scholarly journals Correction: An open-data-driven agent-based model to simulate infectious disease outbreaks

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0211245 ◽  
Author(s):  
Elizabeth Hunter ◽  
Brian Mac Namee ◽  
John Kelleher
Keyword(s):  
Infectious Disease ◽  
Open Data ◽  
Disease Outbreaks ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Infectious Disease Outbreaks

scholarly journals An open-data-driven agent-based model to simulate infectious disease outbreaks

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0208775 ◽  
Author(s):  
Elizabeth Hunter ◽  
Brian Mac Namee ◽  
John Kelleher
Keyword(s):  
Infectious Disease ◽  
Open Data ◽  
Disease Outbreaks ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Infectious Disease Outbreaks

scholarly journals Adapting an Agent-Based Model of Infectious Disease Spread in an Irish County to COVID-19

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 41
Author(s):  
Elizabeth Hunter ◽  
John D. Kelleher
Keyword(s):  
Complex System ◽  
Infectious Disease ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Basic Reproductive Number ◽  
Disease Spread ◽  
Reproductive Number ◽  
Agent Based Model ◽  
School Closures ◽  
Agent Based

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, R0, influence the simulated outbreaks, and find that our model behaves as expected: the higher the R0, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases.

scholarly journals Global health and data-driven policies for emergency responses to infectious disease outbreaks

2020 ◽  
Vol 8 (11) ◽  
pp. e1361-e1363
Author(s):  
Zisis Kozlakidis ◽  
Joud Abduljawad ◽  
Ali M Al Khathaami ◽  
Louise Schaper ◽  
John Stelling
Keyword(s):  
Infectious Disease ◽  
Global Health ◽  
Disease Outbreaks ◽  
Data Driven ◽  
Infectious Disease Outbreaks ◽  
Emergency Responses

scholarly journals Infectious disease outbreaks in the African region: overview of events reported to the World Health Organization in 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
F. Mboussou ◽  
P. Ndumbi ◽  
R. Ngom ◽  
Z. Kassamali ◽  
O. Ogundiran ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Onset ◽  
Disease Outbreaks ◽  
Case Fatality ◽  
World Health ◽  
African Region ◽  
Regional Office ◽  
Member States ◽  
Infectious Disease Outbreaks ◽  
The World

Abstract The WHO African region is characterised by the largest infectious disease burden in the world. We conducted a retrospective descriptive analysis using records of all infectious disease outbreaks formally reported to the WHO in 2018 by Member States of the African region. We analysed the spatio-temporal distribution, the notification delay as well as the morbidity and mortality associated with these outbreaks. In 2018, 96 new disease outbreaks were reported across 36 of the 47 Member States. The most commonly reported disease outbreak was cholera which accounted for 20.8% (n = 20) of all events, followed by measles (n = 11, 11.5%) and Yellow fever (n = 7, 7.3%). About a quarter of the outbreaks (n = 23) were reported following signals detected through media monitoring conducted at the WHO regional office for Africa. The median delay between the disease onset and WHO notification was 16 days (range: 0–184). A total of 107 167 people were directly affected including 1221 deaths (mean case fatality ratio (CFR): 1.14% (95% confidence interval (CI) 1.07%–1.20%)). The highest CFR was observed for diseases targeted for eradication or elimination: 3.45% (95% CI 0.89%–10.45%). The African region remains prone to outbreaks of infectious diseases. It is therefore critical that Member States improve their capacities to rapidly detect, report and respond to public health events.

scholarly journals Statistical methods for the prospective detection of infectious disease outbreaks: a review

2011 ◽  
Vol 175 (1) ◽  
pp. 49-82 ◽  
Author(s):  
Steffen Unkel ◽  
C. Paddy Farrington ◽  
Paul H. Garthwaite ◽  
Chris Robertson ◽  
Nick Andrews
Keyword(s):  
Infectious Disease ◽  
Statistical Methods ◽  
Disease Outbreaks ◽  
Infectious Disease Outbreaks

scholarly journals Dot map cartograms for detection of infectious disease outbreaks: an application to Q fever, the Netherlands and pertussis, Germany

2017 ◽  
Vol 22 (26) ◽  
Author(s):  
Loes Soetens ◽  
Susan Hahné ◽  
Jacco Wallinga
Keyword(s):  
Infectious Disease ◽  
The Netherlands ◽  
Q Fever ◽  
Disease Outbreaks ◽  
Point Pattern ◽  
Infectious Disease Outbreaks ◽  
Public Health Professionals ◽  
Potential Source ◽  
High Incidence ◽  
And Control

Geographical mapping of infectious diseases is an important tool for detecting and characterising outbreaks. Two common mapping methods, dot maps and incidence maps, have important shortcomings. The former does not represent population density and can compromise case privacy, and the latter relies on pre-defined administrative boundaries. We propose a method that overcomes these limitations: dot map cartograms. These create a point pattern of cases while reshaping spatial units, such that spatial area becomes proportional to population size. We compared these dot map cartograms with standard dot maps and incidence maps on four criteria, using two example datasets. Dot map cartograms were able to illustrate both incidence and absolute numbers of cases (criterion 1): they revealed potential source locations (Q fever, the Netherlands) and clusters with high incidence (pertussis, Germany). Unlike incidence maps, they were insensitive to choices regarding spatial scale (criterion 2). Dot map cartograms ensured the privacy of cases (criterion 3) by spatial distortion; however, this occurred at the expense of recognition of locations (criterion 4). We demonstrate that dot map cartograms are a valuable method for detection and visualisation of infectious disease outbreaks, which facilitates informed and appropriate actions by public health professionals, to investigate and control outbreaks.

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