2,840 research outputs found
Analysis of CDC social control measures using an agent-based simulation of an influenza epidemic in a city
Background: the transmission of infectious disease amongst the human population is a complex process which requires advanced, often individual-based, models to capture the space-time details observed in reality.Methods: an Individual Space-Time Activity-based Model (ISTAM) was applied to simulate the effectiveness of non-pharmaceutical control measures including: (1) refraining from social activities, (2) school closure and (3) household quarantine, for a hypothetical influenza outbreak in an urban area.Results: amongst the set of control measures tested, refraining from social activities with various compliance levels was relatively ineffective. Household quarantine was very effective, especially for the peak number of cases and total number of cases, with large differences between compliance levels. Household quarantine resulted in a decrease in the peak number of cases from more than 300 to around 158 for a 100% compliance level, a decrease of about 48.7%. The delay in the outbreak peak was about 3 to 17 days. The total number of cases decreased to a range of 3635-5403, that is, 63.7%-94.7% of the baseline value.When coupling control measures, household quarantine together with school closure was the most effective strategy. The resulting space-time distribution of infection in different classes of activity bundles (AB) suggests that the epidemic outbreak is strengthened amongst children and then spread to adults. By sensitivity analysis, this study demonstrated that earlier implementation of control measures leads to greater efficacy. Also, for infectious diseases with larger basic reproduction number, the effectiveness of non-pharmaceutical measures was shown to be limited.Conclusions: simulated results showed that household quarantine was the most effective control measure, while school closure and household quarantine implemented together achieved the greatest benefit. Agent-based models should be applied in the future to evaluate the efficacy of control measures for a range of disease outbreaks in a range of settings given sufficient information about the given case and knowledge about the transmission processes at a fine scal
Evaluating Temporal Factors in Combined Interventions of Workforce Shift and School Closure for Mitigating the Spread of Influenza
10.1371/journal.pone.0032203PLoS ONE7
Mitigation of infectious disease at school: targeted class closure vs school closure
School environments are thought to play an important role in the community
spread of airborne infections (e.g., influenza) because of the high mixing
rates of school children. The closure of schools has therefore been proposed as
efficient mitigation strategy, with however high social and economic costs:
alternative, less disruptive interventions are highly desirable. The recent
availability of high-resolution contact networks in school environments
provides an opportunity to design micro-interventions and compare the outcomes
of alternative mitigation measures. We consider mitigation measures that
involve the targeted closure of school classes or grades based on readily
available information such as the number of symptomatic infectious children in
a class. We focus on the case of a primary school for which we have
high-resolution data on the close-range interactions of children and teachers.
We simulate the spread of an influenza-like illness in this population by using
an SEIR model with asymptomatics and compare the outcomes of different
mitigation strategies. We find that targeted class closure affords strong
mitigation effects: closing a class for a fixed period of time -equal to the
sum of the average infectious and latent durations- whenever two infectious
individuals are detected in that class decreases the attack rate by almost 70%
and strongly decreases the probability of a severe outbreak. The closure of all
classes of the same grade mitigates the spread almost as much as closing the
whole school. Targeted class closure strategies based on readily available
information on symptomatic subjects and on limited information on mixing
patterns, such as the grade structure of the school, can be almost as effective
as whole-school closure, at a much lower cost. This may inform public health
policies for the management and mitigation of influenza-like outbreaks in the
community
A generalized-growth model to characterize the early ascending phase of infectious disease outbreaks
A better characterization of the early growth dynamics of an epidemic is
needed to dissect the important drivers of disease transmission. We introduce a
2-parameter generalized-growth model to characterize the ascending phase of an
outbreak and capture epidemic profiles ranging from sub-exponential to
exponential growth. We test the model against empirical outbreak data
representing a variety of viral pathogens and provide simulations highlighting
the importance of sub-exponential growth for forecasting purposes. We applied
the generalized-growth model to 20 infectious disease outbreaks representing a
range of transmission routes. We uncovered epidemic profiles ranging from very
slow growth (p=0.14 for the Ebola outbreak in Bomi, Liberia (2014)) to near
exponential (p>0.9 for the smallpox outbreak in Khulna (1972), and the 1918
pandemic influenza in San Francisco). The foot-and-mouth disease outbreak in
Uruguay displayed a profile of slower growth while the growth pattern of the
HIV/AIDS epidemic in Japan was approximately linear. The West African Ebola
epidemic provided a unique opportunity to explore how growth profiles vary by
geography; analysis of the largest district-level outbreaks revealed
substantial growth variations (mean p=0.59, range: 0.14-0.97). Our findings
reveal significant variation in epidemic growth patterns across different
infectious disease outbreaks and highlights that sub-exponential growth is a
common phenomenon. Sub-exponential growth profiles may result from
heterogeneity in contact structures or risk groups, reactive behavior changes,
or the early onset of interventions strategies, and consideration of
"deceleration parameters" may be useful to refine existing mathematical
transmission models and improve disease forecasts.Comment: 31 pages, 9 Figures, 1 Supp. Figure, 1 Table, final accepted version
(in press), Epidemics - The Journal on Infectious Disease Dynamics, 201
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