Abstract
In this study, the two-mode network is used to model and investigate a given set of data regarding the dengue cases that have been reported across the 128 barangays of Baguio City, Philippines for the years 2010–2018. Three different structure perspectives are used: time-location, time, and location-age, from which respective models were patterned from. The study proposes variants of the Newman and weighted Newman projection method to aid in preserving connectivity information and is compared with Newman, and weighted Newman. Measures of global clustering coefficient and two-mode degree are applied upon the two-mode models while strength, closeness, and betweenness measures are used in the analysis of the different one-mode projections. These models have shown that barangays labeled 98, 85, and 80 have the most concentrated and repeated dengue activity based from the barangay-month model. For the month-year model, July to September and February to April were shown to be the peak and hollow seasons of dengue respectively. From the age-barangay and age-district models, age-group of 17–22 years were shown to be the most common among dengue patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
ACPM. Average-case-per-month can be obtained by dividing weighted-Newman strengths over Newman strengths or Second-variant over First-variant strengths. It may also be approximated with sum over binary strengths.
References
Dengue severe dengue frequently asked questions. World Health Organization (2017). http://www.who.int/denguecontrol/faq/en/index5.html
Dengue and severe dengue. World Health Organization (2018). http://www.who.int/en/news-room/fact-sheets/detail/dengue-and-severe-dengue
The Mosquito. World Health Organization (2018). http://www.who.int/denguecontrol/mosquito/en/
Official website of the city government of Baguio: Baguio dengue cases increase by 400 percent (2006). http://www.baguio.gov.ph/content/baguio-dengue-cases-increase-400-percent. Accessed Aug 2017
Libatique, C.P., Pajimola, A.J., Addawe, J.M.: Bifurcation analysis of dengue transmission model in Baguio City, Philippines. In: AIP Conference Proceedings, vol. 1905, p. 030023 (2017). https://doi.org/10.1063/1.5012169
Magsakay, C.B., De Vera, N.U., Libatique, C.P., Addawe, R.C., Addawe, J.M.: Treatment on outliers in UBJ-SARIMA models for forecasting dengue cases on age groups not eligible for vaccination in Baguio City, Philippines. In: AIP Conference Proceedings, vol. 1905, p. 050028 (2017).https://doi.org/10.1063/1.5012247
Addawe, R.C., Addawe, J.M., Magadia, J.C.: Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City. In: AIP Conference Proceedings, vol. 1776, p. 090021 (2016).https://doi.org/10.1063/1.4965385
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
Opsahl, T.: Triadic closure in two-mode networks: redefining the global and local clustering coefficients. Soc. Netw. 35(2), 159–167 (2013). https://doi.org/10.1016/j.socnet.2011.07.001
Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010). https://doi.org/10.1016/j.socnet.2010.03.006
Opsahl, T., Panzarasa, P.: Clustering in weighted networks. Soc. Netw. 31(2), 155–163 (2009). https://doi.org/10.1016/j.socnet.2009.02.002
Malik, H.A.M., Mahesar, A.W., Abid, F., Wagas, A., Wahiddin, M.R.: Two-mode network modeling and analysis of dengue epidemic behavior in Gombak, Malaysia. Appl. Math. Model. 43, 207–220 (2017). https://doi.org/10.1016/j.apm.2016.10.060
Padròn, B., Nogales, M., Traveset, A.: Alternative approaches of transforming bimodal into unimodal mutualistic networks. The usefulness of preserving weighted information. Basic Appl. Ecol. 12(8), 713–721 (2011). https://doi.org/10.1016/j.baae.2011.09.004
Niekamp, A., Mercken, L.A.G., Hoebe, C.J.P.A., Dukers-Muijrers, N.H.T.M.: A sexual affiliation network of swingers, heterosexuals practicing risk behaviours that potentiate the spread of sexually transmitted infections: a two-mode approach. Soc. Netw. 35(2), 223–236 (2013). https://doi.org/10.1016/j.socnet.2013.02.006
Snijders, T.A.B., Lomi, A., Torl, V.J.: A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Soc. Netw. 35(2), 265–276 (2013). https://doi.org/10.1016/j.socnet.2012.05.005
Aksoy, S., Kolda, T.G., Pinar, A.: Measuring and modeling bipartite graphs with community structure. J. Complex Netw. 5, 581–603 (2017)
Everett, M.G.: Centrality and the dual-projection approach for two-mode social network data. Methodol. Innov. 9 (2016). https://doi.org/10.1177/2059799116630662
Broccatelli, C., Everett, M., Koskinen, J.: Temporal dynamics in covert networks. Methodol. Innov. 9 (2016). https://doi.org/10.1177/2059799115622766
Malinick, T.E., Tindall, D.B., Diani, M.: Network centrality and social movement media coverage: a two-mode network analytic approach. Soc. Netw. 35(2), 148–158 (2013). https://doi.org/10.1016/j.socnet.2011.10.005
Saracco, F., Straka, M.J., Di Clemente, R., Gabrielli, A., Caldarelli, G., Squartini, T.: Inferring monopartite projections of bipartite networks: an entropy-based approach. New J. Phys. 19(5), 053022 (2017). http://stacks.iop.org/1367-2630/19/i=5/a=053022
Neal, Z.: The backbone of bipartite projections: inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors. Soc. Netw. 39, 84–97 (2014). https://doi.org/10.1016/j.socnet.2014.06.001
Newman, M.E.J.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. E 64(1), 016132016132 (2001). https://doi.org/10.1103/PhysRevE.64.016132
Newman, M.E.J.: Analysis of weighted networks. Phys. Rev. E 70(5), 056131 (2004). https://doi.org/10.1103/PhysRevE.70.056131
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978). https://doi.org/10.1016/0378-8733(78)90021-7
Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001). https://doi.org/10.1080/0022250X.2001.9990249
Acknowledgements
The authors would like to thank the Baguio City Health Services Office for the data used in the paper gathered through the Philippine Integrated Disease Surveillance and Response program of DOH.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Oryan, R.R., Addawe, J.M., Tubera-Panes, D. (2021). Modeling and Analysis of the Dengue Activity in Baguio City Using Two-Mode and One-Mode Networks. In: Mohd, M.H., Misro, M.Y., Ahmad, S., Nguyen Ngoc, D. (eds) Modelling, Simulation and Applications of Complex Systems. CoSMoS 2019. Springer Proceedings in Mathematics & Statistics, vol 359. Springer, Singapore. https://doi.org/10.1007/978-981-16-2629-6_13
Download citation
DOI: https://doi.org/10.1007/978-981-16-2629-6_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2628-9
Online ISBN: 978-981-16-2629-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)