variables explaining the MDR phenomena across European countries are governance variables, such as corruption control and the rule of law.

On mobility trends analysis of COVID-19 dissemination in Mexico City.

This work presents a tool for forecasting the spread of the new coronavirus in Mexico City, which is based on a mathematical model with a metapopulation structure that uses Bayesian statistics and is inspired by a data-driven approach. The daily mobility of people in Mexico City is mathematically represented by an origin-destination matrix using the open mobility data from Google and the Transportation Mexican Survey. This matrix is incorporated in a compartmental model.

Random perturbations in a mathematical model of bacterial resistance: Analysis and optimal control.

In this work, we study a mathematical model for the interaction of sensitive-resistant bacteria to antibiotics and analyse the effects of introducing random perturbations to this model. We compare the results of existence and stability of equilibrium solutions between the deterministic and stochastic formulations, and show that the conditions for the bacteria to die out are weaker in the stochastic model. Moreover, a corresponding optimal control problem is formulated for the unperturbed and the perturbed system, where the control variable is prophylaxis.

Mathematical modeling on bacterial resistance to multiple antibiotics caused by spontaneous mutations.

We formulate a mathematical model that describes the population dynamics of bacteria exposed to multiple antibiotics simultaneously, assuming that acquisition of resistance is through mutations due to antibiotic exposure. Qualitative analysis reveals the existence of a free-bacteria equilibrium, resistant-bacteria equilibrium and an endemic equilibrium where both bacteria coexist.