Recent results on the spatiotemporal modelling and comparative analysis of Black Death and bubonic plague epidemics.

作者: George Christakos , Ricardo A Olea , H-L Yu , None

DOI: 10.1016/J.PUHE.2006.12.011

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摘要: Summary Background This work demonstrates the importance of spatiotemporal stochastic modelling in constructing maps major epidemics from fragmentary information, assessing population impacts, searching for possible etiologies, and performing comparative analysis epidemics. Methods Based on theory previously published by authors incorporating new knowledge bases, informative composite space–time distributions were generated important characteristics two epidemics: Black Death (14th century Western Europe) bubonic plague (19th–20th Indian subcontinent). Results The led to a number interesting findings: (1) exhibited certain differences their (correlation structures, trends, occurrence patterns propagation speeds) that need be explained means an interdisciplinary effort; (2) geographical epidemic indicators confirmed rigorous quantitative manner partial findings isolated reports time series mortality was orders magnitude higher than plague; (3) modern is rural disease hitting harder small villages countryside whereas devastating indiscriminately attacked large urban centres countryside, while India lasted uninterruptedly five decades, Europe it three half years; (4) had reverse areal extension features response annual seasonal variations. Temperature increase at end winter expansion infected area reduction plague, reaching climax spring when always larger India. Conversely, without exception, during (5) epidemic, disappeared reappeared several times most locations; Europe, once entered place, proportional then years (this on-and-off situation more centuries); (6) average, moved much faster reach virgin territories, despite fact only slightly railroad network almost instantly moving rats, fleas, people one subcontinent other. Conclusions These throw light taken into consideration scientific discussion concerning diseases lessons learned them.

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