The global distribution and burden of dengue.

Dengue is a systemic viral infection transmitted between humans by Aedes mosquitoes. For some patients, dengue is a life-threatening illness. There are currently no licensed vaccines or specific therapeutics, and substantial vector control efforts have not stopped its rapid emergence and global spread. The contemporary worldwide distribution of the risk of dengue virus infection and its public health burden are poorly known. Here we undertake an exhaustive assembly of known records of dengue occurrence worldwide, and use a formal modelling framework to map the global distribution of dengue risk. We then pair the resulting risk map with detailed longitudinal information from dengue cohort studies and population surfaces to infer the public health burden of dengue in 2010. We predict dengue to be ubiquitous throughout the tropics, with local spatial variations in risk influenced strongly by rainfall, temperature and the degree of urbanization. Using cartographic approaches, we estimate there to be 390 million (95% credible interval 284-528) dengue infections per year, of which 96 million (67-136) manifest apparently (any level of disease severity). This infection total is more than three times the dengue burden estimate of the World Health Organization. Stratification of our estimates by country allows comparison with national dengue reporting, after taking into account the probability of an apparent infection being formally reported. The most notable differences are discussed. These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue. We anticipate that they will provide a starting point for a wider discussion about the global impact of this disease and will help to guide improvements in disease control strategies using vaccine, drug and vector control methods, and in their economic evaluation.

Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations.

The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number R0) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: pC, 0.0133-0.150 and R0, 1.09-2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with pC approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, R0 and pC could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.

Multiple estimates of transmissibility for the 2009 influenza pandemic based on influenza-like-illness data from small US military populations.

Rapidly characterizing the amplitude and variability in transmissibility of novel human influenza strains as they emerge is a key public health priority. However, comparison of early estimates of the basic reproduction number during the 2009 pandemic were challenging because of inconsistent data sources and methods. Here, we define and analyze influenza-like-illness (ILI) case data from 2009-2010 for the 50 largest spatially distinct US military installations (military population defined by zip code, MPZ). We used publicly available data from non-military sources to show that patterns of ILI incidence in many of these MPZs closely followed the pattern of their enclosing civilian population. After characterizing the broad patterns of incidence (e.g. single-peak, double-peak), we defined a parsimonious SIR-like model with two possible values for intrinsic transmissibility across three epochs. We fitted the parameters of this model to data from all 50 MPZs, finding them to be reasonably well clustered with a median (mean) value of 1.39 (1.57) and standard deviation of 0.41. An increasing temporal trend in transmissibility ([Formula: see text], p-value: 0.013) during the period of our study was robust to the removal of high transmissibility outliers and to the removal of the smaller 20 MPZs. Our results demonstrate the utility of rapidly available - and consistent - data from multiple populations.

Host and viral ecology determine bat rabies seasonality and maintenance.

Rabies is an acute viral infection that is typically fatal. Most rabies modeling has focused on disease dynamics and control within terrestrial mammals (e.g., raccoons and foxes). As such, rabies in bats has been largely neglected until recently. Because bats have been implicated as natural reservoirs for several emerging zoonotic viruses, including SARS-like corona viruses, henipaviruses, and lyssaviruses, understanding how pathogens are maintained within a population becomes vital. Unfortunately, little is known about maintenance mechanisms for any pathogen in bat populations. We present a mathematical model parameterized with unique data from an extensive study of rabies in a Colorado population of big brown bats (Eptesicus fuscus) to elucidate general maintenance mechanisms. We propose that life history patterns of many species of temperate-zone bats, coupled with sufficiently long incubation periods, allows for rabies virus maintenance. Seasonal variability in bat mortality rates, specifically low mortality during hibernation, allows long-term bat population viability. Within viable bat populations, sufficiently long incubation periods allow enough infected individuals to enter hibernation and survive until the following year, and hence avoid an epizootic fadeout of rabies virus. We hypothesize that the slowing effects of hibernation on metabolic and viral activity maintains infected individuals and their pathogens until susceptibles from the annual birth pulse become infected and continue the cycle. This research provides a context to explore similar host ecology and viral dynamics that may explain seasonal patterns and maintenance of other bat-borne diseases.

Big data opportunities for global infectious disease surveillance.

Simon Hay and colleagues discuss the potential and challenges of producing continually updated infectious disease risk maps using diverse and large volume data sources such as social media.

Persistence of black-tailed prairie-dog populations affected by plague in northern Colorado, USA.

The spatial distribution of prairie dog (Cynomys ludovicianus) colonies in North America has changed from large, contiguous populations to small, isolated colonies in metapopulations. One factor responsible for this drastic change in prairie-dog population structure is plague (caused by the bacterium Yersinia pestis). We fit stochastic patch occupancy models to 20 years of prairie-dog colony occupancy data from two discrete metapopulations (west and east) in the Pawnee National Grassland in Colorado, USA, that differ in connectivity among suitable habitat patches. We conducted model selection between two hypothesized modes of plague movement: independent of prairie-dog dispersal (colony-area) vs. plague movement consistent with prairie-dog dispersal (connectivity to extinct colonies). The best model, which fit the data well (area under the curve [AUC]: 0.94 west area; 0.79 east area), revealed that over time the proportion of extant colonies was better explained by colony size than by connectivity to extinct (plagued) colonies. The idea that prairie dogs are not likely to be the main vector that spreads Y. pestis across the landscape is supported by the observation that colony extinctions are primarily caused by plague, prairie-dog dispersal is short range, and connectivity to extinct colonies was not selected as a factor in the models. We also conducted simulations with the best model to examine long-term patterns of colony occupancy and persistence of prairie-dog metapopulations. In the case where the metapopulations persist, our model predicted that the western metapopulation would have a colony occupancy rate approximately 2.5 times higher than that of the eastern metapopulation (-50% occupied colonies vs. 20%) in 50 years, but that the western metapopulation has -80% chance of extinction in 100 years while the eastern metapopulation has a less than 25% chance. Extinction probability of individual colonies depended on the frequency with which colonies of the same size class occurred in the metapopulation. Thus, the long-term persistence of prairie-dog metapopulations depended on specific details of the metapopulation.

Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics.

Infectious disease ecology has recently raised its public profile beyond the scientific community due to the major threats that wildlife infections pose to biological conservation, animal welfare, human health and food security. As we start unravelling the full extent of emerging infectious diseases, there is an urgent need to facilitate multidisciplinary research in this area. Even though research in ecology has always had a strong theoretical component, cultural and technical hurdles often hamper direct collaboration between theoreticians and empiricists. Building upon our collective experience of multidisciplinary research and teaching in this area, we propose practical guidelines to help with effective integration among mathematical modelling, fieldwork and laboratory work. Modelling tools can be used at all steps of a field-based research programme, from the formulation of working hypotheses to field study design and data analysis. We illustrate our model-guided fieldwork framework with two case studies we have been conducting on wildlife infectious diseases: plague transmission in prairie dogs and lyssavirus dynamics in American and African bats. These demonstrate that mechanistic models, if properly integrated in research programmes, can provide a framework for holistic approaches to complex biological systems.

Using network properties to predict disease dynamics on human contact networks.

Recent studies have increasingly turned to graph theory to model more realistic contact structures that characterize disease spread. Because of the computational demands of these methods, many researchers have sought to use measures of network structure to modify analytically tractable differential equation models. Several of these studies have focused on the degree distribution of the contact network as the basis for their modifications. We show that although degree distribution is sufficient to predict disease behaviour on very sparse or very dense human contact networks, for intermediate density networks we must include information on clustering and path length to accurately predict disease behaviour. Using these three metrics, we were able to explain more than 98 per cent of the variation in endemic disease levels in our stochastic simulations.

The value proposition of the Global Health Security Index.

Infectious disease outbreaks pose major threats to human health and security. Countries with robust capacities for preventing, detecting and responding to outbreaks can avert many of the social, political, economic and health system costs of such crises. The Global Health Security Index (GHS Index)-the first comprehensive assessment and benchmarking of health security and related capabilities across 195 countries-recently found that no country is sufficiently prepared for epidemics or pandemics. The GHS Index can help health security stakeholders identify areas of weakness, as well as opportunities to collaborate across sectors, collectively strengthen health systems and achieve shared public health goals. Some scholars have recently offered constructive critiques of the GHS Index's approach to scoring and ranking countries; its weighting of select indicators; its emphasis on transparency; its focus on biosecurity and biosafety capacities; and divergence between select country scores and corresponding COVID-19-associated caseloads, morbidity, and mortality. Here, we (1) describe the practical value of the GHS Index; (2) present potential use cases to help policymakers and practitioners maximise the utility of the tool; (3) discuss the importance of scoring and ranking; (4) describe the robust methodology underpinning country scores and ranks; (5) highlight the GHS Index's emphasis on transparency and (6) articulate caveats for users wishing to use GHS Index data in health security research, policymaking and practice.

Coordinating the real-time use of global influenza activity data for better public health planning.

Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.

Mathematical modeling of the West Africa Ebola epidemic.

As of November 2015, the Ebola virus disease (EVD) epidemic that began in West Africa in late 2013 is waning. The human toll includes more than 28,000 EVD cases and 11,000 deaths in Guinea, Liberia, and Sierra Leone, the most heavily-affected countries. We reviewed 66 mathematical modeling studies of the EVD epidemic published in the peer-reviewed literature to assess the key uncertainties models addressed, data used for modeling, public sharing of data and results, and model performance. Based on the review, we suggest steps to improve the use of modeling in future public health emergencies.

A global compendium of human Crimean-Congo haemorrhagic fever virus occurrence.

In order to map global disease risk, a geographic database of human Crimean-Congo haemorrhagic fever virus (CCHFV) occurrence was produced by surveying peer-reviewed literature and case reports, as well as informal online sources. Here we present this database, comprising occurrence data linked to geographic point or polygon locations dating from 1953 to 2013. We fully describe all data collection, geo-positioning, database management and quality-control procedures. This is the most comprehensive database of confirmed CCHF occurrence in humans to-date, containing 1,721 geo-positioned occurrences in total.

The global distribution of Crimean-Congo hemorrhagic fever.

BACKGROUND: Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne infection caused by a virus (CCHFV) from the Bunyaviridae family. Domestic and wild vertebrates are asymptomatic reservoirs for the virus, putting animal handlers, slaughter-house workers and agricultural labourers at highest risk in endemic areas, with secondary transmission possible through contact with infected blood and other bodily fluids. Human infection is characterized by severe symptoms that often result in death. While it is known that CCHFV transmission is limited to Africa, Asia and Europe, definitive global extents and risk patterns within these limits have not been well described. METHODS: We used an exhaustive database of human CCHF occurrence records and a niche modeling framework to map the global distribution of risk for human CCHF occurrence. RESULTS: A greater proportion of shrub or grass land cover was the most important contributor to our model, which predicts highest levels of risk around the Black Sea, Turkey, and some parts of central Asia. Sub-Saharan Africa shows more focalized areas of risk throughout the Sahel and the Cape region. CONCLUSIONS: These new risk maps provide a valuable starting point for understanding the zoonotic niche of CCHF, its extent and the risk it poses to humans.

Enhancing disease surveillance with novel data streams: challenges and opportunities.

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Influenza forecasting in human populations: a scoping review.

Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms "influenza AND (forecast* OR predict*)", excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials.

Global database of leishmaniasis occurrence locations, 1960-2012.

The leishmaniases are neglected tropical diseases of significant public health importance. However, information on their global occurrence is disparate and sparse. This database represents an attempt to collate reported leishmaniasis occurrences from 1960 to 2012. Methodology for the collection of data from the literature, abstraction of case locations and data processing procedures are described here. In addition, strain archives and online data resources were accessed. A total of 12,563 spatially and temporally unique occurrences of both cutaneous and visceral leishmaniasis comprise the database, ranging in geographic scale from villages to states. These data can be used for a variety of mapping and spatial analyses covering multiple resolutions.

Global distribution maps of the leishmaniases.

The leishmaniases are vector-borne diseases that have a broad global distribution throughout much of the Americas, Africa, and Asia. Despite representing a significant public health burden, our understanding of the global distribution of the leishmaniases remains vague, reliant upon expert opinion and limited to poor spatial resolution. A global assessment of the consensus of evidence for leishmaniasis was performed at a sub-national level by aggregating information from a variety of sources. A database of records of cutaneous and visceral leishmaniasis occurrence was compiled from published literature, online reports, strain archives, and GenBank accessions. These, with a suite of biologically relevant environmental covariates, were used in a boosted regression tree modelling framework to generate global environmental risk maps for the leishmaniases. These high-resolution evidence-based maps can help direct future surveillance activities, identify areas to target for disease control and inform future burden estimation efforts.