Modelling of poliovirus transmission in Pakistan and Afghanistan to identify optimal vaccination strategies for eradication
File(s)
Author(s)
Molodecky, Natalia A.
Type
Thesis
Abstract
The Global Polio Eradication Initiative (GPEI) is facing significant challenges in achieving its goal, including continued transmission of serotype-1 wild-type poliovirus (WPV1) in Pakistan and Afghani- stan and emerging outbreaks of serotype-2 vaccine-derived poliovirus (VDPV2) following global wi- thdrawal of serotype-2 oral poliovirus vaccine (OPV2). Here I address these challenges using statistical and mathematical analysis of global polio surveillance data.
(1) I fit mixed-effects logistic regression models to the presence of WPV1 cases in districts of Pakistan and Afghanistan between 2010-2016. To accurately capture the force of infection between districts, 6 models of population movement were compared. The best-fitting model was used to produce forecasts of poliomyelitis incidence. The risk of polio cases was predicted to reduce in 2017.
(2) I developed a stochastic mathematical model and fitted it to incidence of WPV1 in Pakistan and Afghanistan for 2010-2016. The developed model reliably captured WPV1 transmission dynamics and identified substantial between-district transmission and seasonality.
(3) In 2015, I estimated population immunity against serotype-2 poliomyelitis in Pakistan and forecasted immunity for the time of planned OPV2 withdrawal using a cohort model. Immunity was forecasted to improve in April 2016. These analyses informed the endorsement of OPV2 withdrawal.
(4) I developed a transmission model and fitted it to VDPV2 cases in Pakistan and Afghanistan for 2010-2016. Using the model, I determined optimal vaccination responses to VDPV2 outbreaks following OPV2 withdrawal. A greater spatial scale of response is required with increasing time since withdrawal and decreasing mucosal immunity. Incorporating inactivated poliovirus vaccine (IPV) into the response increases the probability of stopping transmission by minimizing risk of spread of reverted vaccine virus.
In summary, these analyses have been used by the GPEI to adapt and develop their immediate and longer term strategy as evidenced in publications from the Strategic Advisory Group of Experts on Immunization [1, 2].
(1) I fit mixed-effects logistic regression models to the presence of WPV1 cases in districts of Pakistan and Afghanistan between 2010-2016. To accurately capture the force of infection between districts, 6 models of population movement were compared. The best-fitting model was used to produce forecasts of poliomyelitis incidence. The risk of polio cases was predicted to reduce in 2017.
(2) I developed a stochastic mathematical model and fitted it to incidence of WPV1 in Pakistan and Afghanistan for 2010-2016. The developed model reliably captured WPV1 transmission dynamics and identified substantial between-district transmission and seasonality.
(3) In 2015, I estimated population immunity against serotype-2 poliomyelitis in Pakistan and forecasted immunity for the time of planned OPV2 withdrawal using a cohort model. Immunity was forecasted to improve in April 2016. These analyses informed the endorsement of OPV2 withdrawal.
(4) I developed a transmission model and fitted it to VDPV2 cases in Pakistan and Afghanistan for 2010-2016. Using the model, I determined optimal vaccination responses to VDPV2 outbreaks following OPV2 withdrawal. A greater spatial scale of response is required with increasing time since withdrawal and decreasing mucosal immunity. Incorporating inactivated poliovirus vaccine (IPV) into the response increases the probability of stopping transmission by minimizing risk of spread of reverted vaccine virus.
In summary, these analyses have been used by the GPEI to adapt and develop their immediate and longer term strategy as evidenced in publications from the Strategic Advisory Group of Experts on Immunization [1, 2].
Version
Open Access
Date Issued
2017-12
Date Awarded
2018-08
Advisor
Grassly, Nicholas
Donnelly, Christl
Publisher Department
School of Public Health
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)