Conference Abstract | Volume 8, Abstract ELIC2025211 (Poster 095) | Published: 07 Aug 2025
Nsonghomanyi Fritz Roland Fonkeng1,&, Babatunde Olajumoke2, Manuela Rehr1, Onyebuchi Okoro3, Toluwanimi Adewole3, Emmanuel Agogo1
1FIND, Geneva, Switzerland, 2Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria, 3African Field Epidemiology Network Nigeria, Abuja, Nigeria
&Corresponding author: Nsonghomanyi Fritz Roland Fonkeng, FIND Geneva, Switzerland, Email: fritz.fonkeng@finddx.org
Received: 24 Mar 2025, Accepted: 09 Jul 2025, Published: 07 Aug 2025
Domain: Infectious Disease Epidemiology
Keywords: Geospatial analysis, Diagnostic network optimization, Laboratory access, Lassa Fever, Nigeria, Public health infrastructure
©Nsonghomanyi Fritz Roland Fonkeng et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Nsonghomanyi Fritz Roland Fonkeng et al., Optimization of Laboratory Networks for Epidemic-Prone Diseases in Nigeria: A Geospatial Approach. Journal of Interventional Epidemiology and Public Health. 2025;8(Conf Proc 5):00239. https://doi.org/10.37432/JIEPH-CONFPRO5-00239
Nigeria continues to experience recurring outbreaks of epidemic-prone diseases such as Lassa Fever, Yellow Fever, and Mpox. Despite investments to expand diagnostic access, challenges in geographic coverage and timeliness persist. This study uses a geospatial diagnostic network optimization (DNO) approach to systematically analyze Nigeria’s laboratory network for epidemic-prone diseases and propose improvements for outbreak detection and response.
A national-level geospatial analysis was conducted using ArcGIS Pro and OptiDx. Inputs included population density, disease burden, facility locations, and laboratory capacities. Optimization scenarios evaluated travel time reductions and strategic lab placements under varying disease probability assumptions.
Menu