Assessing complexity and dynamics in epidemics: geographical barriers and facilitators of foot-and-mouth disease dissemination.
| Autor: | Hoogesteyn AL; Department of Human Ecology, CINVESTAV, Merida, Yucatan, Mexico., Rivas AL; Center for Global Health, Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM, United States., Smith SD; Geospatial Research Services, Ithaca, NY, United States., Fasina FO; Department of Veterinary Tropical Diseases, Faculty of Veterinary Science, University of Pretoria, Pretoria, South Africa.; ECTAD Food and Agriculture Organization (FAO), Nairobi, Kenya., Fair JM; Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States., Kosoy M; KB One Health LLC, Fort Collins, CO, United States. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in veterinary science [Front Vet Sci] 2023 May 12; Vol. 10, pp. 1149460. Date of Electronic Publication: 2023 May 12 (Print Publication: 2023). |
| DOI: | 10.3389/fvets.2023.1149460 |
| Abstrakt: | Introduction: Physical and non-physical processes that occur in nature may influence biological processes, such as dissemination of infectious diseases. However, such processes may be hard to detect when they are complex systems. Because complexity is a dynamic and non-linear interaction among numerous elements and structural levels in which specific effects are not necessarily linked to any one specific element, cause-effect connections are rarely or poorly observed. Methods: To test this hypothesis, the complex and dynamic properties of geo-biological data were explored with high-resolution epidemiological data collected in the 2001 Uruguayan foot-and-mouth disease (FMD) epizootic that mainly affected cattle. County-level data on cases, farm density, road density, river density, and the ratio of road (or river) length/county perimeter were analyzed with an open-ended procedure that identified geographical clustering in the first 11 epidemic weeks. Two questions were asked: (i) do geo-referenced epidemiologic data display complex properties? and (ii) can such properties facilitate or prevent disease dissemination? Results: Emergent patterns were detected when complex data structures were analyzed, which were not observed when variables were assessed individually. Complex properties-including data circularity-were demonstrated. The emergent patterns helped identify 11 counties as 'disseminators' or 'facilitators' (F) and 264 counties as 'barriers' (B) of epidemic spread. In the early epidemic phase, F and B counties differed in terms of road density and FMD case density. Focusing on non-biological, geographical data, a second analysis indicated that complex relationships may identify B-like counties even before epidemics occur. Discussion: Geographical barriers and/or promoters of disease dispersal may precede the introduction of emerging pathogens. If corroborated, the analysis of geo-referenced complexity may support anticipatory epidemiological policies. Competing Interests: MK is employed by KB One Health LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Hoogesteyn, Rivas, Smith, Fasina, Fair and Kosoy.) |
| Databáze: | MEDLINE |
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