The role of connectivity on malaria dynamics across areas with contrasting control coverage in the Peruvian Amazon.

Autor: Carrasco-Escobar, Gabriel, Villa, Diego, Barja, Antony, Lowe, Rachel, Llanos-Cuentas, Alejandro, Benmarhnia, Tarik
Předmět:
Zdroj: PLoS Neglected Tropical Diseases; 11/4/2024, Vol. 18 Issue 11, p1-20, 20p
Abstrakt: Network analysis may improve the understanding of malaria epidemiology in rural areas of the Amazon region by explicitly representing the relationships between villages as a proxy for human population mobility. This study tests a comprehensive set of connectivity metrics and their relationship with malaria incidence across villages with contrasting PAMAFRO (a malaria control initiative) coverage levels in the Loreto department of Peru using data from the passive case detection reports from the Peruvian Ministry of Health between 2011 and 2018 at the village level. A total of 24 centrality metrics were computed and tested on 1608 nodes (i.e., villages/cities). Based on its consistency and stability, the betweenness centrality type outperformed other metrics. No appreciable differences in the distributions of malaria incidence were found when using different weights, including population, deforested area, Euclidian distance, or travel time. Overall, villages in the top quintile of centrality have a higher malaria incidence in comparison with villages in the bottom quintile of centrality (Mean Difference in cases per 1000 population; P. vivax = 165.78 and P. falciparum = 76.14). The mean difference between villages at the top and bottom centrality quintiles increases as PAMAFRO coverage increases for both P. vivax (Tier 1 = 155.36; Tier 2 = 176.22; Tier 3 = 326.08) and P. falciparum (Tier 1 = 48.11; Tier 2 = 95.16; Tier 3 = 139.07). The findings of this study support the shift in current malaria control strategies from targeting specific locations based on malaria metrics to strategies based on connectivity neighborhoods that include influential connected villages. Author summary: In our study, we explored how the connections between villages in the Amazon region can help us better understand the spread of malaria. By examining how people move between these villages, we identified key locations that play a significant role in the spread of the disease. We used data from 2011 to 2018, collected from the Peruvian Ministry of Health, focusing on the Loreto department. Our analysis involved 1608 villages, where we computed and tested 24 different connectivity metrics to see which one best predicted malaria cases. We found that the "betweenness centrality" metric, which measures how often a village serves as a bridge between others, was the most reliable predictor of malaria incidence. Interestingly, we discovered that villages with higher connectivity tended to have more malaria cases. This trend was even more pronounced in areas with stronger malaria control efforts. Our findings suggest that current malaria control strategies could be improved by focusing not just on individual villages with high malaria rates but also on those that are well-connected to others. This approach could lead to more effective interventions and a better understanding of how diseases like malaria spread in rural areas. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje