How decision makers can use quantitative approaches to guide outbreak responses.
08 Jul 2019-Philosophical Transactions of the Royal Society B (The Royal Society)-Vol. 374, Iss: 1776, pp 20180365-20180365
TL;DR: A structured approach is needed to develop a structured approach that will improve the quality and timeliness of data collected during outbreaks, establish analytic teams within the response structure and define a research agenda for data analytics in outbreak response.
Abstract: Decision makers are responsible for directing staffing, logistics, selecting public health interventions, communicating to professionals and the public, planning future response needs, and establishing strategic and tactical priorities along with their funding requirements. Decision makers need to rapidly synthesize data from different experts across multiple disciplines, bridge data gaps and translate epidemiological analysis into an operational set of decisions for disease control. Analytic approaches can be defined for specific response phases: investigation, scale-up and control. These approaches include: improved applications of quantitative methods to generate insightful epidemiological descriptions of outbreaks; robust investigations of causal agents and risk factors; tools to assess response needs; identifying and monitoring optimal interventions or combinations of interventions; and forecasting for response planning. Data science and quantitative approaches can improve decision-making in outbreak response. To realize these benefits, we need to develop a structured approach that will improve the quality and timeliness of data collected during outbreaks, establish analytic teams within the response structure and define a research agenda for data analytics in outbreak response. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
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...A key challenge during outbreaks is designing appropriate control interventions, and mathematical models are increasingly used to guide this decision-making (Lofgren et al., 2014; Cunniffe et al., 2016; Cori et al., 2017; Morgan, 2019; Polonsky et al., 2019)....
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...The data needs to be carefully described in terms of time-place-person (animal/food) (Jalava et al., 2018; Morgan, 2019)....
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Cites background from "How decision makers can use quantit..."
...The need for data collection and analysis pipelines to be made routine in epidemic response practice has been the topic of recent widespread discussion [30, 31, 43, 44]....
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References
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01 Jan 1979
11,977 citations
TL;DR: Recent efforts to incorporate human behaviour into disease models are reviewed, and it is proposed that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, andAccording to the assumed effects of such behaviour.
Abstract: Human behaviour plays an important role in the spread of infectious diseases, and understanding the influence of behaviour on the spread of diseases can be key to improving control efforts. While behavioural responses to the spread of a disease have often been reported anecdotally, there has been relatively little systematic investigation into how behavioural changes can affect disease dynamics. Mathematical models for the spread of infectious diseases are an important tool for investigating and quantifying such effects, not least because the spread of a disease among humans is not amenable to direct experimental study. Here, we review recent efforts to incorporate human behaviour into disease models, and propose that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, and according to the assumed effects of such behaviour. We highlight recent advances as well as gaps in our understanding of the interplay between infectious disease dynamics and human behaviour, and suggest what kind of data taking efforts would be helpful in filling these gaps.
1,061 citations
Utrecht University1, Imperial College London2, Roskilde University3, Georgetown University4, University of London5, University of Cambridge6, University of Warwick7, University of Liverpool8, University College London9, Johns Hopkins University10, University of California, Los Angeles11, University of Oxford12, Heriot-Watt University13, University of Florida14, John E. Fogarty International Center15, Massey University16
TL;DR: The development of mathematical models used in epidemiology are reviewed and how these can be harnessed to develop successful control strategies and inform public health policy, using the West African Ebola epidemic as an example.
Abstract: Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.
514 citations
"How decision makers can use quantit..." refers background in this paper
...Over the last 10 or so years, there has been considerable progress in the use of analytic approaches to support decision makers in the control of infectious diseases [1]....
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Journal Article•
TL;DR: A modeling tool was constructed to provide estimates of the potential number of future cases of the current West African epidemic of Ebola and how control and prevention interventions can slow and eventually stop the epidemic.
Abstract: The first cases of the current West African epidemic of Ebola virus disease (hereafter referred to as Ebola) were reported on March 22, 2014, with a report of 49 cases in Guinea. By August 31, 2014, a total of 3,685 probable, confirmed, and suspected cases in West Africa had been reported. To aid in planning for additional disease-control efforts, CDC constructed a modeling tool called EbolaResponse to provide estimates of the potential number of future cases. If trends continue without scale-up of effective interventions, by September 30, 2014, Sierra Leone and Liberia will have a total of approximately 8,000 Ebola cases. A potential underreporting correction factor of 2.5 also was calculated. Using this correction factor, the model estimates that approximately 21,000 total cases will have occurred in Liberia and Sierra Leone by September 30, 2014. Reported cases in Liberia are doubling every 15-20 days, and those in Sierra Leone are doubling every 30-40 days. The EbolaResponse modeling tool also was used to estimate how control and prevention interventions can slow and eventually stop the epidemic. In a hypothetical scenario, the epidemic begins to decrease and eventually end if approximately 70% of persons with Ebola are in medical care facilities or Ebola treatment units (ETUs) or, when these settings are at capacity, in a non-ETU setting such that there is a reduced risk for disease transmission (including safe burial when needed). In another hypothetical scenario, every 30-day delay in increasing the percentage of patients in ETUs to 70% was associated with an approximate tripling in the number of daily cases that occur at the peak of the epidemic (however, the epidemic still eventually ends). Officials have developed a plan to rapidly increase ETU capacities and also are developing innovative methods that can be quickly scaled up to isolate patients in non-ETU settings in a way that can help disrupt Ebola transmission in communities. The U.S. government and international organizations recently announced commitments to support these measures. As these measures are rapidly implemented and sustained, the higher projections presented in this report become very unlikely.
377 citations
"How decision makers can use quantit..." refers background in this paper
...[5], which was a key factor in the decision of the US government to deploy its military to Liberia to support outbreak control activities....
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TL;DR: A systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems, molecular epidemiology, and social network analysis, identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools.
199 citations