Abstract
Urbanization drives the epidemiology of infectious diseases to many threats and new challenges. In this research, we study the interplay between human mobility and dengue outbreaks in the complex urban environment of the city-state of Singapore. We integrate both stylized and mobile phone data-driven mobility patterns in an agent-based transmission model in which humans and mosquitoes are represented as agents that go through the epidemic states of dengue. We monitor with numerical simulations the system-level response to the epidemic by comparing our results with the observed cases reported during the 2013 and 2014 outbreaks. Our results show that human mobility is a major factor in the spread of vector-borne diseases such as dengue even on the short scale corresponding to intra-city distances. We finally discuss the advantages and the limits of mobile phone data and potential alternatives for assessing valuable mobility patterns for modeling vector-borne diseases outbreaks in cities.
Highlights
We use a stochastic population model based on the ordinary differential equation (ODE) framework employed by Lourenco and Recker to describe a dengue outbreak in Madeira, Portugal[11] and used by Wesolowski and colleagues to model the dengue outbreak in Pakistan[14]
In Singapore, according to a study by the Land Transport Authority, about 80% of all trips go to either a work or a home location[42]. This implies that the infection with the dengue virus in Singapore very likely happens either at home or at work, we focus on commuting between these two locations when modeling human mobility in this paper
We can characterize the mosquito population dynamics and the epidemics based on the ODE representation of the previous model
Summary
The code used for our simulations is available online[41].Mobile phone data.Anonymized call detail records (CDRs) were collected over a two month period in 2011 by one of the mobile phone operators in Singapore with a significant market share (a statistical analysis is reported in Fig. S9 in the supplementary materials.). Locations are collected at the cell tower level with further noise applied for privacy reasons. We use this data to assign two “favorite” locations to each user: (i) home and (ii) work. To be able to distinguish between home and work locations, we performed this clustering procedure separately for records generated between 8 pm and 6am on weekdays and during weekend (for home locations) and records generated between 10 am and 4 pm on weekdays (for work locations) After this procedure, we selected the largest clusters for both cases and filtered the list of users who had at least 10 events in both clusters. We display the distribution of these home and work locations in Fig. S2, while we show the nonempty grid cells in Fig. S12 in the Supplementary Material
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