Disease mapping seeks to represent the risk of a disease. This paper focuses on the spatial analysis of risk for pandemic COVID-19 in Europe and the Mediterranean. Morbidity and mortality data for 54 countries in ratio format were used. Two hypotheses were considered, the first one is that the data are homogeneous and the second one is that the ratios are defined in a heterogeneous manner requiring the stratification on the basis of covariables and the methodology of Jenks’ intervals. Spatial risk models were applied as well as methods for the representation of clusters. The results show that the best representation is obtained with the Poisson-Gamma Model under stratification. The variations in the ratios are due to the individual policies of each country for the management of the pandemic. The cluster analysis shows that there is a high mortality process in Eastern Europe. The behavior of the pandemic should be evaluated in the space-time process as well as in other heterogeneous and highly unequal regions.

An ordinary system of differential equations leading to a simulation model is propose as methodological approach to analysis the incidence of infectious-contagious diseases, in this case using SARS-CoV-2 virus as pathogenic model. The dynamics of the model are drive by the interaction between susceptible cells contemplating respiratory epithelial cells and viral infection mediated by two types of lysis response. To perform the simulations, values of some variables and parameters were selected from referenced sources, considering that previous reports suggested that the viral load in the lower respiratory tract might reach its peak in the second week after the beginning of disease symptoms. The scenarios described in the simulations evidence the performance of the cell lysis response from susceptible cells that have been infected. The recommend model shows that an excess response from both the original virus and the mutated virus leads to an increase in the approximate time to control viral infection within the organism.

Severe acute respiratory syndrome coronavirus is a type 2 highly contagious, and transmissible among humans; the natural human immune response to severe acute respiratory syndrome-coronavirus-2 combines cell-mediated immunity (lymphocyte) and antibody production. In the present study, we analyzed the dynamic effects of adaptive immune system cell activation in the human host. The methodology consisted of modeling using a system of ordinary differential equations; for this model, the equilibrium free of viral infection was obtained, and its local stability was determined. Analysis of the model revealed that lymphocyte activation leads to total pathogen elimination by specific recognition of viral antigens; the model dynamics are driven by the interaction between respiratory epithelial cells, viral infection, and activation of helper T, cytotoxic T, and B lymphocytes. Numerical simulations showed that the model solutions match the dynamics involved in the role of lymphocytes in preventing new infections and stopping the viral spread; these results reinforce the understanding of the cellular immune mechanisms and processes of the organism against severe acute respiratory syndrome-coronavirus-2 infection, allowing the understanding of biophysical processes that occur in living systems, dealing with the exchange of information at the cellular level.

The central western area of Venezuela has an unequal distribution of precipitation. Due to its agricultural importance, is necessary to plan water accounting and this requires a evaluation of spatial and temporal variability of precipitation and an estimate of local geophysical effect from the relief. In this research we use an iterative computationally lattice approach to perform a confirmatory analysis of the variability and the spatial correlation structure in monthly precipitation stations. Spatial correlograms and pooled empirical semivariogram were applied to evaluate the most appropriate spatial weighting matrix to estimate the Moran’s I. The altitude effect over monthly rainfall was estimated through spatial regression algorithm which determine the predominant spatial process in each slice. A homogeneous spatial stochastic process with positive spatial autocorrelation is evidenced. There is a trend towards a higher frequency of spatial error and spatial auto-regressive processes between the months of June and August whilst there are not dominant process between October and December. This response is caused by the dynamics of the intertropical convergence zone, which generates a seasonal effect on precipitation. These estimations allows decision-making in modeling and will lead to an improvement for analysis and forecasting in areas strongly affected by climate change and water stress.

The estimation of the minimum inhibitory concentration is usually performed by a method of serial dilutions by a factor of 2, introducing the overestimation of antimicrobial efficacy, quantified by a simulation model that shows that the variability of the bias is higher for the standard deviation, being dependent on the metric distance to the values of the concentrations used. We use a methodological approach through modeling and simulation for the measurement error of physical variables with censored information, proposing a new inference method based on the calculation of the exact probability for the set of possible samples from nmeasurements that allows quantifying the p-value in one or two independent sample tests for the comparison of censored data means. Tests based on exact probability methods offer a reasonable solution for small sample sizes, with statistical power varying according to the hypothesis evaluated, providing insight into the limitations of censored data analysis and providing a tool for decision making in the diagnosis of antimicrobial efficacy.