The Impact of Population, Contact, and Spatial Heterogeneity on Epidemic Model Predictions

Abstract

Our objective was to evaluate the effect that complexity in the form of different levels of spatial, population, and contact heterogeneity has in the predictions of a mechanistic epidemic model. A model that simulates the spatiotemporal spread of infectious diseases between animal populations was developed. Sixteen scenarios of foot-and-mouth disease infection in cattle were analyzed, involving combinations of the following factors: multiple production-types (PT) with heterogeneous contact and population structure versus single PT, random versus actual spatial distribution of population units, high versus low infectivity, and no vaccination versus preemptive vaccination. The epidemic size and duration was larger for scenarios with multiple PT versus single PT. Ignoring the actual unit locations did not affect the epidemic size in scenarios with multiple PT/high infectivity, but resulted in smaller epidemic sizes in scenarios using multiple PT/low infectivity. In conclusion, when modeling fast-spreading epidemics, knowing the actual locations of population units may not be as relevant as collecting information on population and contact heterogeneity. In contrast, both population and spatial heterogeneity might be important to model slower spreading epidemic diseases. Our findings can be used to inform data collection and modeling efforts to inform health policy and planning.

Spatial Transmission Models: A Taxonomy and Framework

“…The use of simple models to produce interesting results has been advocated by Zagmutt et al. (, p.951). However, in the context of risk within a spatial setting, mechanisms for transmission may be nontrivial.…”

“…; Riley, ), but also in fields as diverse as connectivity on wireless networks (Andrews, Ganti, Haenggi, Jindal, & Weber, ), the spread of fire (Rothermel, ), marketing (Bradlow et al., ), and the diffusion of technology (Berger, ). Risk analysis models that model the transmission of threats around a system include analytical models for epidemiological studies (Eisenberg, Seto, Olivieri, & Spear, ; Moreno & Alvar, ; Zagmutt, Schoenbaum, & Hill, ), cellular automata models for nuclear terrorism (Atkinson, Cao, & Wein, ), soil contamination (Cox, ) and species invasion (Sikder, Mal‐Sarkar, & Mal, ), domain‐based studies of exposure to ozone (Fann et al., ), network models for communication (Dettmann & Georgiou, ), power networks (Zio & Sansavini, ), and in the social amplification of risk (Kasperson et al., ).…”

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

“…(1)(2)(3) The importance of wealth exposure evaluation in disaster risk assessment is widely recognized, (4)(5)(6)(7)(8) and spatial variability (or heterogeneity) is increasingly recognized as a key component in environmental exposure and risk assessment, (9)(10)(11) but high spatial resolution data on actually exposed assets are lacking or not sufficiently detailed. (12) Bouwer et al (13) projected both future land use change and future increase of asset value combined with flood scenarios to estimate the flood risk in the Netherlands, and indicated that asset value increase plays a large role in flood losses increase.…”

Preference Functions for Spatial Risk Analysis

“…Malczewski reviews many instances of relevant spatial decision problems. These preference models can also be used to support spatial risk analyses, for instance, in the context of epidemics presented by Zagmutt et al ., or incorporating vulnerability to natural disasters, as explored by Zhou et al …”

“…(2) Malczewski (3) reviews many instances of relevant spatial decision problems. These preference models can also be used to support spatial risk analyses, for instance, in the context of epidemics presented by Zagmutt et al, (4) or incorporating vulnerability to natural disasters, as explored by Zhou et al (5) We will first develop the theory supporting several spatial preference models in Section 2, and then present an example of how one of these models (an additive measurable spatial value function) can be assessed and implemented in Section 3. Section 4 concludes the article.…”