Kyle Gustafson

In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation. Although malaria is still responsible for a great deal of death and illness in many parts of the world, many national control programmes have made great strides in controlling malaria and now are in a position to aim for elimination. However, in order to monitor progress towards elimination and plan interventions, it is crucial to measure malaria transmission and how it varies over space and time. However, traditional metrics used to measure malaria transmission are not suitable in elimination settings. China is one example of a country approaching elimination, with aims to eliminate the disease by 2020. Using a detailed individual level dataset of the times and locations of people showing symptoms of malaria, we use approaches adapted from the study of how information spreads through social networks to estimate the likelihood of transmission occurring between cases. This information is used to estimate how many people we expect each case to go on to infect. In elimination settings, this number is an indication of how quickly elimination will be reached and how likely we are to see a resurgence in cases once elimination is achieved. Our results show a decline in this metric over time, as well as seasonal changes in transmission which are different to the patterns in when the most cases were observed.

The circadian clock in animals orchestrates widespread oscillatory gene expression programs, which underlie 24-h rhythms in behavior and physiology. Several studies have shown the possible roles of transcription factors and chromatin marks in controlling cyclic gene expression. However, how daily active enhancers modulate rhythmic gene transcription in mammalian tissues is not known. Using circular chromosome conformation capture (4C) combined with sequencing (4C-seq), we discovered oscillatory promoter–enhancer interactions along the 24-h cycle in the mouse liver and kidney. Rhythms in chromatin interactions were abolished in arrhythmic Bmal1 knockout mice. Deleting a contacted intronic enhancer element in the Cryptochrome 1 (Cry1) gene was sufficient to compromise the rhythmic chromatin contacts in tissues. Moreover, the deletion reduced the daily dynamics of Cry1 transcriptional burst frequency and, remarkably, shortened the circadian period of locomotor activity rhythms. Our results establish oscillating and clock-controlled promoter–enhancer looping as a regulatory layer underlying circadian transcription and behavior.

Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources. Despite recent progress in analysing and modelling EBOV epidemiological data, a complete characterization of the spatio-temporal spread of Ebola cases remains a challenge. In this work, we offer a novel perspective on the EBOV epidemic in Sierra Leone that uses individual virus genome sequences to inform population-level, spatial models. Calibrated to phylogenetic linkages of virus genomes, these spatial models provide unique insight into the disease mobility of EBOV in Sierra Leone without the need for human mobility data. Consistent with other investigations, our results show that the spread of EBOV during the beginning and middle portions of the epidemic strongly depended on the size of and distance between populations. Our phylodynamic analysis also revealed a change in model preference towards a spatial model with power-law characteristics in the latter portion of the epidemic, correlated with the timing of major intervention campaigns. More generally, we believe this framework, pairing molecular diagnostics with a dynamic model selection procedure, has the potential to be a powerful forecasting tool along with offering operationally relevant guidance for surveillance and sampling strategies during an epidemic.

Human mobility networks facilitate the dynamics of communicable diseases through populations. Similar processes can be observed during population growth or ecological invasion. Disease control and elimination efforts, especially during an outbreak, may depend on numerical modeling of the dynamics of disease on transport networks. However, these networks can be complex, poorly characterized and expensive to simulate at the microscale. As a proof of principle, we computationally studied a parsimonious model for spatiotemporal disease dynamics based on a generalized, or fractional, diffusion equation. We implemented new stochastic simulations of a prototypical influenza-like infection spreading through the United States' highly-connected air travel network. Remarkably, we found that the network-averaged fraction of infected individuals during an SIS outbreak, driven by air traffic in the USA, is most accurately reproduced by a space-fractional diffusion equation consistent with the geographic distribution of airports. This fractional diffusion model could therefore be used to estimate network outbreak dynamics based only on the length distribution of journeys on the network.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs—locomotor bouts—matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior. The brain is never quiet. Even in the absence of environmental cues, neurons receive and produce an ongoing barrage of fluctuating signals. These fluctuations are well studied in the sensory periphery but their potential influence on central circuits and behavior are unknown. In particular, activity fluctuations in action selection circuits—neural populations that drive an animal’s actions from moment to moment—may strongly influence behavior. To shed light on the influence of activity fluctuations on action timing, we developed a computational approach for automatically generating neural network models that reproduce large-scale, high-resolution behavioral measurements of freely walking Drosophila melanogaster. We found that models require stochastic activity fluctuations to reproduce complex Drosophila locomotor patterns. Specific fluctuation-driven dynamics allow these models to produce short and long bouts of locomotion in the absence of sensory cues and to reduce locomotor activity after sensory stimulation. These results support a role for ongoing activity fluctuations in the timing of animal behavior and reveal how behavioral shifts can be brought about through changes in the dynamics of neural circuits. Thus, simple dynamical mechanisms may underlie complex patterns of animal behavior.

We report on recent experimental, numerical and theoretical investigations of suprathermal ion transport and turbulent plasma dynamics in TORPEX. TORPEX is a toroidal device in which field-aligned blobs are intermittently generated and propagate across a confining magnetic field. Suprathermal ions are locally injected using a miniaturized source and detected using gridded energy analyzers. We show evidence for subdiffusive and superdiffusive transport of suprathermal ions, using an unprecedented combination of uniquely resolved three-dimensional measurements and first-principles numerical simulation. We present time-resolved measurements of suprathermal ion current fluctuations, which exhibit a clear causality relation with blobs, as revealed by a transfer entropy method for the superdiffusive case. This link is further investigated by using a conditional sampling technique, which allows resolving the cross-field dynamics of both blobs and the suprathermal ion beam, revealing that suprathermal ions experiencing superdiffusive transport are associated with bursty displacement events, resulting in highly intermittent time traces. This work links observations usually inaccessible in fusion devices and astrophysical plasmas, namely energy resolved three-dimensional time-averaged measurements, with Eulerian time-resolved measurements, which are often the only accessible measurements in such systems.

In the last few years it has been demonstrated, both by data analysis and by numerical simulations, that the transport of energetic particles in the presence of magnetic turbulence can be superdiffusive rather than normal diffusive (Gaussian). The term ‘superdiffusive’ refers to the mean square displacement of particle positions growing superlinearly with time, as compared to the normal linear growth. The so-called anomalous transport, which in general comprises both subdiffusion and superdiffusion, has gained growing attention during the last two decades in many fields including laboratory plasma physics, and recently in astrophysics and space physics. Here we show a number of examples, both from laboratory and from astrophysical plasmas, where superdiffusive transport has been identified, with a focus on what could be the main influence of superdiffusion on fundamental processes like diffusive shock acceleration and heliospheric energetic particle propagation. For laboratory plasmas, superdiffusion appears to be due to the presence of electrostatic turbulence which creates long-range correlations and convoluted structures in perpendicular transport: this corresponds to a similar phenomenon in the propagation of solar energetic particles (SEPs) which leads to SEP dropouts. For the propagation of energetic particles accelerated at interplanetary shocks in the solar wind, parallel superdiffusion seems to be prevailing; this is based on a pitch-angle scattering process different from that envisaged by quasi-linear theory, and this emphasizes the importance of nonlinear interactions and trapping effects. In the case of supernova remnant shocks, parallel superdiffusion is possible at quasi-parallel shocks, as occurring in the interplanetary space, and perpendicular superdiffusion is possible at quasi-perpendicular shocks, as corresponding to Richardson diffusion: therefore, cosmic ray acceleration at supernova remnant shocks should be formulated in terms of superdiffusion. The possible relations among anomalous transport in laboratory, heliospheric, and astrophysical plasmas will be indicated.

The understanding of the transport of suprathermal ions in the presence of turbulence is important for fusion plasmas in the burning regime that will characterize reactors, and for space plasmas to understand the physics of particle acceleration. Here, three-dimensional measurements of a suprathermal ion beam in the toroidal plasma device TORPEX are presented. These measurements demonstrate, in a turbulent plasma, the existence of subdiffusive and superdiffusive transport of suprathermal ions, depending on their energy. This result stems from the unprecedented combination of uniquely resolved measurements and first-principles numerical simulations that reveal the mechanisms responsible for the nondiffusive transport. The transport regime is determined by the interaction of the suprathermal ion orbits with the turbulent plasma dynamics, and is strongly affected by the ratio of the suprathermal ion energy to the background plasma temperature.

The TORPEX basic plasma physics device at the Center for Plasma Physics Research (CRPP) in Lausanne, Switzerland is described. In TORPEX, simple magnetized toroidal configurations, a paradigm for the tokamak scrape-off layer (SOL), as well as more complex magnetic geometries of direct relevance for fusion are produced. Plasmas of different gases are created and sustained by microwaves in the electron-cyclotron (EC) frequency range. Full diagnostic access allows for a complete characterization of plasma fluctuations and wave fields throughout the entire plasma volume, opening new avenues to validate numerical codes. We detail recent advances in the understanding of basic aspects of plasma turbulence, including its development from linearly unstable electrostatic modes, the formation of filamentary structures, or blobs, and its influence on the transport of energy, plasma bulk and suprathermal ions. We present a methodology for the validation of plasma turbulence codes, which focuses on quantitative assessment of the agreement between numerical simulations and TORPEX experimental data.