Author: Sangeeta Bhatia; Britta Lassmann; Emily Cohn; Malwina Carrion; Moritz U.G. Kraemer; Mark Herringer; John Brownstein; Larry Madoff; Anne Cori; Pierre Nouvellet
Title: Using Digital Surveillance Tools for Near Real-Time Mapping of the Risk of International Infectious Disease Spread: Ebola as a Case Study Document date: 2019_11_15
ID: jwesa12u_280
Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101/19011940 doi: medRxiv preprint We estimated the parameters of gravity modelp stay which is the probability of an infectious case staying within a country, and γ which measures the extent to which distance between two locations influences the flow of people between them (Fig 34) . and j is proportional to the product of their populations and inv.....
Document: is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101/19011940 doi: medRxiv preprint We estimated the parameters of gravity modelp stay which is the probability of an infectious case staying within a country, and γ which measures the extent to which distance between two locations influences the flow of people between them (Fig 34) . and j is proportional to the product of their populations and inversely population to the distance between them raised to an exponent γ. The parameter γ thus modulates the influence of distance on the population flow. p stay represents the probability of an individual to stay in a given location during their infectious period. The solid lines represents the median estimates obtained using WHO (yellow), ProMED (blue) and HealthMap (green) data. The shaded regions represent the 95% CrI.
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