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Transmission potential of Rift Valley fever virus over the course of the 2010 epidemic in South Africa.

Métras R, Baguelin M, Edmunds WJ, Thompson PN, Kemp A, Pfeiffer DU, Collins LM, White RG - Emerging Infect. Dis. (2013)

Bottom Line: A Rift Valley fever (RVF) epidemic affecting animals on domestic livestock farms was reported in South Africa during January-August 2010.The epidemic fade-out likely resulted first from the immunization of animals following natural infection or vaccination.Increased availability of vaccine use data would enable evaluation of the effect of RVF vaccination campaigns.

View Article: PubMed Central - PubMed

Affiliation: Royal Veterinary College, Hatfield, UK.

ABSTRACT
A Rift Valley fever (RVF) epidemic affecting animals on domestic livestock farms was reported in South Africa during January-August 2010. The first cases occurred after heavy rainfall, and the virus subsequently spread countrywide. To determine the possible effect of environmental conditions and vaccination on RVF virus transmissibility, we estimated the effective reproduction number (Re) for the virus over the course of the epidemic by extending the Wallinga and Teunis algorithm with spatial information. Re reached its highest value in mid-February and fell below unity around mid-March, when vaccination coverage was 7.5%-45.7% and vector-suitable environmental conditions were maintained. The epidemic fade-out likely resulted first from the immunization of animals following natural infection or vaccination. The decline in vector-suitable environmental conditions from April onwards and further vaccination helped maintain Re below unity. Increased availability of vaccine use data would enable evaluation of the effect of RVF vaccination campaigns.

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Related in: MedlinePlus

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.
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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.

Mentions: The Wallinga and Teunis method (17), extended with spatial information, enables estimation of Re at the farm level by calculating the relative likelihood, or probability (pij), that a specific farm (i) gets infected from another specific farm (j). This probability, pij, is equal to the probability that farm j infects farm i, divided by the probability that farm i had been infected from any other farm (k) in the dataset (Figure 2). These probabilities depend on the number of days separating the onset of symptoms on the 2 farms (i and j) and the distance (in kilometers) separating i and j, and the probabilities were extracted from a probability density function of the generation interval (Technical Appendix). The generation (or serial) interval was defined as the time between onset of symptoms for a primary case and the onset of symptoms for its secondary case (18). In the stylized example in Figure 2, the most likely time difference was 4 days (determined on the basis of the serial interval distribution, given below the x axis), and the most likely distance is short (<1 km). Therefore, farm j is the most likely farm to have infected farm i (this maximized the probability in both dimensions).


Transmission potential of Rift Valley fever virus over the course of the 2010 epidemic in South Africa.

Métras R, Baguelin M, Edmunds WJ, Thompson PN, Kemp A, Pfeiffer DU, Collins LM, White RG - Emerging Infect. Dis. (2013)

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.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3713830&req=5

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.
Mentions: The Wallinga and Teunis method (17), extended with spatial information, enables estimation of Re at the farm level by calculating the relative likelihood, or probability (pij), that a specific farm (i) gets infected from another specific farm (j). This probability, pij, is equal to the probability that farm j infects farm i, divided by the probability that farm i had been infected from any other farm (k) in the dataset (Figure 2). These probabilities depend on the number of days separating the onset of symptoms on the 2 farms (i and j) and the distance (in kilometers) separating i and j, and the probabilities were extracted from a probability density function of the generation interval (Technical Appendix). The generation (or serial) interval was defined as the time between onset of symptoms for a primary case and the onset of symptoms for its secondary case (18). In the stylized example in Figure 2, the most likely time difference was 4 days (determined on the basis of the serial interval distribution, given below the x axis), and the most likely distance is short (<1 km). Therefore, farm j is the most likely farm to have infected farm i (this maximized the probability in both dimensions).

Bottom Line: A Rift Valley fever (RVF) epidemic affecting animals on domestic livestock farms was reported in South Africa during January-August 2010.The epidemic fade-out likely resulted first from the immunization of animals following natural infection or vaccination.Increased availability of vaccine use data would enable evaluation of the effect of RVF vaccination campaigns.

View Article: PubMed Central - PubMed

Affiliation: Royal Veterinary College, Hatfield, UK.

ABSTRACT
A Rift Valley fever (RVF) epidemic affecting animals on domestic livestock farms was reported in South Africa during January-August 2010. The first cases occurred after heavy rainfall, and the virus subsequently spread countrywide. To determine the possible effect of environmental conditions and vaccination on RVF virus transmissibility, we estimated the effective reproduction number (Re) for the virus over the course of the epidemic by extending the Wallinga and Teunis algorithm with spatial information. Re reached its highest value in mid-February and fell below unity around mid-March, when vaccination coverage was 7.5%-45.7% and vector-suitable environmental conditions were maintained. The epidemic fade-out likely resulted first from the immunization of animals following natural infection or vaccination. The decline in vector-suitable environmental conditions from April onwards and further vaccination helped maintain Re below unity. Increased availability of vaccine use data would enable evaluation of the effect of RVF vaccination campaigns.

Show MeSH
Related in: MedlinePlus