Review of Mathematical Optimization and Statistics-based Techniques for Public Health Intervention in India: Balancing Efficiency, Resources, and Policy Goals
Keywords:
Mathematical Optimization, Statistics, Public Health Intervention, Efficiency, Resource Allocation, Modeling, Machine LearningAbstract
The purpose of this academic paper is to present a concise overview of using mathematical optimization and statistics-based methods for public health intervention in India. Through the systematic examination of data, these methods support the efficient use of resources and decision-making based on evidence that aligns with policy objectives. They are vital in addressing constraints related to limited resources and policy goals while maximizing intervention efficiency and effectiveness.
References
C. D. Flagle, “Operational research in the health services,” Annals of the New York Academy of Sciences, vol. 107, no. 2, pp. 748–759, May 1963. DOI: 10.1111/j.1749-6632.1963.tb13318.x. [Online]. Available: https://nyaspubs.onlinelibrary.wiley.com/doi/10.1111/j.17496632.1963.tb13318.x.
A. A. Stinnett and A. D. Paltiel, “Mathematical programming for the efficient allocation of health care resources,” Journal of Health Economics, vol. 15, no. 5, pp. 641–653, Oct. 1996. DOI: 10.1016/s01676296(96) 00493-6. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0167629696004936.
E. S. Pearson, “An appreciation of some aspects of his life and work,” Biometrika, vol. 28, no. 3-4, pp. 193–257, Dec. 1936. DOI: 10 . 1093 / biomet/ 28 . 3 4 . 193. [Online]. Available: https:// www. jstor. org/ stable/2332005?origin=crossref.
F. Brauer, “Mathematical epidemiology: Past, present, and future,” Infectious Disease Modelling, vol. 2, no. 2, pp. 113–127, May 2017. DOI: 10.1016/j.idm.2017.02.001. [Online]. Available: https://www.ncbi. nlm.nih.gov/pmc/articles/PMC6001967/.
M. Holte and C. Mannino, “The implementor/adversary algorithm for the cyclic and robust scheduling problem in health-care,” May 2013. [Online]. Available: https://scite.ai/reports/10.1016/j.ejor.2012.10.029.
H. Zhang, H. Wang, and J. Wei, “Perceptive movement of susceptible individuals with memory,” Mar. 2023. [Online]. Available: https://scite. ai/reports/10.1007/s00285-023-01904-w.
B. Pourbohloul, L. A. Meyers, D. M. Skowronski, M. Krajden, D. M. Patrick, and R. C. Brunham, “Modeling control strategies of respiratory pathogens,” Aug. 2005. [Online]. Available: https://scite.ai/reports/ 10.3201/eid1108.040449.
K. M. Islam and O. M. Araz, “Evaluating the effectiveness of targeted public health control strategies for chlamydia transmission in omaha, nebraska: A mathematical modeling approach,” Jan. 2014. [Online]. Available: https://scite.ai/reports/10.4236/aid.2014.43021.
M. O. Afolabi, O. Wariri, Y. Saidu, et al., “Tracking the uptake and trajectory of covid-19 vaccination coverage in 15 west african countries: An interim analysis,” Dec. 2021. DOI: 10 . 1136 / bmjgh2021 007518. [Online]. Available: https://scite.ai/reports/10.1136/bmjgh-2021007518.
R. Shadi, A. Fakharian, and H. Khaloozadeh, “Modeling and analysis of covid-19 spread: The impacts of nonpharmaceutical protocols,” Sep. 2022. DOI: 10.1155/2022/7706447. [Online]. Available: https://scite.ai/ reports/10.1155/2022/7706447.
Y. Wang, C. Xu, S. Yao, and Z. Yi, “Forecasting the epidemiological trends of covid-19 prevalence and mortality using the advanced -sutte indicator,” Jan. 2020. [Online]. Available: https://scite.ai/reports/10.1017/ s095026882000237x.
O. S. Medina-G´omez and I. S. Medina-Reyes, “Mortalidad por diabetes tipo 2 y la implementaci´on del programa prevenimss: Un estudio de series de tiempo en m´exico, 1998-2015,” May 2018. [Online]. Available: https://scite.ai/reports/10.1590/0102-311x00103117.
Y. Wang, C. Xu, S. Yao, et al., “Estimating the covid-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition,” Nov. 2021. DOI: 10 . 1038 / s41598 021 009486. [Online]. Available: https:// scite. ai/ reports/ 10. 1038/ s41598-021-00948-6.
B. Zhao, Y. Li, B. Wang, et al., “Mathematical modeling and epidemic prediction of covid-19 and its significance to epidemic prevention and control measures,” Jul. 2021. DOI: 10.31579/2690-1897/021. [Online]. Available: https://scite.ai/reports/10.31579/2690-1897/021.
A. Nasif and S. Masroor, “Cardiac surgery and healthcare quality: Is the right question being asked?,” Jun. 2021. [Online]. Available: https:// scite.ai/reports/10.1111/jocs.15709.
J. Y. Lee, R. R. Diaz, H. S. Yu, J. Chung, W. S. Ham, and Y. D. Choi, “Lymphocele after extraperitoneal robot-assisted radical prostatectomy: A propensity score-matching study,” Mar. 2013. [Online]. Available: https://scite.ai/reports/10.1111/iju.12144.
Z. Touma, K. H. Costenbader, B. Hoskin, et al., “Patient-reported outcomes and healthcare resource utilization in systemic lupus erythematosus: Impact of disease activity,” Aug. 2022. [Online]. Available: https://scite.ai/reports/10.21203/rs.3.rs-1669786/v2.
P. Ahmad, S. Qamar, and S. Q. A. Rizvi, “Techniques of data mining in healthcare: A review,” Jun. 2015. DOI: 10.5120/213074126. [Online]. Available: https://scite.ai/reports/10.5120/21307-4126.
B. Ehsani-Moghaddam, K. Martin, and J. Queenan, “Data quality in healthcare: A report of practical experience with the canadian primary care sentinel surveillance network data,” Health Information Management Journal, vol. 50, no. 1-2, pp. 88–92, Dec. 2019. DOI: 10.1177/1833358319887743. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/31805788/.
S. Goli, P. Puri, P. S. Salve, S. Pallikadavath, and K. S. James, “Estimates and correlates of district-level maternal mortality ratio in india,” PLOS global public health, vol. 2, no. 7, e0000441–e0000441, Jul. 2022. [Online]. Available: https://doi.org/10.1371/journal.pgph.0000441.
V. Srivastava, M. Ratna, A. Ray, et al., “Strengthening the immunization supply chain: A time-to-supply based approach to cold chain network optimization extension in madhya pradesh,” Vaccine, vol. 39, no. 45, pp. 6660–6670, Oct. 2021. DOI: 10 . 1016 / j . vaccine . 2021 . 09 . 062. [Online]. Available: https://doi.org/10.1016/j.vaccine.2021.09. 062.
I. Makarova, K. Shubenkova, and A. Pashkevich, Logistical costs minimization for delivery of shot lots by using logistical information systems, Jan. 2017. [Online]. Available: https://doi.org/10.1016/j.proeng. 2017.01.059.
H. Gupta, S. Kumar, D. Yadav, et al., “Data analytics and mathematical modeling for simulating the dynamics of covid-19 epidemic—a case study of india,” Electronics, vol. 10, no. 2, pp. 127–127, Jan. 2021. DOI: 10 . 3390/electronics10020127. [Online]. Available: https://doi.org/10. 3390/electronics10020127.
G. R. Babu, D. Ray, R. Bhaduri, et al., “Covid-19 pandemic in india: Through the lens of modeling,” Global health, science and practice, vol. 9, no. 2, pp. 220–228, Jun. 2021. DOI: 10.9745/ghsp-d-21-00233. [Online]. Available: https://www.ghspjournal.org/content/ghsp/9/2/220. full.pdf.
S. Priyan, “Operations research in healthcare: A review,” JOJ Pub Health, Mar. 2017. DOI: 10.19080/JOJPH.2017.01.555561. [Online]. Available: https://juniperpublishers.com/jojph/JOJPH.MS.ID.555561.php.
B. Ramdurai, “Use of markov chain model for in-patient bed assignment,” Algorithms Research, vol. 6, no. 1, pp. 1–8, DOI: doi:10.5923/j. algorithms.20230601.01.
G. T. Jun, Z. S. Morris, T. Eldabi, et al., “Development of modelling method selection tool for health services management: From problem structuring methods to modelling and simulation methods,” BMC Health Services Research, vol. 11, no. 1, May 2011. DOI: 10.1186/1472-6963-11-108. [Online]. Available: https://bmchealthservres.biomedcentral.com/articles/10.1186/1472-6963-11-108.
R. Zachariah, A. Harries, N. Ishikawa, et al., “Operational research in lowincome countries: What, why, and how?” The Lancet Infectious Diseases, vol. 9, no. 11, pp. 711–717, Nov. 2009. DOI: 10.1016/s14733099(09) 702294. [Online]. Available: http://www.thelancet.com/retrieve/pii/S1473309909702294.
R. Lilford, P. J. Chilton, K. Hemming, A. Girling, C. A. Taylor, and P. Barach, “Evaluating policy and service interventions: Framework to guide selection and interpretation of study end points,” The BMJ, vol. 341, no. aug27 1, pp. c4413–c4413, Aug. 2010. DOI: 10 . 1136 / bmj . c4413. [Online]. Available: https://doi.org/10.1136/bmj.c4413.
