Author Profile

Lone Simonsen

Affiliation: Department of Public Health, University of Copenhagen, Copenhagen, Denmark

Lone Simonsen holds a PhD in population genetics from University of Massachusetts, Amherst and later trained at the Centers for Disease Control (CDC) in infectious disease epidemiology. She is currently a research professor in the department of global health at George Washington University where she happily mentors MPH, DrPH and PhD students in topics relating to global health epidemiology. She spent 6 months as a Lundbeck visiting professor at the University of Copenhagen in 2014, to develop research in historic epidemiology and health transitions. She is a senior fellow in the RAPIDD (mathematical modeling for policy) network hosted at the Fogarty International Center at the National institutes of Health (NIH) and Princeton University. She is an elected member of the American Epidemiological Society AES, and the Danish Royal Academy of Sciences & Letters. Over the past 20+ years she has worked as a researcher at the CDC, World Health Organization (WHO) and NIH on issues including unsafe medical injections, global patterns of HIV/AIDS, TB drug resistance, SARS, pandemic influenza, e-health data, surveillance systems and vaccine program evaluation. Before moving to academia in 2007, she was a senior epidemiologist at the National Institutes of Health-NIAID where she assisted the office of the director with its research response to emerging health issues, including work on rotavirus vaccine adverse events for which she received the Department of Health & Human Services DHHS Secretary’s Distinguished Service Award She has published more than 150 well-cited peer-reviewed papers, book chapters, commentaries and letters, in collaboration with a global network of researchers. Her research currently focuses on modeling of historic and contemporary pandemics and emerging infectious diseases, population transitions in health, modeing the burden of influenza and other vaccine-preventable diseases, and evaluating health benefits associated with vaccine programs on a grant from the Gates Foundation. Simonsen is a frequent speaker at national and international meetings, she served on an influenza expert panel for the Council of Foreign Relations, and presented on pandemic surveillance issues at the President's Council of Advisors on Science andTechnology Policy in 2009. She is currently also working with her RAPIDD collaborators on modeling the Ebola disaster in West africa. She recently led a WHO-sponsored multi-country collaboration to model the 2009 influenza pandemic burden, and is frequently called on by WHO to participate in workshops and meetings on pandemic preparedness, frameworks/policy issues for pandemic preparedness, methodology issues in disease surveillance, including monitoring and evaluation of vaccine program impact. Scientific Productivity (by 2008/ever): H-inex: 44/55 and I10-index:98/111

Recent Posts

Using Phenomenological Models to Characterize Transmissibility and Forecast Patterns and Final Burden of Zika Epidemics

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Background: The World Health Organization declared the ongoing Zika virus (ZIKV) epidemic in the Americas a Public Health Emergency of International Concern on February 1, 2016. ZIKV disease in humans is characterized by a “dengue-like” syndrome including febrile illness and rash. However, ZIKV infection in early pregnancy has been associated with severe birth defects, including microcephaly and other developmental issues. Mechanistic models of disease transmission can be used to forecast trajectories and likely disease burden but are currently hampered by substantial uncertainty on the epidemiology of the disease (e.g., the role of asymptomatic transmission, generation interval, incubation period, and key drivers). When insight is limited, phenomenological models provide a starting point for estimation of key transmission parameters, such as the reproduction number, and forecasts of epidemic impact.

Methods: We obtained daily counts of suspected Zika cases by date of symptoms onset from the Secretary of Health of Antioquia, Colombia during January-April 2016. We calibrated the generalized Richards model, a phenomenological model that accommodates a variety of early exponential and sub-exponential growth kinetics, against the early epidemic trajectory and generated predictions of epidemic size. The reproduction number was estimated by applying the renewal equation to incident cases simulated from the fitted generalized-growth model and assuming gamma or exponentially-distributed generation intervals derived from the literature. We estimated the reproduction number for an increasing duration of the epidemic growth phase.

Results: The reproduction number rapidly declined from 10.3 (95% CI: 8.3, 12.4) in the first disease generation to 2.2 (95% CI: 1.9, 2.8) in the second disease generation, assuming a gamma-distributed generation interval with the mean of 14 days and standard deviation of 2 days. The generalized-Richards model outperformed the logistic growth model and provided forecasts within 22% of the actual epidemic size based on an assessment 30 days into the epidemic, with the epidemic peaking on day 36.

Conclusion: Phenomenological models represent promising tools to generate early forecasts of epidemic impact particularly in the context of substantial uncertainty in epidemiological parameters. Our findings underscore the need to treat the reproduction number as a dynamic quantity even during the early growth phase, and emphasize the sensitivity of reproduction number estimates to assumptions on the generation interval distribution.

Unintended Pregnancies in Brazil – A Challenge for the Recommendation to Delay Pregnancy Due to Zika

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Figure 2.

Because of the potential link between the ongoing Zika virus outbreak and a surge in the number of cases of congenital microcephaly, officials in Latin America have recommended that women postpone pregnancy until this association is firmly established or the outbreak subsides. However, in all these countries a large proportion of babies are still born out of unplanned pregnancies. Teenage girls are particularly at high risk, as they often lack access to preventive contraception methods, or the knowledge to use them appropriately. To gauge the magnitude of the barriers preventing the implementation of such a recommendation in Brazil, the country so far most affected by the Zika epidemic, we evaluated pregnancy rates in teenage girls, and their spatial heterogeneity in the country, in recent years (2012-2014). Nearly 20% of children born in Brazil today (~560,000 live births) are by teenage mothers. Birth incidence is far higher in the tropical and poorer northern states. However, in absolute terms most births occur in the populous southeastern states, matching to a large extent the geographic distribution of dengue (an indicator of suitable climatic and sociodemographic conditions for the circulation of Aedes mosquitoes). These findings indicate that recommendation to delay pregnancy will leave over half a million pregnant adolescents in Brazil vulnerable to infection every year if not accompanied by effective education and real access to prevention.

The Western Africa Ebola Virus Disease Epidemic Exhibits Both Global Exponential and Local Polynomial Growth Rates

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Background:
While many infectious disease epidemics are initially characterized by an exponential growth in time, we show that district-level Ebola virus disease (EVD) outbreaks in West Africa follow slower polynomial-based growth kinetics over several generations of the disease.

Methods:
We analyzed epidemic growth patterns at three different spatial scales (regional, national, and subnational) of the Ebola virus disease epidemic in Guinea, Sierra Leone and Liberia by compiling publicly available weekly time series of reported EVD case numbers from the patient database available from the World Health Organization website for the period 05-Jan to 17-Dec 2014.

Results:
We found significant differences in the growth patterns of EVD cases at the scale of the country, district, and other subnational administrative divisions. The national cumulative curves of EVD cases in Guinea, Sierra Leone, and Liberia show periods of approximate exponential growth. In contrast, local epidemics are asynchronous and exhibit slow growth patterns during 3 or more EVD generations, which can be better approximated by a polynomial than an exponential function.

Conclusions:
The slower than expected growth pattern of local EVD outbreaks could result from a variety of factors, including behavior changes, success of control interventions, or intrinsic features of the disease such as a high level of clustering. Quantifying the contribution of each of these factors could help refine estimates of final epidemic size and the relative impact of different mitigation efforts in current and future EVD outbreaks.

Is West Africa Approaching a Catastrophic Phase or is the 2014 Ebola Epidemic Slowing Down? Different Models Yield Different Answers for Liberia

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An unprecedented epidemic of Zaire ebolavirus (EBOV) has affected West Africa since approximately December 2013, with intense transmission on-going in Guinea, Sierra Leone and Liberia and increasingly important international repercussions. Mathematical models are proving instrumental to forecast the expected number of infections and deaths and quantify the intensity of interventions required to control transmission; however, calibrating mechanistic transmission models to an on-going outbreak is a challenging task owing to limited availability of epidemiological data and rapidly changing interventions. Here we project the trajectory of the EBOV epidemic in Liberia by fitting logistic growth models to the cumulative number of cases. Our model predictions align well with the latest epidemiological reports available as of October 23, and indicates that the exponential growth phase is over in Liberia, with an expected final attack rate of ~0.1-0.12%. Our results indicate that simple phenomenological models can provide complementary insights into the dynamics of an outbreak and capture early signs of changes in population behavior and interventions. In particular, our results underscore the need to treat the effective size of the susceptible population as a dynamic variable rather than a fixed quantity, due to reactive changes in transmission throughout the outbreak. We show that predictions from the logistic model are more variable in the earlier stages of an epidemic (such as the EBOV epidemics in Sierra Leone and Guinea). More research is warranted to compare the performances of mechanistic and phenomenological approaches for disease forecasts, before such predictions can be fully used by public health authorities.

PLOS Currents: Outbreaks — For findings that the world just can’t wait to see

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Editorial There are times in research when speed really is of the essence in getting results published. For those working in the infectious disease field, there is a particularly pressing need when an outbreak occurs and it becomes paramount to get data communicated both efficiently and promptly. The 2009 influenza pandemic highlighted the limited options […]