Modeling spatial pattern of dengue in North Central Mexico using survey data and logistic regression

Dengue is a major public health concern mainly in tropical and subtropical environments worldwide. Despite several attempts to prevent this disease occurring in tropical regions of Mexico, it has not yet been controlled. This work focused on spatial modeling of confirmed dengue fever cases that occurred during the period 2010-2014 in the Huasteca Potosina region of Mexico. Multivariable Logistic Regression Modeling (MLRM) was used to determine the relationship between explanatory variables and the presence/absence of dengue. Model performance was evaluated using the area under curve (AUC) of the relative operating characteristic (ROC); AUC > 0.95. A high spatial resolution map was created to reveal the most probable patterns of dengue risk. Our results can be used for targeted control and prevention programs at local and regional levels. This methodology can be applied to other major diseases that are spatially distributed in accordance with environmental factors.

Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area

We implemented a spatial model for analysing PM10 maxima across the Mexico City metropolitan area during the period 1995–2016. We assumed that these maxima follow a non-identical generalized extreme value (GEV) distribution and modeled the trend by introducing multivariate smoothing spline functions into the probability GEV distribution. A flexible, three-stage hierarchical Bayesian approach was developed to analyse the distribution of the PM10 maxima in space and time. We evaluated the statistical model’s performance by using a simulation study. The results showed strong evidence of a positive correlation between the PM10 maxima and the longitude and latitude. The relationship between time and the PM10 maxima was negative, indicating a decreasing trend over time. Finally, a high risk of PM10 maxima presenting levels above 1000 μg/m3 (return period: 25 yr) was observed in the northwestern region of the study area.

Tree growth response to ENSO in Durango, Mexico

The dynamics of forest ecosystems worldwide have been driven largely by climatic teleconnections. El Niño-Southern Oscillation (ENSO) is the strongest interannual variation of the Earth's climate, affecting the regional climatic regime. These teleconnections may impact plant phenology, growth rate, forest extent, and other gradual changes in forest ecosystems. The objective of this study was to investigate how Pinus cooperi populations face the influence of ENSO and regional microclimates in five ecozones in northwestern Mexico. Using standard dendrochronological techniques, tree-ring chronologies (TRI) were generated. TRI, ENSO, and climate relationships were correlated from 1950-2010. Additionally, multiple regressions were conducted in order to detect those ENSO months with direct relations in TRI (p < 0.1). The five chronologies showed similar trends during the period they overlapped, indicating that the P. cooperi populations shared an interannual growth variation. In general, ENSO index showed correspondences with tree-ring growth in synchronous periods. We concluded that ENSO had connectivity with regional climate in northern Mexico and radial growth of P. cooperi populations has been driven largely by positive ENSO values (El Niño episodes).