Dataset Information

A Model for the Spread of Infectious Diseases in a Region.

ABSTRACT: In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town's place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this article, we take a model for the spread of an infectious disease in a single town and scale it up to simulate a region containing multiple towns. The model is validated by looking at how adding additional towns and commuters influences the outbreak in a single town. We then look at how the centrality of a town within a network influences the outbreak. Our main finding is that the commuters coming into a town have a greater effect on whether an outbreak will spread to a town than the commuters going out. The findings on centrality of a town and how it influences an outbreak could potentially be used to help influence future policy and intervention strategies such as school closure policies.

SUBMITTER: Hunter E

PROVIDER: S-EPMC7246727 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Model for the Spread of Infectious Diseases in a Region.

Hunter Elizabeth E Namee Brian Mac BM Kelleher John D JD

International journal of environmental research and public health 20200430 9

In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town's place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this article, we take a model for the spread of an infectious disease in a single town and scale it up to simulate a region containing multiple towns. The model is validated by looking at how adding additional towns and commuters influences the outbreak in a single town. ...[more]

PMID: 32365775

Similar Datasets

Project description:IntroductionUnderstanding human mixing patterns relevant to infectious diseases spread through close contact is vital for modelling transmission dynamics and optimisation of disease control strategies. Mixing patterns in low-income countries like Malawi are not well known.MethodologyWe conducted a social mixing survey in urban Blantyre, Malawi between April and July 2021 (between the 2nd and 3rd wave of COVID-19 infections). Participants living in densely-populated neighbourhoods were randomly sampled and, if they consented, reported their physical and non-physical contacts within and outside homes lasting at least 5 min during the previous day. Age-specific mixing rates were calculated, and a negative binomial mixed effects model was used to estimate determinants of contact behaviour.ResultsOf 1201 individuals enroled, 702 (58.5%) were female, the median age was 15 years (interquartile range [IQR] 5-32) and 127 (10.6%) were HIV-positive. On average, participants reported 10.3 contacts per day (range: 1-25). Mixing patterns were highly age-assortative, particularly those within the community and with skin-to-skin contact. Adults aged 20-49 y reported the most contacts (median:11, IQR: 8-15) of all age groups; 38% (95%CI: 16-63) more than infants (median: 8, IQR: 5-10), who had the least contacts. Household contact frequency increased by 3% (95%CI: 2-5) per additional household member. Unemployed participants had 15% (95%CI: 9-21) fewer contacts than other adults. Among long range (>30 m away from home) contacts, secondary school children had the largest median contact distance from home (257 m, IQR 78-761). HIV-positive status in adults >=18 years-old was not associated with changed contact patterns (rate ratio: 1.01, 95%CI: (0.91-1.12)). During this period of relatively low COVID-19 incidence in Malawi, 301 (25.1%) individuals stated that they had limited their contact with others due to COVID-19 precautions; however, their reported contacts were 8% (95%CI: 1-13) higher.ConclusionIn urban Malawi, contact rates, are high and age-assortative, with little reported behavioural change due to either HIV-status or COVID-19 circulation. This highlights the limits of contact-restriction-based mitigation strategies in such settings and the need for pandemic preparedness to better understand how contact reductions can be enabled and motivated.

Dataset Information

A Model for the Spread of Infectious Diseases in a Region.

Publications

A Model for the Spread of Infectious Diseases in a Region.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets