Alexander Moffett

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.

MultCSync: a software package for incorporating multiple criteria in conservation planning

MultCSync is a software package designed to aid incorporation of multiple criteria into conservation planning though it can be used in other similar contexts. During such planning, conservation area networks are selected primarily to represent biodiversity but must: (i) incorporate spatial design criteria such as size, dispersion, and connectivity of individual areas; and (ii) negotiate competing social claims on land use including recreation, resource extraction, and development. The social claims can also usually be modeled as (potentially incompatible) criteria to be simultaneously optimized along with the spatial design criteria. MultCSync enables the prioritization of alternative networks on the basis of such criteria after all biodiversity representation targets are satisfied. It begins by computing the set of non-dominated alternatives. If this set is sufficiently small, these alternatives can be presented to political decision makers. However, if this set is intractably large, further prioritization among the non-dominated alternatives is necessary. MultCSync accomplishes this prioritization using the Analytic Hierarchy Process (AHP) as well as a modification of the AHP in accordance with multi-attribute value theory (MAVT). MultCSync is freely downloadable via the world wide web and can be used in conjunction with different place prioritization software packages.

A global public database of disease vector and reservoir distributions.

Alexander Moffett is with UT Austin, Stavana Strutz is with UT Austin, Nelson Guda is with UT Austin, Camila Gonzalez is with National Autonomous University of Mexico, Maria Cristina Ferro is with the National Institute of Health Bogota, Victor Sanchez-Cordero is with National Autonomous University of Mexico, Sahotra Sarkar is with UT Austin.

A Dynamic Graph Automata Approach to Modeling Landscape Change in the Andes and the Amazon

A generalization of cellular automata was developed that allows flexible, dynamic updating of variable neighborhood relationships, which in turn allows the integration of interactions at widely disparate spatial and temporal scales. Cells in the landscapes were modeled as vertices of dynamic graph automata that allow temporally variable causal connectivity between spatially nonadjacent cells. A trial was carried out to represent changes in an Amazonian and a tropical Andean landscape modeled as dynamic graph automata with input from a Landsat TM-derived Level 1 classification with the following classes: for the Amazon—forest, nonforest vegetation, water, and urban or bare (soil); for the Andes—forest, scrub (shrub or grassland), agriculture, and bare or exposed ground. Explicit automata transition rules were used to simulate temporal land-cover change. These rules were derived independently from fieldwork in each area, including vegetation plots or transects and informal interviews. Such a generalization of cellular automata was useful for modeling land-use–land-cover change (LULCC), although it potentially increases the computational complexity of an already data intensive process (involving 5–8 million cells, in 1000 stochastic simulations, with each simulation encompassing 15 annual time steps). The interannual predicted LULCC, while more nuanced in the Andean site, poses a serious threat to compositional and configurational stability in both the Andes and the Amazon, with implications for landscape heterogeneity and habitat fragmentation.

Selecting malaria interventions: A top-down approach

Abstract Malaria continues to be a great burden on both morbidity and mortality as well as economic development across the world. In highly endemic areas, such as Nigeria, malaria can claim hundreds of thousands of lives and millions of dollars yearly. Typically, when selecting intervention strategies to control malaria, research is focused on the cost-effectiveness and general applicability of individual interventions. In separate studies, great care is taken to develop high-fidelity models of malarias economic and morbidity/mortality burden. In this paper, we take a top-down approach to selecting malaria intervention strategies. Instead of studying each element of the problem separately, we combine models for intervention cost-effectiveness, disease burden, and intervention delivery to create a single large-scale geographic optimization. We illustrate our top-down approach with a case study of malaria in Nigeria. Our optimization produces detailed geographic intervention plans, identifies key budget values and specifies the locations of the supply distribution centers.