Presentation abstracts

Diffusive and hierarchical spread of human infections

Ottar Bjornstad
Department of Entomology
Penn State University

I will discuss waiting time approaches to studying the spatial spread of human infectious diseases. The two complementary approaches are hazzard regression and waiting time respose surfaces ('wombling of waiting times').

Integrating the effects of space, environment, and social networks in cholera vaccines

Mike Emch
Department of Geography
University of North Carolina at Chapel Hill

Geographic heterogeneity in vaccine uptake can alter estimates of protective efficacy through analogous variations in vaccine herd protection. The goal of this study is to determine how geographic factors, along with environmental and social ties among vaccinated and unvaccinated individuals can influence indirect protection of unvaccinated individuals. We sought to determine how incidence among unvaccinated individuals varied according to social and environmental ties to vaccinated individuals across space in a randomized cholera vaccine trial conducted in Matlab, Bangladesh, We addressed the impacts of social and environmental connectivity on incidence of non-vaccinees, while also accounting for spatial correlation of incidence. We found that the best supported model incorporated environmental factors and spatial structure, suggesting that spatial and environmental processes are more important than social processes in predicting the incidence of cholera among non-vaccinees. Through this analysis we provide a unified framework to demonstrate how variation in spatial, environmental, and social processes can contribute to heterogeneity of post-baseline infection risk among unvaccinated individuals in vaccine trials.

Spatial heterogeneity and infectious disease dynamics: two case studies on cholera and malaria

Mercedes Pascual
Department of Ecology and Evolutionary Biology
University of Michigan
Large and growing urban centers exhibit considerable demographic, socio-economic, and environmental heterogeneity. Extensive rural environments can also vary significantly with land-use change and associated development in ways that affect the transmission dynamics of water-borne and vector-borne diseases. I present two case studies, respectively on endemic cholera in Dhaka, Bangladesh, and epidemic malaria in arid Northwest India, on the retrospective analysis of spatio-temporal patterns. These studies address how spatial heterogeneity influences the effect of climate variability on disease dynamics and the spatial scale at which to consider this interaction. They illustrate a probabilistic (Markov Chain) model to identify this variation.

Mapping and modelling infection movements in low income regions using novel digital datasets

Andy Tatem
Emerging Pathogens Institute and Department of Geography
University of Florida
Human movements at multiple spatial and temporal scales impact the epidemiology and control of infectious diseases. While movement patterns are becoming relatively well quantified in high income regions of the world, they remain poorly understood in low income regions where the burden from infectious diseases is greatest. The advent of novel spatial digital datasets is increasing our abilities to measure human movement dynamics across large areas in these regions, however, and when combined with mathematical models of disease transmission, can provide valuable guidance on control. Here, I will discuss the potential of these new datasets and methods in disease control strategic planning and present examples of the use of (i) cellphone call data records in malaria control and elimination planning in Zanzibar, Namibia and Kenya, and (ii) satellite-derived night-time lights in measles vaccination strategies in Niger.

Disease invasibility of community networks

Joseph Tien
Department of Mathematics
Ohio State University
Suppose we have a collection of communities, joined together through a network by which pathogen can move. When can a disease invade this network? I will present some theoretical results on this question, and discuss some implications for understanding and controlling actual disease outbreaks, including the current cholera outbreak in Haiti.

Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models

Michael Tildesley
Mathematics Institute
University of Warwick
The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.

Linking fine scale human mobility and dynamic contacts to understand the spatial dimension of infectious disease transmission

Gonzalo M. Vazquez-Prokopec
Dept. of Environmental Studies and Global Health Institute
Emory University
Empiric quantification of the patterns of host movement and co-location is paramount for predicting and responding to infectious disease threats. Although long-time recognized as an important determinant of pathogen transmission and evolution, fine-scale human mobility still remain as one of the least understood dimensions of infectious disease dynamics. This talk will provide a brief overview of the importance of host movement in disease ecology, outline methodologies used to quantify fine-scale patterns of mobility an co-location and provide three examples of how such framework could be applied to understand the transmission and local propagation of three diseases of global health importance: human influenza virus, dengue virus and zoonotic spill-over of Cryptosporidium spp infection.