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Prioritization of the Target Population for Coronavirus Disease 2019 (COVID-19) Vaccination Program in Thailand

Author

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  • Rapeepong Suphanchaimat

    (International Health Policy Program, Ministry of Public Health, Nonthaburi 11000, Thailand
    Division of Epidemiology, Department of Disease Control, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Titiporn Tuangratananon

    (International Health Policy Program, Ministry of Public Health, Nonthaburi 11000, Thailand
    Bureau of Health Promotion, Department of Health, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Nattadhanai Rajatanavin

    (International Health Policy Program, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Mathudara Phaiyarom

    (International Health Policy Program, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Warisara Jaruwanno

    (International Health Policy Program, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Sonvanee Uansri

    (International Health Policy Program, Ministry of Public Health, Nonthaburi 11000, Thailand)

Abstract

Thailand was hit by the second wave of Coronavirus Disease 2019 (COVID-19) in a densely migrant-populated province (Samut Sakhon). COVID-19 vaccines were known to be effective; however, the supply was limited. Therefore, this study aimed to predict the effectiveness of Thailand’s COVID-19 vaccination strategy. We obtained most of the data from the Ministry of Public Health. Deterministic system dynamics and compartmental models were utilized. The reproduction number (R) between Thais and migrants was estimated at 1.25 and 2.5, respectively. Vaccine effectiveness (VE) to prevent infection was assumed at 50%. In Samut Sakhon, there were 500,000 resident Thais and 360,000 resident migrants. The contribution of migrants to the province’s gross domestic product was estimated at 20%. Different policy scenarios were analyzed. The migrant-centric vaccination policy scenario received the lowest incremental cost per one case or one death averted compared with the other scenarios. The Thai-centric policy scenario yielded an incremental cost of 27,191 Baht per one life saved, while the migrant-centric policy scenario produced a comparable incremental cost of 3782 Baht. Sensitivity analysis also demonstrated that the migrant-centric scenario presented the most cost-effective outcome even when VE diminished to 20%. A migrant-centric policy yielded the smallest volume of cumulative infections and deaths and was the most cost-effective scenario, independent of R and VE values. Further studies should address political feasibility and social acceptability of migrant vaccine prioritization.

Suggested Citation

  • Rapeepong Suphanchaimat & Titiporn Tuangratananon & Nattadhanai Rajatanavin & Mathudara Phaiyarom & Warisara Jaruwanno & Sonvanee Uansri, 2021. "Prioritization of the Target Population for Coronavirus Disease 2019 (COVID-19) Vaccination Program in Thailand," IJERPH, MDPI, vol. 18(20), pages 1-17, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:20:p:10803-:d:656483
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    1. Giovanni Bonaccorsi & Francesco Pierri & Matteo Cinelli & Andrea Flori & Alessandro Galeazzi & Francesco Porcelli & Ana Lucia Schmidt & Carlo Michele Valensise & Antonio Scala & Walter Quattrociocchi , 2020. "Economic and social consequences of human mobility restrictions under COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(27), pages 15530-15535, July.
    2. Aronrag Meeyai & Naiyana Praditsitthikorn & Surachai Kotirum & Wantanee Kulpeng & Weerasak Putthasri & Ben S Cooper & Yot Teerawattananon, 2015. "Seasonal Influenza Vaccination for Children in Thailand: A Cost-Effectiveness Analysis," PLOS Medicine, Public Library of Science, vol. 12(5), pages 1-25, May.
    3. Jack H. Buckner & Gerardo Chowell & Michael R. Springborn, 2021. "Dynamic prioritization of COVID-19 vaccines when social distancing is limited for essential workers," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(16), pages 2025786118-, April.
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    Cited by:

    1. Weiwei Zhang & Shiyong Liu & Nathaniel Osgood & Hongli Zhu & Ying Qian & Peng Jia, 2023. "Using simulation modelling and systems science to help contain COVID‐19: A systematic review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 40(1), pages 207-234, January.

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