BACKGROUND: In December 2009, when the H1N1 influenza pandemic appeared to be subsiding, public health officials and unvaccinated individuals faced the question of whether continued H1N1 immunization was still worthwhile. PURPOSE: To delineate what combinations of possible mechanisms could generate a third pandemic wave and then explore whether vaccinating the population at different rates and times would mitigate the wave. METHODS: As part of ongoing work with the Office of the Assistant Secretary for Preparedness and Response at the USDHHS during the H1N1 influenza pandemic, the University of Pittsburgh Models of Infectious Disease Agent Study team employed an agent-based computer simulation model of the Washington DC metropolitan region to delineate what mechanisms could generate a "third pandemic wave" and explored whether vaccinating the population at different rates and times would mitigate the wave. This model included explicit representations of the region's individuals, school systems, workplaces/commutes, households, and communities. RESULTS: Three mechanisms were identified that could cause a third pandemic wave; substantially increased viral transmissibility from seasonal forcing (changing influenza transmission with changing environmental conditions, i.e., seasons) and progressive viral adaptation; an immune escape variant; and changes in social mixing from holiday school closures. Implementing vaccination for these mechanisms, even during the down-slope of the fall epidemic wave, significantly mitigated the third wave. Scenarios showed the gains from initiating vaccination earlier, increasing the speed of vaccination, and prioritizing population subgroups based on Advisory Committee on Immunization Practices recommendations. CONCLUSIONS: Additional waves in an epidemic can be mitigated by vaccination even when an epidemic appears to be waning.
BACKGROUND: In December 2009, when the H1N1influenza pandemic appeared to be subsiding, public health officials and unvaccinated individuals faced the question of whether continued H1N1 immunization was still worthwhile. PURPOSE: To delineate what combinations of possible mechanisms could generate a third pandemic wave and then explore whether vaccinating the population at different rates and times would mitigate the wave. METHODS: As part of ongoing work with the Office of the Assistant Secretary for Preparedness and Response at the USDHHS during the H1N1influenza pandemic, the University of Pittsburgh Models of Infectious Disease Agent Study team employed an agent-based computer simulation model of the Washington DC metropolitan region to delineate what mechanisms could generate a "third pandemic wave" and explored whether vaccinating the population at different rates and times would mitigate the wave. This model included explicit representations of the region's individuals, school systems, workplaces/commutes, households, and communities. RESULTS: Three mechanisms were identified that could cause a third pandemic wave; substantially increased viral transmissibility from seasonal forcing (changing influenza transmission with changing environmental conditions, i.e., seasons) and progressive viral adaptation; an immune escape variant; and changes in social mixing from holiday school closures. Implementing vaccination for these mechanisms, even during the down-slope of the fall epidemic wave, significantly mitigated the third wave. Scenarios showed the gains from initiating vaccination earlier, increasing the speed of vaccination, and prioritizing population subgroups based on Advisory Committee on Immunization Practices recommendations. CONCLUSIONS: Additional waves in an epidemic can be mitigated by vaccination even when an epidemic appears to be waning.
Authors: Neil M Ferguson; Derek A T Cummings; Christophe Fraser; James C Cajka; Philip C Cooley; Donald S Burke Journal: Nature Date: 2006-04-26 Impact factor: 49.962
Authors: Bruce Y Lee; Tina-Marie Assi; Jayant Rajgopal; Bryan A Norman; Sheng-I Chen; Shawn T Brown; Rachel B Slayton; Souleymane Kone; Hailu Kenea; Joel S Welling; Diana L Connor; Angela R Wateska; Anirban Jana; Ann E Wiringa; Willem G Van Panhuis; Donald S Burke Journal: Am J Public Health Date: 2011-11-28 Impact factor: 9.308
Authors: Tina-Marie Assi; Korngamon Rookkapan; Jayant Rajgopal; Vorasith Sornsrivichai; Shawn T Brown; Joel S Welling; Bryan A Norman; Diana L Connor; Sheng-I Chen; Rachel B Slayton; Yongjua Laosiritaworn; Angela R Wateska; Stephen R Wisniewski; Bruce Y Lee Journal: Vaccine Date: 2012-04-24 Impact factor: 3.641
Authors: Bruce Y Lee; Sarah M Bartsch; Yeeli Mui; Leila A Haidari; Marie L Spiker; Joel Gittelsohn Journal: Nutr Rev Date: 2017-01 Impact factor: 7.110
Authors: Jay V DePasse; Kenneth J Smith; Jonathan M Raviotta; Eunha Shim; Mary Patricia Nowalk; Richard K Zimmerman; Shawn T Brown Journal: Am J Epidemiol Date: 2017-05-01 Impact factor: 4.897
Authors: Bruce Y Lee; Sarah M Bartsch; Shawn T Brown; Philip Cooley; William D Wheaton; Richard K Zimmerman Journal: Med Care Date: 2015-03 Impact factor: 2.983
Authors: Tina-Marie Assi; Shawn T Brown; Souleymane Kone; Bryan A Norman; Ali Djibo; Diana L Connor; Angela R Wateska; Jayant Rajgopal; Rachel B Slayton; Bruce Y Lee Journal: Vaccine Date: 2013-04-17 Impact factor: 3.641