Yadav, P. Health product supply chains in developing countries: diagnosis of the root causes of underperformance and an agenda for reform. Health Syst. Reform 1, 142–154 (2015).
UNICEF. The State of the World’s Children 2015: Executive Summary (UN, 2015).
Gallien, J., Leung, N.-H. Z. & Yadav, P. Inventory policies for pharmaceutical distribution in Zambia: improving availability and access equity. Prod. Oper. Manag. 30, 4501–4521 (2021).
Merkuryeva, G., Valberga, A. & Smirnov, A. Demand forecasting in pharmaceutical supply chains: a case study. Procedia Comput. Sci. 149, 3–10 (2019).
Yenet, A., Nibret, G. & Tegegne, B. A. Challenges to the availability and affordability of essential medicines in African countries: a scoping review. Clin. Outcomes Res. 15, 443–458 (2023).
De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019).
Ngiam, K. Y. & Khor, W. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 20, e262–e273 (2019).
Bastani, H. et al. Efficient and targeted COVID-19 border testing via reinforcement learning. Nature 599, 108–113 (2021).
Yang, X. et al. A large language model for electronic health records. npj Digit. Med. 5, 194 (2022).
Zuma, S. M. & Modiba, L. M. Challenges associated with provision of essential medicines in the Republic of South Africa and other selected African countries. World J. Pharm. Res. 8, 1532–1547 (2019).
World Health Organization. Service availability and readiness assessment (SARA): an annual monitoring system for service delivery. Implementation guide. IRIS https://iris.who.int/server/api/core/bitstreams/a3003454-b93e-4a11-b96a-5db7a544efef/content (2015).
Droti, B., O’Neill, K. P., Mathai, M., Dovlo, D. Y. T. & Robertson, J. Poor availability of essential medicines for women and children threatens progress towards Sustainable Development Goal 3 in Africa. BMJ Glob. Health 4, e001306 (2019).
Tetteh, E. Creating reliable pharmaceutical distribution networks and supply chains in African countries: Implications for access to medicines. Res. Soc. Adm. Pharm. 5, 286–297 (2009).
Cachon, G. & Terwiesch, C. Matching Supply with Demand: An Introduction to Operations Management 5th edn (McGraw-Hill, 2024).
Fenta, T. G. Human resources for public health supply chain management in Ethiopia: competency mapping and training needs. Ethiop. J. Health Dev. 31, 266–275 (2017).
Johnson, A., Peter, K. & Shital, M. Inventory management practices and supply chain performance of antiretroviral medicines in public hospitals in Nyamira County, Kenya. Rwanda J. Med. Health Sci. 4, 257–268 (2021).
Mwashiuya, S., Mchopa, A. D. & Shayo, F. A. Effect of inventory control on medical supply chain performance: the moderating role of information technology among medical stores department customers in Tanzania. Afr. J. Procure. Logist. Supply Chain Manag. 8, 1–14 (2025).
Braa, J. & Muquinge, H. Building collaborative networks in Africa on health information systems and open source software development-experiences from the HISP/BEANISH Network. In IST Afr. 3 (IST-Africa Initiative, 2007).
Donnelly, J. How did Sierra Leone provide free health care? Lancet 377, 1393–1396 (2011).
Linnander, E. et al. Process evaluation of knowledge transfer across industries: leveraging Coca-Cola’s supply chain expertise for medicine availability in Tanzania. PLoS One 12, e0186832 (2017).
Linnander, E., LaMonaca, K., Brault, M. A., Vyavahare, M. & Curry, L. A mixed methods evaluation of a multi-country, cross-sectoral knowledge transfer partnership to improve health systems across Africa. Int. J. Mult. Res. Approaches 10, 136–148 (2018).
Project Last Mile. Project Last Mile Annual Report January–December 2023. https://www.projectlastmile.com/wp-content/uploads/2024/06/2023-PLM-Annual-Report-240605s.pdf (2023).
Zhu, X., Ninh, A., Zhao, H. & Liu, Z. Demand forecasting with supply-chain information and machine learning: evidence in the pharmaceutical industry. Prod. Oper. Manag. 30, 3231–3252 (2021).
Mbonyinshuti, F., Nkurunziza, J., Niyobuhungiro, J. & Kayitare, E. The prediction of essential medicines demand: a machine learning approach using consumption data in Rwanda. Processes 10, 26 (2021).
Bastani, H., Bastani, O. & Chung, T.-H. in Responsible and Sustainable Operations: The New Frontier 187–202 (Springer, 2024).
Bertsimas, D. & Thiele, A. A robust optimization approach to inventory theory. Oper. Res. 54, 150–168 (2006).
Yang, C., Hu, Z. & Zhou, S. X. Multilocation newsvendor problem: Centralization and inventory pooling. Manag. Sci. 67, 185–200 (2021).
Aviv, Y. The effect of collaborative forecasting on supply chain performance. Manag. Sci. 47, 1326–1343 (2001).
USAID Deliver Project. Quantification of Health Commodities: A Guide to Forecasting and Supply Planning for Procurement (USAID, 2014).
Reproductive and Maternal Health Services Unit. National Guidelines for Quantification, Procurement, and Pipeline Monitoring for Family Planning Commodities in Kenya (Ministry of Health, 2016).
Fredrick, M. W. & Muturi, W. Factors influencing frequent stock-outs of essential medicines in public health facilities in Kisii County, Kenya. IOSR J. Bus. Manag. 18, 63–75 (2016).
Ban, G.-Y. & Rudin, C. The big data newsvendor: practical insights from machine learning. Oper. Res. 67, 90–108 (2019).
Caruana, R. Multitask learning. Mach. Learn. 28, 41–75 (1997).
Donti, P., Amos, B. & Kolter, J. Z. Task-based end-to-end model learning in stochastic optimization. In Proc. 31st International Conference on Neural Information Processing Systems (eds von Luxburg, U. et al.) 5490–5500 (Curran Associates, 2017).
Elmachtoub, A. N. & Grigas, P. Smart “predict, then optimize”. Manag. Sci. 68, 9–26 (2022).
Bertsimas, D. & Kallus, N. From predictive to prescriptive analytics. Manag. Sci. 66, 1025–1044 (2020).
Wilder, B., Dilkina, B. & Tambe, M. Melding the data-decisions pipeline: decision-focused learning for combinatorial optimization. In Proc. AAAI Conference on Artificial Intelligence Vol. 33, 1658–1665 (2019).
Wang, K., Wilder, B., Perrault, A. & Tambe, M. Automatically learning compact quality-aware surrogates for optimization problems. Adv. Neural Inf. Process. Syst. 33, 9586–9596 (2020).
Shah, S., Wang, K., Wilder, B., Perrault, A. & Tambe, M. Decision-focused learning without decision-making: Learning locally optimized decision losses. Adv. Neural Inf. Process. Syst. 35, 1320–1332 (2022).
Huang, D., Stein, N., Rubin, D. B. & Kou, S. Catalytic prior distributions with application to generalized linear models. Proc. Natl Acad. Sci. USA 117, 12004–12010 (2020).
Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W. & Wager, S. Synthetic difference-in-differences. Am. Econ. Rev. 111, 4088–4118 (2021).
World Bank. Crude birth rate (per 1,000 people) – Sierra Leone. World Bank Group https://data.worldbank.org/indicator/SP.DYN.CBRT.IN?locations=SL (2025).
UNICEF. Sierra Leone: Demographics, Health & Infant Mortality. UNICEF https://data.unicef.org/country/sle/ (2025).
Souza, J. P. et al. A global analysis of the determinants of maternal health and transitions in maternal mortality. Lancet Glob. Health 12, e306–e316 (2024).
World Health Organization. Maternal mortality: fact sheet. WHO https://www.who.int/news-room/fact-sheets/detail/maternal-mortality (2024).
Shafiq, Y. et al. Causes of maternal deaths in Sierra Leone from 2016 to 2019: analysis of districts’ maternal death surveillance and response data. BMJ Open 14, e076256 (2024).
Birge, J. R. & Louveaux, F. Introduction to Stochastic Programming (Springer, 2011).
Gilbert, K. An ARIMA supply chain model. Manag. Sci. 51, 305–310 (2005).
Bastani, H. Predicting with proxies: Transfer learning in high dimension. Manag. Sci. 67, 2964–2984 (2021).
Xu, K. & Bastani, H. Multitask learning and bandits via robust statistics. Manag. Sci. 71, 7752–7773 (2025).
Rubin, D. B. Inference and missing data. Biometrika 63, 581–592 (1976).
Kallus, N. & Mao, X. Stochastic optimization forests. Manag. Sci. 69, 1975–1994 (2022).
Card, D. E. & Krueger, A. B. Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania. Working paper no. w4509 (National Bureau of Economic Research, 1993).
Abadie, A., Diamond, A. & Hainmueller, J. Synthetic control methods for comparative case studies: estimating the effect of California’s tobacco control program. J. Am. Stat. Assoc. 105, 493–505 (2010).
Clarke, D., Pailañir, D., Athey, S. & Imbens, G. Synthetic Difference In Differences Estimation. IZA Discussion Papers 15907 (IZA Network, 2023).
Candes, E. & Recht, B. Exact matrix completion via convex optimization. Commun. ACM 55, 111–119 (2012).
Penno, E., Gauld, R. & Audas, R. How are population-based funding formulae for healthcare composed? A comparative analysis of seven models. BMC Health Serv. Res. 13, 470 (2013).
MoHS & Center for International Earth Science Information Network. Sierra Leone National Health Facilities Dataset Version 01. Columbia University https://doi.org/10.7916/ag19-s334 (Columbia University, 2023).
Weiss, D. et al. Global maps of travel time to healthcare facilities. Nat. Med. 26, 1835–1838 (2020).
WorldPop Global Project Population Data: Estimated Residential Population per 100x100m Grid Square. WorldPop https://worldpop.org (2021).
Statistics Sierra Leone. 2015 Population and Housing Census. Stats SL https://www.statistics.sl/index.php/census/census-2015.html (2015).
The Humanitarian Data Exchange. Sierra Leone – subnational population statistics. HDX https://data.humdata.org/dataset/cod-ps-sle (2023).
Kotary, J., Fioretto, F., Van Hentenryck, P. & Wilder, B. End-to-end constrained optimization learning: a survey. In Proc. Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (ed. Zhou, Z.-H.) 4475–4482 (International Joint Conferences on Artificial Intelligence, 2021); https://www.ijcai.org/proceedings/2021/0610.pdf.
Agrawal, A. et al. Differentiable convex optimization layers. In Adv. Neural Inf. Process. Syst. Vol. 32 (NeurIPS, 2019).
Chung, A. T.-H. et al. Data from: Improving access to essential medicines via decision-aware machine learning [Dataset]. Dryad https://doi.org/10.5061/dryad.h9w0vt4tw (2026).
