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Epidemic Science in Real Time
Abstract
Few situations more dramatically illustrate the salience of science to policy than an epidemic. The relevant science takes place rapidly and continually, in the laboratory, clinic, and community. In facing the current swine flu (H1N1 influenza) outbreak, the world has benefited from research investment over many years, as well as from preparedness exercises and planning in many countries. The global public health enterprise has been tempered by the outbreak of severe acute respiratory syndrome (SARS) in 2002–2003, the ongoing threat of highly pathogenic avian flu, and concerns over bioterrorism. Researchers and other experts are now able to make vital contributions in real time. By conducting the right science and communicating expert judgment, scientists can enable policies to be adjusted appropriately as an epidemic scenario unfolds.
... The ability to respond quickly in the face of an emerging infectious disease is critical for global patient safety efforts. Indeed, the capacity to collaborate across institutions and countries has proved critical in the success of recent pandemics such as severe acute respiratory syndrome (SARS) [2,3]. Furthermore, the ability to both rapidly mobilize investigations and then disseminate the findings is vital to identify both successes and failures in responding to emerging infectious diseases. ...
... Furthermore, the ability to both rapidly mobilize investigations and then disseminate the findings is vital to identify both successes and failures in responding to emerging infectious diseases. Indeed, from a scientific standpoint, these experiences provide an invaluable opportunity to elucidate the epidemiology of the epidemic in "real time", presenting the clear opportunity for such research to inform ongoing policy decisions [2]. ...
... It is encouraging that the majority of respondents believed they had adequate support and resources to deal with the H1N1 crisis. Indeed, the experience with SARS, and the threats of bioterrorism and avian influenza has led to considerable efforts to address pandemic preparedness at healthcare institutions throughout the country [2]. The H1N1 crisis represents the first real test of these initiatives, and suggests that these efforts have been largely successful in preparing hospitals to address emerging infectious diseases. ...
The emergence of H1N1 influenza is cause for great concern. Although one of the most important components of the response to the H1N1 crisis is the work of health care epidemiology professionals, the beliefs and experiences of this community are unknown, and the optimal approach to managing H1N1 in the future has not been delineated.
To assess attitudes and responses of health care epidemiology professionals to the H1N1 influenza crisis, we conducted a cross-sectional survey of members of the Society for Healthcare Epidemiology of America. We assessed beliefs regarding (1) importance of H1N1, (2) institutional preparedness, (3) time spent on the H1N1 crisis, and (4) the institution's response to H1N1.
Of 323 respondents, 195 (60.4%) reported that their hospitals were well prepared for a pandemic. Furthermore, the majority reported that senior administrators provided adequate political support and resources (85.1% and 80.2%, respectively) to respond to H1N1. However, 163 (50.9%) respondents reported that other important infection prevention activities were neglected during the H1N1 crisis. Shortages of antiviral medication were reported by 99 (30.7%) respondents. Furthermore, 126 (39.0%) reported that personal stockpiling of antiviral medications occurred at their institution, and 166 (51.4%) reported that institutional actions were initiated to prevent personal stockpiling. Also, 294 (91.0%) respondents believed that H1N1 influenza would reappear later this year. Vaccine development, health care worker education, and revisions of pandemic influenza plans were identified as the most important future initiatives. Finally, 251 (77.7%) respondents felt that health care workers should be mandated to receive influenza vaccine.
Although generally institutions are well prepared for the H1N1 crisis, substantial revisions of pandemic preparedness plans appear to be necessary. Future efforts to optimize the response to H1N1 should include curtailing personal stockpiling of antivirals and vaccine development with consideration of mandatory vaccination of health care workers.
... Moreover, the robustness of the network against virus attacks can be comprehensively and accurately measured by considering the spread velocity. Furthermore, the spreading velocity describes the changes in the propagation over time, which is very suitable for measuring the robustness of the network in real time [17]. ...
... Epidemics in social networks can theoretically be described using biological epidemic models, through which the spreading mechanism of the viruses can be described and analyzed. For example, the SI epidemic model and the SIS model are often used to model the spread of pandemics [13]- [17]. In the SI model, the S-state nodes can pass to the infected state through contagion by infected ones, and the rate of an S-state node being infected by a single infected neighbor is . ...
Spread velocity, epidemic threshold, and infection density at steady state are three non-negligible features describing the spread of epidemics. Combining these three features together, a new network robustness metric with respect to epidemics was proposed in this paper. The real-time robustness of the network was defined and analyzed. By using the susceptible–infected (SI) and susceptible–infected–susceptible (SIS) epidemic models, the robustness of different networks was analyzed based on the proposed network robustness metric. The simulation results showed that homogeneous networks present stronger robustness than do heterogeneous networks at the early stage of the epidemic, and the robustness of the heterogeneous networks becomes stronger than that of the homogeneous ones with the progress of the epidemic. Moreover, the irregularity of the degree distribution decreases the network robustness in homogeneous networks. The network becomes more vulnerable as the average degree grows in both homogeneous and heterogeneous networks.
... Time series based prediction models such as ARIMA, Grey Model, Markov Chain models have been used to describe dependence structure over of the disease spread over time [48][49][50][51][52]. On the other hand, statistical models, so-called phenomenological models, which follow certain laws of epidemiology [53,54] are widely used in real-time forecasting for infection trajectory or size of epidemics in early stages of pandemic [18,41,55]. Statistical models can be easily extended to the framework of hierarchical models (multilevel models [56]) to analyze data within a nested hierarchy, eventually harnessing the data integration [57][58][59][60]. ...
... ONEReal-time forecasting for COVID-19 infection trajectory for multiple countries using data integration may use each of the γ = 0.9, 0.99, 0.999, and 0.9999 to further investigate evolvement of flattening phase over time.For the US, the posterior means of the flat time points t flat,γ are May 30th, July 16th, August 30th, and October 15th when corresponding γ's are chosen by 0.9, 0.99, 0.999, and 0.9999, respectively. It is important to emphasize that the extrapolated infection trajectory is real-time prediction of COVID-19 outbreaks[31,41] based on observations tracked until May 14th. Certainly, incorporation of new information such as compliance with social distancing or advances in medical and biological sciences for this disease will change the inference outcomes.Fig 8 shows the extrapolated infection trajectories for Russia, UK, and Brazil. ...
Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.
... W hen an outbreak of an emergent disease or an environmental disaster occurs, public health officials and researchers are expected to set up in real time the best possible epidemiological investigations to understand what happens, what the health consequences of the event are and how to mitigate them as quickly as possible (1). ...
... Building the draft of the questionnaire First, the user must select the candidate questions pertaining to the event of interest: questions according to categories of the questions, time of study, study population and so on. For example, the user may want to consult all the questions belonging to the 'Knowledge' category, concerning healthcare workers and to be collected during the outbreak, which would require three 1 A password can be obtained upon request from the corresponding author (A-J V). In this example, the user chose the subcategory 'Travel history' in the 'Personal history' category (left column). ...
Background
Should an emerging infectious disease outbreak or an environmental disaster occur, the collection of epidemiological data must start as soon as possible after the event's onset. Questionnaires are usually built de novo for each event, resulting in substantially delayed epidemiological responses that are detrimental to the understanding and control of the event considered. Moreover, the public health and/or academic institution databases constructed with responses to different questionnaires are usually difficult to merge, impairing necessary collaborations. We aimed to show that e-commerce concepts and software tools can be readily adapted to enable rapid collection of data after an infectious disease outbreak or environmental disaster. Here, the ‘customers’ are the epidemiologists, who fill their shopping ‘baskets’ with standardised questions.
Methods
For each epidemiological field, a catalogue of questions is constituted by identifying the relevant variables based on a review of the published literature on similar circumstances. Each question is tagged with information on its source papers. Epidemiologists can then tailor their own questionnaires by choosing appropriate questions from this catalogue. The software immediately provides them with ready-to-use forms and online questionnaires. All databases constituted by the different EpiBasket users are interoperable, because the corresponding questionnaires are derived from the same corpus of questions.
Results
A proof-of-concept prototype was developed for Knowledge, Attitudes and Practice (KAP) surveys, which is one of the fields of the epidemiological investigation frequently explored during, or after, an outbreak or environmental disaster. The catalogue of questions was initiated from a review of the KAP studies conducted during or after the 2003 severe acute respiratory syndrome epidemic.
Conclusion
Rapid collection of standardised data after an outbreak or environmental disaster can be facilitated by transposing the e-commerce paradigm to epidemiology, taking advantage of the powerful software tools already available.
... L'impatto che può avere l'utilizzo dei modelli epidemiologici e, più in generale, dell'epidemiologia computazionale è stato discusso in modo approfondito nell'articolo di Fineberg e Wilson apparso nel 2009 sulla prestigiosa rivista Science [30]. Gli autori di questo studio hanno identificato cinque aree dove il contributo scientifico dell'epidemiologia computazionale può supportare le azioni dei policy-makers 2 : ...
A distanza di un anno e mezzo dall'inizio della pandemia causata dal SARS-CoV-2, è ormai evidente la necessità di politiche volte alla convivenza ed al controllo di questa malattia. L'obiettivo dello studio è quello di esaminare alcune di queste politiche con lo scopo di minimizzare l'impatto delle attività scolastiche in presenza sulla diffusione dell'epidemia.
A questo proposito viene sviluppato un modello basato sugli agenti (ABM) in Netlogo per valutare diverse strategie di controllo dell’infezione confrontandole tra di loro cercando di capire quali sono le strategie migliori.
Il modello descrive le attività all'interno di una scuola che comprende più classi, ambienti condivisi, il personale della scuola (insegnanti, preside e bidelli) e una descrizione dettagliata delle dinamiche di infezione. Nello specifico, nel modello il virus si diffonde per contatto diretto tra una persona infetta e una suscettibile o attraverso l'aerosol all'interno di un ambiente chiuso.
Le strategie studiate nel corso di questa tesi sono volte alla scoperta di un'eventuale focolaio all'interno della scuola per mezzo di campagne di screening. Gli esperimenti condotti in questa tesi prevedono diverse soglie di partecipazione della popolazione scolastica e diverse politiche di contenimento come, ad esempio:
- Test su tutti ogni settimana (politica A1).
- Test su 1/4 della classe ogni settimana, a rotazione (politica D1).
- Test su 1/4 della classe ogni settimana, a rotazione, suddiviso in due giorni settimanali (politica D2).
Queste strategie sono state messe a punto utilizzando diversi studi presenti in letteratura.
I risultati ottenuti da queste analisi possono essere di aiuto per mettere a punto delle strategie con l'obiettivo di contenere i contagi nelle scuole e rappresentano quindi degli strumenti utili a supporto dei policy-makers.
... Real-time tracking of the transmission and evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for public health during the COVID-19 pandemic [1,2]. Since January 2020 more than two million genomic sequences have been deposited into public databases, such as National Center for Biotechnology Information (NCBI) GenBank [3], Global Initiative on Sharing All Inf luenza Data (GISAID) [4,5]. ...
Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2.
... Menschen weltweit zu Tode kamen [95]. Zu dieser Zeit war es aufgrund technologischer und methodischer Innovationen erstmals möglich, die Epidemie in Echtzeit zu beobachten und zukünftige Entwicklungen zu prognostizieren [96]. Diese Fortschritte halfen jedoch wenig in der Prävention der Entstehung von Pandemien. ...
... Li et al. put forward their experience on training to combat COVID-19 infection, recommending continuous education as a step to tackle future emergencies [21]. Experience during such unprecedented situations helps in developing real-time policies [22,23]. ...
Introduction
During a large-scale disease outbreak, one needs to respond to the situation quickly towards capacity building, by identifying areas that require training and planning a workable strategy and implementing it. There are limited studies focused on fast-track workforce creation under challenging circumstances that demand mandatory social distancing and discouragement of gatherings. This study was conducted to analyze the planning process and implementation of fast-track training during the Coronavirus disease (COVID-19) pandemic, and evaluate its effectiveness in building a rapid, skilled, and massive workforce.
Methods
A cross-sectional study was conducted to evaluate rapid preparedness training delivered from March to June 2020, based on documents and data regarding the process, planning, and implementation for large-scale capacity building. Pre-test and post-test scores were compared to assess the effectiveness of training. The number of personnel trained was evaluated to determine the efficiency of the training program. Data on COVID-19 among health care workers (HCWs) were analyzed.
Results
The Advanced Center of Continuous Professional Development acted as the central facility, quickly responding to the situation. A total of 327 training sessions were conducted, including 76 online sessions with 153 instructors. The capacity-building of 2,706 individuals (913 clinicians and 1,793 nurses, paramedics, and non-medical staff) was achieved through multiple parallel sessions on general precautionary measures and specialized skills within four months. The rate of hospital staff infected with COVID-19 was found to be 0.01% over five months.
Conclusions
A fast-track, efficient, large-scale workforce can be created through a central facility even under challenging circumstances which restrict gatherings and require physical distancing. A training action plan for disease outbreaks would be a useful resource to tackle such medical emergencies affecting substantial populations in future.
... Real-time tracking of the epidemic trajectory and infection incidence is fundamental for public health planning and intervention during a pandemic (1,2). In the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic, key epidemiological parameters such as the effective reproductive number, Rt, have typically been estimated using the time-series of observed case counts, hospitalizations, or deaths, usually based on reverse-transcription quantitative polymerase chain reaction (RT-qPCR) testing. ...
Added value of PCR testing for COVID-19
During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, polymerase chain reaction (PCR) tests were generally reported only as binary positive or negative outcomes. However, these test results contain a great deal more information than that. As viral load declines exponentially, the PCR cycle threshold (Ct) increases linearly. Hay et al. developed an approach for extracting epidemiological information out of the Ct values obtained from PCR tests used in surveillance for a variety of settings (see the Perspective by Lopman and McQuade). Although there are challenges to relying on single Ct values for individual-level decision-making, even a limited aggregation of data from a population can inform on the trajectory of the pandemic. Therefore, across a population, an increase in aggregated Ct values indicates that a decline in cases is occurring.
Science , abh0635, this issue p. eabh0635 ; see also abj4185, p. 280
... It is well-known that the infectious disease transmission is a complex diffusion process due to social relationships. Different models have been widely developed in the literature to study the transmission process of infectious diseases theoretically, that allows us accurately predict the future development trend of infectious diseases, see among others [3][4][5][6][7][8][9][10]. While the traditional epidemiological models describe the dynamic behavior of the diseases through differential equations allowing the laws of transmission within the population, the statistic models (also so-called phenomenological models) which follow certain laws of epidemiology [9,11], are widely used in real-time forecasting for infection trajectory or size of epidemics in early stages of pandemic [12,13]. ...
The COVID-19 pandemic has highlighted the need for finding mathematical models to forecast the evolution of the contagious disease and evaluate the success of particular policies in reducing infections. In this work, we perform Bayesian inference for a non-homogeneous Poisson process with an intensity function based on the Gompertz curve. We discuss the prior distribution of the parameter and we generate samples from the posterior distribution by using Markov Chain Monte Carlo (MCMC) methods. Finally, we illustrate our method analyzing real data associated with COVID-19 in a specific region located at the south of Spain.
... Tracking trends in the incidence of infection during an epidemic are vital for deciding on 26 appropriate public health response measures (1)(2)(3). This information can help decision-makers 27 understand the need for and efficacy of non-pharmaceutical interventions, to plan the deployment 28 of public health resources, and the use of scarce hospital beds and personal protective 29 equipment. ...
Estimating an epidemic's trajectory is crucial for developing public health responses to infectious diseases, but incidence data used for such estimation are confounded by variable testing practices. We show instead that the population distribution of viral loads observed under random or symptom-based surveillance, in the form of cycle threshold (Ct) values, changes during an epidemic and that Ct values from even limited numbers of random samples can provide improved estimates of an epidemic's trajectory. Combining multiple such samples and the fraction positive improves the precision and robustness of such estimation. We apply our methods to Ct values from surveillance conducted during the SARS-CoV-2 pandemic in a variety of settings and demonstrate new approaches for real-time estimates of epidemic trajectories for outbreak management and response.
... Menschen weltweit zu Tode kamen [95]. Zu dieser Zeit war es aufgrund technologischer und methodischer Innovationen erstmals möglich, die Epidemie in Echtzeit zu beobachten und zukünftige Entwicklungen zu prognostizieren [96]. Diese Fortschritte halfen jedoch wenig in der Prävention der Entstehung von Pandemien. ...
Das Buch rekonstruiert die Implementierung des Lockdowns als Bekämpfungsstrategie der Covid-19-Pandemie im Frühjahr 2020 in den Ländern des globalen Nordens aus einer sozialwissenschaftlichen Perspektive. Auf der Basis zahlreicher empirischer Studien wird die These vertreten, dass der Lockdown zwar nicht notwendig, aber unvermeidbar war. Zentrales Problem in nahezu allen Ländern war die mangelnde Vorbereitung auf ein solches Ereignis, und dies obwohl wissenschaftliche und staatliche Stellen immer wieder auf die Gefahr hingewiesen hatten. Zusätzlich zu den biologischen und epidemiologischen Dynamiken entwickelten sich psychologische und gesellschaftliche Dynamiken, welche kaum eine andere Option als umsetzbar erscheinen liessen.
... Indeed, these situations provide an opportunity to help in policy making in real time. [9,10] Health Care Workers (HCW) are at higher risk themselves of contracting COVID-19, and thus they can place their patients at risk. In China, more than 3300 HCW have been infected and at least 22 had died. ...
Objective:
Coronavirus Disease 2019 is a new threat to human lives worldwide. Preparedness of institutions during epidemic outbreak has a pivotal role in saving lives and preventing further spread. At the same time, these pandemics impact badly on professional and personal life of Health care workers. The objective of this study is to find the opinion of Health care workers regarding their level of preparedness, concerns and perceived impact related to this pandemic outbreak.
Materials and methods:
In this study, random samples of doctors and nurses was provided with a self-administered questionnaire regarding their preparedness, work and non-work related concerns and impact on their lives during Covid-19 outbreak.
Results:
Most of the Health Care Workers believed that their institute preparation to fight Covid-19 pandemic is better than prior to onset of this crisis (P < 0.001). Work related stress was seen more commonly in nurses whereas higher frequency of non-work related stress was observed among doctors. Nurses (75.55%) faith in their employer was more than doctors faith (46.66%) regarding their medical needs. There was more acceptance of hydroxychloroquine as a prophylactic drug for Covid-19 in doctors compared to nurses (P < 0.01).
Conclusions:
Though this institute was more prepared at the time of pandemic spread, substantial opportunity of improvement remains. The consistency of work and non work related anxiety and stress in health care workers is very high in present study group. Concerns and risks of Health Care Workers should be addressed ethically and adequately by strengthening safety measures and building trust in the system they work.
... Mathematical modeling of infectious disease dynamics provides an efficient environment to design and test intervention policies in a virtual computational framework [17]- [21]. ...
The study objective is to develop an epidemiological model of brucellosis transmission dynamics among cattle in India and to estimate the impact of different prevention and control strategies. The prevention and control strategies are test-and-slaughter, transmission rate reduction, and mass vaccination. We developed a mathematical model based on the susceptible-infectious-recovered epidemic model to simulate brucellosis transmission dynamics, calibrated to the endemically stable levels of bovine brucellosis prevalence of cattle in India. We analyzed the epidemiological benefit of different rates of reduced transmission and vaccination. Test-and-slaughter is an effective strategy for elimination and eradication of brucellosis, but socio-cultural constraints forbid culling of cattle in India. Reducing transmission rates lowered the endemically stable levels of brucellosis prevalence correspondingly. One-time vaccination lowered prevalence initially but increased with influx of new susceptible births. While this epidemiological model is a basic representation of brucellosis transmission dynamics in India and constrained by limitations in surveillance data, this study illustrates the comparative epidemiological impact of different bovine brucellosis prevention and control strategies.
... Modelling tools and data gaps: Modelling may provide essential information on potential scenarios for outbreak progression, intervention design, and logistics planning [107]. Data gaps in the outbreak region have been significant, however, limiting the full use of this tool set and our ability to address operational needs. ...
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.
... Epidemiological models of infectious diseases are useful to predict the epidemiological morbidity and mortality, identify vulnerable populations, assess the beneficial impact of available interventions, compare different implementation options, and improve public understanding of infectious disease dynamics [26]. We presented the ecological niche model based on geographically weighted regression to predict the incidence and prevalence of H1N1 influenza in different regions of Vellore, India, thereby assisting in prioritizing high risk areas for implementation of optimal prevention interventions. ...
The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009-2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013-2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013-2014.
... II. METHODS Mathematical modeling of infectious disease dynamics provides an efficient environment to design and test intervention policies in a virtual computational framework[17]–[21]. ...
The study objective is to develop an epidemiological model of brucellosis transmission dynamics among cattle in India and to estimate the impact of different prevention and control strategies. The prevention and control strategies are test-and-slaughter, transmission rate reduction, and mass vaccination. We developed a mathematical model based on the susceptible-infectious-recovered epidemic model to simulate brucellosis transmission dynamics, calibrated to the endemically stable levels of bovine brucellosis prevalence of cattle in India. We analyzed the epidemiological benefit of different rates of reduced transmission and vaccination. Test-and-slaughter is an effective strategy for elimination and eradication of brucellosis, but socio-cultural constraints forbid culling of cattle in India. Reducing transmission rates lowered the endemically stable levels of brucellosis prevalence correspondingly. One-time vaccination lowered prevalence initially but increased with influx of new susceptible births. While this epidemiological model is a basic representation of brucellosis transmission dynamics in India and constrained by limitations in surveillance data, this study illustrates the comparative epidemiological impact of different bovine brucellosis prevention and control strategies.
... Decreasing variety of nutritional baskets and the broad adoption of a Western diet have had direct consequences on nutritional health, as well as critical implications for immune systems (Myles 2014). Similarly, as the genetic heterogeneity of our agricultural products declines, the susceptibility to a single catastrophic disease increases (Fineberg & Wilson 2009). In perhaps the most obvious causal feedback system, the climate change produced by the human global system might produce environments in which new diseases can develop (Altizer et al. 2013, Daszak et al. 2001). ...
In this article, we discuss the increasing interdependence of societies, focusing specifically on issues of systemic instability and fragility generated by the new and unprecedented level of connectedness and complexity resulting from globalization. We define the global system as a set of tightly coupled interactions that allow for the continued flow of information, capital, goods, services, and people. Using the general concepts of globality, complexity, networks, and the nature of risk, we analyze case studies of trade, finance, infrastructure, climate change, and public health to develop empirical support for the concept of global systemic risk. We seek to identify and describe the sources and nature of such risks and methods of thinking about risks that may inform future academic research and policy-making decisions.
... Modelling tools and data gaps: Modelling may provide essential information on potential scenarios for outbreak progression, intervention design, and logistics planning [96]. Data gaps in the outbreak region have been significant, however, limiting the full use of this tool set and our ability to address operational needs. ...
... More fundamentally, the 2009 H1N1 experience reminds us that uncertainty is inherent in infectious disease outbreaks, especially those involving emerging pathogens, so should be expected and planned for [6]. For instance, the first evidence of an outbreak should trigger efforts to learn more, rather than disproportionate control measures based on worst-case scenarios [11,102]. The requirement for better risk clarification in the WHO's new interim pandemic influenza guidance is another example [101]. ...
... Modelling tools and data gaps: Modelling may provide essential information on potential scenarios for outbreak progression, intervention design, and logistics planning [96]. Data gaps in the outbreak region have been significant, however, limiting the full use of this tool set and our ability to address operational needs. ...
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.
http://blogs.plos.org/speakingofmedicine/2014/11/11/factors-might-led-emergence-ebola-west-africa/
... Computational models play an important role in elucidating the space-time dynamics of epidemics. The H1N1 pandemic reaffirmed the need for developing analytical tools and methods to detect, assess, and respond to future pandemics; see Lipsitch et al. [2011], Kerkhove and Ferguson [2012], Wu and Cowling [2011], and Fineberg and Wilson [2009]. The role of computational models is all the more important due to ethical reasons and lack of data. ...
We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface.
Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented.
... In this context, the first evidence of an outbreak should initiate efforts to learn more about the pathogen's characteristics rather than triggering disproportionate control measures based on worst-case scenarios. 13 The risk management approach in WHO's new pandemic influenza guidance is one example. 14 Previously, pandemic influenza viruses were assumed to be highly virulent, and pandemic stages were defined in terms of spread of the virus in the population and between countries and regions. ...
The biosurveillance capabilities needed to rapidly detect and characterize emerging biological threats are an essential part of the Global Health Security Agenda (GHSA). The analyses of the global public health system's functioning during the 2009 H1N1 pandemic suggest that while capacities such as those identified in the GHSA are essential building blocks, the global biosurveillance system must possess 3 critical capabilities: (1) the ability to detect outbreaks and determine whether they are of significant global concern, (2) the ability to describe the epidemiologic characteristics of the pathogen responsible, and (3) the ability to track the pathogen's spread through national populations and around the world and to measure the impact of control strategies. The GHSA capacities-laboratory and diagnostic capacity, reporting networks, and so on-were essential in 2009 and surely will be in future events. But the 2009 H1N1 experience reminds us that it is not just detection but epidemiologic characterization that is necessary. Similarly, real-time biosurveillance systems are important, but as the 2009 H1N1 experience shows, they may contain inaccurate information about epidemiologic risks. Rather, the ability of scientists in Mexico, the United States, and other countries to make sense of the emerging laboratory and epidemiologic information that was critical-an example of global social capital-enabled an effective global response. Thus, to ensure that it is meeting its goals, the GHSA must track capabilities as well as capacities.
... Big Data driving Real-time EpidemiologyReal-time epidemiology, a rapidly developing area within public health epidemiology seeks to support policy makers in near real-time as the epidemic is unfolding[13]. A natural use of real-time epidemiology is in disease surveillance, i.e., the problem of monitoring the spacetime progression of disease. ...
Public health epidemiology aims to understand the spatiotemporal spread of diseases and to develop methods to control such spread. Computational epidemiology has become increasingly multidisciplinary and has led to novel computational methods for understanding and controlling spatiotemporal disease spread. Recent advances focus specifically on modeling, data mining, and inferential and planning questions.
... This real example annotates epidemics in real time -a phrase recently used again in a Science editorial by Fineberg and Wilson [11]: "In the face of a threatened pandemic, policy-makers will want real-time answers in at least five areas where science can help: pandemic risk, vulnerable populations, available interventions, implementation possibilities and pitfalls, and public understanding." Building real-time simulations is well motivated in the modeling and simulation community. ...
To respond to the serious threat of pandemics (e.g. 2009 H1N1 influenza) to human society, we developed Indemics (Interactive Epidemic Simulation), an interactive, data intensive, high performance modeling environment for realtime pandemic planning, situation assessment, and course of action analysis. Indemics was built upon a model of interactive data intensive scientific computation, supporting online interactions between users and simulations and enabling epidemic simulations over detailed social contact networks and realistic representations of complex public policies and intervention strategies. Instead of simply making a highly optimized parallel application run even faster, Indemics introduced several innovative ideas such as online interactive computation and HPC-DBMS integration that significantly improved the functionality, flexibility, modularity, and usability of HPC software. Our performance evaluation suggests that additional computational overhead incurred by Indemics compared to non-interactive simulations is easily offset by its new capabilities. Preliminary results show that Indemics significantly broadens the range of course of action scenarios that can be simulated and enables domain experts to analyze problems that were previously not possible to study.
... In order to plan and respond proportionately to such pandemics, public health officials need to have a systematic assessment of the socio-economic and health impact of the disease, interventions, and other mitigation efforts (Blendon et al., 2008; Epstein, et al., 2007; Philipson, 2000). Policy makers desire an understanding of intervention possibilities and pitfalls for limiting pandemic risk and assisting vulnerable populations (Fineberg & Wilson, 2009). These interventions may include social distancing, a prioritized governmental distribution of vaccines and antiviral medications, and pharmaceutical consumption in the private sector (Bruin et al., 2006; Whitley et al., 2006). ...
We aim to determine the economic and social impact of typical interventions proposed by the public health officials and preventive behavioral changes adopted by the private citizens in the event of a "flu-like" epidemic.
We apply an individual-based simulation model to the New River Valley area of Virginia for addressing this critical problem. The economic costs include not only the loss in productivity due to sickness but also the indirect cost incurred through disease avoidance and caring for dependents.
The results show that the most important factor responsible for preventing income loss is the modification of individual behavior; it drops the total income loss by 62% compared to the base case. The next most important factor is the closure of schools which reduces the total income loss by another 40%.
The preventive behavior of the private citizens is the most important factor in controlling the epidemic.
... Epidemics and outbreaks caused by emerging infectious diseases continue to challenge medical and public health authorities. Outbreak and epidemic control requires swift action, but real-time identification and characterization of epidemics remains difficult [1]. Methods are needed to inform real-time decision making through rapid characterization of disease epidemiology, prediction of shortterm disease trends, and evaluation of the projected impacts of different intervention measures. ...
Management of emerging infectious diseases such as the 2009 influenza pandemic A (H1N1) poses great challenges for real-time mathematical modeling of disease transmission due to limited information on disease natural history and epidemiology, stochastic variation in the course of epidemics, and changing case definitions and surveillance practices.
The Richards model and its variants are used to fit the cumulative epidemic curve for laboratory-confirmed pandemic H1N1 (pH1N1) infections in Canada, made available by the Public Health Agency of Canada (PHAC). The model is used to obtain estimates for turning points in the initial outbreak, the basic reproductive number (R0), and for expected final outbreak size in the absence of interventions. Confirmed case data were used to construct a best-fit 2-phase model with three turning points. R0 was estimated to be 1.30 (95% CI 1.12-1.47) for the first phase (April 1 to May 4) and 1.35 (95% CI 1.16-1.54) for the second phase (May 4 to June 19). Hospitalization data were also used to fit a 1-phase model with R0 = 1.35 (1.20-1.49) and a single turning point of June 11.
Application of the Richards model to Canadian pH1N1 data shows that detection of turning points is affected by the quality of data available at the time of data usage. Using a Richards model, robust estimates of R0 were obtained approximately one month after the initial outbreak in the case of 2009 A (H1N1) in Canada.
... For example, scientists recently demonstrated that human gene expression profiles are an effective tool for determining the etiology of respiratory infections, provid ing a striking example of rapid translation from basic research to potential clinical and public health application [43]. In a recent editorial titled 'Epidemic science in real time', Fineberg and Wilson underscored the critical importance of 'conducting the right science and communicating expert judgment' to 'enable policies to be adjusted appropriately as an epidemic scenario unfolds' [44]. They emphasized that in times of diminishing public health resources, scientists from diverse disciplines -epidemiology, laboratory, social sciences -must work together to respond to immediate threats and follow through with research to understand key attributes of the affected populations and the disease process. ...
Public health preparedness requires effective surveillance of and rapid response to infectious disease outbreaks. Inclusion of research activities within the outbreak setting provides important opportunities to maximize limited resources, to enhance gains in scientific knowledge, and ultimately to increase levels of preparedness. With rapid advances in laboratory technologies, banking and analysis of human genomic specimens can be conducted as part of public health investigations, enabling valuable research well into the future.
... Indeed, the onboard quarantine inspection in Japan was based on the response policy against the highly pathogenic avian influenza A(H5N1) and attempted to block all cases of the pandemic influenza A(H1N1) virus from entering the country. However, this method was ineffective in preventing the spread of the infection, and scientific policy-making would have been needed to minimise the adverse effects of this intervention [12, 13]. In the current study, we have highlighted a method of accomplishing evidence-based public health policy making for emerging infectious diseases. ...
We simulated the early phase of the 2009 influenza A(H1N1) pandemic and assessed the effectiveness of public health interventions in Japan. We show that the detection rate of border quarantine was low and the timing of the intervention was the most important factor involved in the control of the pandemic, with the maximum reduction in daily cases obtained after interventions started on day 6 or 11. Early interventions were not always effective.
... There are two different approaches to assessing the latter aspect of a pandemic, the virulence of infection. One is to explore specific genetic markers of the virus that are known to be associated with severe influenza (e.g. the PB1 gene) [6], although the absence of a known marker, as was for example the case in a novel swine-origin influenza A (H1N1) virus (S-OIV), does not necessarily indicate that the virus is benign [7]. Another is an epidemiological approach to quantification of the case fatality ratio (CFR), the conditional probability of death given infection (or disease; see below). ...
The case fatality ratio (CFR), the ratio of deaths from an infectious disease to the number of cases, provides an assessment of virulence. Calculation of the ratio of the cumulative number of deaths to cases during the course of an epidemic tends to result in a biased CFR. The present study develops a simple method to obtain an unbiased estimate of confirmed CFR (cCFR), using only the confirmed cases as the denominator, at an early stage of epidemic, even when there have been only a few deaths.
Our method adjusts the biased cCFR by a factor of underestimation which is informed by the time from symptom onset to death. We first examine the approach by analyzing an outbreak of severe acute respiratory syndrome in Hong Kong (2003) with known unbiased cCFR estimate, and then investigate published epidemiological datasets of novel swine-origin influenza A (H1N1) virus infection in the USA and Canada (2009). Because observation of a few deaths alone does not permit estimating the distribution of the time from onset to death, the uncertainty is addressed by means of sensitivity analysis. The maximum likelihood estimate of the unbiased cCFR for influenza may lie in the range of 0.16-4.48% within the assumed parameter space for a factor of underestimation. The estimates for influenza suggest that the virulence is comparable to the early estimate in Mexico. Even when there have been no deaths, our model permits estimating a conservative upper bound of the cCFR.
Although one has to keep in mind that the cCFR for an entire population is vulnerable to its variations among sub-populations and underdiagnosis, our method is useful for assessing virulence at the early stage of an epidemic and for informing policy makers and the public.
Preparing for the next pandemic became not only a national task, but a problem with international, even global dimensions. No single nation on its own is able to stop the next pandemic; no borders are strong enough to keep microbial threats outside the nation-state’s territory. This essay focuses on the role of the WHO in pandemic preparedness planning in the 1990s and early 2000s: What vision of the global future did the international organization follow and design? What measures did it propose to reduce risks? What narratives did it use to define its own role in these processes?
Schon unmittelbar nach Beginn des Corona-Lockdowns wurde über dessen Notwendigkeit diskutiert: War das Virus vielleicht doch »nur« eine harmlose Grippe? Wäre das »Schwedische Modell« eine realistische Alternative auch in anderen Ländern gewesen? Wieso hat die Politik in vielen Ländern das Risiko des größten Wirtschaftseinbruchs seit Jahrzehnten in Kauf genommen? Dirk Richter analysiert die Pandemie-Entwicklung im Frühjahr 2020 sowie deren Hintergründe und kommt zu dem Schluss: Die Maßnahmen wären nicht zwingend notwendig gewesen, aber länger zurückliegende Fehleinschätzungen und aktuelle gesellschaftliche Entwicklungen haben sie unvermeidlich gemacht.
This chapter describes the emergence of " global health security " as a strategic framework designed to prepare for and respond to the threat of catastrophic disease.
This paper analyses the mechanisms through which experts in the field of global health work to manage the future well-being of populations. It develops a contrast between two ways of approaching disease threats: actuarial and sentinel devices. If actuarial devices seek to map disease over time and across populations in order to gauge and mitigate risk, sentinel devices treat unprecedented diseases that cannot be mapped over time, but can only be anticipated and prepared for. The paper shows that a recent controversy over vaccination in Europe in response to the H1N1 pandemic can be understood in terms of the tension between these two kinds of security mechanisms.
This paper proposes a new approach that uses people's social interaction behavior collected by mobile phones and vital signs collected by wireless body area networks (WBAN) for epidemic control. By this approach, infectious people who are socially active can be quickly identified to be quarantined. To realize this approach, we introduce a notion of critical network and critical node identification algorithm. Observing some resource constraints such as quarantine cost and hardware limitation, we focus on optimizing the proposed approach such that high epidemic control effectiveness is achieved while the corresponding overhead is minimized. Our simulation results demonstrate that our approach can effectively control the spread of epidemic diseases in various situations.
Los virus de la influenza A y B son de relevancia médica en Costa Rica y en el mundo. Causan infecciones respiratorias graves que se manifiestan como epidemias estacionales o interpandémicas. Estos virus están conformados por genomas inestables, lo que condiciona frecuentes variaciones antigénicas y, por ende, la población en general es muy susceptible a sus efectos. Las epidemias de influenza han sido responsables de una cantidad importante de muertes (250000 a 500000 defunciones por año) y hospitalizaciones, que afectan a todos los grupos etarios, pero principalmente a niños menores de 2 años y adultos mayores de 65........
Public health policy decision makers need analytical and interactive features in epidemic simulation systems, along with the ability to simulate disease propagation over large scale populations, ranging over millions of individuals. To fulfill these requirements, we decided to re-engineer existing epidemiological software systems and integrate them together such that the performance of the overall system was minimally affected. The systems that were part of the integration effort included EpiFast, an HPC-based simulation engine, that simulates disease diffusion over multiple regions; ISIS, a web-based visual interface tool, used for analyzing the role of different parameters in disease propagation; and a database management system, storing and operating on the demographic and geographic information about different city populations. We analyzed the feasibility of existing middleware platforms to support the integration and developed a new architecture that achieves seamless and efficient integration of component systems. The integrated software system provides a combination of capability along with usability and flexibility, required by public health policy decision makers to study epidemics holistically. It also allows reuse of complex intervention strategies defined by multiple users through the web-based interface and reduces the overall time to set-up experiments and manage data. In this paper, we describe the flexible architecture that made the integration of these distinct software components possible and report on the case studies that show considerable improvement in productivity of decision makers and epidemiologists using the new integrated tool.
Many kinds of FRXQWHUmeasure could be adopted to contain or mitigate the spread of contagious disease; however, their effects are not very clear. We developed an agent-based model for assessment of those intervention policies. The population in a community was model as a large-scale artificial society, which is consistent with survey data in statisticDO� FKDUDFWHULVWLF. This artificial society is composed of numbers of households, schools, workplaces and social places. All those places, exclude households, are further divided into mixing groups. Individual schedules his/her daily behavior, travels to other places, contacts with others, and may result to transmission of disease. Contacts among individuals FRQVWUXFW the dynamic network in the fly. Control policies change the behavior of individuals, and may lead to mitigation of disease. We developed agent-based simulation software, and validated the model with sample runs. The results show that this model may be helpful in exploring the effects of countermeasure for infectious disease in large-scale population.
Host responses can contribute to the severity of viral infection, through the failure of innate antiviral mechanisms to recognize and restrict the pathogen, the development of intense systemic inflammation leading to circulatory failure or through tissue injury resulting from overly exuberant cell-mediated immune responses. High-throughput genomics methods are now being used to identify the biochemical pathways underlying ineffective or damaging host responses in a number of acute and chronic viral infections. This article reviews recent gene expression studies of 1918 H1N1 influenza and Ebola hemorrhagic fever in cell culture and animal models, focusing on how genomics experiments can be used to increase our understanding of the mechanisms that permit those viruses to cause rapidly overwhelming infection. Particular attention is paid to how evasion of type I IFN responses in infected cells might contribute to over-activation of inflammatory responses. Reviewing recent research and describing how future studies might be tailored to understand the relationship between the infected cell and its environment, this article discusses how the rapidly growing field of high-throughput genomics can contribute to a more complete understanding of severe, acute viral infections and identify novel targets for therapeutic intervention.
The sudden emergence of 2009 H1N1 influenza in the spring of that year sparked a surge in visits to emergency departments in New York City and other communities. A larger, second wave of cases was anticipated the following autumn. To reduce a potential surge of health system utilization without denying needed care, we enlisted the input of experts from medicine, public health, nursing, information technology, and other disciplines to design, test, and deploy clinical algorithms to help minimally trained health care workers and laypeople make informed decisions about care-seeking for influenza-like illness. The product of this collaboration, named Strategy for Off-Site Rapid Triage (SORT) was disseminated in 2 forms. Static algorithms, posted on the Centers for Disease Control and Prevention's Web site, offered guidance to clinicians and telephone call centers on how to manage adults and children with influenza-like illness. In addition, 2 interactive Web sites, http://www.Flu.gov and http://www.H1N1ResponseCenter.com, were created to help adults self-assess their condition and make an informed decision about their need for treatment. Although SORT was anchored in a previously validated clinical decision rule, incorporated the input of expert clinicians, and was subject to small-scale formative evaluations during rapid standup, prospective evaluation is lacking. If its utility and safety are confirmed, SORT may prove to be a useful tool to blunt health system surge and rapidly collect epidemiologic data on future disease outbreaks.
When the pandemic influenza A (H1N1) 2009 virus emerged, one of the early priorities was to estimate its virulence. This is measured by the case-fatality ratio (CFR), the proportion of deaths from influenza among the total number of cases. Epidemiological studies in the very early stages of the pandemic estimated the confirmed CFR (cCFR), using confirmed cases as the denominator, to be approximately 0.5%. However, later studies estimated the symptomatic CFR (sCFR) as approximately 0.05% of all medically attended symptomatic cases. Although subjective, the virulence of influenza A (H1N1) 2009 can be perceived as mild. Further epidemiological investigations showed that both the cCFR and sCFR varied greatly by age and risk group. When assessing the efficacy of a specific intervention in reducing the risk of influenza-related death, particular care is required regarding the inclusion criteria and in matching age and underlying conditions between patients with, and without, the intervention.
The European Academies Science Advisory Council has published a series of reports on infectious disease policy issues, analyzing priorities for building the science base as part of public health strategy. Among current challenges facing the European Union are the needs to tackle antibiotic resistance, promote vaccine innovation, prepare for the emergence of novel zoonoses, and integrate research approaches to human and animal health. The scientific community must help public policy-makers to address the organization, balance, and sustainability of research funding and infrastructure; encourage the creation of a more supportive regulatory environment for translational medicine; and evaluate new models for public-private partnership to facilitate innovation.
WHO has revised its definition of pandemic flu in response to current experience with A/H1N1. Peter Doshi argues that our plans for pandemics need to take into account more than the worst case scenarios
Epidemic Science in Real Time Editor's Summary This copy is for your personal, non-commercial use only
- Science Harvey
- V Fineberg
- Mary Elizabeth Wilson
Science
Harvey V. Fineberg and Mary Elizabeth Wilson (May 21, 2009)
Epidemic Science in Real Time
Editor's Summary
This copy is for your personal, non-commercial use only.