The global distribution of yellow fever and dengue

Relationship between exposure to vector bites and antibody responses to mosquito salivary gland extracts

Mosquito-borne diseases are major health problems worldwide. Serological responses to mosquito saliva proteins may be useful in estimating individual exposure to bites from mosquitoes transmitting these diseases. However, the relationships between the levels of these IgG responses and mosquito density as well as IgG response specificity at the genus and/or species level need to be clarified prior to develop new immunological markers to assess human/vector contact. To this end, a kinetic study of antibody levels against several mosquito salivary gland extracts from southeastern French individuals living in three areas with distinct ecological environments and, by implication, distinct Aedes caspius mosquito densities were compared using ELISA. A positive association was observed between the average levels of IgG responses against Ae. caspius salivary gland extracts and spatial Ae. caspius densities. Additionally, the average level of IgG responses increased significantly during the peak exposure to Ae. caspius at each site and returned to baseline four months later, suggesting short-lived IgG responses. The species-specificity of IgG antibody responses was determined by testing antibody responses to salivary gland extracts from Cx. pipiens, a mosquito that is present at these three sites at different density levels, and from two other Aedes species not present in the study area (Ae. aegypti and Ae. albopictus). The IgG responses observed against these mosquito salivary gland extracts contrasted with those observed against Ae. caspius salivary gland extracts, supporting the existence of species-specific serological responses. By considering different populations and densities of mosquitoes linked to environmental factors, this study shows, for the first time, that specific IgG antibody responses against Ae. caspius salivary gland extracts may be related to the seasonal and geographical variations in Ae. caspius density. Characterisation of such immunological-markers may allow the evaluation of the effectiveness of vector-control strategies or estimation of the risk of vector-borne disease transmission.

The effects of spatial population dataset choice on estimates of population at risk of disease

Background

The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.

Methods

The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.

Results

The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.

Conclusions

Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

Spatial analysis of notified dengue fever infections

This study aimed to investigate the spatial clustering and dynamic dispersion of dengue incidence in Queensland, Australia. We used Moran's I statistic to assess the spatial autocorrelation of reported dengue cases. Spatial empirical Bayes smoothing estimates were used to display the spatial distribution of dengue in postal areas throughout Queensland. Local indicators of spatial association (LISA) maps and logistic regression models were used to identify spatial clusters and examine the spatio-temporal patterns of the spread of dengue. The results indicate that the spatial distribution of dengue was clustered during each of the three periods of 1993-1996, 1997-2000 and 2001-2004. The high-incidence clusters of dengue were primarily concentrated in the north of Queensland and low-incidence clusters occurred in the south-east of Queensland. The study concludes that the geographical range of notified dengue cases has significantly expanded in Queensland over recent years.

The arrival, establishment and spread of exotic diseases: patterns and predictions

The impact of human activities on the principles and processes governing the arrival, establishment and spread of exotic pathogens is illustrated by vector-borne diseases such as malaria, dengue, chikungunya, West Nile, bluetongue and Crimean-Congo haemorrhagic fevers. Competent vectors, which are commonly already present in the areas, provide opportunities for infection by exotic pathogens that are introduced by travel and trade. At the same time, the correct combination of environmental conditions (both abiotic and biotic) makes many far-flung parts of the world latently and predictably, but differentially, permissive to persistent transmission cycles. Socioeconomic factors and nutritional status determine human exposure to disease and resistance to infection, respectively, so that disease incidence can vary independently of biological cycles.

How important is vertical transmission in mosquitoes for the persistence of dengue? Insights from a mathematical model

In many regions dengue incidence fluctuates seasonally with few if any infections reported in unfavourable periods. It has been hypothesized that vertical transmission within the mosquito population allows the virus to persist at these times. A review of the literature shows that vertical infection efficiencies are 1-4%. Using a mathematical model we argue that at these infection rates vertical transmission is not an important factor for long term virus persistence. In endemic situations, increases in reproductive number, half-life and persistence times of the disease only become significant when vertical infection efficiency exceeds 20-30%. In epidemic situations vertical infection accelerates the course of the outbreak and may actually reduce persistence time. These results stem from the fact that the mosquito life-cycle is relatively rapid and vertically acquired infections are multiplicatively diluted with every generation. When the efficiency of vertical infection is as low as reported from empirical studies, the virus is rapidly lost unless there is regular amplification in the human population. Processes such as asymptomatic human dengue cases are therefore more likely to be important in persistence than transmission within the vector population. The empirical data are not, however, unequivocal and we identify several areas of research that would further clarify the role of vertical transmission in the epidemiology of dengue.

Assessment of yellow fever epidemic risk: an original multi-criteria modeling approach

Background

Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in the early 1980s. We developed an original modeling approach to assess YF epidemic risk (vulnerability) and to prioritize the populations to be vaccinated.

Methods and Findings

We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with “exposure” to virus/vector and one with “susceptibility” of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns.

Conclusion

This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors.

This article describes the use of an original modeling approach to assess the risk of yellow fever (YF) epidemics. YF is a viral hemorrhagic fever responsible in past centuries for devastating outbreaks. Since the 1930s, a vaccine has been available that protects the individual for at least 10 years, if not for life. However, immunization of populations in African countries was gradually discontinued after the 1960s. With the decrease in immunity against YF in African populations the disease reemerged in the 1980s. In 2005, WHO, UNICEF, and the GAVI Alliance decided to support preventive vaccination of at-risk populations in West African endemic countries in order to tackle the reemergence of YF and reduce the risk of urban YF outbreaks. Financial resources were made available to scale up a global YF vaccine stockpile and to support countries with limited resources in the management of preventive vaccination campaigns. This article describes the process we used to determine the most at-risk populations using a mathematical model to prioritize targeted immunization campaigns. We believe that this approach could be useful for other diseases for which decision making process is difficult because of limited data availability, complex risk variables, and a need for rapid decisions and implementation.

Urban structure and dengue fever in Puntarenas, Costa Rica

Dengue is currently the most important arboviral disease globally and is usually associated with built environments in tropical areas. Remotely sensed information can facilitate the study of urban mosquito-borne diseases by providing multiple temporal and spatial resolutions appropriate to investigate urban structure and ecological characteristics associated with infectious disease. In this study, coarse, medium and fine resolution satellite imagery (Moderate Resolution Imaging Spectrometer, Advanced Spaceborne Thermal Emission and Reflection Radiometer and QuickBird respectively) and ground-based data were analyzed for the Greater Puntarenas area, Costa Rica for the years 2002-04. The results showed that the mean normalized difference vegetation index (NDVI) was generally higher in the localities with lower incidence of dengue fever during 2002, although the correlation was statistically significant only in the dry season (r=-0.40; p=0.03). Dengue incidence was inversely correlated to built area and directly correlated with tree cover (r=0.75, p=0.01). Overall, the significant correlations between dengue incidence and urban structural variables (tree cover and building density) suggest that properties of urban structure may be associated with dengue incidence in tropical urban settings.

Local and global effects of climate on dengue transmission in Puerto Rico

The four dengue viruses, the agents of dengue fever and dengue hemorrhagic fever in humans, are transmitted predominantly by the mosquito Aedes aegypti. The abundance and the transmission potential of Ae. aegypti are influenced by temperature and precipitation. While there is strong biological evidence for these effects, empirical studies of the relationship between climate and dengue incidence in human populations are potentially confounded by seasonal covariation and spatial heterogeneity. Using 20 years of data and a statistical approach to control for seasonality, we show a positive and statistically significant association between monthly changes in temperature and precipitation and monthly changes in dengue transmission in Puerto Rico. We also found that the strength of this association varies spatially, that this variation is associated with differences in local climate, and that this relationship is consistent with laboratory studies of the impacts of these factors on vector survival and viral replication. These results suggest the importance of temperature and precipitation in the transmission of dengue viruses and suggest a reason for their spatial heterogeneity. Thus, while dengue transmission may have a general system, its manifestation on a local scale may differ from global expectations.

Dengue viruses are a major health problem throughout the tropical and subtropical regions of the world. Because they are transmitted by mosquitoes that are sensitive to changes in rainfall and temperature, transmission intensity may be regulated by weather and climate. Laboratory studies have shown this to be biologically plausible, but studies of transmission in real-life situations have been inconclusive. Here we demonstrate that increased temperature and rainfall are associated with increased dengue transmission in subsequent months across Puerto Rico. We also show that differences in local climate within Puerto Rico can explain local differences observed in the relationship between weather and dengue transmission. This finding is important because it suggests that the determinants of transmission occur on a local level such that although dengue viruses have a basically universal transmission cycle, changes in temperature or rainfall may have diverse local effects.

Human population, urban settlement patterns and their impact on Plasmodium falciparum malaria endemicity

BACKGROUND

The efficient allocation of financial resources for malaria control and the optimal distribution of appropriate interventions require accurate information on the geographic distribution of malaria risk and of the human populations it affects. Low population densities in rural areas and high population densities in urban areas can influence malaria transmission substantially. Here, the Malaria Atlas Project (MAP) global database of Plasmodium falciparum parasite rate (PfPR) surveys, medical intelligence and contemporary population surfaces are utilized to explore these relationships and other issues involved in combining malaria risk maps with those of human population distribution in order to define populations at risk more accurately.

METHODS

First, an existing population surface was examined to determine if it was sufficiently detailed to be used reliably as a mask to identify areas of very low and very high population density as malaria free regions. Second, the potential of international travel and health guidelines (ITHGs) for identifying malaria free cities was examined. Third, the differences in PfPR values between surveys conducted in author-defined rural and urban areas were examined. Fourth, the ability of various global urban extent maps to reliably discriminate these author-based classifications of urban and rural in the PfPR database was investigated. Finally, the urban map that most accurately replicated the author-based classifications was analysed to examine the effects of urban classifications on PfPR values across the entire MAP database.

RESULTS

Masks of zero population density excluded many non-zero PfPR surveys, indicating that the population surface was not detailed enough to define areas of zero transmission resulting from low population densities. In contrast, the ITHGs enabled the identification and mapping of 53 malaria free urban areas within endemic countries. Comparison of PfPR survey results showed significant differences between author-defined 'urban' and 'rural' designations in Africa, but not for the remainder of the malaria endemic world. The Global Rural Urban Mapping Project (GRUMP) urban extent mask proved most accurate for mapping these author-defined rural and urban locations, and further sub-divisions of urban extents into urban and peri-urban classes enabled the effects of high population densities on malaria transmission to be mapped and quantified.

CONCLUSION

The availability of detailed, contemporary census and urban extent data for the construction of coherent and accurate global spatial population databases is often poor. These known sources of uncertainty in population surfaces and urban maps have the potential to be incorporated into future malaria burden estimates. Currently, insufficient spatial information exists globally to identify areas accurately where population density is low enough to impact upon transmission. Medical intelligence does however exist to reliably identify malaria free cities. Moreover, in Africa, urban areas that have a significant effect on malaria transmission can be mapped.

Influence of hosts on the ecology of arboviral transmission: potential mechanisms influencing dengue, Murray Valley encephalitis, and Ross River virus in Australia

Ecological interactions are fundamental to the transmission of infectious disease. Arboviruses are particularly elegant examples, where rich arrays of mechanisms influence transmission between vectors and hosts. Research on host contributions to the ecology of arboviral diseases has been undertaken within multiple subdisciplines, but significant gaps in knowledge remain and multidisciplinary approaches are needed. Through our multidisciplinary review of the literature we have identified five broad areas where hosts may influence the ecology of arboviral transmission: host immunity; cross-protective immunity and antibody-dependent enhancement; host abundance; host diversity; and pathogen spillover and dispersal. Herein we discuss the known and theoretical roles of hosts within these topics and then apply this knowledge to three epidemiologically important mosquito-borne arboviruses that occur in Australia: dengue virus (DENV), Murray Valley encephalitis virus (MVEV), and Ross River virus (RRV). We argue that the underlying mechanisms by which hosts influence arboviral activity are numerous and attempts to delineate these mechanisms further are needed. Investigations that focus on hosts of vector-borne diseases are likely to be rewarding, particularly where the ecology of vectors is relatively well understood. From an applied perspective, enhanced knowledge of host influences upon vector-borne disease transmission is likely to enable better management of disease burden. Finally, we suggest a framework that may be useful to identify and determine host contributions to the ecology of arboviruses.

Dynamics of dengue epidemics in urban contexts

Dengue, similar to other arboviral diseases, exhibits complex spatiotemporal dynamics. Even at town or village level, individual-based spatially explicit models are required to correctly reproduce epidemic curves. This makes modelling at the regional level (province, country or continent) very difficult and cumbersome. We propose here a first step to build a hierarchized model by constructing a simple analytical expression which reproduces the model output from macroscopic parameters describing each 'village'. It also turns out to be a good approximation of real urban epidermic outbreaks. Subsequently, a regional model could be built by coupling these equations on a lattice.

A geographical sampling method for surveys of mosquito larvae in an urban area using high-resolution satellite imagery

Entomological surveys in urban areas are often biased by selecting houses or locations with known high vector densities. A sampling strategy was developed for Puntarenas, Costa Rica, using high-resolution satellite imagery. Grids from the Advanced Spaceborne Thermal Emission and Reflection Radiometer and a QuickBird classified land cover map were used to determine the optimal final grid area for surveys. A random sample (10% of cells) was selected, and sample suitability was assessed by comparing the mean percentage of tree cover between sample and total cells. Sample cells were used to obtain entomological data from 581 locations: 26.3% of all locations positive for mosquito larvae were not households, they contained 29.5% of mosquito-positive habitats and 16% of Aedes aegypti pupae collected. Entomological indices for Ae. aegypti (pupae per person, Breteau index, container index, location index) were slightly lower when only household data were analyzed. High-resolution satellite imagery and geographical information systems appear useful for evaluating urban sites and randomly selecting locations for accurate entomological surveys.

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data

Background

Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics.

Methodology/Principal Findings

We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005.

Conclusions/Significance

Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed.