Malaria in Africa: Vector Species' Niche Models and Relative Risk Maps

A central theoretical goal of epidemiology is the construction of spatial models of disease prevalence and risk, including maps for the potential spread of infectious disease. We provide three continent-wide maps representing the relative risk of malaria in Africa based on ecological niche models of vector species and risk analysis at a spatial resolution of 1 arc-minute (9 185 275 cells of approximately 4 sq km). Using a maximum entropy method we construct niche models for 10 malaria vector species based on species occurrence records since 1980, 19 climatic variables, altitude, and land cover data (in 14 classes). For seven vectors (Anopheles coustani, A. funestus, A. melas, A. merus, A. moucheti, A. nili, and A. paludis) these are the first published niche models. We predict that Central Africa has poor habitat for both A. arabiensis and A. gambiae, and that A. quadriannulatus and A. arabiensis have restricted habitats in Southern Africa as claimed by field experts in criticism of previous models. The results of the niche models are incorporated into three relative risk models which assume different ecological interactions between vector species. The “additive” model assumes no interaction; the “minimax” model assumes maximum relative risk due to any vector in a cell; and the “competitive exclusion” model assumes the relative risk that arises from the most suitable vector for a cell. All models include variable anthrophilicity of vectors and spatial variation in human population density. Relative risk maps are produced from these models. All models predict that human population density is the critical factor determining malaria risk. Our method of constructing relative risk maps is equally general. We discuss the limits of the relative risk maps reported here, and the additional data that are required for their improvement. The protocol developed here can be used for any other vector-borne disease

Hybrid Particle-Field Model for Conformational Dynamics of Peptide Chains

We propose the first model of a polypeptide chain based on a hybrid-particle field approach. The intramolecular potential is built on a two-bead coarse grain mapping for each amino acid. We employ a combined potential for the bending and the torsional degrees of freedom that ensures the stabilization of secondary structure elements in the conformational space of the polypeptide. The electrostatic dipoles associated with the peptide bonds of the main chain are reconstructed by a topological procedure. The intermolecular interactions comprising both the solute and the explicit solvent are treated by a density functional-based mean-field potential. Molecular dynamics simulations on a series of test systems show how the model here introduced is able to capture all the main features of polypeptides. In particular, homopolymers of different lengths yield a complex folding phase diagram, covering from the collapsed to swollen state. Moreover, simulations on models of a four-helix bundle and of an alpha + beta peptide evidence how the collapse of the hydrophobic core drives the appearance of both folded motifs and the stabilization of tertiary or quaternary assemblies. Finally, the polypeptide model is able to structurally respond to the environmental changes caused by the presence of a lipid bilayer