| Autor: |
Eneanya OA; Guinea Worm Eradication Program, The Carter Center, Atlanta, Georgia., Delea MG; Guinea Worm Eradication Program, The Carter Center, Atlanta, Georgia., Cano J; Expanded Special Project for Elimination of Neglected Tropical Diseases, World Health Organization, Brazzaville, Republic of Congo., Tchindebet PO; Guinea Worm Eradication Program, Ministry of Public Health, N'Djamena, Chad., Richards RL; School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia., Zhao Y; Guinea Worm Eradication Program, The Carter Center, Atlanta, Georgia., Meftuh A; Guinea Worm Eradication Program, The Carter Center, N'Djamena, Chad., Unterwegner K; Guinea Worm Eradication Program, The Carter Center, Atlanta, Georgia., Guagliardo SAJ; Parasitic Diseases Branch, Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia., Hopkins DR; Guinea Worm Eradication Program, The Carter Center, Atlanta, Georgia., Weiss A; Guinea Worm Eradication Program, The Carter Center, Atlanta, Georgia. |
| Abstrakt: |
A comprehensive understanding of the spatial distribution and correlates of infection are key for the planning of disease control programs and assessing the feasibility of elimination and/or eradication. In this work, we used species distribution modeling to predict the environmental suitability of the Guinea worm (Dracunculus medinensis) and identify important climatic and sociodemographic risk factors. Using Guinea worm surveillance data collected by the Chad Guinea Worm Eradication Program (CGWEP) from 2010 to 2022 in combination with remotely sensed climate and sociodemographic correlates of infection within an ensemble machine learning framework, we mapped the environmental suitability of Guinea worm infection in Chad. The same analytical framework was also used to ascertain the contribution and influence of the identified climatic risk factors. Spatial distribution maps showed predominant clustering around the southern regions and along the Chari River. We also identified areas predicted to be environmentally suitable for infection. Of note are districts near the western border with Cameroon and southeastern border with Central African Republic. Key environmental correlates of infection as identified by the model were proximity to permanent rivers and inland lakes, farmlands, land surface temperature, and precipitation. This work provides a comprehensive model of the spatial distribution of Guinea worm infections in Chad 2010-2022 and sheds light on potential environmental correlates of infection. As the CGWEP moves toward elimination, the methods and results in this study will inform surveillance activities and help optimize the allocation of intervention resources. |
