Browsing by Author "Eubank, Stephen"

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    Comparing Effectiveness of Top-Down and Bottom-Up Strategies in Containing Influenza
    Marathe, Achla; Lewis, Bryan L.; Barrett, Christopher L.; Chen, Jiangzhuo; Marathe, Madhav V.; Eubank, Stephen; Ma, Yifei (Public Library of Science, 2011-09-22)
    This research compares the performance of bottom-up, self-motivated behavioral interventions with top-down interventions targeted at controlling an “Influenza-like-illness”. Both types of interventions use a variant of the ring strategy. In the first case, when the fraction of a person's direct contacts who are diagnosed exceeds a threshold, that person decides to seek prophylaxis, e.g. vaccine or antivirals; in the second case, we consider two intervention protocols, denoted Block and School: when a fraction of people who are diagnosed in a Census Block (resp., School) exceeds the threshold, prophylax the entire Block (resp., School). Results show that the bottom-up strategy outperforms the top-down strategies under our parameter settings. Even in situations where the Block strategy reduces the overall attack rate well, it incurs a much higher cost. These findings lend credence to the notion that if people used antivirals effectively, making them available quickly on demand to private citizens could be a very effective way to control an outbreak.
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    Detail in network models of epidemiology: are we there yet?
    Eubank, Stephen; Barrett, Christopher L.; Beckman, Richard J.; Bisset, Keith R.; Durbeck, L.; Kuhlman, Christopher J.; Lewis, Bryan L.; Marathe, Achla; Marathe, Madhav V.; Stretz, P. (Taylor & Francis, 2010)
    Network models of infectious disease epidemiology can potentially provide insight into how to tailor control strategies for specific regions, but only if the network adequately reflects the structure of the region’s contact network. Typically, the network is produced by models that incorporate details about human interactions. Each detail added renders the models more complicated and more difficult to calibrate, but also more faithful to the actual contact network structure. We propose a statistical test to determine when sufficient detail has been added to the models and demonstrate its application to the models used to create a synthetic population and contact network for the USA.
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    Enhancing disease surveillance with novel data streams: challenges and opportunities
    Althouse, Benjamin M.; Scarpino, Samuel V.; Meyers, Lauren Ancel; Ayers, John W.; Bargsten, Marisa; Baumbach, Joan; Brownstein, John S.; Castro, Lauren; Clapham, Hannah; Cummings, Derek A. T.; Del Valle, Sara; Eubank, Stephen; Fairchild, Geoffrey; Finelli, Lyn; Generous, Nicholas; George, Dylan; Harper, David R.; Hebert-Dufresne, Laurent; Johansson, Michael A.; Konty, Kevin; Lipsitch, Marc; Millinovich, Gabriel; Miller, Joseph D.; Nsoesie, Elaine O.; Olson, Donald R.; Paul, Michael; Priedhorsky, Reid; Read, Jonathan M.; Rodriguez-Barraquer, Isabel; Smith, Derek J.; Stefansen, Christian; Swerdlow, David L.; Thompson, Deborah; Vespignani, Alessandro; Wesolowski, Amy; Polgreen, Philip M. (Springer, 2015)
    Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.
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    Estimating Human Cases of Avian Influenza A(H7N9) from Poultry Exposure
    Rivers, Caitlin; Lum, Kristian; Lewis, Bryan L.; Eubank, Stephen (PLOS, 2013-05-15)
    In March 2013 an outbreak of avian influenza A(H7N9) was first recognized in China. To date there have been 130 cases in human, 47% of which are in men over the age of 55.The influenza strain is a novel subtype not seen before in humans; little is known about zoonotic transmission of the virus, but it is hypothesized that contact with poultry in live bird markets may be a source of exposure. The purpose of this study is to estimate the transmissibility of the virus from poultry to humans by estimating the amount of time shoppers, farmers, and live bird market retailers spend exposed to poultry each day. Results suggest that increased risk among older men is not due to greater exposure time at live bird markets.
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    in silico Public Health: The Essential Role of Highly Detailed Simulations in Support of Public Health Decision-Making
    Lewis, Bryan L. (Virginia Tech, 2011-01-19)
    Public Health requires a trans-disciplinary approach to tackle the breadth and depth of the issues it faces. Public health decisions are reached through the compilation of multiple data sources and their thoughtful synthesis. The complexity and importance of these decisions necessitates a variety of approaches, with simulations increasingly being relied upon. This dissertation describes several research efforts that demonstrate the utility of highly detailed simulations in public health decision-making. Simulations are frequently used to represent dynamic processes and to synthesize data to predict future outcomes, which can be used in cost-benefit and course of action analyses. The threat of pandemic influenza and its subsequent arrival prompted many simulation-based studies. This dissertation details several such studies conducted at the federal policy level. Their use for planning and the rapid response to the unfolding crisis demonstrates the integration of highly detailed simulations into the public health decision-making process. Most analytic methods developed by public health practitioners rely on historical data sources, but are intended to be broadly applicable. Oftentimes this data is limited or incomplete. This dissertation describes the use of highly detailed simulations to evaluate the performance of outbreak detection algorithms. By creating methods that generate realistic and configurable synthetic data, the reliance on these historical samples can be reduced, thus facilitating the development and improvement of methods for public health practice. The process of decision-making itself can significantly influence the decisions reached. Many fields use simulations to train and evaluate, however, public health has yet to fully adopt these approaches. This dissertation details the construction of highly detailed synthetic data that was used to build an interactive environment designed to evaluate the decision-making processes for pertussis control. The realistic data sets provide sufficient face validity to experienced public health practitioners, creating a natural and effective medium for training and evaluation purposes. Advances in high-performance computing, information sciences, computer science, and epidemiology are enabling increasing innovation in the application of simulations. This dissertation illustrates several applications of simulations to relevant public health practices and strongly argues that highly detailed simulations have an essential role to play in Public Health decision-making.
  • Innovation networks and complex contagion in East Africa: modeling adoption of conservation agriculture in the Mt. Elgon region of Kenya and Uganda
    Rivers Gunter, J. C. M.; Moore, Keith M.; Eubank, Stephen; Kuhlman, C.; Lamb, Jennifer Nicole; Laker-Ojok, Rita; Ngosia Sikuku, D. (2014)
    Community support networks play a key role in smallholder farmers’ willingness to adopt
  • Innovation networks and social contagion in East Africa
    Gunter, J.; Rivers, Caitlin; Eubank, Stephen; Moore, Keith M.; Kuhlman, C.; Lamb, Jennifer Nicole; Norton, James B.; Omondi, Emmanuel C.; Ojok, R. L.; Sikuku, Dominic Ngosia; Ashilenje, Dennis S.; Odera, J. (2012)
    This study seeks to understand the pathway by which new technology and the associated knowledge passes through community networks in western Kenya and eastern Uganda. Previous research in the region emphasizes the importance of community support to promote widespread adoption of Conservation Agriculture practices. We will simulate complex contagions of information in these networks using the simulation platform EpiSimdemics. This work complements and expands on the growing body of research that uses network analysis to study the effects of network structure and social contagion on complex health and social systems.
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    Modeling Commodity Flow in the Context of Invasive Species Spread: Study of Tuta absoluta in Nepal
    Sridhar, Venkataramana; Wu, S.; Shi, B.; Marathe, Achla; Sah, L.P.; Giri, A.P.; Colavito, L.A.; Nitin, K.S.; Asokan, R.; Muniappan, Rangaswamy (Muni); Norton, George W.; Adiga, A.; Eubank, Stephen (Virginia Tech, 2017)
    Trade and transport of goods is widely accepted as a primary pathway for the introduction and dispersal of invasive species. However, understanding commodity flows remains a challenge owing to its complex nature, unavailability of quality data and lack of systematic modeling methods. A robust network-based approach is proposed to model seasonal flow of agricultural produce and examine its role in pest spread. It is applied to study the spread of Tuta absoluta, a devastating pest of tomato in Nepal. Further, the long-term establishment potential of the pest and its economic impact on the country are assessed. Preliminary analyses indicate that T. absoluta will invade most major tomato production regions within a year of introduction and the economic impact of invasion could range from $17-25 million. The proposed approach is generic and particularly suited for data-poor scenarios.
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    Modeling targeted layered containment of an influenza pandemic in the United States
    Halloran, Elizabeth M.; Ferguson, Neil M.; Eubank, Stephen; Longini, Ira M. Jr.; Cummings, Derek A. T.; Lewis, Bryan L.; Xu, Shufu; Fraser, Christophe; Vullikanti, Anil; Germann, Timothy C.; Wagener, Diane; Beckman, Richard J.; Kadau, Kai; Barrett, Christopher L.; Macken, Catherine A.; Burke, Donald S.; Cooley, Philip (NAS, 2008-03-25)
    Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with 8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.
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    Modeling the Ebola Outbreak in West Africa, August 4th 2014 update
    Lewis, Bryan L.; Rivers, Caitlin; Eubank, Stephen; Marathe, Marathe; Barrett, Christopher L. (2014)
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    Modeling the effect of transient populations on epidemics in Washington DC
    Parikh, Nidhi; Youssef, Mina; Swarup, Samarth; Eubank, Stephen (Nature Publishing Group, 2013-11)
    Large numbers of transients visit big cities, where they come into contact with many people at crowded areas. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. We evaluate the effect of transients on epidemics by extending a synthetic population model for the Washington DC metro area to include leisure and business travelers. A synthetic population is obtained by combining multiple data sources to build a detailed minute-by-minute simulation of population interaction resulting in a contact network. We simulate an influenza-like illness over the contact network to evaluate the effects of transients on the number of infected residents. We find that there are significantly more infections when transients are considered. Since much population mixing happens at major tourism locations, we evaluate two targeted interventions: closing museums and promoting healthy behavior (such as the use of hand sanitizers, covering coughs, etc.) at museums. Surprisingly, closing museums has no beneficial effect. However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak. We analytically derive the reproductive number and perform stability analysis using an ODE-based model.
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    Models of the Mucosal Inflammatory and Regulatory Immune Pathways: The Role of Host Response in Microbial Persistence and Pathogenesis
    Wendelsdorf, Katherine Veronica (Virginia Tech, 2011-11-08)
    The scientific method requires the creation of a unifying hypothesis that reconciles an observed health outcome of infection with experimental data gathered about the disease process following infection. In this era of unprecedented amounts of data and information for various disease models, the creation and articulation of such hypothesis are often beyond human capacity. Modeling offers a means to generate hypothesis that provide complex mechanisms that reconcile seemingly contradictory data as well as quantitatively assess the relative plausibility of different mechanisms proposed to explain the same data/health outcome association. Here I explain the modeling approach to hypothesis generation and offer several examples of its implementation to address the role of the natural host immune response in determining outcomes of infection by a specific microbe including pathogenesis and microbial clearance. Such knowledge is key to devising sophisticated disease intervention strategies. The systems studied are i) Inflammatory Bowel Disease, where I explore mechanisms of inflammation regulation and how they break down to give rise to a chronic inflammatory disease, ii)H. pylori infection, in which I explore potential bacterial strategies for persistence as a commensal of the microflora or as a pathogen, and iii) HIV infection, where I explore the role of inflammatory and anti-inflammatory mechanisms in establishing viral infection. I present both mathematical, equation based models as well as agent-based, computational models offering a comparison of each method.
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    Opinion: Mathematical models: A key tool for outbreak response
    Lofgren, Eric T.; Halloran, M. Elizabeth; Rivers, Caitlin; Drake, John M.; Porco, Travis C.; Lewis, Bryan L.; Yang, Wan; Vespignani, Alessandro; Shaman, Jeffrey; Eisenberg, Joseph N.S.; Eisenberg, Marisa C.; Marathe, Madhav V.; Scarpino, Samuel V.; Alexander, Kathleen A.; Meza, Rafael; Ferrari, Matthew J.; Hyman, James M.; Meyers, Lauren Ancel; Eubank, Stephen (NAS, 2015-01-13)
    The 2014 outbreak of Ebola in West Africa is unprecedented in its size and geographic range, and demands swift, effective action from the international community. Understanding the dynamics and spread of Ebola is critical for directing interventions and extinguishing the epidemic; however, observational studies of local conditions have been incomplete and limited by the urgent need to direct resources to patient care. Mathematical and computational models can help address this deficiency through work with sparse observations, inference on missing data, and incorporation of the latest information. These models can clarify how the disease is spreading and provide timely guidance to policymakers. However, the use of models in public health often meets resistance (1), from doubts in peer review about the utility of such analyses to public skepticism that models can contribute when the means to control an epidemic are already known (2). Even when they are discussed in a positive light, models are often portrayed as arcane and largely inaccessible thought experiments (3). However, the role of models is crucial: they can be used to quantify the effect of mitigation efforts, provide guidance on the scale of interventions required to achieve containment, and identify factors that fundamentally influence the course of an outbreak.
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    Predictive Computational Modeling of the Mucosal Immune Responses during Helicobacter pylori Infection
    Carbo, Adria; Bassaganya-Riera, Josep; Pedragosa, Mireia; Viladomiu, Monica; Marathe, Madhav; Eubank, Stephen; Wendesdorf, Katherine; Bisset, Keith R.; Hoops, Stefan; Deng, Xinwei; Alam, Maksudul; Kronsteiner, Barbara; Mei, Yongguo; Hontecillas, Raquel (Public Library of Science, 2013-09-05)
    T helper (Th) cells play a major role in the immune response and pathology at the gastric mucosa during Helicobacter pylori infection. There is a limited mechanistic understanding regarding the contributions of CD4+ T cell subsets to gastritis development during H. pylori colonization. We used two computational approaches: ordinary differential equation (ODE)-based and agent-based modeling (ABM) to study the mechanisms underlying cellular immune responses to H. pylori and how CD4+ T cell subsets influenced initiation, progression and outcome of disease. To calibrate the model, in vivo experimentation was performed by infecting C57BL/6 mice intragastrically with H. pylori and assaying immune cell subsets in the stomach and gastric lymph nodes (GLN) on days 0, 7, 14, 30 and 60 post-infection. Our computational model reproduced the dynamics of effector and regulatory pathways in the gastric lamina propria (LP) in silico. Simulation results show the induction of a Th17 response and a dominant Th1 response, together with a regulatory response characterized by high levelｙs of mucosal Treg) cells. We also investigated the potential role of peroxisome proliferator-activated receptor γ (PPARγ) activation on the modulation of host responses to H. pylori by using loss-of-function approaches. Specifically, in silico results showed a predominance of Th1 and Th17 cells in the stomach of the cell-specific PPARγ knockout system when compared to the wild-type simulation. Spatio-temporal, object-oriented ABM approaches suggested similar dynamics in induction of host responses showing analogous T cell distributions to ODE modeling and facilitated tracking lesion formation. In addition, sensitivity analysis predicted a crucial contribution of Th1 and Th17 effector responses as mediators of histopathological changes in the gastric mucosa during chronic stages of infection, which were experimentally validated in mice. These integrated immunoinformatics approaches characterized the induction of mucosal effector and regulatory pathways controlled by PPARγ during H. pylori infection affecting disease outcomes.
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    Quantifying Interhospital Patient Sharing as a Mechanism for Infectious Disease Spread
    Huang, Susan S.; Avery, Takuser R.; Song, Yeohan H.; Elkins, Kristen R.; Nguyen, Christopher C.; Nutter, Sandra K.; Nafday, Alaka. A.; Condon, Curtis J.; Chang, Michael T.; Chrest, David; Boos, John; Bobashev, Georgiy; Wheaton, William; Frank, Steven A.; Platt, Richard; Lipsitch, Marc; Bush, Robin M.; Eubank, Stephen; Burke, Donald S.; Lee, Bruce Y. (University of Chicago Press, 2010-11)
    BACKGROUND. Assessments of infectious disease spread in hospitals seldom account for interfacility patient sharing. This is particularly important for pathogens with prolonged incubation periods or carrier states. METHODS. We quantified patient sharing among all 32 hospitals in Orange County (OC), California, using hospital discharge data. Same-day transfers between hospitals were considered "direct" transfers, and events in which patients were shared between hospitals after an intervening stay at home or elsewhere were considered "indirect" patient-sharing events. We assessed the frequency of readmissions to another OC hospital within various time points from discharge and examined interhospital sharing of patients with Clostridium difficile infection. RESULTS. In 2005, OC hospitals had 319,918 admissions. Twenty-nine percent of patients were admitted at least twice, with a median interval between discharge and readmission of 53 days. Of the patients with 2 or more admissions, 75% were admitted to more than 1 hospital. Ninety-four percent of interhospital patient sharing occurred indirectly. When we used 10 shared patients as a measure of potential interhospital exposure, 6 (19%) of 32 hospitals "exposed" more than 50% of all OC hospitals within 6 months, and 17 (53%) exposed more than 50% within 12 months. Hospitals shared 1 or more patient with a median of 28 other hospitals. When we evaluated patients with C. difficile infection, 25% were readmitted within 12 weeks; 41% were readmitted to different hospitals, and less than 30% of these readmissions were direct transfers. CONCLUSIONS. In a large metropolitan county, interhospital patient sharing was a potential avenue for transmission of infectious agents. Indirect sharing with an intervening stay at home or elsewhere composed the bulk of potential exposures and occurred unbeknownst to hospitals.
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    Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection
    Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith R.; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav (PLOS, 2015-09-01)
    Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.
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    Sensitivity of Household Transmission to Household Contact Structure and Size
    Marathe, Achla; Lewis, Bryan L.; Chen, Jiangzhuo; Eubank, Stephen (PLOS, 2011-08-01)
    Objective: Study the influence of household contact structure on the spread of an influenza-like illness. Examine whether changes to in-home care giving arrangements can significantly affect the household transmission counts. Method: We simulate two different behaviors for the symptomatic person; either s/he remains at home in contact with everyone else in the household or s/he remains at home in contact with only the primary caregiver in the household. The two different cases are referred to as full mixing and single caregiver, respectively. Results: The results show that the household’s cumulative transmission count is lower in case of a single caregiver configuration than in the full mixing case. The household transmissions vary almost linearly with the household size in both single caregiver and full mixing cases. However the difference in household transmissions due to the difference in household structure grows with the household size especially in case of moderate flu. Conclusions: These results suggest that details about human behavior and household structure do matter in epidemiological models. The policy of home isolation of the sick has significant effect on the household transmission count depending upon the household size.
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    Systems Modeling of Molecular Mechanisms Controlling Cytokine-driven CD4+ T Cell Differentiation and Phenotype Plasticity
    Carbo, Adria; Hontecillas, Raquel; Kronsteiner, Barbara; Viladomiu, Monica; Pedragosa, Mireia; Lu, Pinyl; Philipson, Casandra W.; Hoops, Stefan; Marathe, Madhav; Eubank, Stephen; Bisset, Keith R.; Wendelsdorf, Katherine; Jarrah, Abdul Salam; Mei, Yongguo; Bassaganya-Riera, Josep (Public Library of Science, 2013-04-04)
    Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPARc) in modulating plasticity between Th17 and iTreg cells. PPARc regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPARc activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPARc. Deletion of PPARc in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPARc in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.
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    What Factors Might Have Led to the Emergence of Ebola in West Africa?
    Alexander, Kathleen A.; Sanderson, Claire E.; Marathe, Madhav V.; Lewis, Bryan L.; Rivers, Caitlin; Lofgren, Eric T.; Eubank, Stephen; Eisenberg, Marisa C.; Drake, John M.; Shaman, Jeffrey (PLOS, 2015-06-04)
    An Ebola outbreak of unprecedented scope emerged in West Africa in December 2013 and presently continues unabated in the countries of Guinea, Sierra Leone, and Liberia. Ebola is not new to Africa, and outbreaks have been confirmed as far back as 1976. The current West African Ebola outbreak is the largest ever recorded and differs dramatically from prior outbreaks in its duration, number of people affected, and geographic extent. The emergence of this deadly disease in West Africa invites many questions, foremost among these: why now, and why in West Africa? Here, we review the sociological, ecological, and environmental drivers that might have influenced the emergence of Ebola in this region of Africa and its spread throughout the region. Containment of the West African Ebola outbreak is the most pressing, immediate need. A comprehensive assessment of the drivers of Ebola emergence and sustained human-to-human transmission is also needed in order to prepare other countries for importation or emergence of this disease. Such assessment includes identification of country-level protocols and interagency policies for outbreak detection and rapid response, increased understanding of cultural and traditional risk factors within and between nations, delivery of culturally embedded public health education, and regional coordination and collaboration, particularly with governments and health ministries throughout Africa. Public health education is also urgently needed in countries outside of Africa in order to ensure that risk is properly understood and public concerns do not escalate unnecessarily. To prevent future outbreaks, coordinated, multiscale, early warning systems should be developed that make full use of these integrated assessments, partner with local communities in high-risk areas, and provide clearly defined response recommendations specific to the needs of each community.
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    What to know before forecasting the flu
    Chakraborty, Prithwish; Lewis, Bryan L.; Eubank, Stephen; Brownstein, John S.; Marathe, Madhav V.; Ramakrishnan, Naren (PLOS, 2018-10-12)
    Accurate and timely influenza (flu) forecasting has gained significant traction in recent times. If done well, such forecasting can aid in deploying effective public health measures. Unlike other statistical or machine learning problems, however, flu forecasting brings unique challenges and considerations stemming from the nature of the surveillance apparatus and the end utility of forecasts. This article presents a set of considerations for flu forecasters to take into account prior to applying forecasting algorithms.