Article Text
Abstract
Objectives This research studies the role of slums in the spread and control of infectious diseases in the National Capital Territory of India, Delhi, using detailed social contact networks of its residents.
Methods We use an agent-based model to study the spread of influenza in Delhi through person-to-person contact. Two different networks are used: one in which slum and non-slum regions are treated the same, and the other in which 298 slum zones are identified. In the second network, slum-specific demographics and activities are assigned to the individuals whose homes reside inside these zones. The main effects of integrating slums are that the network has more home-related contacts due to larger family sizes and more outside contacts due to more daily activities outside home. Various vaccination and social distancing interventions are applied to control the spread of influenza.
Results Simulation-based results show that when slum attributes are ignored, the effectiveness of vaccination can be overestimated by 30%–55%, in terms of reducing the peak number of infections and the size of the epidemic, and in delaying the time to peak infection. The slum population sustains greater infection rates under all intervention scenarios in the network that treats slums differently. Vaccination strategy performs better than social distancing strategies in slums.
Conclusions Unique characteristics of slums play a significant role in the spread of infectious diseases. Modelling slums and estimating their impact on epidemics will help policy makers and regulators more accurately prioritise allocation of scarce medical resources and implement public health policies.
- delhi
- epidemic
- interventions
- slum population
- synthetic social contact network
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Footnotes
Contributors AA, SE, CJK, AM, MM, SS, AV designed and conceived the study. SC carried out the experiments and simulations. SC, CJK, AM performed data analysis. CJK, BL, AM, MM, EKN, MLW helped with reviewing the results and writing the paper.
Funding This work has been partially supported by the Defense Threat Reduction Agency (DTRA) (grant no. HDTRA1-11-1-0016 and HDTRA1-11-D-0016-0001), National Institutes of Health (NIH) (grant no. 1R01GM109718), National Science Foundation (NSF) (grant no. CCF-1216000, CNS-1011769 and NRT-DESE-154362), and NIH Models of Infectious Disease Agent Study (MIDAS) (grant no. 2U01GM070694-11 and 3U01FM070694-09S1).
Competing interests None declared.
Patient consent Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Data pertaining to figures and statistical analysis are partially provided in the supplementary file, and also can be obtained by contacting the corresponding author through email.