osrm: Interface Between R and the OpenStreetMap-Based
Routing Service OSRM

Giraud, Timothée

doi:10.21105/joss.04574

Cited by 30 publications

(10 citation statements)

References 21 publications

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“…We constructed this matrix using the Open Source Routing Machine (OSRM) tool which employs Dijkstra’s routing algorithm to find the shortest path between two nodes in a network, applied to the complete set of buildings and paths available on OpenStreetMap for Ifanadiana District [30]. We used the R package “osrm” for implementation of OSRM with R software [31]. Via the OSRM routing algorithm, we obtained two outputs: a paired matrix of travel times between each building (including the CHS), and the shortest travel path between each pair of points.…”

Section: Methodsmentioning

confidence: 99%

Combining participatory mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile

Randriamihaja,

Ihantamalala,

Rafenoarimalala

et al. 2024

Preprint

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Community health programs are gaining relevance within national health systems and becoming inherently more complex. To ensure that community health programs lead to equitable geographic access to care, the WHO recommends adapting the target population and workload of community health workers (CHWs) according to the local geographic context and population size of the communities they serve. Geographic optimization could be particularly beneficial for those activities that require CHWs to visit households door-to-door for last mile delivery of care. The goal of this study was to demonstrate the feasibility and utility of geographic optimization in the context of community health programs in rural areas of the developing world. We developed a decision-making tool based on participatory mapping and route optimization algorithms in order to inform the micro-planning and implementation of two kinds of community health interventions requiring door-to-door delivery: mass distribution campaigns and proactive community case management (proCCM) programs. We applied the Vehicle Routing Problem with Time Windows (VRPTW) algorithm to optimize the on-foot routes that CHWs take to visit households in their catchment, using a geographic dataset obtained from participatory mapping onOpenStreetMapcomprising over 100,000 buildings and 20,000 km of footpaths in the rural district of Ifanadiana, Madagascar. We found that personnel-day requirements ranged from less than 15 to over 60 per CHW catchment for mass distribution campaigns, and from less than 5 to over 20 for proCCM programs, assuming 1 visit per month. To facilitate local use of VRPTW algorithms by operational teams, we developed an “e-health” platform to visualize resource requirements, CHW optimal schedules and itineraries according to customizable intervention designs and hypotheses. Further development and scale-up of these tools could help optimize community health programs and other last mile delivery activities, in line with WHO recommendations, linking a new era of big data analytics with the most basic forms of frontline care in resource poor areas.

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Section: Methodsmentioning

confidence: 99%

Combining participatory mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile

Randriamihaja,

Ihantamalala,

Rafenoarimalala

et al. 2024

Preprint

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“…We calculated the median of this value to obtain fokontany-level estimates. The distance to a primary health center was estimated for each building over the full transport network using the Open Source Routing Machine (OSRM) routing algorithm via the package in R (Giraud 2022), and the average distance was calculated for each fokontany.…”

Section: Methodsmentioning

confidence: 99%

Increasing the resolution of malaria early warning systems for use by local health actors

Evans,

Ihantamalala,

Randriamihaja

et al. 2024

Preprint

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Background: The increasing availability of electronic health system data and remotely-sensed environmental variables has led to the emergence of statistical models capable of producing malaria forecasts. Many of these models have been operationalized into malaria early warning systems (MEWSs), which provide predictions of malaria dynamics several months in advance at national and regional levels. However, MEWSs do not generally produce predictions at the village-level, the operational scale of community health systems and the first point of contact for the majority of rural populations in malaria-endemic countries. Methods: We developed a hyper-local MEWS for use within a health-system strengthening intervention in rural Madagascar. It combined bias-corrected, village-level case notification data with remotely sensed environmental variables at spatial scales as fine as a 10m resolution. A spatio-temporal hierarchical generalized linear regression model was trained on monthly malaria case data from 195 communities from 2017-2020 and evaluated via cross-validation. The model was then integrated into an automated workflow with environmental data updated monthly to create a continuously updating MEWS capable of predicting malaria cases up to three months in advance at the village-level. Predictions were transformed into indicators relevant to health system actors by estimating the quantities of medical supplies required at each health clinic and the number of cases remaining untreated at the community level. Results: The statistical model was able to accurately reproduce village-level case data, performing nearly five times as well as a null model during cross-validation. The dynamic environmental variables, particularly those associated with standing water and rice field dynamics, were strongly associated with malaria incidence, allowing the model to accurately predict future incidence rates. When compared to historical stock data, the MEWS predicted stock requirements within 50 units of reported stock requirements 68% of the time. Conclusion: We demonstrate the feasibility of developing an automatic, hyper-local MEWS leveraging remotely-sensed environmental data at fine spatial scales. As health system data become increasingly digitized, this method can be easily applied to other regions and be updated with near real-time health data to further increase performance.

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“…The shortest road paths between premises were calculated using the premises’ geolocations and the OpenStreetMap® calculation paths service (Giraud, 2022; Luxen and Vetter, 2011). The distance of 25,524 unique pairs of origin-destination shipments was calculated.…”

Section: Methodsmentioning

confidence: 99%

Analyzing the intrastate and interstate swine movement network in the United States

Cardenas,

Valencio,

Sanchez

et al. 2024

Preprint

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Disease prevention and control tactics rely on identifying and restricting animal movement to attenuate the between premises spread of disease in livestock systems. Therefore, it is essential to uncover between premises movement dynamics, including shipment distances and network based control strategies. Here, we analyzed three years of between premises pig movements, which include 197,022 unique animal shipments, 3,973 premises, and 391,625,374 pigs shipped across 20 U.S. states. We constructed unweighted, directed, temporal networks at 180 day intervals to calculate premises to premises movement distances, the size of connected components, network loyalty, and degree distributions, and, based on the out going contact chains, identified network based control actions. Our results show that the median distance between premises pig movements was 74.37 km, with median intrastate and interstate movements of 52.71 km and 328.76 km, respectively. On average, 2,842 premises were connected via 6,705 edges, resulting in a weak giant connected component that included 91% of the premises. The premises level network exhibited loyalty, with a median of 0.65 (IQR: 0.45, 0.77). Results highlight the effectiveness of node targeting and disease spread; we demonstrated that targeting 25% of farms with the highest degree or betweenness limited spread to 1.23% and 1.7% of premises, respectively.

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osrm: Interface Between R and the OpenStreetMap-Based Routing Service OSRM

Cited by 30 publications

References 21 publications

Combining participatory mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile

Combining participatory mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile

Increasing the resolution of malaria early warning systems for use by local health actors

Analyzing the intrastate and interstate swine movement network in the United States

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