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A crowdsourcing-based optimal route selection for drug delivery in low- and middle-income countries

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Abstract

The timely delivery of life-saving products such as medicines from the pharmacy to the patient’s location requires availability of an adequate road infrastructure and reliable directions to the patient’s home. However, in many regions of low- and middle-income countries (LMIC), the road infrastructure is in poor state, and medicine delivery is affected by flooded roads, unsafe routes, congestion, traffic disruptions due to accidents, and lack of reliable navigation directions. Owing to the proliferation of smartphones and increasing mobile connectivity, these days the vehicle users rely heavily on routing software apps to select and follow shortest or fastest routes for reaching the destination and deliver life-saving medicinal products. However, routing software apps such as Google and Waze are not able to detect flooded roads or insecure and unsafe roads in various locations in LMIC countries, which causes disruption in drug delivery. Hence, this paper proposes a crowdsourcing-based approach to select optimal drug delivery routes with the objective to prevent drug delivery disruption and to guarantee the required delivery time-widow. The novelty of the proposed approach is that it determines optimal drug delivery routes based on real-time crowdsourced data and using communication services such as SMS. Furthermore, it overcomes the limitation of routing software apps. The tests conducted using the proposed approach show promised results with no drug delivery disruption. In the rainy season, 71% all selected drug delivery routes recommended by proposed system were optimal compared with 89% in the dry season. The similar tests using Google Maps are less successful, where in the rainy season only 11% and in the dry season 49% of the selected routes were found to be optimal.

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Acknowledgements

We acknowledge previous help of Mrs. SOSSOU Dolorès Valérie from University of Abomey Calvi for assisting us during the testing of proposed NoMap routing system.

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Authors and Affiliations

  1. Technical University of Munich, Chair of Applied Software Engineering, Boltzmannstr. 3, 85748, Garching, Munich, Germany

    Thierry O. C. Edoh

  2. Department of Computer Science, State University of New York, Korea, Songdo, Incheon, South Korea

    Pravin Amrut Pawar

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  1. Thierry O. C. Edoh
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  2. Pravin Amrut Pawar
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Correspondence to Pravin Amrut Pawar.

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Edoh, T.O.C., Pawar, P.A. A crowdsourcing-based optimal route selection for drug delivery in low- and middle-income countries. Pers Ubiquit Comput 28, 289–307 (2024). https://doi.org/10.1007/s00779-020-01424-0

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