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EXPLORE IDM’S CURRENT RESEARCH PUBLICATIONS

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Dr Mami Taniuchi, PhD, Michael Famulare, PhD, Khalequ Zaman, PhD, Md Jashim Uddin, MSc, Alexander M Upfill-Brown, MSc, Tahmina Ahmed, MSc, Parimalendu Saha, MSc, Rashidul Haque, PhD, Ananda S Bandyopadhyay, MBBS, Prof John F Modlin, MD, James A Platts-Mills, MD, Prof Eric R Houpt, MD, Mohammed Yunus, MBBS, Prof William A Petri Jr, MD

THE LANCET INFECTIOUS DISEASES

Background

Trivalent oral polio vaccine (tOPV) was replaced worldwide from April, 2016, by bivalent types 1 and 3 oral polio vaccine (bOPV) and one dose of inactivated polio vaccine (IPV) where available. The risk of transmission of type 2 poliovirus or Sabin 2 virus on re-introduction or resurgence of type 2 poliovirus after this switch is not understood completely. We aimed to assess the risk of Sabin 2 transmission after a polio vaccination campaign with a monovalent type 2 oral polio vaccine (mOPV2).

Methods

We did an open-label cluster-randomised trial in villages in the Matlab region of Bangladesh. We randomly allocated villages (clusters) to either: tOPV at age 6 weeks, 10 weeks, and 14 weeks; or bOPV at age 6 weeks, 10 weeks, and 14 weeks and either one dose of IPV at age 14 weeks or two doses of IPV at age 14 weeks and 18 weeks. After completion of enrolment, we implemented an mOPV2 vaccination campaign that targeted 40% of children younger than 5 years, regardless of enrolment status. The primary outcome was Sabin 2 incidence in the 10 weeks after the campaign in per-protocol infants who did not receive mOPV2, as assessed by faecal shedding of Sabin 2 by reverse transcriptase quantitative PCR (RT-qPCR). The effect of previous immunity on incidence was also investigated with a dynamical model of poliovirus transmission to observe prevalence and incidence of Sabin 2 virus. This trial is registered at ClinicalTrials.gov, number NCT02477046.

Findings

Between April 30, 2015, and Jan 14, 2016, individuals from 67 villages were enrolled to the study. 22 villages (300 infants) were randomly assigned tOPV, 23 villages (310 infants) were allocated bOPV and one dose of IPV, and 22 villages (329 infants) were assigned bOPV and two doses of IPV. Faecal shedding of Sabin 2 in infants who did not receive the mOPV2 challenge did not differ between children immunised with bOPV and one or two doses of IPV and those who received tOPV (15 of 252 [6%] vs six of 122 [4%]; odds ratio [OR] 1·29, 95% CI 0·45–3·72; p=0·310). However, faecal shedding of Sabin 2 in household contacts was increased significantly with bOPV and one or two doses of IPV compared with tOPV (17 of 751 [2%] vs three of 353 [1%]; OR 3·60, 95% CI 0·82–15·9; p=0·045). Dynamical modelling of within-household incidence showed that immunity in household contacts limited transmission.

Interpretation

In this study, simulating 1 year of tOPV cessation, Sabin 2 transmission was higher in household contacts of mOPV2 recipients in villages receiving bOPV and either one or two doses of IPV, but transmission was not increased in the community as a whole as shown by the non-significant difference in incidence among infants. Dynamical modelling indicates that transmission risk will be higher with more time since cessation.

Funding

Bill & Melinda Gates Foundation.

Neil Sherborne, Joel C. Miller, Konstantin B. Blyuss, Istvan Z. Kiss

JOURNAL OF MATHEMATICAL BIOLOGY

This paper introduces a novel extension of the edge-based compartmental model to epidemics where the transmission and recovery processes are driven by general independent probability distributions. Edge-based compartmental modelling is just one of many different approaches used to model the spread of an infectious disease on a network; the major result of this paper is the rigorous proof that the edge-based compartmental model and the message passing models are equivalent for general independent transmission and recovery processes. This implies that the new model is exact on the ensemble of configuration model networks of infinite size. For the case of Markovian transmission the message passing model is re-parametrised into a pairwise-like model which is then used to derive many well-known pairwise models for regular networks, or when the infectious period is exponentially distributed or is of a fixed length.

Adam Akullian, Anna Bershteyn, Daniel Klein, Alain Vandormael, Till Barnighausen, and Frank Tanser

AIDS

Objective
To quantify the contribution of specific sexual partner age groups to the risk of HIV acquisition in men and women in hyperendemic region of South Africa.

Design
We conducted a population-based cohort study among women (15–49 years of age) and men (15–55 years of age) between 2004 and 2015 in KwaZulu-Natal, South Africa.

Methods:
Generalized additive models were used to estimate smoothed HIV incidence rates across partnership age pairings in men and women. Cox proportional hazards regression was used to estimate the relative risk of HIV acquisition by partner age group.

Results
A total of 882 HIV seroconversions were observed in 15 935 person-years for women, incidence rate¼5.5 per 100 person-years [95% confidence interval (CI), 5.2– 5.9] and 270 HIV seroconversions were observed in 9372 person-years for men, incidence rate¼2.9 per 100 person-years (95% CI, 2.6–3.2). HIV incidence was highest among 15–24-year-old women reporting partnerships with 30–34-year-old men, incidence rate¼9.7 per 100 person-years (95% CI, 7.2–13.1). Risk of HIV acquisition in women was associated with male partners aged 25–29 years (adjusted hazard ratio; aHR¼1.44, 95% CI, 1.02–2.04) and 30–34 years (aHR¼1.50, 95% CI, 1.08–2.09) relative to male partners aged 35 and above. Risk of HIV acquisition in men was associated with 25–29-year-old (aHR¼1.72, 95% CI, 1.02–2.90) and 30–34- year-old women (aHR¼2.12, 95% CI, 1.03–4.39) compared to partnerships with women aged 15–19 years.

MALARIA JOURNAL

Background

reactive case detection is best poised to push a region to elimination. This study uses mathematical modelling to assess how baseline transmission intensity and local interconnectedness affect the impact of reactive activities in the context of other possible intervention packages.

Methods

Communities in Southern Province, Zambia, where elimination operations are currently underway, were used as representatives of three archetypes of malaria transmission: low-transmission, high household density; high-transmission, low household density; and high-transmission, high household density. Transmission at the spatially-connected household level was simulated with a dynamical model of malaria transmission, and local variation in vectorial capacity and intervention coverage were parameterized according to data collected from the area. Various potential intervention packages were imposed on each of the archetypical settings and the resulting likelihoods of elimination by the end of 2020 were compared.

Results

Simulations predict that success of elimination campaigns in both low- and high-transmission areas is strongly dependent on stemming the flow of imported infections, underscoring the need for regional-scale strategies capable of reducing transmission concurrently across many connected areas. In historically low-transmission areas, treatment of clinical malaria should form the cornerstone of elimination operations, as most malaria infections in these areas are symptomatic and onward transmission would be mitigated through health system strengthening; reactive case detection has minimal impact in these settings. In historically high-transmission areas, vector control and case management are crucial for limiting outbreak size, and the asymptomatic reservoir must be addressed through reactive case detection or mass drug campaigns.

FIG. 5

Elimination in Luumbo HFCA requires limiting importations, increasing case management rate, maintaining vector control, and either RCD or MDA campaigns. a Probability of elimination in Luumbo under three potential case management rates and five potential intervention packages implemented in the external high-transmission area. b Probability of elimination in Luumbo under various case management rates and potential RCD implementations. Importation rates correspond to intervention packages in the external high-transmission area as described in the caption to Fig. 4d. c Probability of elimination in Luumbo under various vector control packages implemented in Luumbo. All intervention scenarios were simulated under low importation (intervention package #4 in external high-transmission area)

Conclusions

Reactive case detection is recommended only for settings where transmission has recently been reduced rather than all low-transmission settings. This is demonstrated in a modelling framework with strong out-of-sample accuracy across a range of transmission settings while including methodologies for understanding the most resource-effective allocations of health workers. This approach generalizes to providing a platform for planning rational scale-up of health systems based on locally-optimized impact according to simplified stratification.

Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor, Eurika Kaiser & J. Nathan Kutz

NATURE COMMUNICATIONS

Understanding the interplay of order and disorder in chaos is a central challenge in modern quantitative science. Approximate linear representations of nonlinear dynamics have long been sought, driving considerable interest in Koopman theory. We present a universal, data-driven decomposition of chaos as an intermittently forced linear system. This work combines delay embedding and Koopman theory to decompose chaotic dynamics into a linear model in the leading delay coordinates with forcing by low-energy delay coordinates; this is called the Hankel alternative view of Koopman (HAVOK) analysis. This analysis is applied to the Lorenz system and real-world examples including Earth’s magnetic field reversal and measles outbreaks. In each case, forcing statistics are non-Gaussian, with long tails corresponding to rare intermittent forcing that precedes switching and bursting phenomena. The forcing activity demarcates coherent phase space regions where the dynamics are approximately linear from those that are strongly nonlinear.

Oliver J Brady, Hannah C Slater, Peter Pemberton-Ross, Edward Wenger, Richard J Maude, Azra C Ghani, Melissa A Penny, Jaline Gerardin, Lisa J White, Nakul Chitnis, Ricardo Aguas, Simon I Hay, David L Smith, Erin M Stuckey, Emelda A Okiro, Thomas A Smith, Lucy C Okell

THE LANCET

Background

Mass drug administration for elimination of Plasmodium falciparum malaria is recommended by WHO in some settings. We used consensus modelling to understand how to optimise the effects of mass drug administration in areas with low malaria transmission.

Methods

We collaborated with researchers doing field trials to establish a standard intervention scenario and standard transmission setting, and we input these parameters into four previously published models. We then varied the number of rounds of mass drug administration, coverage, duration, timing, importation of infection, and pre-administration transmission levels. The outcome of interest was the percentage reduction in annual mean prevalence of P falciparum parasite rate as measured by PCR in the third year after the final round of mass drug administration.

Findings

The models predicted differing magnitude of the effects of mass drug administration, but consensus answers were reached for several factors. Mass drug administration was predicted to reduce transmission over a longer timescale than accounted for by the prophylactic effect alone. Percentage reduction in transmission was predicted to be higher and last longer at lower baseline transmission levels. Reduction in transmission resulting from mass drug administration was predicted to be temporary, and in the absence of scale-up of other interventions, such as vector control, transmission would return to pre-administration levels. The proportion of the population treated in a year was a key determinant of simulated effectiveness, irrespective of whether people are treated through high coverage in a single round or new individuals are reached by implementation of several rounds. Mass drug administration was predicted to be more effective if continued over 2 years rather than 1 year, and if done at the time of year when transmission is lowest.

Interpretation

Mass drug administration has the potential to reduce transmission for a limited time, but is not an effective replacement for existing vector control. Unless elimination is achieved, mass drug administration has to be repeated regularly for sustained effect.

Funding

Bill & Melinda Gates Foundation.

Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor and J. Nathan Kutz

SCIENCE ADVANCES

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg–de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

FRACTIONAL DIFFUSION EMULATES A HUMAN MOBILITY NETWORK DURING A SIMULATED DISEA…

Mobility networks facilitate the growth of populations, the success of invasive species, and the spread of communicable diseases among social animals, including humans.Disease control and elimination efforts, especially during an outbreak, can be optimized by numerical modeling of disease dynamics on transport networks. This is especially true when incidence data from an emerging epidemic is sparse and unreliable. However, mobility networks can be complex, challenging to characterize, and expensive to simulate with agent-based models. We therefore studied a parsimonious model for spatiotemporal disease dynamics based on a fractional diffusion equation. We implemented new stochastic simulations of a prototypical influenza-like infection spreading through the United States’ highly-connected air travel network. We found that the national-averaged infected fraction during an outbreak is accurately reproduced by a space-fractional diffusion equation consistent with the connectivity of airports. Fractional diffusion therefore seems to be a better model of network outbreak dynamics than a diffusive model. Our fractional reaction-diffusion method and the result could be extended to other mobility networks in a variety of applications for population dynamics.

Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor, and J. Nathan Kutz

SCIENCE ADVANCES

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg–de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

2017 AMERICAN CONTROL CONFERENCE (ACC)

Utilizing the concept of observability, in conjunction with tools from graph theory and optimization, this paper develops an algorithm for network synthesis with privacy guarantees. In particular, we propose an algorithm for the selection of optimal weights for the communication graph in order to maximize the privacy of nodes in the network, from a control theoretic perspective. In this direction, we propose an observability-based design of the communication topology that improves the privacy of the network in presence of an intruder. The resulting adaptive network responds to the intrusion by changing the topology of the network-in an online manner- in order to reduce the information exposed to the intruder.