ArticleScientific reports2018
Mapping road network communities for guiding disease surveillance and control strategies.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Influence of spatial connectivity on the spread of Mpox in Maï-Ndombe province, Democratic Republic of the Congo, 2025.International journal of health geographics · 2026Article
- Links between infrastructure for human movement and early Ebola outbreak trajectories.Scientific reports · 2026Article
- Deep learning-based extraction of Kenya's historical road network from topographic maps.Scientific data · 2025Article
- Spatiotemporal Drivers of the African Swine Fever Epidemic in Lao PDR.Transboundary and emerging diseases · 2023Article
- COVID-19 contagion across remote communities in tropical forests.Scientific reports · 2022Article
- The impact of super-spreader cities, highways, and intensive care availability in the early stages of the COVID-19 epidemic in Brazil.Scientific reports · 2021Article
- Examining the diffusion of coronavirus disease 2019 cases in a metropolis: a space syntax approach.International journal of health geographics · 2021Article
- High levels of imported asymptomatic malaria but limited local transmission in KwaZulu-Natal, a South African malaria-endemic province nearing malaria elimination.Malaria journal · 2020Article
- Travel Time to Health Facilities as a Marker of Geographical Accessibility Across Heterogeneous Land Coverage in Peru.Frontiers in public health · 2020Article
- Utilizing geospatial information to implement SDGs and monitor their Progress.Environmental monitoring and assessment · 2019Review
- Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine.Journal of travel medicine · 2019Review
- Article
- Synergy between roads and disturbance favourPeerJ · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Human mobility is increasing in its volume, speed and reach, leading to the movement and introduction of pathogens through infected travelers. An understanding of how areas are connected, the strength of these connections and how this translates into disease spread is valuable for planning surveillance and designing control and elimination strategies. While analyses have been undertaken to identify and map connectivity in global air, shipping and migration networks, such analyses have yet to be undertaken on the road networks that carry the vast majority of travellers in low and middle income settings. Here we present methods for identifying road connectivity communities, as well as mapping bridge areas between communities and key linkage routes. We apply these to Africa, and show how many highly-connected communities straddle national borders and when integrating malaria prevalence and population data as an example, the communities change, highlighting regions most strongly connected to areas of high burden. The approaches and results presented provide a flexible tool for supporting the design of disease surveillance and control strategies through mapping areas of high connectivity that form coherent units of intervention and key link routes between communities for targeting surveillance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.