#Socioeconomic #disparities associated with 29 common #infectious #diseases in #Sweden, 2005–14: an individually matched case-control study (Lancet Infect Dis., abstract)

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

Socioeconomic disparities associated with 29 common infectious diseases in Sweden, 2005–14: an individually matched case-control study

Alessandro Pini, MD, Magnus Stenbeck, PhD, Ilias Galanis, MSc, Henrik Kallberg, PhD, Kostas Danis, PhD, Anders Tegnell, MD, Anders Wallensten, MD

Published: December 14, 2018 / DOI: https://doi.org/10.1016/S1473-3099(18)30485-7




Although the association between low socioeconomic status and non-communicable diseases is well established, the effect of socioeconomic factors on many infectious diseases is less clear, particularly in high-income countries. We examined the associations between socioeconomic characteristics and 29 infections in Sweden.


We did an individually matched case-control study in Sweden. We defined a case as a person aged 18–65 years who was notified with one of 29 infections between 2005 and 2014, in Sweden. Cases were individually matched with respect to sex, age, and county of residence with five randomly selected controls. We extracted the data on the 29 infectious diseases from the electronic national register of notified infections and infectious diseases (SmiNet). We extracted information on country of birth, educational and employment status, and income of cases and controls from Statistics Sweden’s population registers. We calculated adjusted matched odds ratios (amOR) using conditional logistic regression to examine the association between infections or groups of infections and place of birth, education, employment, and income.


We included 173 729 cases notified between Jan 1, 2005, and Dec 31, 2014 and 868 645 controls. Patients with invasive bacterial diseases, blood-borne infectious diseases, tuberculosis, and antibiotic-resistant infections were more likely to be unemployed (amOR 1·59, 95% CI 1·49–1·70; amOR 3·62, 3·48–3·76; amOR 1·88, 1·65–2·14; and amOR 1·73, 1·67–1·79, respectively), to have a lower educational attainment (amOR 1·24, 1·15–1·34; amOR 3·63, 3·45–3·81; amOR 2·14, 1·85–2·47; and amOR 1·07, 1·03–1·12, respectively), and to have a lowest income (amOR 1·52, 1·39–1·66; amOR 3·64, 3·41–3·89; amOR 3·17, 2·49–4·04; and amOR 1·2, 1·14–1·25, respectively). By contrast, patients with food-borne and water-borne infections were less likely than controls to be unemployed (amOR 0·74, 95% CI 0·72–0·76), to have lower education (amOR 0·75, 0·73–0·77), and lowest income (amOR 0·59, 0·58–0·61).


These findings indicate persistent socioeconomic inequalities in infectious diseases in an egalitarian high-income country with universal health care. We recommend using these findings to identify priority interventions and as a baseline to monitor programmes addressing socioeconomic inequalities in health.


The Public Health Agency of Sweden.

Keywords: Society; Public Health; Sweden; Poverty.



#Epidemic #preparedness: why is there a need to accelerate the #development of #diagnostics? (Lancet Infect Dis., summary)

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

Epidemic preparedness: why is there a need to accelerate the development of diagnostics?

Prof Rosanna W Peeling, PhD, Maurine Murtagh, PhD, Piero L Olliaro, MD

Published: December 11, 2018 / DOI: https://doi.org/10.1016/S1473-3099(18)30594-2



Global epidemics of infectious diseases are increasing in frequency and severity. Diagnostics are needed for rapid identification of the cause of the epidemic to facilitate effective control and prevention. Lessons learned from the recent Ebola virus and Zika virus epidemics are that delay in developing the right diagnostic for the right population at the right time has been a costly barrier to disease control and prevention. We believe that it is possible to accelerate and optimise diagnostic development through a five-pronged strategy: by doing a global landscape analysis of diagnostic availability worldwide; through strategic partnerships for accelerating test development, in particular with vaccine companies to identify novel diagnostic targets; by creating and sharing repositories of data, reagents, and well characterised specimens for advancing the development process; by involving key public and private stakeholders, including appropriate regulatory bodies and policy makers, to ensure rapid access for researchers to diagnostics; and last, by fostering an enabling environment for research and access to diagnostics in the countries that need them. The need is great, but not insurmountable and innovative and faster development pathways are urgently required to address current shortfalls.

Keywords: Emerging Diseases; Infectious Diseases; Pandemic preparedness; Diagnostic tests.


Measurability of the #epidemic #R0 in #data-driven #contact #networks (Proc Natl Acad Sci USA, abstract)

[Source: Proceedings of the National Academy of Sciences of the United States of America, full page: (LINK). Abstract, edited.]

Measurability of the epidemic reproduction number in data-driven contact networks

Quan-Hui Liu, Marco Ajelli, Alberto Aleta, Stefano Merler, Yamir Moreno, and Alessandro Vespignani

PNAS published ahead of print November 21, 2018 / DOI: https://doi.org/10.1073/pnas.1811115115

Edited by Simon A. Levin, Princeton University, Princeton, NJ, and approved October 16, 2018 (received for review June 27, 2018)



The analysis of real epidemiological data has raised issues of the adequacy of the classic homogeneous modeling framework and quantities, such as the basic reproduction number in real-world situations. Based on high-quality sociodemographic data, here we generate a multiplex network describing the contact pattern of the Italian and Dutch populations. By using a microsimulation approach, we show that, for epidemics spreading on realistic contact networks, it is not possible to define a steady exponential growth phase and a basic reproduction number. We show the operational use of the instantaneous reproduction rate as a good descriptor of the transmission dynamics.



The basic reproduction number is one of the conceptual cornerstones of mathematical epidemiology. Its classical definition as the number of secondary cases generated by a typical infected individual in a fully susceptible population finds a clear analytical expression in homogeneous and stratified mixing models. Along with the generation time (the interval between primary and secondary cases), the reproduction number allows for the characterization of the dynamics of an epidemic. A clear-cut theoretical picture, however, is hardly found in real data. Here, we infer from highly detailed sociodemographic data two multiplex contact networks representative of a subset of the Italian and Dutch populations. We then simulate an infection transmission process on these networks accounting for the natural history of influenza and calibrated on empirical epidemiological data. We explicitly measure the reproduction number and generation time, recording all individual-level transmission events. We find that the classical concept of the basic reproduction number is untenable in realistic populations, and it does not provide any conceptual understanding of the epidemic evolution. This departure from the classical theoretical picture is not due to behavioral changes and other exogenous epidemiological determinants. Rather, it can be simply explained by the (clustered) contact structure of the population. Finally, we provide evidence that methodologies aimed at estimating the instantaneous reproduction number can operationally be used to characterize the correct epidemic dynamics from incidence data.

computational modeling – infectious diseases – multiplex networks – reproduction number – generation time

Keywords: Infectious Diseases; Mathematical models.


#Fogarty International Center collaborative #networks in #infectious disease modeling: Lessons learnt in research and capacity building (Epidemics, abstract)

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

Epidemics. 2018 Oct 23. pii: S1755-4365(18)30029-X. doi: 10.1016/j.epidem.2018.10.004. [Epub ahead of print]

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building.

Nelson MI1, Lloyd-Smith JO2, Simonsen L3, Rambaut A4, Holmes EC5, Chowell G6, Miller MA1, Spiro DJ1, Grenfell B7, Viboud C8.

Author information: 1 Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda MD, USA. 2 Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda MD, USA; Department of Ecology & Evolutionary Biology, University of California, Los Angeles CA, USA. 3 Department of Science and Environment, Roskilde University, Roskilde, Denmark. 4 Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda MD, USA; Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, Scotland. 5 Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, The University of Sydney, Sydney NSW, Australia. 6 Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda MD, USA; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta GA, USA. 7 Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda MD, USA; Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ, USA. 8 Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda MD, USA. Electronic address: viboudc@mail.nih.gov.



Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the ‘big data’ revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.

KEYWORDS: Capacity building; Computational models; Control; Emerging disease threats; Infectious diseases; Influenza; Pathogen evolution; Policy; Transmission models

PMID: 30446431 DOI: 10.1016/j.epidem.2018.10.004

Keywords: Infectious Diseases; Emerging Diseases; Pandemic Preparedness; International Cooperation; Mathermatical Models.


#MERS #coronavirus #outbreak: Implications for emerging viral #infections (Diagn Microbiol Infect Dis., abstract)

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

Diagn Microbiol Infect Dis. 2018 Oct 18. pii: S0732-8893(18)30502-9. doi: 10.1016/j.diagmicrobio.2018.10.011. [Epub ahead of print]

MERS coronavirus outbreak: Implications for emerging viral infections.

Al-Omari A1, Rabaan AA2, Salih S3, Al-Tawfiq JA4, Memish ZA5.

Author information: 1 Critical Care and Infection Control Department, Dr. Sulaiman Al-Habib Medical Group, and Al-Faisal University, Riyadh, Saudi Arabia. 2 Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran, Saudi Arabia. Electronic address: arabaan@gmail.com. 3 Internal Medicine Department, Dr.Sulaiman Al-Habib Medical Group, Riyadh, Saudi Arabia. 4 Medical Department, Johns Hopkins Aramco Healthcare, Dhahran, Saudi Arabia, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA. 5 College of Medicine, Al-Faisal University, Riyadh, Saudi Arabia.



In September 2012, a novel coronavirus was isolated from a patient who died in Saudi Arabia after presenting with acute respiratory distress and acute kidney injury. Analysis revealed the disease to be due to a novel virus which was named Middle East Respiratory Coronavirus (MERS-CoV). There have been several MERS-CoV hospital outbreaks in KSA, continuing to the present day, and the disease has a mortality rate in excess of 35%. Since 2012, the World Health Organization has been informed of 2220 laboratory-confirmed cases resulting in at least 790 deaths. Cases have since arisen in 27 countries, including an outbreak in the Republic of Korea in 2015 in which 36 people died, but more than 80% of cases have occurred in Saudi Arabia.. Human-to-human transmission of MERS-CoV, particularly in healthcare settings, initially caused a ‘media panic’, however human-to-human transmission appears to require close contact and thus far the virus has not achieved epidemic potential. Zoonotic transmission is of significant importance and evidence is growing implicating the dromedary camel as the major animal host in spread of disease to humans. MERS-CoV is now included on the WHO list of priority blueprint diseases for which there which is an urgent need for accelerated research and development as they have the potential to cause a public health emergency while there is an absence of efficacious drugs and/or vaccines. In this review we highlight epidemiological, clinical, and infection control aspects of MERS-CoV as informed by the Saudi experience. Attention is given to recommended treatments and progress towards vaccine development.

KEYWORDS: Coronavirus; Infection; MERS; Middle East; Respiratory; Saudi Arabia; Transmission

PMID: 30413355 DOI: 10.1016/j.diagmicrobio.2018.10.011

Keywords: MERS-CoV; Emerging Diseases; Infectious Diseases; Nosocomial Outbreaks.


#Familiar #barriers still unresolved—a #perspective on the #Zika virus #outbreak #research response (Lancet Infect Dis., abstract)

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

Familiar barriers still unresolved—a perspective on the Zika virus outbreak research response

Prof Marion Koopmans, PhD, Prof Xavier de Lamballerie, MD, Thomas Jaenisch, MD PhD on behalf of ZIKAlliance Consortium †

Published: November 09, 2018 / DOI: https://doi.org/10.1016/S1473-3099(18)30497-3



Research is an important component of an effective response to the increasing frequency of widespread infectious disease outbreaks. In turn, the ability to do such studies relies on willingness of partners in different regions to collaborate and the capacity to mount a rapid research response. The EU-funded ZIKAlliance Consortium has initiated a multicountry epidemiological, clinical, and laboratory research agenda to determine the incidence, risk factors, and outcomes of Zika virus infection in pregnant women and their children. We reviewed the timeline of patient cohort initiation in relation to the Zika virus epidemic and mapped key events regarding funding, regulatory approvals, and site preparation during this timeline. We then assessed barriers and delays that the international research team experienced through a systematic telephone interview. We have identified three major bottlenecks in the implementation of a swift response: the absence of a timeline for the funding process, delays in regulatory and ethical approval, and the challenging logistics of laboratory support, including diagnostics. These bottlenecks illustrate the clear and urgent need for implementing a strong and permanent global emerging infectious diseases research capacity that has structured funding, enables long-term partnerships, and develops basic clinical and laboratorial research and a response infrastructure that is ready to deploy.

Keywords: Zika Virus; Global Health; International Cooperation; Emerging Diseases.


#Identification of acutely #sick people: individual differences and #social information use (Proc Roy Soc B., abstract)

[Source: Proceedings of the Royal Society B, Biological Sciences, full page: (LINK). Summary, edited.]

Identification of acutely sick people: individual differences and social information use

Ralf H. J. M. Kurvers, Max Wolf

Published 24 October 2018. DOI: 10.1098/rspb.2018.1274


Can humans discriminate between healthy and sick individuals, and if so by what cues? In order to study these important questions, in a recent study, Axelsson et al. let 62 untrained raters decide for 32 facial photos whether the person in the photo was healthy or sick [1]. Photos were taken of 16 healthy volunteers, injected once with a lipopolysaccharide injection (Escherichia coliendotoxin) and once with a placebo injection (with a three to four week interval in between). Photos were taken 2 h after injection. While the authors used this ingenious study design to estimate the average discrimination ability of raters, their analysis did not address two keys aspects pertaining to their research questions. First, they did not report whether and how individuals differ in discrimination ability. Individuals may differ in their ability to discriminate between healthy and sick individuals, and in how they balance the trade-off between sensitivity (i.e. the frequency of sick individuals classified as sick) and specificity (i.e. the frequency of healthy individuals classified as healthy), a.k.a. response bias [2]. Importantly, while the authors did not use their data to investigate the presence and structure of such differences, the implications of their work for disease dynamics—and consequently prevention strategies—will differ substantially between populations with and without individual differences [3–5].


Data accessibility

Data of the original study can be found at https://osf.io/btc7p/. The code of the analyses is included as electronic supplementary material.

Authors’ contributions

R.H.J.M.K. and M.W. conceived the presented idea and wrote the paper; R.H.J.M.K. performed the analysis.

Competing interests

We declare we have no competing interests.


We received no funding for this study.


Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9.figshare.c.4264085.

The accompanying reply can be viewed at http://dx.doi.org/10.1098/rspb.2018.2005.

Received June 8, 2018. Accepted August 13, 2018.

© 2018 The Author(s) http://royalsocietypublishing.org/licencePublished by the Royal Society. All rights reserved.

Keywords: Infectious Diseases; Society.