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An Analytical Approach for Temporal Infection Mapping and Composite Index Development

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  • Weiwei Wang

    (School of Medicine, Xiamen University, Xiamen 361005, China
    National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China)

  • Futian Weng

    (School of Medicine, Xiamen University, Xiamen 361005, China
    National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China)

  • Jianping Zhu

    (National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China
    School of Management, Xiamen University, Xiamen 361005, China)

  • Qiyuan Li

    (School of Medicine, Xiamen University, Xiamen 361005, China
    National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China)

  • Xiaolong Wu

    (School of Medicine, Xiamen University, Xiamen 361005, China
    National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China)

Abstract

Significant and composite indices for infectious disease can have implications for developing interventions and public health. This paper presents an investment for developing access to further analysis of the incidence of individual and multiple diseases. This research mainly comprises two steps: first, an automatic and reproducible procedure based on functional data analysis techniques was proposed for analyzing the dynamic properties of each disease; second, orthogonal transformation was adopted for the development of composite indices. Between 2000 and 2019, nineteen class B notifiable diseases in China were collected for this study from the National Bureau of Statistics of China. The study facilitates the probing of underlying information about the dynamics from discrete incidence rates of each disease through the procedure, and it is also possible to obtain similarities and differences about diseases in detail by combining the derivative features. There has been great success in intervening in the majority of notifiable diseases in China, like bacterial or amebic dysentery and epidemic cerebrospinal meningitis, while more efforts are required for some diseases, like AIDS and virus hepatitis. The composite indices were able to reflect a more complex concept by combining individual incidences into a single value, providing a simultaneous reflection for multiple objects, and facilitating disease comparisons accordingly. For the notifiable diseases included in this study, there was superior management of gastro-intestinal infectious diseases and respiratory infectious diseases from the perspective of composite indices. This study developed a methodology for exploring the prevalent properties of infectious diseases. The development of effective and reliable analytical methods provides special insight into infectious diseases’ common dynamics and properties and has implications for the effective intervention of infectious diseases.

Suggested Citation

  • Weiwei Wang & Futian Weng & Jianping Zhu & Qiyuan Li & Xiaolong Wu, 2023. "An Analytical Approach for Temporal Infection Mapping and Composite Index Development," Mathematics, MDPI, vol. 11(20), pages 1-16, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4358-:d:1263766
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    1. Sokhna Dieng & Pierre Michel & Abdoulaye Guindo & Kankoe Sallah & El-Hadj Ba & Badara Cissé & Maria Patrizia Carrieri & Cheikh Sokhna & Paul Milligan & Jean Gaudart, 2020. "Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies," IJERPH, MDPI, vol. 17(11), pages 1-23, June.
    2. Juan Li & Shengjie Lai & George F. Gao & Weifeng Shi, 2021. "The emergence, genomic diversity and global spread of SARS-CoV-2," Nature, Nature, vol. 600(7889), pages 408-418, December.
    3. Padhan, Rakesh & Prabheesh, K.P., 2021. "The economics of COVID-19 pandemic: A survey," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 220-237.
    4. López-Pintado, Sara & Romo, Juan, 2009. "On the Concept of Depth for Functional Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 718-734.
    5. Kate E. Jones & Nikkita G. Patel & Marc A. Levy & Adam Storeygard & Deborah Balk & John L. Gittleman & Peter Daszak, 2008. "Global trends in emerging infectious diseases," Nature, Nature, vol. 451(7181), pages 990-993, February.
    6. Buonomo, Bruno & Della Marca, Rossella, 2019. "Oscillations and hysteresis in an epidemic model with information-dependent imperfect vaccination," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 162(C), pages 97-114.
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