Spatial model for risk prediction and sub-national prioritization to aid poliovirus eradication in Pakistan

BMC Medicine

Research Article

Sue Gerber¹  Alexander Upfill-Brown²  Guillaume Chabot-Couture³  Steve J. Kroiss³  Hil Lyons³  Laina D. Mercer³  Frank Salet⁴  Rana M. Safdar⁴  Mike Ryan⁴  Aiden O’Leary⁵  Jamal Ahmed⁶  Abdirahman Mahamud⁶  M. Muzaffar Khan⁶ 

[1] Bill and Melinda Gates Foundation, Seattle, WA, USA;David Geffen School of Medicine, University of California, Los Angeles, CA, USA;Institute for Disease Modeling, 3150 138th Ave SE, 98005, Bellevue, WA, USA;National Emergency Operations Centre for Polio Eradication, Islamabad, Pakistan;National Emergency Operations Centre for Polio Eradication, Islamabad, Pakistan;United Nations Children’s Fund (UNICEF), Islamabad, Pakistan;National Emergency Operations Centre for Polio Eradication, Islamabad, Pakistan;World Health Organization, Islamabad, Pakistan;

关键词: Disease mapping;    Polio eradication;    Risk mapping;    Spatial epidemiology;    Hurdle models;    Pakistan;    Risk prioritization;    Vaccination campaigns;    Supplementary immunization activities;   

DOI  :  10.1186/s12916-017-0941-2

 received in 2017-04-07, accepted in 2017-09-06,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundPakistan is one of only three countries where poliovirus circulation remains endemic. For the Pakistan Polio Eradication Program, identifying high risk districts is essential to target interventions and allocate limited resources.MethodsUsing a hierarchical Bayesian framework we developed a spatial Poisson hurdle model to jointly model the probability of one or more paralytic polio cases, and the number of cases that would be detected in the event of an outbreak. Rates of underimmunization, routine immunization, and population immunity, as well as seasonality and a history of cases were used to project future risk of cases.ResultsThe expected number of cases in each district in a 6-month period was predicted using indicators from the previous 6-months and the estimated coefficients from the model. The model achieves an average of 90% predictive accuracy as measured by area under the receiver operating characteristic (ROC) curve, for the past 3 years of cases.ConclusionsThe risk of poliovirus has decreased dramatically in many of the key reservoir areas in Pakistan. The results of this model have been used to prioritize sub-national areas in Pakistan to receive additional immunization activities, additional monitoring, or other special interventions.

【 授权许可】

CC BY   
© The Author(s). 2017