Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting

Vector Surveillance, Host Species Richness, and Demographic Factors as West Nile Disease Risk Indicators

West Nile virus (WNV) is the most common arthropod-borne virus (arbovirus) in the United States (US) and is the leading cause of viral encephalitis in the country. The virus has affected tens of thousands of US persons total since its 1999 North America introduction, with thousands of new infections reported annually. Approximately 1% of humans infected with WNV acquire neuroinvasive West Nile Disease (WND) with severe encephalitis and risk of death. Research describing WNV ecology is needed to improve public health surveillance, monitoring, and risk assessment. We applied Bayesian joint-spatiotemporal modeling to assess the association of vector surveillance data, host species richness, and a variety of other environmental and socioeconomic disease risk factors with neuroinvasive WND throughout the conterminous US. Our research revealed that an aging human population was the strongest disease indicator, but climatic and vector-host biotic interactions were also significant in determining risk of neuroinvasive WND. Our analysis also identified a geographic region of disproportionately high neuroinvasive WND disease risk that parallels the Continental Divide, and extends southward from the US-Canada border in the states of Montana, North Dakota, and Wisconsin to the US-Mexico border in western Texas. Our results aid in unraveling complex WNV ecology and can be applied to prioritize disease surveillance locations and risk assessment.

The Role of Vector Trait Variation in Vector-Borne Disease Dynamics

Many important endemic and emerging diseases are transmitted by vectors that are biting arthropods. The functional traits of vectors can affect pathogen transmission rates directly and also through their effect on vector population dynamics. Increasing empirical evidence shows that vector traits vary significantly across individuals, populations, and environmental conditions, and at time scales relevant to disease transmission dynamics. Here, we review empirical evidence for variation in vector traits and how this trait variation is currently incorporated into mathematical models of vector-borne disease transmission. We argue that mechanistically incorporating trait variation into these models, by explicitly capturing its effects on vector fitness and abundance, can improve the reliability of their predictions in a changing world. We provide a conceptual framework for incorporating trait variation into vector-borne disease transmission models, and highlight key empirical and theoretical challenges. This framework provides a means to conceptualize how traits can be incorporated in vector borne disease systems, and identifies key areas in which trait variation can be explored. Determining when and to what extent it is important to incorporate trait variation into vector borne disease models remains an important, outstanding question.

Data-driven modelling and spatial complexity supports heterogeneity-based integrative management for eliminating Simulium neavei-transmitted river blindness

Concern is emerging regarding the challenges posed by spatial complexity for modelling and managing the area-wide elimination of parasitic infections. While this has led to calls for applying heterogeneity-based approaches for addressing this complexity, questions related to spatial scale, the discovery of locally-relevant models, and its interaction with options for interrupting parasite transmission remain to be resolved. We used a data-driven modelling framework applied to infection data gathered from different monitoring sites to investigate these questions in the context of understanding the transmission dynamics and efforts to eliminate Simulium neavei- transmitted onchocerciasis, a macroparasitic disease that causes river blindness in Western Uganda and other regions of Africa. We demonstrate that our Bayesian-based data-model assimilation technique is able to discover onchocerciasis models that reflect local transmission conditions reliably. Key management variables such as infection breakpoints and required durations of drug interventions for achieving elimination varied spatially due to site-specific parameter constraining; however, this spatial effect was found to operate at the larger focus level, although intriguingly including vector control overcame this variability. These results show that data-driven modelling based on spatial datasets and model-data fusing methodologies will be critical to identifying both the scale-dependent models and heterogeneity-based options required for supporting the successful elimination of S. neavei-borne onchocerciasis.

Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment

Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.

West Nile virus infections are here! Are we prepared to face another flavivirus epidemic?

Emerging arthropod-borne viruses (arboviruses), such as chikungunya and Zika viruses, are a major threat to public health in countries like Brazil where biodiversity is high and medical care is sometimes precarious. West Nile fever is a disease caused by the West Nile Virus (WNV), an RNA virus belonging to the Flaviviridae family. It is transmitted by infected mosquitoes to numerous animals like birds, reptiles and mammals, including human and non-human primates. In the last decade, the number of reported cases of WNV infection in humans and animals has increased in the Americas. Circulation of WNV in forests and rural areas in Brazil has been detected based on serological surveys and, in 2014, the first case of West Nile fever was confirmed in a patient from Piauí State. In 2018, the virus was isolated for the first time from a horse from a rural area in the state of Espírito Santo presenting with a neurological disorder; this raises the possibility that other cases of WNV encephalitis may have occurred without clinical recognition and without laboratory diagnosis by specific assays. The imminent WNV outbreak poses a challenge for Brazilian clinicians and researchers. In this review, we summarize the basic biological and ecological characteristics of this virus and the clinical presentation and treatment of febrile illnesses caused by WNV. We also discuss the epidemiological aspects, prophylaxis of WNV infections, and monitoring strategies that could be applied in the possibility of a WNV outbreak in Brazil.

Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease

Emerging infectious diseases increasingly threaten wildlife populations. Most studies focus on managing short-term epidemic properties, such as controlling early outbreaks. Predicting long-term endemic characteristics with limited retrospective data is more challenging. We used individual-based modeling informed by individual variation in pathogen load and transmissibility to predict long-term impacts of a lethal, transmissible cancer on Tasmanian devil (Sarcophilus harrisii) populations. For this, we employed approximate Bayesian computation to identify model scenarios that best matched known epidemiological and demographic system properties derived from 10 yr of data after disease emergence, enabling us to forecast future system dynamics. We show that the dramatic devil population declines observed thus far are likely attributable to transient dynamics (initial dynamics after disease emergence). Only 21% of matching scenarios led to devil extinction within 100 yr following devil facial tumor disease (DFTD) introduction, whereas DFTD faded out in 57% of simulations. In the remaining 22% of simulations, disease and host coexisted for at least 100 yr, usually with long-period oscillations. Our findings show that pathogen extirpation or host-pathogen coexistence are much more likely than the DFTD-induced devil extinction, with crucial management ramifications. Accounting for individual-level disease progression and the long-term outcome of devil-DFTD interactions at the population-level, our findings suggest that immediate management interventions are unlikely to be necessary to ensure the persistence of Tasmanian devil populations. This is because strong population declines of devils after disease emergence do not necessarily translate into long-term population declines at equilibria. Our modeling approach is widely applicable to other host-pathogen systems to predict disease impact beyond transient dynamics.

Demographic stochasticity drives epidemiological patterns in wildlife with implications for diseases and population management

Infectious diseases raise many concerns for wildlife and new insights must be gained to manage infected populations. Wild ungulates provide opportunities to gain such insights as they host many pathogens. Using modelling and data collected from an intensively monitored population of Pyrenean chamois, we investigated the role of stochastic processes in governing epidemiological patterns of pestivirus spread in both protected and hunted populations. We showed that demographic stochasticity led to three epidemiological outcomes: early infection fade-out, epidemic outbreaks with population collapse, either followed by virus extinction or by endemic situations. Without re-introduction, the virus faded out in >50% of replications within 4 years and did not persist >20 years. Test-and-cull of infected animals and vaccination had limited effects relative to the efforts devoted, especially in hunted populations in which only quota reduction somewhat improve population recovery. Success of these strategies also relied on the maintenance of a high level of surveillance of hunter-harvested animals. Our findings suggested that, while surveillance and maintenance of population levels at intermediate densities to avoid large epidemics are useful at any time, a 'do nothing' approach during epidemics could be the 'least bad' management strategy in populations of ungulates species facing pestivirus infection.

Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination

Background

Mathematical models are increasingly being used to evaluate strategies aiming to achieve the control or elimination of parasitic diseases. Recently, owing to growing realization that process-oriented models are useful for ecological forecasts only if the biological processes are well defined, attention has focused on data assimilation as a means to improve the predictive performance of these models.

Methodology and principal findings

We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. The relative information contribution of site-specific data collected at the time points proposed by the WHO monitoring framework was evaluated using model-data updating procedures, and via calculations of the Shannon information index and weighted variances from the probability distributions of the estimated timelines to parasite extinction made by each model. Results show that data-informed models provided more precise forecasts of elimination timelines in each site compared to model-only simulations. Data streams that included year 5 post-MDA microfilariae (mf) survey data, however, reduced each model’s uncertainty most compared to data streams containing only baseline and/or post-MDA 3 or longer-term mf survey data irrespective of MDA coverage, suggesting that data up to this monitoring point may be optimal for informing the present LF models. We show that the improvements observed in the predictive performance of the best data-informed models may be a function of temporal changes in inter-parameter interactions. Such best data-informed models may also produce more accurate predictions of the durations of drug interventions required to achieve parasite elimination.

Significance

Knowledge of relative information contributions of model only versus data-informed models is valuable for improving the usefulness of LF model predictions in management decision making, learning system dynamics, and for supporting the design of parasite monitoring programmes. The present results further pinpoint the crucial need for longitudinal infection surveillance data for enhancing the precision and accuracy of model predictions of the intervention durations required to achieve parasite elimination in an endemic location.

Although parasite transmission models offer powerful tools for predicting the impacts of interventions, there is growing realization that these models can be useful for this purpose only if their governing biological processes are well defined. Recently, model-data assimilation has been applied to address this problem and improve the performance of process-oriented models for ecological forecasting. Here, we developed an analytical framework that allowed the sequential coupling of the three existing lymphatic filariasis (LF) models with longitudinal infection monitoring data collected in field sites undergoing mass drug administrations (MDAs) to examine the relative value of such data for parameterizing these models and for improving their predictions of the required durations of drug interventions to break parasite transmission. We found that data-informed models provided more precise and reliable forecasts of elimination timelines in the study sites compared to model-only predictions, and that data collected up to 5 years post-MDA reduced each model’s predictive uncertainty most. We also found that this improved performance may be intriguingly related to temporal changes in system dynamics. Our results underscore the significance of sequential model-data fusion for enhancing the understanding of LF transmission dynamics, design of surveillance, and generation of reliable model predictions for management decision making.

Role of monkeys in the sylvatic cycle of chikungunya virus in Senegal

Arboviruses spillover into humans either as a one-step jump from a reservoir host species into humans or as a two-step jump from the reservoir to an amplification host species and thence to humans. Little is known about arbovirus transmission dynamics in reservoir and amplification hosts. Here we elucidate the role of monkeys in the sylvatic, enzootic cycle of chikungunya virus (CHIKV) in the region around Kédougou, Senegal. Over 3 years, 737 monkeys were captured, aged using anthropometry and dentition, and tested for exposure to CHIKV by detection of neutralizing antibodies. Infant monkeys were positive for CHIKV even when the virus was not detected in a concurrent survey of mosquitoes and when population immunity was too high for monkeys alone to support continuous transmission. We conclude that monkeys in this region serve as amplification hosts of CHIKV. Additional efforts are needed to identify other hosts capable of supporting continuous circulation.

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020

BACKGROUND

There are growing demands for predicting the prospects of achieving the global elimination of neglected tropical diseases as a result of the institution of large-scale nation-wide intervention programs by the WHO-set target year of 2020. Such predictions will be uncertain due to the impacts that spatial heterogeneity and scaling effects will have on parasite transmission processes, which will introduce significant aggregation errors into any attempt aiming to predict the outcomes of interventions at the broader spatial levels relevant to policy making. We describe a modeling platform that addresses this problem of upscaling from local settings to facilitate predictions at regional levels by the discovery and use of locality-specific transmission models, and we illustrate the utility of using this approach to evaluate the prospects for eliminating the vector-borne disease, lymphatic filariasis (LF), in sub-Saharan Africa by the WHO target year of 2020 using currently applied or newly proposed intervention strategies. METHODS AND RESULTS: We show how a computational platform that couples site-specific data discovery with model fitting and calibration can allow both learning of local LF transmission models and simulations of the impact of interventions that take a fuller account of the fine-scale heterogeneous transmission of this parasitic disease within endemic countries. We highlight how such a spatially hierarchical modeling tool that incorporates actual data regarding the roll-out of national drug treatment programs and spatial variability in infection patterns into the modeling process can produce more realistic predictions of timelines to LF elimination at coarse spatial scales, ranging from district to country to continental levels. Our results show that when locally applicable extinction thresholds are used, only three countries are likely to meet the goal of LF elimination by 2020 using currently applied mass drug treatments, and that switching to more intensive drug regimens, increasing the frequency of treatments, or switching to new triple drug regimens will be required if LF elimination is to be accelerated in Africa. The proportion of countries that would meet the goal of eliminating LF by 2020 may, however, reach up to 24/36 if the WHO 1% microfilaremia prevalence threshold is used and sequential mass drug deliveries are applied in countries.

CONCLUSIONS

We have developed and applied a data-driven spatially hierarchical computational platform that uses the discovery of locally applicable transmission models in order to predict the prospects for eliminating the macroparasitic disease, LF, at the coarser country level in sub-Saharan Africa. We show that fine-scale spatial heterogeneity in local parasite transmission and extinction dynamics, as well as the exact nature of intervention roll-outs in countries, will impact the timelines to achieving national LF elimination on this continent.

Projection of Climate Change Influences on U.S. West Nile Virus Vectors

While estimates of the impact of climate change on health are necessary for health care planners and climate change policy makers, models to produce quantitative estimates remain scarce. We describe a freely available dynamic simulation model parameterized for three West Nile virus vectors, which provides an effective tool for studying vector-borne disease risk due to climate change. The Dynamic Mosquito Simulation Model is parameterized with species specific temperature-dependent development and mortality rates. Using downscaled daily weather data, we estimate mosquito population dynamics under current and projected future climate scenarios for multiple locations across the country. Trends in mosquito abundance were variable by location, however, an extension of the vector activity periods, and by extension disease risk, was almost uniformly observed. Importantly, mid-summer decreases in abundance may be off-set by shorter extrinsic incubation periods resulting in a greater proportion of infective mosquitoes. Quantitative descriptions of the effect of temperature on the virus and mosquito are critical to developing models of future disease risk.

Coinfection by Ixodes Tick-Borne Pathogens: Ecological, Epidemiological, and Clinical Consequences

Ixodes ticks maintain a large and diverse array of human pathogens in the enzootic cycle, including Borrelia burgdorferi and Babesia microti. Despite the poor ecological fitness of B. microti, babesiosis has recently emerged in areas endemic for Lyme disease. Studies in ticks, reservoir hosts, and humans indicate that coinfection with B. burgdorferi and B. microti is common, promotes transmission and emergence of B. microti in the enzootic cycle, and causes greater disease severity and duration in humans. These interdisciplinary studies may serve as a paradigm for the study of other vector-borne coinfections. Identifying ecological drivers of pathogen emergence and host factors that fuel disease severity in coinfected individuals will help guide the design of effective preventative and therapeutic strategies.

Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission

Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10-15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector-pathogen systems.

Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis

Background

Mathematical models of parasite transmission can help integrate a large body of information into a consistent framework, which can then be used for gaining mechanistic insights and making predictions. However, uncertainty, spatial variability and complexity, can hamper the use of such models for decision making in parasite management programs.

Methods

We have adapted a Bayesian melding framework for calibrating simulation models to address the need for robust modelling tools that can effectively support management of lymphatic filariasis (LF) elimination in diverse endemic settings. We applied this methodology to LF infection and vector biting data from sites across the major LF endemic regions in order to quantify model parameters, and generate reliable predictions of infection dynamics along with credible intervals for modelled output variables. We used the locally calibrated models to estimate breakpoint values for various indicators of parasite transmission, and simulate timelines to parasite extinction as a function of local variations in infection dynamics and breakpoints, and effects of various currently applied and proposed LF intervention strategies.

Results

We demonstrate that as a result of parameter constraining by local data, breakpoint values for all the major indicators of LF transmission varied significantly between the sites investigated. Intervention simulations using the fitted models showed that as a result of heterogeneity in local transmission and extinction dynamics, timelines to parasite elimination in response to the current Mass Drug Administration (MDA) and various proposed MDA with vector control strategies also varied significantly between the study sites. Including vector control, however, markedly reduced the duration of interventions required to achieve elimination as well as decreased the risk of recrudescence following stopping of MDA.

Conclusions

We have demonstrated how a Bayesian data-model assimilation framework can enhance the use of transmission models for supporting reliable decision making in the management of LF elimination. Extending this framework for delivering predictions in settings either lacking or with only sparse data to inform the modelling process, however, will require development of procedures to estimate and use spatio-temporal variations in model parameters and inputs directly, and forms the next stage of the work reported here.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-015-1132-7) contains supplementary material, which is available to authorized users.

AGE AND REPEATED BIOPSY INFLUENCE ANTEMORTEM PRP(CWD) TESTING IN MULE DEER (ODOCOILEUS HEMIONUS) IN COLORADO, USA

Biopsy of rectal mucosa-associated lymphoid tissue provides a useful, but imperfect, live-animal test for chronic wasting disease (CWD) in mule deer (Odocoileus hemionus). It is difficult and expensive to complete these tests on free-ranging animals, and wildlife health managers will benefit from methods that can accommodate test results of varying quality. To this end, we developed a hierarchical Bayesian model to estimate the probability that an individual is infected based on test results. Our model was estimated with the use of data on 210 adult female mule deer repeatedly tested during 2010-14. The ability to identify infected individuals correctly declined with age and may have been influenced by repeated biopsy. Fewer isolated lymphoid follicles (where PrP(CWD) accumulates) were obtained in biopsies of older deer and the proportion of follicles showing PrP(CWD) was reduced. A deer's genotype in the prion gene (PRNP) also influenced detection. At least five follicles were needed in a biopsy to assure a 95% accurate test in PRNP genotype 225SS deer.

Climate change and Ixodes tick-borne diseases of humans

The evidence that climate warming is changing the distribution of Ixodes ticks and the pathogens they transmit is reviewed and evaluated. The primary approaches are either phenomenological, which typically assume that climate alone limits current and future distributions, or mechanistic, asking which tick-demographic parameters are affected by specific abiotic conditions. Both approaches have promise but are severely limited when applied separately. For instance, phenomenological approaches (e.g. climate envelope models) often select abiotic variables arbitrarily and produce results that can be hard to interpret biologically. On the other hand, although laboratory studies demonstrate strict temperature and humidity thresholds for tick survival, these limits rarely apply to field situations. Similarly, no studies address the influence of abiotic conditions on more than a few life stages, transitions or demographic processes, preventing comprehensive assessments. Nevertheless, despite their divergent approaches, both mechanistic and phenomenological models suggest dramatic range expansions of Ixodes ticks and tick-borne disease as the climate warms. The predicted distributions, however, vary strongly with the models' assumptions, which are rarely tested against reasonable alternatives. These inconsistencies, limited data about key tick-demographic and climatic processes and only limited incorporation of non-climatic processes have weakened the application of this rich area of research to public health policy or actions. We urge further investigation of the influence of climate on vertebrate hosts and tick-borne pathogen dynamics. In addition, testing model assumptions and mechanisms in a range of natural contexts and comparing their relative importance as competing models in a rigorous statistical framework will significantly advance our understanding of how climate change will alter the distribution, dynamics and risk of tick-borne disease.

Climate change and species interactions: ways forward

With ongoing and rapid climate change, ecologists are being challenged to predict how individual species will change in abundance and distribution, how biotic communities will change in structure and function, and the consequences of these climate-induced changes for ecosystem functioning. It is now well documented that indirect effects of climate change on species abundances and distributions, via climatic effects on interspecific interactions, can outweigh and even reverse the direct effects of climate. However, a clear framework for incorporating species interactions into projections of biological change remains elusive. To move forward, we suggest three priorities for the research community: (1) utilize tractable study systems as case studies to illustrate possible outcomes, test processes highlighted by theory, and feed back into modeling efforts; (2) develop a robust analytical framework that allows for better cross-scale linkages; and (3) determine over what time scales and for which systems prediction of biological responses to climate change is a useful and feasible goal. We end with a list of research questions that can guide future research to help understand, and hopefully mitigate, the negative effects of climate change on biota and the ecosystem services they provide.

Integrated assessment of biological invasions

As the main witnesses of the ecological and economic impacts of invasions on ecosystems around the world, ecologists seek to provide the relevant science that informs managers about the potential for invasion of specific organisms in their region(s) of interest. Yet, the assorted literature that could inform such forecasts is rarely integrated to do so, and further, the diverse nature of the data available complicates synthesis and quantitative prediction. Here we present a set of analytical tools for synthesizing different levels of distributional and/or demographic data to produce meaningful assessments of invasion potential that can guide management at multiple phases of ongoing invasions, from dispersal to colonization to proliferation. We illustrate the utility of data-synthesis and data-model assimilation approaches with case studies of three well-known invasive species--a vine, a marine mussel, and a freshwater crayfish--under current and projected future climatic conditions. Results from the integrated assessments reflect the complexity of the invasion process and show that the most relevant climatic variables can have contrasting effects or operate at different intensities across habitat types. As a consequence, for two of the study species climate trends will increase the likelihood of invasion in some habitats and decrease it in others. Our results identified and quantified both bottlenecks and windows of opportunity for invasion, mainly related to the role of human uses of the landscape or to disruption of the flow of resources. The approach we describe has a high potential to enhance model realism, explanatory insight, and predictive capability, generating information that can inform management decisions and optimize phase-specific prevention and control efforts for a wide range of biological invasions.

Improving the modeling of disease data from the government surveillance system: a case study on malaria in the Brazilian Amazon

The study of the effect of large-scale drivers (e.g., climate) of human diseases typically relies on aggregate disease data collected by the government surveillance network. The usual approach to analyze these data, however, often ignores a) changes in the total number of individuals examined, b) the bias towards symptomatic individuals in routine government surveillance, and; c) the influence that observations can have on disease dynamics. Here, we highlight the consequences of ignoring the problems listed above and develop a novel modeling framework to circumvent them, which is illustrated using simulations and real malaria data. Our simulations reveal that trends in the number of disease cases do not necessarily imply similar trends in infection prevalence or incidence, due to the strong influence of concurrent changes in sampling effort. We also show that ignoring decreases in the pool of infected individuals due to the treatment of part of these individuals can hamper reliable inference on infection incidence. We propose a model that avoids these problems, being a compromise between phenomenological statistical models and mechanistic disease dynamics models; in particular, a cross-validation exercise reveals that it has better out-of-sample predictive performance than both of these alternative models. Our case study in the Brazilian Amazon reveals that infection prevalence was high in 2004–2008 (prevalence of 4% with 95% CI of 3–5%), with outbreaks (prevalence up to 18%) occurring during the dry season of the year. After this period, infection prevalence decreased substantially (0.9% with 95% CI of 0.8–1.1%), which is due to a large reduction in infection incidence (i.e., incidence in 2008–2010 was approximately one fifth of the incidence in 2004–2008).We believe that our approach to modeling government surveillance disease data will be useful to advance current understanding of large-scale drivers of several diseases.

Disease data collected by the government surveillance system are frequently used to understand the influence of large-scale phenomena (e.g., climate) on human health because these data often have a large temporal and/or geographical span. The down side is that a) these data are often biased towards individuals that come to the health facilities (i.e., symptomatic individuals); and b) the number of individuals examined can vary substantially regardless of concurrent changes in prevalence or incidence (e.g., due to shortage of personnel or supplies in health facilities), directly impacting the number of disease cases detected. Current modeling approaches typically ignore these peculiarities of the government data. Furthermore, current approaches do not take into account that observations directly influence disease dynamics since individuals with a positive diagnosis are often subsequently treated for the disease. In this article, we develop a novel model to circumvent these shortcomings and apply it to simulated data, highlighting how inference on infection incidence and prevalence might be misleading when some of the issues mentioned above are ignored. Finally, we illustrate this model using malaria data from the Brazilian Amazon, revealing the strong role of precipitation on infection prevalence seasonality and striking patterns in infection incidence.

Sequential modelling of the effects of mass drug treatments on anopheline-mediated lymphatic filariasis infection in Papua New Guinea

BACKGROUND

Lymphatic filariasis (LF) has been targeted by the WHO for global eradication leading to the implementation of large scale intervention programs based on annual mass drug administrations (MDA) worldwide. Recent work has indicated that locality-specific bio-ecological complexities affecting parasite transmission may complicate the prediction of LF extinction endpoints, casting uncertainty on the achievement of this initiative. One source of difficulty is the limited quantity and quality of data used to parameterize models of parasite transmission, implying the important need to update initially-derived parameter values. Sequential analysis of longitudinal data following annual MDAs will also be important to gaining new understanding of the persistence dynamics of LF. Here, we apply a Bayesian statistical-dynamical modelling framework that enables assimilation of information in human infection data recorded from communities in Papua New Guinea that underwent annual MDAs, into our previously developed model of parasite transmission, in order to examine these questions in LF ecology and control.

RESULTS

Biological parameters underlying transmission obtained by fitting the model to longitudinal data remained stable throughout the study period. This enabled us to reliably reconstruct the observed baseline data in each community. Endpoint estimates also showed little variation. However, the updating procedure showed a shift towards higher and less variable values for worm kill but not for any other drug-related parameters. An intriguing finding is that the stability in key biological parameters could be disrupted by a significant reduction in the vector biting rate prevailing in a locality.

CONCLUSIONS

Temporal invariance of biological parameters in the face of intervention perturbations indicates a robust adaptation of LF transmission to local ecological conditions. The results imply that understanding the mechanisms that underlie locally adapted transmission dynamics will be integral to identifying points of system fragility, and thus countermeasures to reliably facilitate LF extinction both locally and globally.

Remote sensing of climatic anomalies and West Nile virus incidence in the northern Great Plains of the United States

The northern Great Plains (NGP) of the United States has been a hotspot of West Nile virus (WNV) incidence since 2002. Mosquito ecology and the transmission of vector-borne disease are influenced by multiple environmental factors, and climatic variability is an important driver of inter-annual variation in WNV transmission risk. This study applied multiple environmental predictors including land surface temperature (LST), the normalized difference vegetation index (NDVI) and actual evapotranspiration (ETa) derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) products to establish prediction models for WNV risk in the NGP. These environmental metrics are sensitive to seasonal and inter-annual fluctuations in temperature and precipitation, and are hypothesized to influence mosquito population dynamics and WNV transmission. Non-linear generalized additive models (GAMs) were used to evaluate the influences of deviations of cumulative LST, NDVI, and ETa on inter-annual variations of WNV incidence from 2004–2010. The models were sensitive to the timing of spring green up (measured with NDVI), temperature variability in early spring and summer (measured with LST), and moisture availability from late spring through early summer (measured with ETa), highlighting seasonal changes in the influences of climatic fluctuations on WNV transmission. Predictions based on these variables indicated a low WNV risk across the NGP in 2011, which is concordant with the low case reports in this year. Environmental monitoring using remote-sensed data can contribute to surveillance of WNV risk and prediction of future WNV outbreaks in space and time.

Surveillance of dengue fever virus: a review of epidemiological models and early warning systems

Dengue fever affects over a 100 million people annually hence is one of the world's most important vector-borne diseases. The transmission area of this disease continues to expand due to many direct and indirect factors linked to urban sprawl, increased travel and global warming. Current preventative measures include mosquito control programs, yet due to the complex nature of the disease and the increased importation risk along with the lack of efficient prophylactic measures, successful disease control and elimination is not realistic in the foreseeable future. Epidemiological models attempt to predict future outbreaks using information on the risk factors of the disease. Through a systematic literature review, this paper aims at analyzing the different modeling methods and their outputs in terms of acting as an early warning system. We found that many previous studies have not sufficiently accounted for the spatio-temporal features of the disease in the modeling process. Yet with advances in technology, the ability to incorporate such information as well as the socio-environmental aspect allowed for its use as an early warning system, albeit limited geographically to a local scale.

Despite mass vaccination campaigns and large scaled improvements in global surveillance, infectious diseases are a worldwide problem. In recent years, the ability to use models as a tool to help visualize, understand and combat infectious diseases has become more feasible and reliable. In this context, modelling focuses on transmission patterns between the different animal, human or vector components as well as including parameters which affect these pathways such as environmental, climatic or geographic ones. The output of these models can help in decision making processes concerning control purposes, surveillance methods and hopefully also as good predictive tools. Prediction forms part of surveillance systems, and more specifically in early warning systems. It is the timely collection and analysis of data as well as the use of risk-based assessments in order to aid in prompt health interventions such as movement control, vaccination campaigns or the distribution of important information. Early warning systems for vector borne diseases are especially complex due to the involvement of various factors originating from the human, animal and insect sector as well the disease itself. The authors investigate the variety and depth of available models for dengue fever surveillance and their use as early warning tools.