» Articles » PMID: 27668435

Spatial Dynamics and High Risk Transmission Pathways of Poliovirus in Nigeria 2001-2013

Overview
Journal PLoS One
Date 2016 Sep 27
PMID 27668435
Citations 3
Authors
Affiliations
Soon will be listed here.
Abstract

The polio eradication programme in Nigeria has been successful in reducing incidence to just six confirmed cases in 2014 and zero to date in 2015, but prediction and management of future outbreaks remains a concern. A Poisson mixed effects model was used to describe poliovirus spread between January 2001 and November 2013, incorporating the strength of connectivity between districts (local government areas, LGAs) as estimated by three models of human mobility: simple distance, gravity and radiation models. Potential explanatory variables associated with the case numbers in each LGA were investigated and the model fit was tested by simulation. Spatial connectivity, the number of non-immune children under five years old, and season were associated with the incidence of poliomyelitis in an LGA (all P < 0.001). The best-fitting spatial model was the radiation model, outperforming the simple distance and gravity models (likelihood ratio test P < 0.05), under which the number of people estimated to move from an infected LGA to an uninfected LGA was strongly associated with the incidence of poliomyelitis in that LGA. We inferred transmission networks between LGAs based on this model and found these to be highly local, largely restricted to neighbouring LGAs (e.g. 67.7% of secondary spread from Kano was expected to occur within 10 km). The remaining secondary spread occurred along routes of high population movement. Poliovirus transmission in Nigeria is predominantly localised, occurring between spatially contiguous areas. Outbreak response should be guided by knowledge of high-probability pathways to ensure vulnerable children are protected.

Citing Articles

Review of poliovirus modeling performed from 2000 to 2019 to support global polio eradication.

Thompson K, Kalkowska D Expert Rev Vaccines. 2020; 19(7):661-686.

PMID: 32741232 PMC: 7497282. DOI: 10.1080/14760584.2020.1791093.


Responding to Communicable Diseases in Internationally Mobile Populations at Points of Entry and along Porous Borders, Nigeria, Benin, and Togo.

Merrill R, Rogers K, Ward S, Ojo O, Kakai C, Agbeko T Emerg Infect Dis. 2017; 23(13).

PMID: 29155668 PMC: 5711311. DOI: 10.3201/eid2313.170520.


Risk factors and short-term projections for serotype-1 poliomyelitis incidence in Pakistan: A spatiotemporal analysis.

Molodecky N, Blake I, OReilly K, Wadood M, Safdar R, Wesolowski A PLoS Med. 2017; 14(6):e1002323.

PMID: 28604777 PMC: 5467805. DOI: 10.1371/journal.pmed.1002323.

References
1.
. Surveillance systems to track progress towards global polio eradication,2012–2013. Wkly Epidemiol Rec. 2014; 89(17):165-73. View

2.
Tatem A, Huang Z, Narib C, Kumar U, Kandula D, Pindolia D . Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malar J. 2014; 13:52. PMC: 3927223. DOI: 10.1186/1475-2875-13-52. View

3.
Famulare M, Hu H . Extracting transmission networks from phylogeographic data for epidemic and endemic diseases: Ebola virus in Sierra Leone, 2009 H1N1 pandemic influenza and polio in Nigeria. Int Health. 2015; 7(2):130-8. PMC: 4379986. DOI: 10.1093/inthealth/ihv012. View

4.
Grassly N, Fraser C, Wenger J, Deshpande J, Sutter R, Heymann D . New strategies for the elimination of polio from India. Science. 2006; 314(5802):1150-3. DOI: 10.1126/science.1130388. View

5.
Eggo R, Cauchemez S, Ferguson N . Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States. J R Soc Interface. 2010; 8(55):233-43. PMC: 3033019. DOI: 10.1098/rsif.2010.0216. View