#Outbreak #response as an essential #component of #vaccine #development (Lancet Infect Dis., abstract)

[Source: The Lancet Infectious Diseases, full page: (LINK). Abstract, edited.]

Outbreak response as an essential component of vaccine development

Richard Hatchett, MD, Prof Nicole Lurie, MD

Published: June 27, 2019 / DOI: https://doi.org/10.1016/S1473-3099(19)30305-6

 

Summary

The Coalition for Epidemic Preparedness Innovations (CEPI) was created as a result of an emerging global consensus that a coordinated, international, and intergovernmental effort was needed to develop and deploy new vaccines to prevent future epidemics. Although some disease outbreaks can be relatively brief, early outbreak response activities can provide important opportunities to make progress on vaccine development. CEPI has identified six such areas and is prepared to work with other organisations in the global community to combat WHO priority pathogens, including the hypothetical Disease X, by supporting early activities in these areas, even when vaccine candidates are not yet available.

Keywords: Pandemic Preparedness; Vaccines.

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#Clinical #management of respiratory syndrome in #patients hospitalized for suspected #MERS #coronavirus #infection in the #Paris area from 2013 to 2016 (BMC Infect Dis., abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

BMC Infect Dis. 2018 Jul 16;18(1):331. doi: 10.1186/s12879-018-3223-5.

Clinical management of respiratory syndrome in patients hospitalized for suspected Middle East respiratory syndrome coronavirus infection in the Paris area from 2013 to 2016.

Bleibtreu A1,2,3,4, Jaureguiberry S5, Houhou N6, Boutolleau D7, Guillot H5, Vallois D8, Lucet JC9,10,11, Robert J12,13, Mourvillier B10,11,14, Delemazure J15, Jaspard M5, Lescure FX8,10,11, Rioux C8, Caumes E5, Yazdanapanah Y8,10,11.

Author information: 1 APHP, Hôpital Bichat Claude Bernard, Service des Maladies Infectieuses et Tropicales, Paris Diderot University, Paris, France. alexandre.bleibtreu@aphp.fr. 2 APHP, Hôpitaux Universitaires Pitié Salpêtrière-Charles Foix, Service des Maladies Infectieuses et Tropicales, Paris, France. alexandre.bleibtreu@aphp.fr. 3 INSERM, IAME, UMR 1137, Paris, France. alexandre.bleibtreu@aphp.fr. 4 Univ Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, Paris, France. alexandre.bleibtreu@aphp.fr. 5 APHP, Hôpitaux Universitaires Pitié Salpêtrière-Charles Foix, Service des Maladies Infectieuses et Tropicales, Paris, France. 6 Virology Department, APHP-Bichat-Claude Bernard Hospital, Paris, France. 7 AP-HP, Hôpitaux Universitaires Pitié Salpêtrière-Charles Foix, Service de Virologie, et Sorbonne Universités, UPMC Univ Paris 06, CR7, CIMI, INSERM U1135, Paris, France. 8 APHP, Hôpital Bichat Claude Bernard, Service des Maladies Infectieuses et Tropicales, Paris Diderot University, Paris, France. 9 APHP, Infection control unit, Bichat Claude Bernard hospital, Paris Diderot University, Paris, France. 10 INSERM, IAME, UMR 1137, Paris, France. 11 Univ Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, Paris, France. 12 AP-HP, Hôpitaux Universitaires Pitié Salpêtrière-Charles Foix, Bactériologie-Hygiène Hospitalière, Paris, France. 13 Faculté de Médecine P. & M. Curie Paris-6 – Site Pitié, Centre d’Immunologie et des Maladies Infectieuses (CIMI) – E13, Paris, France. 14 APHP- Hôpital Bichat Claude Bernard, Service de Réanimation médicale et Infectieuse, Paris, France. 15 Service de pneumologie et réanimation Département R3S, AP-HP, Hôpitaux Universitaires Pitié Salpêtrière-Charles Foix, unité de Soin de Réadaptation Post Réanimation (SRPR), Paris, France.

 

Abstract

BACKGROUND:

Patients with suspected Middle East respiratory syndrome coronavirus (MERS-CoV) infection should be hospitalized in isolation wards to avoid transmission. This suspicion can also lead to medical confusion and inappropriate management of acute respiratory syndrome due to causes other than MERS-CoV.

METHODS:

We studied the characteristics and outcome of patients hospitalized for suspected MERS-CoV infection in the isolation wards of two referral infectious disease departments in the Paris area between January 2013 and December 2016.

RESULTS:

Of 93 adult patients (49 male (52.6%), median age 63.4 years) hospitalized, 82 out of 93 adult patients had returned from Saudi Arabia, and 74 of them were pilgrims (Hajj). Chest X-ray findings were abnormal in 72 (77%) patients. The 93 patients were negative for MERS-CoV RT-PCR, and 70 (75.2%) patients had documented infection, 47 (50.5%) viral, 22 (23.6%) bacterial and one Plasmodium falciparum malaria. Microbiological analysis identified Rhinovirus (27.9%), Influenza virus (26.8%), Legionella pneumophila (7.5%), Streptococcus pneumoniae (7.5%), and non-MERS-coronavirus (6.4%). Antibiotics were initiated in 81 (87%) cases, with two antibiotics in 63 patients (67.7%). The median duration of hospitalization and isolation was 3 days (1-33) and 24 h (8-92), respectively. Time of isolation decreased over time (P < 0.01). Two patients (2%) died.

CONCLUSION:

The management of patients with possible MERS-CoV infection requires medical facilities with trained personnel, and rapid access to virological results. Empirical treatment with neuraminidase inhibitors and an association of antibiotics effective against S. pneumoniae and L. pneumophila are the cornerstones of the management of patients hospitalized for suspected MERS-CoV infection.

KEYWORDS: Isolation ward; Legionella; Middle East respiratory syndrome coronavirus (MERS-CoV); Pilgrims; Respiratory tract infection; Saudi Arabia

PMID: 30012113 PMCID: PMC6048819 DOI: 10.1186/s12879-018-3223-5 [Indexed for MEDLINE]  Free PMC Article

Keywords: MERS-CoV; France.

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The #effect of #global #change on #mosquito-borne #disease (Lancet Infect Dis., abstract)

[Source: The Lancet Infectious Diseases, full page: (LINK). Abstract, edited.]

The effect of global change on mosquito-borne disease

Lydia H V Franklinos, MSc, Prof Kate E Jones, PhD, David W Redding, PhD, Prof Ibrahim Abubakar, PhD

Published: June 18, 2019 / DOI: https://doi.org/10.1016/S1473-3099(19)30161-6

 

Summary

More than 80% of the global population is at risk of a vector-borne disease, with mosquito-borne diseases being the largest contributor to human vector-borne disease burden. Although many global processes, such as land-use and socioeconomic change, are thought to affect mosquito-borne disease dynamics, research to date has strongly focused on the role of climate change. Here, we show, through a review of contemporary modelling studies, that no consensus on how future changes in climatic conditions will impact mosquito-borne diseases exists, possibly due to interacting effects of other global change processes, which are often excluded from analyses. We conclude that research should not focus solely on the role of climate change but instead consider growing evidence for additional factors that modulate disease risk. Furthermore, future research should adopt new technologies, including developments in remote sensing and system dynamics modelling techniques, to enable a better understanding and mitigation of mosquito-borne diseases in a changing world.

Keywords: Arbovirus; Mosquitoes; Emerging Diseases; Climate Change.

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Estimating #undetected #Ebola #spillovers (PLoS Negl Trop Dis., abstract)

[Source: PLoS Neglected Tropical Diseases, full page: (LINK). Abstract, edited.]

OPEN ACCESS /  PEER-REVIEWED / RESEARCH ARTICLE

Estimating undetected Ebola spillovers

Emma E. Glennon , Freya L. Jephcott, Olivier Restif, James L. N. Wood

Published: June 13, 2019 / DOI: https://doi.org/10.1371/journal.pntd.0007428

 

Abstract

The preparedness of health systems to detect, treat, and prevent onward transmission of Ebola virus disease (EVD) is central to mitigating future outbreaks. Early detection of outbreaks is critical to timely response, but estimating detection rates is difficult because unreported spillover events and outbreaks do not generate data. Using three independent datasets available on the distributions of secondary infections during EVD outbreaks across West Africa, in a single district (Western Area) of Sierra Leone, and in the city of Conakry, Guinea, we simulated realistic outbreak size distributions and compared them to reported outbreak sizes. These three empirical distributions lead to estimates for the proportion of detected spillover events and small outbreaks of 26% (range 8–40%, based on the full outbreak data), 48% (range 39–62%, based on the Sierra Leone data), and 17% (range 11–24%, based on the Guinea data). We conclude that at least half of all spillover events have failed to be reported since EVD was first recognized. We also estimate the probability of detecting outbreaks of different sizes, which is likely less than 10% for single-case spillover events. Comparing models of the observation process also suggests the probability of detecting an outbreak is not simply the cumulative probability of independently detecting any one individual. Rather, we find that any individual’s probability of detection is highly dependent upon the size of the cluster of cases. These findings highlight the importance of primary health care and local case management to detect and contain undetected early stage outbreaks at source.

 

Author summary

Emerging infectious diseases are often not investigated in rural Africa unless outbreaks involve a sizeable number of cases. A number of different Ebola virus disease (EVD) outbreaks have been reported in the literature and in surveillance reports since its discovery in 1976. The majority of the reports are of large outbreaks. Given the low reported rate of transmission of Ebola, and the high frequency with which cases infect no one else, one might expect most outbreaks to be very small (<5 people). This is the first study to the authors’ knowledge that quantitatively estimates the number of undetected EVD outbreaks or probabilities of EVD outbreak detection by outbreak size. Although the total amount of evidence in this area is still limited, this study’s main result—that at least half of EVD outbreaks go undetected—is consistent under many different sets of assumptions. This is the most thorough estimation of EVD outbreak detection to date and corroborates the majority of more qualitative work on EVD surveillance, suggesting greater investment in primary health care and local surveillance will be important to detect EVD outbreaks early and consistently.

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Citation: Glennon EE, Jephcott FL, Restif O, Wood JLN (2019) Estimating undetected Ebola spillovers. PLoS Negl Trop Dis 13(6): e0007428. https://doi.org/10.1371/journal.pntd.0007428

Editor: Benjamin Althouse, Institute for Disease Modeling, UNITED STATES

Received: October 30, 2018; Accepted: May 1, 2019; Published: June 13, 2019

Copyright: © 2019 Glennon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data and R code used in this analysis are publicly available at https://github.com/eeg31/evd-detection (doi: 10.5281/zenodo.2602105).

Funding: EEG is funded by the Gates-Cambridge Trust (Bill & Melinda Gates Foundation [OPP1144]). OR and JLNW are funded by the ALBORADA Trust. JLNW is funded by the Medical Research Council (MR/P025226/1). The funding bodies had no involvement in the design, writing, or decision to publish of this manuscript.

Competing interests: The authors have declared that no competing interests exist.

Keywords: Infectious Diseases; Emerging Diseases; Ebola.

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Increased #frequency of #travel in the presence of cross-immunity may act to decrease the #chance of a #global #pandemic (Philos Transact Roy Soc B., abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Philos Trans R Soc Lond B Biol Sci. 2019 Jun 24;374(1775):20180274. doi: 10.1098/rstb.2018.0274.

Increased frequency of travel in the presence of cross-immunity may act to decrease the chance of a global pandemic.

Thompson RN1,2,3, Thompson CP2, Pelerman O4, Gupta S2, Obolski U2,5,6.

Author information: 1 Mathematical Institute, University of Oxford , Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG , UK. 2 Department of Zoology, University of Oxford , South Parks Road, Oxford OX1 3PS , UK.
3 Christ Church, University of Oxford , St Aldate’s, Oxford OX1 1DP , UK. 4 The Chaim Rosenberg School of Jewish Studies, Tel Aviv University , Tel Aviv 69978 , Israel. 5 School of Public Health , Tel Aviv University, Tel Aviv , Israel. 6 Porter School of the Environment and Earth Sciences, Tel Aviv University , Israel.

 

Abstract

The high frequency of modern travel has led to concerns about a devastating pandemic since a lethal pathogen strain could spread worldwide quickly. Many historical pandemics have arisen following pathogen evolution to a more virulent form. However, some pathogen strains invoke immune responses that provide partial cross-immunity against infection with related strains. Here, we consider a mathematical model of successive outbreaks of two strains-a low virulence (LV) strain outbreak followed by a high virulence (HV) strain outbreak. Under these circumstances, we investigate the impacts of varying travel rates and cross-immunity on the probability that a major epidemic of the HV strain occurs, and the size of that outbreak. Frequent travel between subpopulations can lead to widespread immunity to the HV strain, driven by exposure to the LV strain. As a result, major epidemics of the HV strain are less likely, and can potentially be smaller, with more connected subpopulations. Cross-immunity may be a factor contributing to the absence of a global pandemic as severe as the 1918 influenza pandemic in the century since. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

KEYWORDS: antigenic variation; cross-immunity; major epidemic; mathematical modelling; pathogen diversity

PMID: 31056047 DOI: 10.1098/rstb.2018.0274

Keywords: Emerging diseases; Infectious Diseases; Pandemic Influenza, Mathematical models.

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One #model to rule them all? Modelling #approaches across #OneHealth for #human, #animal and #plant #epidemics (Philos Transact Roy Soc B., abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Philos Trans R Soc Lond B Biol Sci. 2019 Jun 24;374(1775):20180255. doi: 10.1098/rstb.2018.0255.

One model to rule them all? Modelling approaches across OneHealth for human, animal and plant epidemics.

Kleczkowski A1, Hoyle A2, McMenemy P2.

Author information: 1 Department of Mathematics and Statistics, University of Strathclyde , Glasgow G1 1XH , UK. 2 Computing Science and Mathematics, University of Stirling , Stirling FK9 4LA , UK.

 

Abstract

One hundred years after the 1918 influenza outbreak, are we ready for the next pandemic? This paper addresses the need to identify and develop collaborative, interdisciplinary and cross-sectoral approaches to modelling of infectious diseases including the fields of not only human and veterinary medicine, but also plant epidemiology. Firstly, the paper explains the concepts on which the most common epidemiological modelling approaches are based, namely the division of a host population into susceptible, infected and removed (SIR) classes and the proportionality of the infection rate to the size of the susceptible and infected populations. It then demonstrates how these simple concepts have been developed into a vast and successful modelling framework that has been used in predicting and controlling disease outbreaks for over 100 years. Secondly, it considers the compartmental models based on the SIR paradigm within the broader concept of a ‘disease tetrahedron’ (comprising host, pathogen, environment and man) and uses it to review the similarities and differences among the fields comprising the ‘OneHealth’ approach. Finally, the paper advocates interactions between all fields and explores the future challenges facing modellers. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

KEYWORDS: OneHealth; bio-economic models; compartmental models; epidemiological data; infectious disease; plant pathogens

PMID: 31056049 DOI: 10.1098/rstb.2018.0255

Keywords: Infectious Diseases; Emerging Diseases; Pandemic Influenza; Mathematical models.

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#Detection, #forecasting and #control of #infectious #disease #epidemics: modelling #outbreaks in #humans, #animals and #plants (Philos Transact Roy Soc B., abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Philos Trans R Soc Lond B Biol Sci. 2019 Jun 24;374(1775):20190038. doi: 10.1098/rstb.2019.0038.

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants.

Thompson RN1,2,3, Brooks-Pollock E4,5.

Author information: 1 Mathematical Institute, University of Oxford , Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG , UK. 2 Department of Zoology, University of Oxford , Peter Medawar Building, South Parks Road, Oxford OX1 3SY , UK. 3 Christ Church, University of Oxford , St Aldates, Oxford OX1 1DP , UK. 4 Bristol Veterinary School, University of Bristol , Langford BS40 5DU , UK. 5 National Institute for Health Research, Health Protection Research Unit in Evaluation of Interventions, Bristol Medical School , Bristol BS8 2BN , UK.

 

Abstract

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

KEYWORDS: animal disease; human disease; mathematical modelling; one health; plant disease; public health

PMID: 31056051 DOI: 10.1098/rstb.2019.0038

Keywords: Infectious Diseases; Emerging Diseases; Pandemic Influenza; Mathematical models.

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