SEARCH · bioRxiv Science

Results for “Epidemiology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8Linked to original sources

bioRxiv2018

Genetic association of photoplethysmography-derived arterial stiffness index with blood pressure and coronary artery disease

BackgroundArterial stiffness index (ASI) is independently associated with blood pressure and coronary artery disease (CAD) in epidemiologic studies. However, it is unknown whether these associations represent causal relationships.\n\nObjectivesHere, we assess whether genetic predisposition to increased ASI is associated with elevated blood pressure and CAD risk.\n\nMethodsGenome-wide association analysis (GWAS) of finger photoplethysmography-derived ASI was performed in 131,686 participants from the UK Biobank. Across UK Biobank participants not in the ASI GWAS, a 6-variant ASI polygenic risk score was calculated. The ASI polygenic score was associated with systolic and diastolic blood pressures (SBP, DBP, N=208,897), and with incident CAD over 10 years follow-up (N=223,061; 7,534 cases). The lack of CAD association observed was replicated among 184,305 participants (60,810 cases) from the Coronary Artery Disease Genetics Consortium (CARDIOGRAMplusC4D).\n\nResultsWe replicated prior reports of the epidemiologic association of ASI with SBP (Beta 0.55mmHg, [95% CI, 0.45-0.65], P=5.77x10-24), DBP (Beta 1.05mmHg, [95% CI, 0.99-1.11], P=7.27x10-272), and incident CAD (HR 1.08 [95% CI, 1.04-1.11], P=1.5x10-6) in multivariable models. While each SD increase in genetic predisposition to elevated ASI was highly associated with SBP (Beta 4.63 mmHg [95% CI, 2.1-7.2]; P=3.37x10-4), and DBP (Beta 2.61 mmHg [95% CI, 1.2-4.0]; P=2.85x10-4), no association was observed with incident CAD in UK Biobank (HR 1.12 [95% CI, 0.55-2.3]; P=0.75), or with prevalent CAD in CARDIOGRAMplusC4D (OR 0.56 [95% CI, 0.26-1.24]; P=0.15).\n\nConclusionsA genetic predisposition to higher ASI was associated with elevated blood pressure but not with increased risk of developing CAD.\n\nCondensed AbstractArterial stiffness index (ASI) is proposed by some as a surrogate of blood pressure and coronary artery disease (CAD) risk based on epidemiologic analyses. We tested whether genetic predisposition to increased ASI is associated with elevated blood pressure and CAD risk to assess whether these represent causal relationships. We find that a genetic predisposition to higher ASI is associated with elevated systolic (Beta 4.63 mmHg [95% CI, 2.1-7.2]) and diastolic blood pressures (Beta 2.61 mmHg [95% CI, 1.2-4.0]) in the UK Biobank, but not associated with incident CAD in the UK Biobank (P=0.75) or with prevalent CAD in CARDIOGRAMplusC4D (P=0.15). These data support a causal relationship of ASI with blood pressure but do not support the notion that ASI is a suitable surrogate for CAD risk.

bioinformatics↗

bioRxiv2016

How human behavior drives the propagation of an emerging infection: the case of the 2014 Chikungunya outbreak in Martinique

Understanding the spatio-temporal dynamics of endemic infections is of critical importance for a deeper understanding of pathogen transmission, and for the design of more efficient public health strategies. However, very few studies in this domain have focused on emerging infections, generating a gap of knowledge that hampers epidemiological response planning. Here, we analyze the case of a Chikungunya outbreak that occurred in Martinique in 2014. Using time series estimates from a network of sentinel practitioners covering the entire island, we first analyze the spatio-temporal dynamics and show that the largest city has served as the epicenter of this epidemic. We further show that the epidemic spread from there through two different propagation waves moving northwards and southwards, probably by individuals moving along the road network. We then develop a mathematical model to explore the drivers of the temporal dynamics of this mosquito-borne virus. Finally, we show that human behavior, inferred by a textual analysis of messages published on the social network Twitter, is required to explain the epidemiological dynamics over time. Overall, our results suggest that human behavior has been a key component of the outbreak propagation, and we argue that such results can lead to more efficient public health strategies specifically targeting the propagation process.

Epidemiology↗

bioRxiv2016

Quantitative, model-based estimates of variability in the serial interval of Plasmodium falciparum malaria

Background: The serial interval is a fundamentally important quantity in infectious disease epidemiology that has numerous applications to inferring patterns of transmission from case data. Many of these applications are apropos to efforts to eliminate Plasmodium falciparum (Pf) malaria from locations throughout the world, yet the serial interval for this disease is poorly understood quantitatively.\n\nResults: To obtain a quantitative estimate of the serial interval for Pf malaria, we took the sum of components of the Pf malaria transmission cycle based on a combination of mathematical models and empirical data. During this process, we identified a number of factors that account for substantial variability in the serial interval across different contexts. Treatment with antimalarial drugs roughly halves the serial interval, seasonality results in different serial intervals at different points in the transmission season, and variability in within-host dynamics results in many individuals whose serial intervals do not follow average behavior.\n\nConclusions: These results have important implications for epidemiological applications that rely on quantitative estimates of the serial interval of Pf malaria and other diseases characterized by prolonged infections and complex ecological drivers.

Epidemiology↗

bioRxiv2016

Inference of transmission network structure from HIV phylogenetic trees

Phylogenetic inference is an attractive mean to reconstruct transmission histories and epidemics. As the interest lies in how HIV-1 spread in a human population, many previous studies have ignored details about the evolutionary process of the pathogen. Because phylogenetics investigates the evolutionary history of the pathogen rather than the spread between hosts per se, we first investigated the effects of including a within-host evolutionary model in epidemiological simulations. In particular, we investigated if the resulting phvlogenv could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivitv across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phvlogenv (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phvlogenv reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivitv dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.

Epidemiology↗

bioRxiv2016

Animals in the Zika virus life cycle: what to expect from megadiverse Latin American countries.

Zika virus (ZIKV) was first isolated in 1947 in primates in Uganda, West Africa. The virus remained confined to the equatorial regions of Africa and Asia, cycling between infecting monkeys, arboreal mosquitoes, and occasional humans. The ZIKV Asiatic strain was probably introduced into Brazil in 2013. In the current critical human epidemic in the Americas, ZIKV is transmitted primarily by Aedes aegypti mosquitoes, especially where the human population density is combined with poor sanitation. Presently, ZIKV is in contact with the rich biodiversity in all Brazilian biomes, bordering on other Latin American countries. Infections in Brazilian primates have been reported recently, but the overall impact of this virus on wildlife in the Americas is still unknown. The current epidemic in the Americas requires knowledge on the role of mammals, especially non-human primates, in ZIKV transmission to humans. The article discusses the available data on ZIKV in host animals, besides issues of biodiversity, rapid environmental change, and impact on human health in megadiverse Latin American countries. The authors reviewed scientific articles and recent news stories on ZIKV in animals, showing that 47 animal species from three orders (mammals, reptiles, and birds) have been investigated for the potential to establish a sylvatic cycle. The review aims to contribute to epidemiological studies and the knowledge on the natural history of ZIKV. The article concludes with questions that require urgent attention in epidemiological studies involving wildlife in order to understand their role as ZIKV hosts and to effectively control the epidemic.

Epidemiology↗

bioRxiv2016

New method to reconstruct phylogenetic and transmission trees with sequence data from infectious disease outbreaks

Whole-genome sequencing (WGS) of pathogens from host samples becomes more and more routine during infectious disease outbreaks. These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. However, the relationship between transmission events and WGS data is obscured by uncertainty arising from four largely unobserved processes: transmission, case observation, within-host pathogen dynamics and mutation. To properly resolve transmission events, these processes need to be taken into account. Recent years have seen much progress in theory and method development, but applications are tailored to specific datasets with matching model assumptions and code, or otherwise make simplifying assumptions that break up the dependency between the four processes. To obtain a method with wider applicability, we have developed a novel approach to reconstruct transmission trees with WGS data. Our approach combines elementary models for transmission, case observation, within-host pathogen dynamics, and mutation. We use Bayesian inference with MCMC for which we have designed novel proposal steps to efficiently traverse the posterior distribution, taking account of all unobserved processes at once. This allows for efficient sampling of transmission trees from the posterior distribution, and robust estimation of consensus transmission trees. We implemented the proposed method in a new R package phybreak. The method performs well in tests of both new and published simulated data. We apply the model to to five datasets on densely sampled infectious disease outbreaks, covering a wide range of epidemiological settings. Using only sampling times and sequences as data, our analyses confirmed the original results or improved on them: the more realistic infection times place more confidence in the inferred transmission trees.\n\nAuthor SummaryIt is becoming easier and cheaper to obtain whole genome sequences of pathogen samples during outbreaks of infectious diseases. If all hosts during an outbreak are sampled, and these samples are sequenced, the small differences between the sequences (single nucleotide polymorphisms, SNPs) give information on the transmission tree, i.e. who infected whom, and when. However, correctly inferring this tree is not straightforward, because SNPs arise from unobserved processes including infection events, as well as pathogen growth and mutation within the hosts. Several methods have been developed in recent years, but none so generic and easily accessible that it can easily be applied to new settings and datasets. We have developed a new model and method to infer transmission trees without putting prior limiting constraints on the order of unobserved events. The method is easily accessible in an R package implementation. We show that the method performs well on new and previously published simulated data. We illustrate applicability to a wide range of infectious diseases and settings by analysing five published datasets on densely sampled infectious disease outbreaks, confirming or improving the original results.

Epidemiology↗

bioRxiv2016

Containing Emerging Epidemics: a Quantitative Comparison of Quarantine and Symptom Monitoring

Strategies for containing an emerging infectious disease outbreak must be non-pharmaceutical when drugs or vaccines for the pathogen do not yet exist or are unavailable. The success of these non-pharmaceutical strategies will depend not only on the effectiveness of quarantine or other isolation measures but also on the epidemiological characteristics of the infection. However, there is currently no systematic framework to assess the relationship between different containment strategies and the natural history and epidemiological dynamics of the pathogen. Here, we compare the effectiveness of quarantine and symptom monitoring, implemented via contact tracing, in controlling epidemics using an agent-based branching model. We examine the relationship between epidemic containment and the disease dynamics of symptoms and infectiousness for seven case study diseases with diverse natural histories including Ebola, Influenza A, and Severe Acute Respiratory Syndrome (SARS). We show that the comparative effectiveness of symptom monitoring and quarantine depends critically on the natural history of the infectious disease, its inherent transmissibility, and the intervention feasibility in the particular healthcare setting. The benefit of quarantine over symptom monitoring is generally maximized for fast-course diseases, but we show the conditions under which symptom monitoring alone can control certain outbreaks. This quantitative framework can guide policy-makers on how best to use non-pharmaceutical interventions to contain emerging outbreaks and prioritize research during an outbreak of a novel pathogen.\n\nSIGNIFICANCEQuarantine and symptom monitoring of contacts with suspected exposure to an infectious disease are key interventions for the control of emerging epidemics; however, there does not yet exist a quantitative framework for comparing the control performance of each. Here, we use a mathematical model of seven case study diseases to show how the choice of intervention is influenced by the natural history of the infectious disease, its inherent transmissibility, and the intervention feasibility in the particular healthcare setting. We use this information to identify the most important characteristics of the disease and setting that need to be characterized for an emerging pathogen in order to make an informed decision between quarantine and symptom monitoring.

Epidemiology↗

bioRxiv2016

Phylodynamics on local sexual contact networks

Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.

epidemiology↗

bioRxiv2016

How polio vaccination affects poliovirus transmission

The oral polio vaccine (OPV) contains live-attenuated polioviruses that induce immunity by causing low virulence infections in vaccine recipients and their close contacts. Widespread immunization with OPV has reduced the annual global burden of paralytic poliomyelitis by a factor of ten thousand or more and has driven wild poliovirus (WPV) to the brink of eradication. However, in instances that have so far been rare, OPV can paralyze vaccine recipients and generate vaccine-derived polio outbreaks. To complete polio eradication, OPV use should eventually cease, but doing so will leave a growing population fully susceptible to infection. If poliovirus is reintroduced after OPV cessation, under what conditions will OPV vaccination be required to interrupt transmission? Can conditions exist where OPV and WPV reintroduction present similar risks of transmission? To answer these questions, we built a multiscale mathematical model of infection and transmission calibrated to data from clinical trials and field epidemiology studies. At the within-host level, the model describes the effects of vaccination and waning immunity on shedding and oral susceptibility to infection. At the between-host level, the model emulates the interaction of shedding and oral susceptibility with sanitation and person-to-person contact patterns to determine the transmission rate in communities. Our results show that inactivated polio vaccine is sufficient to prevent outbreaks in low transmission rate settings, and that OPV can be reintroduced and withdrawn as needed in moderate transmission rate settings. However, in high transmission rate settings, the conditions that support vaccine-derived outbreaks have only been rare because population immunity has been high. Absent population immunity, the Sabin strains from OPV will be nearly as capable of causing outbreaks as WPV. If post-cessation outbreak responses are followed by new vaccine-derived outbreaks, strategies to restore population immunity will be required to ensure the stability of polio eradication.\n\nAuthor SummaryOral polio vaccine (OPV) has played an essential role in the elimination of wild poliovirus (WPV). OPV contains attenuated yet transmissible viruses that can spread from person-to-person. When OPV transmission persists uninterrupted, vaccine-derived outbreaks occur. After OPV is no longer used in routine immunization, as with the cessation of type 2 OPV in 2016, population immunity will decline. A key question is how this affects the potential of OPV viruses to spread within and across communities. To address this, we examined the roles of immunity, sanitation, and social contact in limiting OPV transmission. Our results derive from an extensive review and synthesis of vaccine trial data and community epidemiological studies. Shedding, oral susceptibility to infection, and transmission data are analyzed to systematically explain and model observations of WPV and OPV circulation. We show that in high transmission rate settings, falling population immunity after OPV cessation will lead to conditions where OPV and WPV are similarly capable of causing outbreaks, and that this conclusion is compatible with the known safety of OPV prior to global cessation. Novel strategies will be required to ensure the stability of polio eradication for all time.

epidemiology↗

bioRxiv2016

Power calculator for instrumental variable analysis in pharmacoepidemiology

BackgroundInstrumental variable analysis, for example with physicians prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomisation power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research.\n\nMethods and resultsThe formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. A web application is provided for the implementation of the formula by others.\n\nConclusionsThe statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power.\n\nFUNDING STATEMENTThis work was supported by the Perros Trust and the Integrative Epidemiology Unit. The Integrative Epidemiology Unit is supported by the Medical Research Council and the University of Bristol [grant number MC_UU_12013/9]. Stephen Burgess is supported by a post-doctoral fellowship from the Wellcome Trust [100114].\n\nKey MessagesO_LIResearch questions using instrumental variable analysis in pharmacoepidemiology have distinct structures that have previously not been catered for by instrumental variable analysis power calculators.\nC_LIO_LIPower can be calculated for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome in the context of pharmacoepidemiology using the presented formula and online power calculator.\nC_LIO_LIThe use of this power calculator will allow investigators to determine whether a pharmacoepidemiology study is likely to detect clinically meaningful treatment effects prior to the studys commencement.\nC_LI

epidemiology↗

bioRxiv2016

Livestock and microcephaly, traces of an association?

While there is no doubt about the participation of Zika virus in microcephaly, its epidemiology is not entirely clear and doubts remain about the intervention of other factors. In studies on the epidemiology of dengue, the infestation by Aedes aegypti peridomiciliary and the population density are the main determinants for viral spread. However, in Rio Grande do Norte state (RN), the counties that have confirmed cases of microcephaly overlapped the river basins regions surrounded by agriculture and livestock. In addition, the prevalence of microcephaly at the end of the first year of the epidemic was higher in small towns than in larger ones, elements that seem to contradict what is known about the epidemic by other arboviruses. Methods: 234 cases of microcephaly were analyzed from three states and 144 counties. Results: An exponential trend of higher prevalence of microcephaly in the smaller cities (r2=0,7121) was found.The correlation coefficients (R) between the Prevalence of microcephaly and the variables that measured the density of animals in the territory ranged from moderate to strong. Discussion: Concerning microcephaly, studies in progress point to the possibility of association between the Zika Virus and the BVDV, a virus known to produce birth defects in farm animals but perceived as innocuous in humans. Conclusions: The overlap of cases of microcephaly in river basins, their higher prevalence in smaller cities, the strength of the correlation coefficient, render necessary new etiological and pathophysiological studies.\n\n4. ABBREVIATIONS

epidemiology↗

bioRxiv2016

Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo

Genetic sequences from pathogens can provide information about infectious disease dynamics that may supplement or replace information from other epidemiological observations. Currently available methods first estimate phylogenetic trees from sequence data, then estimate a transmission model conditional on these phylogenies. Outside limited classes of models, existing methods are unable to enforce logical consistency between the model of transmission and that underlying the phylogenetic reconstruction. Such conflicts in assumptions can lead to bias in the resulting inferences. Here, we develop a general, statistically efficient, plug-and-play method to jointly estimate both disease transmission and phylogeny using genetic data and, if desired, other epidemiological observations. This method explicitly connects the model of transmission and the model of phylogeny so as to avoid the aforementioned inconsistency. We demonstrate the feasibility of our approach through simulation and apply it to estimate stage-specific infectiousness in a subepidemic of HIV in Detroit, Michigan. In a supplement, we prove that our approach is a valid sequential Monte Carlo algorithm. While we focus on how these methods may be applied to population-level models of infectious disease, their scope is more general. These methods may be applied in other biological systems where one seeks to infer population dynamics from genetic sequences, and they may also find application for evolutionary models with phenotypic rather than genotypic data.

epidemiology↗

bioRxiv2017

Directly Estimating Epidemic Curves From Genomic Data

Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death or behaviour change). Birth-death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models, and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth-death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

epidemiology↗

bioRxiv2017

Reverse immunodynamics: a new method to identifying targets of protective immunity.

Despite a dramatic increase in our ability to catalogue variation among pathogen genomes, we have made far fewer advances in using this information to identify targets of protective immunity. We propose a novel methodology that combines predictions from epidemiological models with phylogenetic and structural analyses to identify such targets. Epidemiological models predict that strong immune selection can cause antigenic variants to exist in non-overlapping combinations. A corollary of this theory is that targets of immunity may be identified by searching for non-overlapping associations among antigenic variants. We applied this concept to the AMA-1 protein of the malaria parasite Plasmodium falciparum and found strong signatures of immune selection among certain regions of low variability which could render them ideal vaccine candidates.

epidemiology↗

bioRxiv2017

Multi-scale immune selection and the transmission-diversity feedback of Plasmodium falciparum malaria

Antigenic diversity is a key factor underlying the complex epidemiology of Plasmodium falciparum malaria. Within-host clonal antigenic variation limits host exposure to the parasites antigenic repertoire, while the high degree of diversity at the population-level requires multiple exposures for hosts to acquire anti-disease immunity. This diversity is predominantly generated through mitotic and meiotic recombination between individual genes and multi-gene repertoires and is therefore expected to respond dynamically to changes in transmission and immune selection. We hypothesised that this coupling creates a positive feedback mechanism whereby infection and disease transmission promotes the generation of diversity, which itself facilitates immune evasion and hence further infection and transmission. To investigate the link between diversity and malaria prevalence in more detail we developed an individual-based model in which antigenic diversity emerges as a dynamic property from the underlying transmission processes. We show that the balance between stochastic extinction and the generation of new antigenic variants is intrinsically linked to within-host and between-host immune selection, which in turn determines the level of diversity that can be maintained in a given population. We further show that the transmission-diversity feedback can lead to temporal lags in the response to natural or intervention-induced perturbations in transmission rates. These results will add to our understanding of the epidemiological dynamics of P. falciparum malaria in different transmission settings and will have important implications for monitoring and assessing the effectiveness of disease control efforts.

epidemiology↗

bioRxiv2017

Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100046). The recent Ebola virus (EBOV) outbreak in West Africa witnessed considerable efforts to obtain viral genomic data as the epidemic was unfolding. If such data can be deployed in real-time, molecular epidemiological investigations could play a role in complementing contact tracing undertaken by public health agencies. Analysing the EBOV genomes accumulated to date can also deliver insights into epidemic dynamics. Such analyses have been shown that metapopulation dynamics were critical for EBOV dispersal between rural and urban areas during the epidemic, but the implications for specific intervention scenarios remain unclear. Here, we address this issue using a collection of phylodynamic approaches. We show that long-distance dispersal events (between administrative areas >250 km apart) were not crucial for epidemic expansion and that preventing viral lineage movement to any given administrative area would, in most cases, have had little impact. However, urban areas - specifically those encompassing the three capital cities and their suburbs - were critical in attracting and further disseminating the virus: preventing viral lineage movement to all three simultaneously would have contained epidemic size by two-thirds. Using continuous phylogeographic reconstructions we estimate a distance kernel for EBOV spread and reveal considerable heterogeneity in dispersal velocity through time. We also show that announcements of border closures were followed by a significant but transient effect on international virus dispersal. By quantifying the hypothetical impact of different intervention strategies as well as the impact of barriers on dispersal frequency, our study illustrates how phylodynamic analyses can help to address specific epidemiological and outbreak control questions.

epidemiology↗

bioRxiv2017

Practical unidentifiability of a simple vector-borne disease model: implications for parameter estimation and intervention assessment

BackgroundMathematical modeling has an extensive history in vector-borne disease epidemiology, and is increasingly used for prediction, intervention design, and understanding mechanisms. Many of these studies rely on parameter estimation to link models and data, and to tailor predictions and counterfactuals to specific settings. However, few studies have formally evaluated whether vector-borne disease models can properly estimate the parameters of interest given the constraints of a particular dataset.\n\nMethodology/Principle FindingsIdentifiability methods allow us to examine whether model parameters can be estimated uniquely--a lack of consideration of such issues can result in misleading or incorrect parameter estimates and model predictions. Here, we evaluate both structural (theoretical) and practical identifiability of a commonly used compartmental model of mosquitoborne disease, using 2010 dengue epidemic in Taiwan as a case study. We show that while the model is structurally identifiable, it is practically unidentifiable under a range of human and mosquito time series measurement scenarios. In particular, the transmission parameters form a practically identifiable combination and thus cannot be estimated separately, which can lead to incorrect predictions of the effects of interventions. However, in spite of unidentifiability of the individual parameters, the basic reproduction number was successfully estimated across the unidentifiable parameter ranges. These identifiability issues can be resolved by directly measuring several additional human and mosquito life-cycle parameters both experimentally and in the field.\n\nConclusionsWhile we only consider the simplest case for the model, without explicit environmental drivers, we show that a commonly used model of vector-borne disease is unidentifiable from human and mosquito incidence data, making it difficult or impossible to estimate parameters or assess intervention strategies. This work illustrates the importance of examining identifiability when linking models with data to make predictions, and particularly highlights the importance of combining experimental, field, and case data if we are to successfully estimate epidemiological and ecological parameters using models.\n\nAuthor SummaryMathematical models have seen increasing use in understanding transmission processes, developing interventions, and predicting disease incidence and prevalence. Vector-borne diseases in particular present both a challenge and an opportunity for modeling, due to the complex interactions between host and vector species. A key step in many of these studies is connecting transmission models with data to infer parameters and make useful predictions, which requires careful consideration of identifiability and uncertainty of the model parameters. Whether due to intrinsic limitations of the model structure, or practical limitations of the data collected, is common that many different parameter values may yield the same or very similar fits to the data, making it impossible to successfully estimate the parameters. This issue of parameter unidentifiability can have broad implications for our ability to draw conclusions from mechanistic models--in some cases making it difficult or impossible to generate specific predictions, forecasts, or parameter estimates from a given model and data. Here, we evaluate these questions for a commonly-used model of vectorborne disease, examining how parameter uncertainty and unidentifiability can affect intervention predictions, estimation of the basic reproduction number, and other public health conclusions drawn from the model.

epidemiology↗

bioRxiv2017

Targeting Vaccinations for the Licensed Dengue Vaccine: Considerations for Serosurvey Design

ObjectiveWHO recommends countries consider dengue vaccination in geographic settings only where epidemiological data indicate a high burden of disease. In defining target populations, WHO recommend that prior infection with any dengue serotype should be >70% seroprevalence. Here we address considerations for serosurvey design in the context of the newly licensed CYD-TDV vaccine.\n\nMethodsTo explore how the design of seroprevalence surveys (age range, survey size) would affect estimates of the force of infection, for every combination of age range, total survey size, transmission setting, and test sensitivity/specificity, 100 age-specific seroprevalence surveys were simulated using a beta-binomial distribution and a simple catalytic model. The transmission intensity was then re-estimated using a Metropolis-Hastings Markov Chain Monte-Carlo algorithm.\n\nFindingsSampling from a wide age range led to more accurate estimates than having a larger sample size. This finding was consistent across all transmission settings. The optimal age range to sample from differed by transmission intensity, with younger and older ages being important in high and low transmission settings respectively. The optimum test sensitivity and specificity given an imperfect test also differed by transmission setting with high sensitivity being important in high transmission settings and high specificity important in low transmission settings.\n\nConclusionsWhen assessing the suitability for vaccination by seroprevalence surveys, countries should ensure that an appropriate age range is sampled, taking into account epidemiological evidence about the local burden of dengue.

epidemiology↗