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Volume 19, Number 6—June 2013
Research

Transmission Potential of Rift Valley Fever Virus over the Course of the 2010 Epidemic in South Africa

Raphaëlle MétrasComments to Author , Marc Baguelin, W. John Edmunds, Peter N. Thompson, Alan Kemp, Dirk U. Pfeiffer, Lisa M. Collins, and Richard G. White
Author affiliations: Royal Veterinary College, Hatfield, UK (R. Métras, D.U. Pfeiffer); London School of Hygiene and Tropical Medicine, London, UK (R. Métras, M. Baguelin, W.J. Edmunds, R.G. White); Health Protection Agency, London (M. Baguelin); University of Pretoria, Pretoria, South Africa (P.N. Thompson); National Institute for Communicable Diseases, Sandringham, South Africa (A. Kemp); Queen’s University Belfast, Belfast, Northern Ireland, UK (L.M. Collins)

Main Article

Figure 2

Schematic representation of the Wallinga-Teunis algorithm extended with spatial information. Farm i could get infection from Farm j, but it also could get infection from Farms k1, k2, and k3. In this example, the most likely time difference between onset of symptoms is 4 days (based on the serial interval distribution, given below the x axis), and the most likely distance between farms is short (<1 km). Therefore, Farm j is the most likely farm to have infected Farm i (this scenario maximizes

Figure 2. . Schematic representation of the Wallinga-Teunis algorithm extended with spatial information. Farm i could get infection from Farm j, but it also could get infection from Farms k1, k2, and k3. In this example, the most likely time difference between onset of symptoms is 4 days (based on the serial interval distribution, given below the x-axis), and the most likely distance between farms is short (<1 km). Therefore, Farm j is the most likely farm to have infected Farm i (this scenario maximizes the probability in both dimensions). See the online Technical Appendix (wwwnc.cdc.gov/EID/article/19/6/12-1641-Techapp1.pdf) for details.

Main Article

Page created: May 20, 2013
Page updated: May 20, 2013
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