Journal of Peking University (Health Sciences) ›› 2026, Vol. 58 ›› Issue (3): 446-454. doi: 10.19723/j.issn.1671-167X.2026.03.002

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Evaluation of bilateral vaccine cooperation priority countries on Chinese human papillomavirus vaccine using hierarchical K-means hybrid clustering

Yinge WU, Yangmu HUANG*()   

  1. Department of Global Health, Peking University School of Public Health, Beijing 100191, China
  • Received:2025-10-16 Online:2026-06-18 Published:2026-04-28
  • Contact: Yangmu HUANG
  • Supported by:
    the National Natural Science Foundation of China and Gates Foundation Joint Project(72361127504-2023YFVA1003)

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Abstract:

Objective: To evaluate priority target countries and formulate context-specific cooperation modalities for China ' s bilateral international collaboration on human papillomavirus (HPV) vaccines, thereby furnishing an evidence-based reference for optimizing China ' s bilateral vaccine cooperation stra-tegies and contributing to global health equity. Methods: Using a previously established index system designed for selecting preferred countries in China ' s international vaccine cooperation, we comprehensively applied a weighted-scoring approach to quantitatively rank 29 Phases 3 and 4 Global Alliance for Vaccines and Immunization (Gavi) eligible countries in HPV vaccine cooperation. Based on 5 first-level index (legal and regulatory factors, economic factors, political factors, social factors, and technological factors) scores, a hierarchical K-means hybrid method was used for clustering the countries. Hierarchical clustering was initially utilized to explore the underlying data structure and determine the optimal number of clusters, followed by K-means clustering to precisely categorize the evaluated nations based on their multidimensional characteristics. Results: The top10 countries were mostly Asian countries. African countries were mostly in the lower ranks. The countries were clustered into three types: (1) Technology transfer priority (n=3: Indonesia, Viet Nam, Uzbekistan), characterized by above-average economic, techno-scientific and political scores, robust vaccine-import volumes, non-negligible domestic manufacturing capacity and high political stability—recommended for priority collaboration; (2) Basic assistance (n=25), approximating mean indicator values, typically low-population or low-income economies suitable for aid-based or concessional-procurement models; (3) High barrier (n=1: India), exhibiting high socio-demographic and scientific base yet minimal legal-regulatory scores, indicating large demand, established vaccine industry, but formidable registration barriers. Policy recommendations included: concessional product export plus clinical-trial cooperation with Indonesia, with prospective licensing and technology transfer; clinical-trial collaboration with Viet Nam; and licensing/technology-transfer schemes with Uzbekistan. Conclusion: Indonesia, Viet Nam, and Uzbekistan are identified as the priority target countries for China ' s bilateral international cooperation on HPV vaccines. For these technology-transfer priority nations, cooperation should be strategically conducted through diverse modalities encompassing vaccine product export, clinical trial collaboration, authorized licensing, and technology transfer.

Key words: International cooperation, Human papillomavirus vaccine, Cluster analysis, Hierarchical clustering, K-means clustering

CLC Number: 

  • R186

Table 1

Primary and secondary indicators and their weights in the index system for identifying preferred countries in China ' s international vaccine cooperation"

Primary indicators (weights) Secondary indicators (weights) Combined weights of secondary indicators
Legal and regulatory factors (0.48) Vaccine product registration (0.40) 0.192
Vaccine clinical trial approval (0.20) 0.096
Requirements for overseas vaccine enterprises (0.40) 0.192
Economic factors (0.23) Target country economic scale (0.23) 0.053
Target country vaccine import scale (0.67) 0.154
Target country economic system (0.10) 0.023
Political factors (0.15) Political stability and absence of violence (0.23) 0.035
Government effectiveness (0.12) 0.018
Public service capacity (0.23) 0.035
Bilateral diplomatic relations (0.42) 0.063
Social factors (0.095) Population size and growth rate (0.20) 0.019
Burden of vaccine-preventable diseases (0.60) 0.057
Health system evaluation (0.20) 0.019
Technological factors (0.045) Vaccine research and development capacity (0.67) 0.030
Vaccine manufacturing capacity (0.33) 0.015

Table 2

Scores of primary indicators and total score ranking for human papillomavirus vaccine cooperation"

Countries Primary indicators Total score Rank
Legal and regulatory factors Economic factors Political factors Social factors Technological factors
Indonesia 31.87 19.18 11.00 2.14 2.64 66.83 1
Sri Lanka 38.40 9.60 10.36 1.74 0.03 60.13 2
Georgia 38.40 9.11 10.94 1.35 0.22 60.02 3
Viet Nam 32.19 12.14 12.37 1.63 1.69 60.02 4
Azerbaijan 38.40 9.41 9.64 1.20 0.13 58.78 5
Bhutan 38.40 8.37 7.40 1.13 0.00 55.30 6
Armenia 38.40 7.51 6.01 1.37 1.58 54.87 7
Moldova 38.40 8.94 6.26 0.95 0.06 54.61 8
Uzbekistan 28.80 9.04 12.99 1.71 1.58 54.12 9
Nigeria 41.47 2.21 8.17 1.76 0.00 53.61 10
Mongolia 28.80 9.57 12.13 1.60 0.07 52.17 11
Kenya 37.76 1.29 9.51 1.57 0.59 50.72 12
Lao PDR 28.80 8.76 11.47 1.12 0.06 50.21 13
Sao Tome and Principe 28.80 9.49 10.08 1.62 0.00 49.99 14
Bangladesh 34.56 2.39 9.44 1.71 1.36 49.46 15
Nicaragua 28.80 8.65 8.99 1.45 1.50 49.39 16
Djibouti 28.80 8.87 9.87 1.00 0.01 48.55 17
India 16.94 10.04 9.07 7.99 3.50 47.54 18
Papua New Guinea 28.80 7.49 9.41 1.06 0.01 46.77 19
Cuba 31.36 4.43 7.55 1.76 1.60 46.70 20
Angola 28.80 7.06 9.15 1.49 0.01 46.51 21
Côte d’Ivoire 28.80 6.61 8.39 1.37 1.05 46.22 22
Guyana 28.80 7.67 5.88 1.35 0.00 43.70 23
Honduras 28.80 7.78 4.72 1.49 0.40 43.19 24
Solomon Islands 28.80 1.84 10.80 1.56 0.00 43.00 25
Bolivia 28.80 3.66 9.07 1.33 0.00 42.86 26
Timor-Leste 28.80 2.13 10.59 1.26 0.00 42.78 27
Ghana 28.03 1.93 9.59 1.57 0.19 41.31 28
Kiribati 28.80 0.64 6.46 1.32 0.00 37.22 29

Figure 1

Hierarchical clustering dendrogram of 29 countries"

Figure 2

Variation of sum of squared errors (SSE) with candidate K values in K-means clustering"

Table 3

Final K-means cluster centers for 29 countries (K=3)"

Items Cluster
1 2 3
Economic factors 1.593 64 -0.220 75 0.737 87
Political factors 1.418 68 -0.167 36 -0.072 14
Social factors 0.121 29 -0.217 69 5.078 29
Technological factors 1.449 09 -0.298 05 3.104 07
Legal and regulatory factors -0.124 72 0.127 03 -2.801 65

Table 4

Clustering results of HPV vaccine cooperation"

Cluster Countries (ranked in descending order of total score within each cluster)
1 Technology transfer priority Indonesia, Viet Nam, Uzbekistan
2 Basic assistance Sri Lanka, Georgia, Azerbaijan, Bhutan, Armenia, Moldova, Nigeria, Mongolia, Kenya, Lao PDR, Sao Tome and Principe, Bangladesh, Niagara, Djibouti, Papua New Guinea, Cuba, Angola, Côte d’Ivoire, Guyana, Honduras, Solomon Island, Bolivia, Timor-Leste, Ghana, Kiribati
3 High barrier India

Figure 3

Score breakdown of countries in each cluster"

Figure 4

Scoring rates across five factors for countries in each cluster"

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