Certainty, uncertainty, and the spatiality of disease: a West Nile Virus example

Abstract

The problem is not uncertainty-proposed here as an inevitable condition-but the chimera of certainty asserted by most contemporary researchers. Problems of data definition, collection, and their use are reviewed in terms of spatial epidemiology and health data with examples drawn from several areas of contemporary health research. The argument is that preconceptions limit data modeled in a manner assuming its completeness. The result, as the West Nile Virus example seeks to demonstrate, may obscure other patterns and limit avenues of research.

Why populations are not planets_ gravity and the limits of disease modeling by analogy

“…Instead, the disease barely spread to nearby Iowa or Ohio, dying out despite clear transportation and commuter ties. Similar discontinuities were observed in previous epidemics of, for example West Nile Virus (Koch and Denike, 2007) and 2009 H1N1 Influenza (Koch, 2021).…”

Why populations are not planets_ gravity and the limits of disease modeling by analogy

“…Instead, the disease barely spread to nearby Iowa or Ohio, dying out despite clear transportation and commuter ties. Similar discontinuities were observed in previous epidemics of, for example West Nile Virus (Koch and Denike, 2007) and 2009 H1N1 Influenza (Koch, 2021).…”

The Effect of Data Inaccuracy on Tests of Space‐Time Interaction

“…To assume that one can precisely and accurately represent the complex and dynamic world in which we live in a static spatial or spatio‐temporal database is an illusion that we have become experts at propagating (Koch and Denike ). As shown here, conducting analyses based on this assumption without careful thought about its possible consequences can yield misleading results.…”

Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China

“…27–31 In the present study, the nearest-neighbour hierarchical clustering (NHC) model (Appendix 3) was used to identify spatial patterns of SARS distribution. Points were randomly distributed in space and circled in a spatial cluster, if their inter-distance was significantly smaller than the mean distance.…”

Why populations are not planets_ gravity and the limits of disease modeling by analogy

“…Instead, the disease barely spread to nearby Iowa or Ohio, dying out despite clear transportation and commuter ties. Similar discontinuities were observed in previous epidemics of, for example West Nile Virus (Koch and Denike, 2007) and 2009 H1N1 Influenza (Koch, 2021).…”

The Effect of Data Inaccuracy on Tests of Space‐Time Interaction

“…To assume that one can precisely and accurately represent the complex and dynamic world in which we live in a static spatial or spatio‐temporal database is an illusion that we have become experts at propagating (Koch and Denike ). As shown here, conducting analyses based on this assumption without careful thought about its possible consequences can yield misleading results.…”

Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China

“…27–31 In the present study, the nearest-neighbour hierarchical clustering (NHC) model (Appendix 3) was used to identify spatial patterns of SARS distribution. Points were randomly distributed in space and circled in a spatial cluster, if their inter-distance was significantly smaller than the mean distance.…”

Why populations are not planets_ gravity and the limits of disease modeling by analogy

“…Instead, the disease barely spread to nearby Iowa or Ohio, dying out despite clear transportation and commuter ties. Similar discontinuities were observed in previous epidemics of, for example West Nile Virus (Koch and Denike, 2007) and 2009 H1N1 Influenza (Koch, 2021).…”

The Effect of Data Inaccuracy on Tests of Space‐Time Interaction

“…To assume that one can precisely and accurately represent the complex and dynamic world in which we live in a static spatial or spatio‐temporal database is an illusion that we have become experts at propagating (Koch and Denike ). As shown here, conducting analyses based on this assumption without careful thought about its possible consequences can yield misleading results.…”

Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China

“…27–31 In the present study, the nearest-neighbour hierarchical clustering (NHC) model (Appendix 3) was used to identify spatial patterns of SARS distribution. Points were randomly distributed in space and circled in a spatial cluster, if their inter-distance was significantly smaller than the mean distance.…”