Measuring the uncertainties of pandemic influenza

Fair, Jeanne M.; Powell, Dennis R.; Leclaire, Rene J.; Moore, Leslie M.; Wilson, Michael L.; Dauelsberg, L. B.; Samsa, M.E.; Deland, Sharon M.; Hirsch, Gary B.; Bush, Brian

doi:10.1504/ijram.2012.047550

Cited by 9 publications

(12 citation statements)

References 69 publications

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“…In addition, artificial intelligence and smart algorithms are needed to build an antitransmission and detection system such as face recognition and thermal screening in public areas [48,49]. Moreover, wide monitoring and detection in the areas of positive clusters will ease social isolation, diagnosis and care [50][51][52].…”

Section: Discussionmentioning

confidence: 99%

Mitigation strategies to fight the COVID-19 pandemic—present, future and beyond

Abideen

Mohamad

Hassan

2020

JHR

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PurposeThe latest novel coronavirus disease 2019 (COVID-10) pandemic continues to have a significant social and financial impact globally. It is very essential to study, categorize and systematize published research on mitigation strategies adopted during previous pandemic scenario that could provide an insight into improving the current crisis. The goal of this paper is to systematize and identify gaps in previous research and suggest potential recommendations as a conceptual framework from a strategic point of view.Design/methodology/approachA systematic review of Scopus and Web of Science (WoS) core collection databases was performed based on strict keyword search selections followed by a bibliometric meta-analysis of the final dataset.FindingsThis study indicated that the traditional mitigation techniques adopted during past pandemics are in place but are not capable of managing the transmission capability and virulence of COVID-19. There is a greater need for rethinking and re-engineering short and long-term approaches to prevent, control and contain the current pandemic situation.Practical implicationsIntegrating various mitigation approaches shall assist in flattening the pandemic curve and help in the long run.Originality/valueArticles, conference proceedings, books, book chapters and other references from two extensive databases (Scopus and WoS) were purposively considered for this study. The search was confined to the selected keywords outlined in the methodology section of this paper.

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Section: Discussionmentioning

confidence: 99%

Mitigation strategies to fight the COVID-19 pandemic—present, future and beyond

Abideen

Mohamad

Hassan

2020

JHR

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“…The disease input parameters sampled in the experiment are subclinical and clinical stage times. These were chosen due to inherent biological uncertainty and based on previous studies showing result sensitivity with these parameters [ 30 ]. For example, the average time of both subclinical and clinical disease stage period in days varies greatly with what is known about H5N1 influenza in birds and human.…”

Section: Methodsmentioning

confidence: 99%

Constructing Rigorous and Broad Biosurveillance Networks for Detecting Emerging Zoonotic Outbreaks

et al. 2015

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Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

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“…However, the modeler might also consider that people are sick but their cases are just not reported. For example, estimates on the fraction of reported influenza cases, hospitalization rates or case fatalities are subject to uncertainty [15,16]. Asymptomatic infections might also happen for influenza [17,18].…”

Section: Unknown Infectionsmentioning

confidence: 99%

Agent-Based Methods for Simulation of Epidemics with a Low Number of Infected Persons

Miksch¹,

Pichler²,

Espinosa³

et al. 2014

Information and Communication Technology

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Part 1: Information & Communication Technology-EurAsia Conference 2014, ICT-EurAsia 2014International audienceModeling of infectious diseases with a low number of infections is a task that often arises since most real epidemics affect only a small fraction of the population. Agent-based methods simulate individuals and their behavior. When the model is simulated, the epidemic automatically arises without being explicitly defined. Surprisingly, it is not easy to produce such epidemics with small infection numbers. Instead, it needs model improvements to accomplish that task. In this paper, we show different extensions, addressing the person’s behavior, the pathogen’s behavior and the environmental impacts. It turns out that the discussed improvements have different consequences. Hence, they need to be used deliberately to overcome modeling issues of a specific epidemic in an appropriate and valid way. Even more, these improvements address the underlying behavior of epidemics and hence have the ability to provide a deeper insight into the real spreading process of a disease

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Measuring the uncertainties of pandemic influenza

Cited by 9 publications

References 69 publications

Mitigation strategies to fight the COVID-19 pandemic—present, future and beyond

Mitigation strategies to fight the COVID-19 pandemic—present, future and beyond

Constructing Rigorous and Broad Biosurveillance Networks for Detecting Emerging Zoonotic Outbreaks

Agent-Based Methods for Simulation of Epidemics with a Low Number of Infected Persons

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