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Spatio-Temporal Analysis of Infectious Diseases

Antonio López-Quílez
Department of Statistics and Operations Research, Faculty of Mathematics, University of Valencia, 46100 Valencia, Spain
Int. J. Environ. Res. Public Health 2019, 16(4), 669;
Submission received: 16 February 2019 / Accepted: 20 February 2019 / Published: 25 February 2019
(This article belongs to the Special Issue Spatio-Temporal Analysis of Infectious Diseases)
Epidemiological research on the pathogenesis, diagnosis, and treatment of infectious diseases is a broad field of study with renewed validity in the face of social changes and new threats. The spatio-temporal distribution of diseases is central to the knowledge of their development, transmission, spread, and dynamics.
New technologies and geographic information system (GIS) analysis together with highly structured mathematical and statistical techniques have a special utility in describing and analyzing the incidence of infectious diseases. Specifically, Bayesian inference methods allow the analysis of models with complex and flexible structures suitable to represent the diverse characteristics present in each geographical environment and disease.
Tuberculosis, hepatitis, human immunodeficiency virus (HIV), influenza, malaria, dengue, zika, and other vector-borne diseases are a constant concern for health authorities, practitioners, and patients. A variety of environmental, climatic, and socio-economic factors underlie their spatio-temporal patterns. In addition, factors such as changes in climate, habits, or land use intervene and complicate the understanding of these processes.
This Special Issue compiles contributions on the spatio-temporal analysis of infectious diseases and related themes. The collection of studies presented in this Special Issue contributes to a better understanding of which methods are currently available related with space and time in the data analysis of infectious diseases. These articles provide novel insights into the interaction between space and time in the context of each specific infectious disease, providing new perspectives in the understanding of these pathologies and their extent. A total of 11 manuscripts [1,2,3,4,5,6,7,8,9,10,11] were accepted after a single-blind review process by at least two international experts using the journal-specific review guidelines.
The different works corresponding to these articles were carried out in different geographical areas of several countries: China [2,5,6,7,8,10], Colombia [4], Ecuador [3], Korea [9], and Taiwan [1]. One of the studies deals with the analysis of an intracellular geometric model [11].
This volume contains a blend of papers focusing on the modeling, inference, and prediction of the behavior of infectious diseases. The scope of applications covers a wide range of topics. Several infectious diseases are addressed by studying their differentiated characteristics and relating them to relevant explanatory variables. The list of diseases studied includes typhoid and paratyphoid fevers [10]; Middle East respiratory syndrome-related coronavirus (MERS-CoV) [9]; tuberculosis [8]; bacillary dysentery [7]; cystic echinococcosis [6]; hand, foot, and mouth disease [5]; zika virus disease (ZVD) [4]; dengue [1,3,4]; and hepatitis [2,11].
While the contributions collected in this Special Issue are encouraging, it is evident that more discussion and research is needed to improve our understanding of the complexity of modeling infectious diseases in the space-time context. I hope that this Special Issue will stimulate and promote new action and research on this important topic.


This research received no external funding.


I am deeply grateful to the authors who responded to the call for papers, as well as to the reviewers, whose critical and constructive comments on the manuscripts contributed greatly to the quality of this publication. Finally, my sincere thanks to the IJERPH staff for their highly professional editorial assistance during the preparation of this Special Issue.

Conflicts of Interest

The author declares no conflict of interest.


The following abbreviations are used in this manuscript:
GISGeographic information system
HIVHuman immunodeficiency virus
MERS-CoVMiddle East respiratory syndrome-related coronavirus
ZVDZika virus disease


  1. Chuang, T.-W.; Ng, K.-C.; Nguyen, T.L.; Chaves, L.F. Epidemiological Characteristics and Space-Time Analysis of the 2015 Dengue Outbreak in the Metropolitan Region of Tainan City, Taiwan. Int. J. Environ. Res. Public Health 2018, 15, 396. [Google Scholar] [CrossRef] [PubMed]
  2. Zhu, B.; Liu, J.; Fu, Y.; Zhang, B.; Mao, Y. Spatio-Temporal Epidemiology of Viral Hepatitis in China (2003–2015): Implications for Prevention and Control Policies. Int. J. Environ. Res. Public Health 2018, 15, 661. [Google Scholar] [CrossRef] [PubMed]
  3. Lippi, C.A.; Stewart-Ibarra, A.M.; Muñoz, Á.G.; Borbor-Cordova, M.J.; Mejía, R.; Rivero, K.; Castillo, K.; Cárdenas, W.B.; Ryan, S.J. The Social and Spatial Ecology of Dengue Presence and Burden during an Outbreak in Guayaquil, Ecuador, 2012. Int. J. Environ. Res. Public Health 2018, 15, 827. [Google Scholar] [CrossRef] [PubMed]
  4. Martínez-Bello, D.A.; López-Quílez, A.; Torres Prieto, A. Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia. Int. J. Environ. Res. Public Health 2018, 15, 1376. [Google Scholar] [CrossRef] [PubMed]
  5. Song, C.; He, Y.; Bo, Y.; Wang, J.; Ren, Z.; Yang, H. Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models. Int. J. Environ. Res. Public Health 2018, 15, 1476. [Google Scholar] [CrossRef] [PubMed]
  6. Huang, D.; Li, R.; Qiu, J.; Sun, X.; Yuan, R.; Shi, Y.; Qu, Y.; Niu, Y. Geographical Environment Factors and Risk Mapping of Human Cystic Echinococcosis in Western China. Int. J. Environ. Res. Public Health 2018, 15, 1729. [Google Scholar] [CrossRef] [PubMed]
  7. Yan, C.; Chen, Y.; Miao, Z.; Qin, S.; Gu, H.; Cai, J. Spatiotemporal Characteristics of Bacillary Dysentery from 2005 to 2017 in Zhejiang Province, China. Int. J. Environ. Res. Public Health 2018, 15, 1826. [Google Scholar] [CrossRef] [PubMed]
  8. Bao, H.; Liu, K.; Wu, Z.; Chai, C.; He, T.; Wang, W.; Wang, F.; Peng, Y.; Wang, X.; Chen, B.; et al. Tuberculosis among Full-Time Teachers in Southeast China, 2005–2016. Int. J. Environ. Res. Public Health 2018, 15, 2024. [Google Scholar] [CrossRef] [PubMed]
  9. Kim, Y.; Ryu, H.; Lee, S. Agent-Based Modeling for Super-Spreading Events: A Case Study of MERS-CoV Transmission Dynamics in the Republic of Korea. Int. J. Environ. Res. Public Health 2018, 15, 2369. [Google Scholar] [CrossRef] [PubMed]
  10. Gu, H.; Yan, C.; Jiang, Z.; Li, X.; Chen, E.; Jiang, J.; Jiang, Q.; Zhou, Y. Epidemiological Trend of Typhoid and Paratyphoid Fevers in Zhejiang Province, China from 1953 to 2014. Int. J. Environ. Res. Public Health 2018, 15, 2427. [Google Scholar] [CrossRef]
  11. Knodel, M.M.; Targett-Adams, P.; Grillo, A.; Herrmann, E.; Wittum, G. Advanced Hepatitis C virus replication PDE models within a realistic intracellular geometric environment. Int. J. Environ. Res. Public Health 2019, 16, 513. [Google Scholar] [CrossRef] [PubMed]

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López-Quílez, A. Spatio-Temporal Analysis of Infectious Diseases. Int. J. Environ. Res. Public Health 2019, 16, 669.

AMA Style

López-Quílez A. Spatio-Temporal Analysis of Infectious Diseases. International Journal of Environmental Research and Public Health. 2019; 16(4):669.

Chicago/Turabian Style

López-Quílez, Antonio. 2019. "Spatio-Temporal Analysis of Infectious Diseases" International Journal of Environmental Research and Public Health 16, no. 4: 669.

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