The Timing and Targeting of Treatment in Influenza Pandemics Influences the Emergence of Resistance in Structured Populations

abstract: Antiviral resistance in influenza is rampant and has the possibility of causing major morbidity and mortality. Previous models have identified treatment regimes to minimize total infections and keep resistance low. However, the bulk of these studies have ignored stochasticity and heterogeneous contact structures. Here we develop a network model of influenza transmission with treatment and resistance, and present both standard mean-field approximations as well as simulated dynamics. We find differences in the final epidemic sizes for identical transmission parameters (bistability) leading to different optimal treatment timing depending on the number initially infected. We also find, contrary to previous results, that treatment targeted by number of contacts per individual (node degree) gives rise to more resistance at lower levels of treatment than non-targeted treatment. Finally we highlight important differences between the two methods of analysis (mean-field versus stochastic simulations), and show where traditional mean-field approximations fail. Our results have important implications not only for the timing and distribution of influenza chemotherapy, but also for mathematical epidemiological modeling in general. Antiviral resistance in influenza may carry large consequences for pandemic mitigation efforts, and models ignoring contact heterogeneity and stochasticity may provide misleading policy recommendations.The article is published at http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.100291

Localization, epidemic transitions, and unpredictability of multistrain epidemics with an underlying genotype network

Mathematical disease modelling has long operated under the assumption that any one infectious disease is caused by one transmissible pathogen spreading among a population. This paradigm has been useful in simplifying the biological reality of epidemics and has allowed the modelling community to focus on the complexity of other factors such as population structure and interventions. However, there is an increasing amount of evidence that the strain diversity of pathogens, and their interplay with the host immune system, can play a large role in shaping the dynamics of epidemics. Here, we introduce a disease model with an underlying genotype network to account for two important mechanisms. One, the disease can mutate along network pathways as it spreads in a host population. Two, the genotype network allows us to define a genetic distance across strains and therefore to model the transcendence of immunity often observed in real world pathogens. We study the emergence of epidemics in this model, through its epidemic phase transitions, and highlight the role of the genotype network in driving cyclicity of diseases, large scale fluctuations, sequential epidemic transitions, as well as localization around specific strains of the associated pathogen. More generally, our model illustrates the richness of behaviours that are possible even in well-mixed host populations once we consider strain diversity and go beyond the "one disease equals one pathogen" paradigm

Correlation of influenza infection with glycan array

Poster Presentation: SPB1 / SPB2 - Virus Host Interaction/Pathogensis/Transmission: abstract no. B109PINTRODUCTION: The past 6 years has seen the introduction of glycan arrays containing large numbers of sialic acid (Sia) containing compounds and these arrays have been used to demonstrate the relative binding affinity of influenza viruses to different glycans. Though infor...postprin

An analysis of target recipient groups for monovalent 2009 pandemic influenza vaccine and trivalent seasonal influenza vaccines in 2009-10 and 2010-11

Poster Presentation: SPA5 - How to Evaluate Vaccine Effectiveness and Efficacy?: abstract no. A513PINTRODUCTION: Vaccination is generally considered to be the best primary prevention measure against influenza virus infection. Many countries encourage specific target groups of people to undertake vaccination, often with financial subsidies or a list of priority. To understand differential patterns of national target groups for influenza vaccination before, during and after the 2009 influenza pandemic, we reviewed and identified changes in national target groups for trivalent seasonal influenza and the monovalent 2009 pandemic influenza vaccines dur...postprin

Efficacy of live attenuated seasonal and pandemic influenza vaccine in school-age children: a randomized controlled trial

Poster Presentation: SPA5 - How to Evaluate Vaccine Effectiveness and Efficacy?: abstract no. A508PBACKGROUND: A novel pandemic influenza A(H1N1) virus emerged in North America in early 2009 and rapidly spread worldwide. Monovalent pH1N1 vaccines were licensed later in 2009 based on preliminary studies demonstrating their immunogenicity and safety. In this study we report the efficacy of live attenuated monovalent pH1N1 vacc...postprin

Spatial and Temporal Dynamics of Influenza

Despite the significant amount of research conducted on the epidemiology of seasonal influenza, the patterns in the annual oscillations of influenza epidemics have not been fully described or understood. Furthermore, the current understanding of the intrinsic properties of influenza epidemics is limited by the geographic scales used to evaluate the data. Analyses conducted at larger spatial scales may potentially conceal local trends in disease structure which may reveal the effect of population structure or environmental factors on disease spread. By using influenza incidence data from the Commonwealth of Pennsylvania and United States influenza mortality data, this dissertation characterizes seasonal influenza epidemics, evaluates factors that drive local influenza epidemics, and provides an initial assessment in how administrative borders influence surveillance for local and regional influenza epidemics.Evidence of spatial heterogeneity existed in the distribution of influenza epidemics for Pennsylvania counties resulting in a cluster of elevated incidence in the South Central region of the state that persisted during the entire study period (2003-2009). Lower monthly precipitation levels during the influenza season (OR = 0.52, p = 0.0319), fewer residents over age 64 (OR = 0.27, p = 0.01) and fewer residents with more than a high school education (OR = 0.76, p = 0.0148) were significantly associated with membership in this cluster. In addition, significant synchrony in the timing of epidemics existed across the entire state and decayed with distance (regional correlation r = 62%). Synchrony as a function of population size displayed evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations was the best predictor of influenza spread suggesting that non-routine and leisure travel drive local epidemics. Within the United States, clusters of epidemic synchronization existed, most notably in densely populated regions where connectivity is stronger. Observation of county and state epidemic clusters highlights the importance and necessity of correctly identifying the ontologic unit of epidemicity for influenza and other diseases. Recognition of the appropriate geographic unit to implement effective surveillance and prevention methods can strengthen the public health response and minimize inefficient mechanisms

Challenges for modelling interventions for future pandemics

Funding: This work was supported by the Isaac Newton Institute (EPSRC grant no. EP/R014604/1). MEK was supported by grants from The Netherlands Organisation for Health Research and Development (ZonMw), grant number 10430022010001, and grant number 91216062, and by the H2020 Project 101003480 (CORESMA). RNT was supported by the UKRI, grant number EP/V053507/1. GR was supported by Fundação para a Ciência e a Tecnologia (FCT) project reference 131_596787873. and by the VERDI project 101045989 funded by the European Union. LP and CO are funded by the Wellcome Trust and the Royal Society (grant 202562/Z/16/Z). LP is also supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1) and by The Alan Turing Institute for Data Science and Artificial Intelligence. HBS is funded by the Wellcome Trust and Royal Society (202562/Z/16/Z), and the Alexander von Humboldt Foundation. DV had support from the National Council for Scientific and Technological Development of Brazil (CNPq - Refs. 441057/2020-9, 424141/2018-3, 309569/2019-2). FS is supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). EF is supported by UKRI (Medical Research Council)/Department of Health and Social Care (National Insitute of Health Research) MR/V028618/1. JPG's work was supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care.Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.Publisher PDFPeer reviewe

Emerg Infect Dis

Emerging Infectious Diseases is providing access to these abstracts on behalf of the ICEID 2022 program committee (http://www.iceid.org), which performed peer review. ICEID is organized by the Centers for Disease Control and Prevention and Task Force for Global Health, Inc.Emerging Infectious Diseases has not edited or proofread these materials and is not responsible for inaccuracies or omissions. All information is subject to change. Comments and corrections should be brought to the attention of the authors.Suggested citation: Authors. Title [abstract]. International Conference on Emerging Infectious Diseases 2022 poster and oral presentation abstracts. Emerg Infect Dis. 2022 Sep [date cited]. http://www.cdc.gov/EID/pdfs/ICEID2022.pdf2022PMC94238981187

On the socio-economic impact of pandemics in Africa: Lessons learned from COVID-19, Trypanosomiasis, HIV, Yellow Fever and Cholera

Throughout history, nothing has killed more human beings than infectious diseases. Although, death rates from pandemics dropped globally by about 0.8 % per year, all the way through the 20th century, the number of new infectious diseases like Sars, HIV and Covid-19 increased by nearly fourfold over the past century. In Africa, there were reported a total of 4,522,489 confirmed COVID-19 cases and 119,816 death, as of 23 April 2021. The pandemic impacted seriously on the economic and social sectors in almost all African countries. It is threatening to push up to 58 m people into extreme poverty. However, apart from the African poor, the Covid pandemic also affects the growing African middle class, i.e. about 170 million out of Africa's 1.3 billion people currently classified as middle class. Nearly eight million of may be thrust into poverty because of the coronavirus and its economic aftermath. This setback will be felt for decades to come. Moreover, in recent African History also other infectouse diseases like the 1896-1906 Congo Basin Trypanosomiasis with a death-toll of over 500.000 as well as the 1900-1920 Uganda African trypanosomiasis epidemic with 200,000-300,000 death had tremendous negative impact on Africa's societies and economies. Actually, other pandemics, like Yellow Fever, Cholera, Meningitis and Measles - not to mention Malaria - contributed to long-lasting economic downturns and seriously affect the social wellbeing for decades.Im Laufe der Geschichte hat nichts mehr Menschen getötet als Infektionskrankheiten. Obwohl die Sterblichkeitsrate durch Pandemien im Laufe des 20. Jahrhunderts weltweit um etwa 0,8% pro Jahr gesunken ist, hat sich die Zahl der neuen Infektionskrankheiten wie Sars, HIV und Covid-19 im vergangenen Jahrhundert fast vervierfacht. In Afrika wurden zum 23. April 2021 insgesamt 4.522.489 bestätigte COVID-19-Fälle und 119.816 Todesfälle gemeldet. Die Pandemie hatte schwerwiegende Auswirkungen auf den wirtschaftlichen und sozialen Sektor in fast allen afrikanischen Ländern. Sie droht, bis zu 58 Millionen Menschen in extreme Armut zu treiben. Abgesehen von den afrikanischen Armen betrifft die Covid-Pandemie jedoch auch die wachsende afrikanische Mittelschicht, d. h. etwa 170 Millionen der 1,3 Milliarden Menschen in Afrika, die derzeit als Mittelschicht eingestuft sind. Fast acht Millionen von ihnen könnten aufgrund des Coronavirus und seiner wirtschaftlichen Folgen in Armut geraten. Dieser Rückschlag wird noch Jahrzehnte zu spüren sein. Darüber hinaus hatten in der jüngeren afrikanischen Geschichte auch andere Infektionskrankheiten wie die Trypanosomiasis (Schlafkrankheit) im Kongobecken von 1896-1906 mit einer Zahl von über 500.000 Todesopfern sowie die Trypanosomiasis-Epidemie in Uganda von 1900-1920 mit 200.000-300.000 Todesfällen enorme negative Auswirkungen auf die afrikanischen Gesellschaften und Volkswirtschaften. Tatsächlich haben andere Pandemien wie Gelbfieber, Cholera, Meningitis und Masern - ganz zu schweigen von Malaria - zu lang anhaltenden wirtschaftlichen Abschwüngen beigetragen und das soziale Wohlbefinden über Jahrzehnte hinweg ernsthaft beeinträchtigt.Au cours de l’histoire, rien n’a tué plus d’êtres humains que les maladies infectieuses et la fièvre hémorragique. Bien que les taux de mortalité dus aux pandémies aient chuté de près de 1% par an dans le monde, environ 0,8% par an, tout au long du XXe siècle, le nombre de nouvelles maladies infectieuses comme le Sars, le VIH et le Covid-19 a presque quadruplé par rapport au passé. En Afrique, on a signalé un total de 4 522 489 cas confirmés de COVID-19 et 119 816 décès, au 23 avril 2021. La pandémie a eu de graves répercussions sur les secteurs économique et social dans presque tous les pays africains. Il menace de pousser jusqu'à 58 millions de personnes dans l'extrême pauvreté. Cependant, outre les Africains pauvres, la pandémie de Covid affecte également la classe moyenne africaine en pleine croissance, c'est-à-dire environ 170 millions sur les 1,3 milliard d'africains actuellement classés dans la classe moyenne. Près de huit millions d'entre eux pourraient être plongés dans la pauvreté à cause du coronavirus et de ses conséquences économiques. Ce revers se fera sentir pendant des décennies. En outre, dans l'histoire récente de l'Afrique, d'autres maladies infectieuses comme la trypanosomiase du bassin du Congo de 1896 à 1906 avec un nombre des morts de plus de 500 000 ainsi que l'épidémie de trypanosomose africaine en Ouganda de 1900 à 1920 avec 200 000 à 300 000 décès ont eu un impact négatif considérable sur les sociétés et économies africaines. En fait, d'autres pandémies, comme la fièvre jaune, le choléra, la méningite et la rougeole - sans parler du paludisme - ont contribué à des ralentissements économiques durables et affectent gravement le bien-être social pendant des décennies

Emerging infectious diseases

Emerging Infectious Diseases is providing access to these abstracts on behalf of the ICEID 2012 program committee (www.iceid.org), which performed peer review. Emerging Infectious Diseases has not edited or proofread these materials and is not responsible for inaccuracies or omissions. All information is subject to change. Comments and corrections should be brought to the attention of the authors.Influenza preparedness: lessons learned -- Policy implications and infectious diseases -- Improving preparedness for infectious diseases -- New or rapid diagnostics -- Foodborne and waterborne infections -- Effective and sustainable surveillance platforms -- Healthcare-associated infections -- Molecular epidemiology -- Antimicrobial resistance -- Tropical infections and parasitic diseases -- H1N1 influenza -- Risk Assessment -- Laboratory Support -- Zoonotic and Animal Diseases -- Viral Hepatitis -- E1. Zoonotic and animal diseases -- E2. Vaccine issues -- E3. H1N1 influenza -- E4. Novel surveillance systems -- E5. Antimicrobial resistance -- E6. Late-breakers I -- Antimicrobial resistance -- Influenza preparedness: lessons learned -- Zoonotic and animal diseases -- Improving preparedness for infectious diseases -- Laboratory support -- Early warning systems -- H1N1 influenza -- Policy implications and infectious diseases -- Modeling -- Molecular epidemiology -- Novel surveillance systems -- Tropical infections and parasitic diseases -- Strengthening public health systems -- Immigrant and refugee health -- Foodborne and waterborne infections -- Healthcare-associated infections -- Foodborne and waterborne infections -- New or rapid diagnostics -- Improving global health equity for infectious diseases -- Vulnerable populations -- Novel agents of public health importance -- Influenza preparedness: lessons learned -- Molecular epidemiology -- Zoonotic and animal diseases -- Vaccine-preventable diseases -- Outbreak investigation: lab and epi response -- H1N1 influenza -- laboratory support -- effective and sustainable surveillance platforms -- new vaccines -- vector-borne diseases and climate change -- travelers' health -- J1. Vectorborne diseases and climate change -- J2. Policy implications and infectious diseases -- J3. Influenza preparedness: lessons learned -- J4. Effective and sustainable surveillance platforms -- J5. Outbreak investigation: lab and epi response I -- J6. Late-breakers II -- Strengthening public health systems -- Bacterial/viral coinfections -- H1N1 influenza -- Novel agents of public health importance -- Foodborne and waterborne infections -- New challenges for old vaccines -- Vectorborne diseases and climate change -- Novel surveillance systems -- Geographic information systems (GIS) -- Improving global health equity for infectious diseases -- Vaccine preventable diseases -- Vulnerable populations -- Laboratory support -- Prevention challenges for respiratory diseases -- Zoonotic and animal diseases -- Outbreak investigation: lab and epi response -- Vectorborne diseases and climate change -- Outbreak investigation: lab and epi response -- Laboratory proficiency testing/quality assurance -- Effective and sustainable surveillance platforms -- Sexually transmitted diseases -- H1N1 influenza -- Surveillance of vaccine-preventable diseases -- Foodborne and waterborne infections -- Role of health communication -- Emerging opportunistic infections -- Host and microbial genetics -- Respiratory infections in special populations -- Zoonotic and animal diseases -- Laboratory support -- Antimicrobial resistance -- Vulnerable populations -- Global vaccine initiatives -- Tuberculosis -- Prevention challenges for respiratory diseases -- Infectious causes of chronic diseases -- O1. Outbreak investigation: lab and epi response II -- O2. Prevention challenges for respiratory diseases -- O3. Populations at high risk for infectious diseases -- O4. Foodborne and waterborne infections -- O5. Laboratory support: surveillance and monitoring infections -- O6. Late-breakers IIIAbstracts published in advance of the conference