Research

Research in the Rohani lab focuses on population biology, usually of host-natural enemy interactions, with a view to understanding fundamental processes in ecology and evolution. We use a combination of mathematical modelling, data analysis and statistical inference to understand the ecology and evolution of infectious diseases of humans and wildlife, including childhood infections and emerging infectious diseases.

Dynamics of Human Infectious Diseases

measles

Measles virus budding off from an infected cell.

Much of current research in the lab is based on understanding long-term data sets on the spatio-temporal patterns of morbidity and mortality caused by the great childhood microparasitic infections (such as measles, whooping cough and polio). The analyses of these data have provided interesting insights into the mechanisms of  transmission and the ecology of infectious diseases.

Ecology, Immunology and Evolution of Pertussis

The study of pertussis is a major focus in the lab, funded by a 5-year grant from the National Institutes of Health.  Our work on this project has involved a multitude of approaches:

  • Age structure and transmission dynamics  We study age-stratified incidence reports in conjunction with age structured mathematical models with a view to deciphering the transmission consequences of pertussis vaccination.  This work has focused on long-term data from Sweden (Rohani 2010), England & Wales (Riolo et al., in prep) and Thailand (Blackwood et al., in prep).
    Age-specific contact patterns

    Illustration of age-specific patterns of contact, following Rohani et al. (2010; Science). Data based on Mossong et al. (2009; PLoS Med).

  • Spatio-temporal dynamics  We have been documenting the diversity of patterns in pertussis incidence using data from a number of sources.  We have demonstrated how clear waves of pertussis incidence in the US during the 1950s gave way to spatially unorganized and irregular epidemics in the 2000s (Choisy & Rohani 2012).  In contrast, we find no evidence for geographical pulsing in pre-vaccination era pertussis epidemics in England & Wales (Conlan et al., in prep), contemporary reports from the provinces of Thailand (Blackwood et al. 2013) or regions of Italy (Magpantay et al., in prep).
    Waves of pertussis epidemics in the US from 1951-1962 (Choisy & Rohani 2012)

    Waves of pertussis epidemics in the US from 1951-1962 (Choisy & Rohani 2012)

  • Nature and duration of immunity A key issue in the epidemiology of pertussis remains the duration of immunity, both derived following vaccination and natural infection. Using probe-matching approaches, Wearing & Rohani (2009), explored when model dynamics are in parsimony withepidemiological data. The main conclusion of this study was that mean duration of natural immunity may be in excess of 30 years.  Last year, in the paper by Blackwood et al. (2013), we competed a range of models to explain contemporary pertussis incidence data from Thailand; we found long-term duration of immunity provided the most parsimonious explanation for the data.  Recently, we have developed theory on the epidemiological consequences of different mechanisms behind immune “failure”, specifically the distinction between leaky and all-or-nothing immunity (Magpantay et al. 2014).  Currently, we are in the process of evaluating the empirical evidence in support for these alternative explanations using computational statistics approaches on incidence data from England & Wales (Domenech de Celles et al., in prep) and Italy (Magpantay et al., in prep).
    Comparison of different models with pertussis incidence data from Thailand (Blackwood et al. 2013; PNAS).

    Comparison of different models with pertussis incidence data from Thailand (Blackwood et al. 2013; PNAS).

Designing effective booster vaccination schedules Despite much uncertainty over the mechanisms that have shaped the resurgence of pertussis in countries such as the US and the UK, public health policy makers have had to implement responsive control strategies, in the shape of boosters introduced into the existing schedule.  Given the complexity and high dimensionality of the problem, it isn’t well-suited to traditional optimization methods.  So, we have been using a genetic algorithm approach to identify effective booster strategies under different assumed underlying causes of resurgence (Riolo & Rohani 2015).

Schematic of genetic algorithm used in Riolo & Rohani (2015).

Multi-pathogen systems

For more than a decade, we have been interested in studying ecological interactions between infectious diseases, which may arise from unavailability to contract a disease following infection with another or via immunological mechanisms (Rohani et al. 2003; 2008).  Theoretical analyses of such mechanisms predict pronounced phase-differences between different disease (or strains of the same disease), which are consistent with observed patterns in mortality data.  Statistical methods for the detection of such interactions, especially in antigenically polymorphic infectious diseases such as dengue (Wearing & Rohani 2006) are very timely, and a main focus of research in the lab.

  • Dengue serotype dynamics In Shrestha et al. (2011), we examined the plausibility of using likelihood-based inference to detect interaction among pathogens and the limits to this approach.  Since then, we have obtained funding from NIH as part of the MIDAS network to make this analysis more formal.  This project is in collaboration with Derek Cummings, Bobby Reiner and Aaron King.  In a related effort, we will also be exploring the impact of climatic drivers on dengue transmission, with Diego Ruiz Moreno.
  • Interaction between influenza and pneumococcal bacteria  Numerous studies focused at the level of the individual—including lab challenge studies on mice and autopsy reports on humans—have established that infection with the influenza virus affects the outcome of exposure to bacteria.  In contrast, efforts to uncover a clear signal of this phenomenon in population-level incidence reports have been unequivocal.  We have developed a likelihood-based hypothesis-testing approach using a mechanistic pneumococcal transmission model and identified a clear, unambiguous interaction, resulting from a short-lived but notable increase in susceptibility to bacterial pneumonia following influenza infection (Shrestha et al. 2013).  Ongoing work will extend this to all-cause pneumonia.

 

Likelihood profiles testing alternative hypotheses regarding the impact of influenza infection on pneumococcal pneumonia.  From Shrestha et al. (2013; Sci. Trans. Med.).

Likelihood profiles testing alternative hypotheses regarding the impact of influenza infection on pneumococcal pneumonia. From Shrestha et al. (2013; Sci. Trans. Med.).

Mixed transmission systems

Over the past five or so years, we have been working with David Stallknecht and John Drake (both at the University of Georgia) to understand the transmission dynamics of Influenza A viruses among their avian hosts.  In particular, this work has focused on the consequences of environmental transmission via long-lived viral reservoir.

  • Transmission ecology  We have shown that indirect transmission chains can have important consequences for the invasion (Rohani et al. 2009), persistence (Breban et al. 2009) and coexistence of these viruses (Breban et al. 2010; Roche & Rohani 2010).  In Delaware Bay, described as a global hotspot for avian influenza viruses, environmental transmission is shown to be a crucial component in the triple prevalence peaks reported during the year (Brown & Rohani 2012).
  • Virulence evolution  In collaboration with Benjamin Roche and John Drake, we have studied the impact of mixed transmission dynamics on the evolution of virulence, documenting the necessary conditions for evolutionary bistability (Roche et al. 2011).
  • Influenza phylodynamics  The study by Roche et al. (2014) documented contrasting patterns of viral genetic diversity among human and avian influenza viruses.  Within comparable subtypes, we observe an order of magnitude greater HA diversity among avian viruses.  We examined putative explanations and found that HA genetic diversity in avian viruses is determined by a combination of factors, predominantly subtype-specific differences in host immune selective pressure and the ecology of transmission (in particular, the durability of subtypes in aquatic environments).  Our conclusion that environmental transmission plays an important role in the evolutionary biology of avian influenza viruses—a manifestation of the ecological phenomenon called the ‘‘storage effect’’—highlights the potentially unpredictable impact of wildlife reservoirs.
Schematic of avian influenza transmission dynamics and evolution.

Schematic of avian influenza transmission dynamics and evolution. Illustration by John Megahan.

Host-parasitoid assemblages

We have been collaborating with Dr Steve Sait (Department of Biology, University of Leeds, UK) to study laboratory populations of insect host-parasitoid-pathogen assemblages.  The main study organism is the Indian meal moth, Plodia interpunctella (a stored-product pest) and its competitor, the Almond moth, Ephestia cautella. Both species are subject to attack by a suite of natural enemies, including a solitary ichneumonid wasp (Venturia canescens) and and two species of baculoviruses (the P. interpunctella granulovirus and E. cautellanucleopolyhedrovirus; PiGV and EcNPV respectively).

Indian_Meal_MothUsing this system, we have explored a number of ecological questions, typically by testing model predictions in the laboratory population assemblages. These topics include:

  • Understanding the dynamical consequences of specialized versus generalist natural enemies (Rohani et al. 2003Wearing et al. 2004a)
  • Exploring the importance of development variability and demographic noise in determining the fluctuations observed in our laboratory populations (Wearing et al. 2004b).
  • Examining the role of periodic resource dynamics in generating cycles of different periods (Wearing et al., in prep).
  • Understanding the co-evolutionary consequences of seasonality. We are using mathematical models to explore how temporal fluctuations affect the (co-) evolutionary dynamics of host resistence and parasitoid virulence. Seasonality affects our system in two separate mechanisms: (i) periodic changes in pathogen transmission and parasitism, and (ii) temporally varying environment. This work is done in collaboration with Steven White, at the CEH, UK.

Work on host-parasitoid-pathogen assemblages has been funded by two grants from the UK’s Natural Environment Research Council.