Land cover and land use mapping

LULC includes, for example, vegetation type, human settlements, urban features, and water bodies. Fine-resolution data, provided, for example, by the Landsat series, have been used widely for custom land cover mapping and applied to identification of suitable vector and host breeding sites [2,3,18,53], and have been used for mapping urban areas [54]. There are also several moderate-resolution global land cover maps, which have also seen wide use (e.g., [7,14,55]). An example is shown in Fig 1. Global land cover maps are summarized in Table 2, and an overview is provided in S1 Text.

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Fig 1. Examples of MODIS products for the Ningxia Hui Autonomous Region (NHAR), China (top left).

The top right image shows the 500 m x 500 m annual land cover map (MCD12Q1) for 2012. It uses the IGBP classification scheme (see S1 Text). Only classes covering more than 1% of the NHAR area are shown. The second row shows MODIS (MOD13A3) 1 x 1 km NDVI (bottom left) and pixel reliability (bottom right) maps for July 2012. Pixels flagged as “check metadata” were still of high quality, but flagged because of a moderate atmospheric aerosol load, which can reduce image quality.

https://doi.org/10.1371/journal.pntd.0004164.g001

VFR imagery from aerial surveys has been available for several decades. Over the last 15 years, a variety of VFR satellite imagery (Table 3) has become available [56]. De Castro et al. [57] used aerial photographs to identify potential mosquito breeding grounds in Dar es Salaam, Tanzania. Reis et al. [58] used 16 cm-resolution aerial photography to identify potential leptospirosis risk factors, including open sewers, refuse sites, vegetation, and water bodies in Salvador, Brazil. Limited use has been made of VFR satellite imagery, although Soti et al. [59] used 2.4 m-resolution Quickbird images to detect ponds in a semi-arid area of north Senegal. Addink et al. [60] demonstrated that 2.4 m multispectral Quickbird imagery can be used to delineate the burrows of the great gerbil (Rhombomys opimus), an important host for the plague bacterium (Yersinia pestis), for a 10 × 6 km test site in Kazakhstan. This study was then extended to a 200 × 250 km area by Wilschut et al. [61] using a Landsat 7 30 m and SPOT-5 2.5 m imagery. An important limitation of VFR data is the lack of a regular acquisition cycle, which limits their utility for monitoring and means that historic data for a given study site may not be available.

Digital elevation models

DEMs are derived from satellite or airborne RS data [63]. Elevation and the derived variables (such as slope and aspect) may give a measure of habitat suitability or may be correlated with other relevant environmental variables (e.g., temperature, rainfall) [23,64,65]. DEMs can also be used to identify water bodies and potential areas of flooding [58,66]. Freely available DEMs that cover much of the globe at resolutions of 30 m and coarser have been used widely [7,20,24,61,66]. These are listed in Table 2 and summarized in S1 Text. For any given study area, finer-resolution, more accurate DEMs may be available via a private company or government agency [15,58].

Land surface and vegetation dynamics

The repetivity for fine-resolution sensors is considered too long to monitor environmental dynamics, and their use tends to be restricted to static maps [2,3]. Moderate- and coarse-resolution sensors typically acquire data daily, although they are aggregated over several days for time-series products. Data from these sensors, particularly the NOAA Advanced Very High Resolution Radiometer (AVHRR), have been used for monitoring environmental dynamics [10,67,68]. AVHRR provides a 10-day 8 × 8 km-resolution time series of land surface temperature (LST), middle infrared reflectance (MIR), and NDVI going back to 1981 [41,42]. These variables have been used widely in NTD applications [10,12,20,69]. The time series of monthly NDVI, LST, and MIR data (August 1981 to September 2001) have been processed using temporal Fourier analysis (TFA) and made available to the community by Hay et al. [17]. TFA gives a per-pixel summary of the time series that can be used as a covariate in subsequent analysis [22,69,70]. TFA is of particular interest because it describes the mean, variance, and seasonality in the signal. Other possibilities for summarizing time series include simple summary statistics (e.g., mean, minimum, and maximum) [10,71].

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is carried on the NASA Terra and Aqua satellites, launched in 1999 and 2001, respectively [18], as part of the NASA Earth Observing System (EOS). A particular feature of EOS is the provision of a suite of MODIS data products at resolutions of 250, 500, or 1,000 m, with a temporal resolution of 1 day to 1 year. MODIS products are required to be fully documented, including a user guide and quality assurance and validation reports [72–74]. MODIS products are not, however, simply ready to use out of the box. Each product is the outcome of a substantial scientific investigation, and it is necessary to understand the fundamentals of the product and the quality report [75]. MODIS products commonly used in infectious disease studies include land cover type, NDVI, Enhanced Vegetation Index (EVI), and LST (see Table 2), which have seen increased use in recent years [23,55,76]. MODIS 8-day 1 × 1 km time-series for 2001 to 2005 for MIR, NDVI, EVI, and day and night LST have also been processed using TFA and made available to the commnity [75].

LST is a measure of the temperature of the land or vegetation surface. LST is not the same as air temperature, measured using conventional meteorological networks, although it is correlated with it. Temperature is an important control on pathogens, hosts, and vectors. Hence, LST is used widely in NTD studies [3,19,22,77]. NDVI has been very widely used in remote sensing applications over several decades [78] and has been used widely as a covariate for studying the epidemiology of NTDs [3,5,12,34,77]. NDVI allows vegetated and nonvegetated surfaces to be distinguished, and high values are associated with vegetation properties such as biomass, leaf area index (LAI), productivity, and health [78], and is illustrated in Fig 1 where high values are associated with agricultural production. Time series of NDVI values are available from AVHRR (since 1981), MODIS (since 2000), and Satellite Pour l’Observation de la Terre VeGeTation (SPOT VGT) (since 1998), and have been used to study vegetation dynamics and phenology [78]. Furthermore, since healthy vegetation tends to be associated with favourable climatic conditions, it is also used as a surrogate for meteorology [3,67,77]. Despite its succesful application, NDVI is limited because it uses only two wavebands [79], and there are now numerous vegetation and other indices available that use different wavebands and may be more suitable in any given situation [34,80]. Furthermore, there are now MODIS EO products that are based on the modelling of biophysical principles that are generated in a consistent and standardized way [81]. These include vegetation leaf area index (LAI) (MCD15A2 & 3) and net primary productivity (MOD17A3), as well as EVI and land cover dynamics (MCDQ1 & 2). We expect that NDVI will continue to be useful, but to gain a richer understanding of the system under investigation, alternatives should be considered.

Seasonal meteorological dynamics

Meteorological data are important for NTD studies. Vapour pressure deficit (VPD) can be estimated from AVHRR 8 × 8 km TIR data [82] and MODIS 1 × 1 km LST data [83]. VPD, precipitation, and temperature can also be interpolated from weather station data [82,84]. The Worldclim 1 × 1 km climate summaries [84], which give long-term summaries of monthly precipitation, mean, minimum, and maximum temperature grids for 1950 to 2000, have been used widely in infectious disease studies (with 150+ citations accrued on Web of Science), including NTDs (e.g., [7,14]). In the future, more detailed datasets may become available. For example, Kilibarda et al. [85] published a proof-of-concept global, daily, 1 km-resolution temperature map for 2011 that integrated remotely sensed LST, in situ air temperature, and other remotely sensed covariates.

Earth observation: New directions

Recent developments in EO may be of future relevance in NTD epidemiology. First, sensors mounted on unmanned aerial vehicles (UAVs/drones) have recently gained increased interest for civilian applications [86]. We found no scientific papers that used UAVs for disease applications, although there is a rapidly developing literature for environmental surveys and urban mapping. Second, Light Detection And Ranging (LiDAR) is used to calculate the distance between the sensor and a target by measuring the response of a reflected laser pulse and can be used to build up highly detailed profiles of surfaces (up to 10 to 20 points per m²). Example applications include the development of detailed digital terrain models, 3D vegetation modelling, and the development of 3D models of urban areas [87]. We found very few examples in epidemiology or public health of applications using LiDAR, although Upegui and Viel [88] did use LiDAR for urban mapping in a public health context. Third, in the coming years, the Sentinel missions will be launched by the European Space Agency (ESA). Sentinel-2 (two satellites) will deliver 13 bands in the visible and near infrared (VNIR) and short-wave infrared (SWIR) part of the electromagnetic spectrum. Sentinel 2A was launched on 23 June 2015, and 2B is scheduled for launch in 2016 [89]. Spatial resolution will be 10 to 60 m with a repetivity of 5 days at the equator [90]. Sentinel-3 (three satellites, scheduled for launch in 2015 to 2020 [91]), will carry moderate-resolution sensors with a 1- to 2-day repetivity [92]. Fourth, we expect a wider range of in situ observations (e.g., weather, water level) from official sensor networks and private individuals, to be made available over the internet [93]. The information technology infrastructure to support this “sensor web” is developing rapidly [94]. Fifth, further useful data products may be obtained from integrating multiple remotely sensed and in situ data. An interesting example is provided by Soti et al. [66], who combined fine resolution Quickbird imagery, the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), and a hydrological model to simulate pond dynamics, which are relevant to mosquito breeding, in north Senegal. Walz et al. [8] call for similar approaches to support schistosomiasis research. Finally, land cover mapping continues to be an active area of research. Attention has turned to the provision of fine resolution global land cover maps [95], such as the 30 m-resolution GlobeLand30 [50,96]. GlobeLand30 was only released to the public in September 2014, and we could not find epidemiological studies that make use of it.

Spatial scale of EO data

EO data are constrained by the measurement process, specifically sampling (support, extent, sample density) and measurement error [101]. Each individual observation occupies a volume or area, referred to as the support (e.g., a 1 × 1 km-resolution MODIS pixel). For raster grid, the support is often referred to as the resolution. The support may also be defined in terms of a buffer drawn around a specific object (e.g., a clinic or other location attached to a disease incidence). A set of observations covers a defined extent (e.g., Queensland, Australia) and is gathered according to a sampling scheme [102]. The property or attribute (e.g., rainfall, NDVI) is subject to measurement error.

EO data may be aggregated or disaggregated to smaller or larger supports [101,103]. Of key importance is that aggregation or disaggregation should be documented explicitly [104] because it leads to new variables with specific statistical properties [105]. Different aggregations (support size and shape) may display different spatial patterns, leading to different conclusions about the variable of interest, a phenomenon known as the modifiable areal unit problem (MAUP) [105–107]. Furthermore, it is common to use multiple EO covariates with different resolutions and where the grids may not be aligned and need to be processed onto the same grid prior to use [55,98]. Such data have been described as incompatible spatial units or spatially misaligned data [108]. We advocate formal, properly documented approaches to aggregation, disaggregation, and spatial misalignments of the type described by Stasch et al. [104] and Atkinson [101], although the tools to implement this need further development.

The scale of variation of disease risk may be fine relative to the often-used moderate- to coarse- resolution EO data [102]. This places a limit on the resolution of the analysis and the resulting disease maps, because if the support is too large, important fine-scale spatial variation may be missed. The appropriate size of support is a function of the objective of study, the research goal, and the analysis method, and may be difficult to identify precisely [109,110]. The support size has received little explicit attention in NTD disease studies, although Soti et al. [59] studied the impact of spatial resolution on the identification of ponds, and Addink et al. [60] explicitly chose 2.4 m-resolution imagery because 0.6 m-resolution imagery was too heterogeneous to permit mapping of the burrows of the great gerbil (R. opimus), an important reservoir of the Bubonic plague bacterium. Danson et al. [111,112] and Pleydell et al.[6] investigated the impact of buffer size on the relationship between environmental drivers and echinococcus incidence, although they only investigated the buffer size and not the resolution (pixels size) of the associated RS image. Danson et al. [111,112] chose the buffer size that yielded the largest correlation, whereas Pleydell et al. [6] incorporated it as a parameter in their model. Of importance is that the choice of support of the EO data may influence the results. We did not find quantitative methods for choosing the resolution of EO data; however, the researcher needs to consider whether the support of their EO data reflects the variability in the area that they are studying.

Spatial studies in NTD epidemiology cover a range of extents, from a single village [113] or individual suburb (0.5 km²) [58] to a small island (140 km²) [15], countries [22], and the entire globe [14]. Studies over different extents often come to different conclusions about the environmental and socioeconomic drivers of disease. Simoonga et al. [5] noted that, for schistosomiasis, local studies tend to highlight socioeconomic drivers, whereas larger-extent studies highlight environmental drivers. Similar observations were made by Danson et al. [111] for human alveolar echinococcosis (AE). These conclusions are, however, not generalizable. Reis et al. [58] and Lau et al. [15] were able to identify environmental drivers of leptospirosis, including vegetation, elevation, and distance from refuse sites and sewers. In their study of Chagas disease and schistosomiasis, Kitron et al. [114] showed that disease transmission can be affected by factors outside the extent of the study area. Gracie et al. [115] presented an exploratory study showing that the variability in drivers of leptospirosis was associated with different spatial extents, but did not draw strong conclusions. Clearly, the extent of the study area and the support size should relate to the study objectives and the phenomenon being investigated. In particular, the extent is usually determined by the subject of the investigator’s research (e.g., a suburb in Salvador, Brazil [58]); however, explicit attention is still required here because these choices can affect the results.

The last 15 years have seen the development of sensors with a wide range of spatial resolutions; however, of the 40 papers identified (S2 Text), 27 did not justify the choice of the spatial resolution of the EO data. We recommend that researchers be explicit and consider the implications of these choices. Furthermore, we recommend that the development and application of quantitative methods to identify the relevant extent and support size for a given study objective need further attention.

Temporal scale of EO data

Spatial sampling considerations of support, extent, and sample density also apply in the temporal domain. Remotely sensed data typically represent a snapshot in time, whereas in situ data may have a defined temporal support (e.g., daily rainfall). Extent refers to the length of the time series. In epidemiological studies, it is common to use temporal aggregates as covariates [28]. For example, the summaries reported in the Worldclim dataset [84] cover 1950 to 2010, giving a temporal support of 50 years. The TFA summaries presented by Hay et al. [17] and Scharlemann et al. [75] cover 20 years (1981 to 2001) and 5 years (2001 to 2005), respectively. In computing and using these summaries, it is assumed the series is stationary (i.e., has a constant mean and variance) over the aggregated support. The consequence of violating this assumption is the estimation of temporal summaries that do not represent the entire aggregated support or the temporal extent of the disease data. This may lead to misleading conclusions about the relationship between disease outcomes and environmental variables. It is, therefore, important that the investigator properly justifies the temporal support of EO data. Considering the 40 identified articles (S2 Text), for 19 articles, there was a mismatch between the timing of the epidemiological and EO data, and only 16 articles explicitly acknowledged the assumption of temporal stationarity. To ensure that this is addressed properly, we recommend that researchers be explicit about the assumptions made and justify whether they are reasonable in the context of their investigation. Possible consequences are outlined below.

The above discussion raises questions for NTD studies. First, we must consider whether the EO data are really stationary over the aggregated support. Notwithstanding potential climate change, land use and land cover can change rapidly, particularly in fast-developing parts of the world [13,30,116]. Second, if the EO data are not stationary, the investigator needs to decide what a suitable temporal support would be. When choosing this, the modifiable temporal unit problem (MTUP) becomes important, particularly when the data show a seasonal periodicity [117]. Hence, both the temporal support and the starting time require careful evaluation, because choices made here may affect the modelled association between the disease data and the EO data. Third, studies tend to use multiple datasets that are defined over temporal supports of different or unspecified lengths and with different start and end dates. In some cases, the temporal dimensions of different EO datasets may not overlap each other or the epidemiological data. The measure of exposure to the environmental conditions may, therefore, be inaccurate, and that this may, in turn, affect the modelled association between the disease data and the EO environmental data. This was noted by Rogers et al. [68], but the effect on the eventual epidemiological analysis remains to be assessed. Finally, we note that modelling disease responses to temporally resolved covariates will require the development and application of spatiotemporal models that can support this [28].

Uncertainty in EO products

When evaluating spatial data, it is usual to consider the elements of position, time, and attribute. We might measure temperature (the attribute) at a particular location at a particular point in time. Any one of these elements might be uncertain [118]. A set of measurements may be processed further to yield an EO product. For example, the data used to compile the Worldclim EO product are both aggregated temporally and interpolated spatially. Furthermore, EO products based on RS also undergo complex processing, including radiometric and atmospheric correction and geometric correction onto a standard grid [63], as well as further processing that is dependent on the specific product. This will introduce further uncertainty into the final per-pixel attribute value.

Uncertainty may be evaluated by validation against a reference dataset, yielding a measure of accuracy [73,119,120]. Accuracy assessment for land cover mapping based on remote sensing has received extensive attention by Congalton and colleagues [121–123] and by Foody [119]. The reference data should be semantically similar to the data of interest, implying that they should describe the same attribute at the same spatial and temporal support. An extensive system has been developed for the validation of MODIS products [73,74]; for example, the NDVI image shown in Fig 1 has a stated accuracy of ±0.025 [124]. If reference data are not available, other approaches can be taken to evaluate uncertainty. For example, EO products produced using statistical interpolation yield a spatially explicit prediction variance, which is a measure of uncertainty [102,125]. Finally, uncertainty in the input data can be propagated through processing chains to yield a measure of uncertainty in the final result [126,127]. A possible consequence of inaccuracy in EO data is bias in the results of statistical epidemiological analyses. Consider, for example, that the MODIS Collection 5 land cover product (MOD12Q1) (used in, for example, [4,128]) is stated to have an overall accuracy of 75%, and individual classes may be classified less accurately [129].

Ambiguity is an important consideration for the interpretation of land cover maps, because land cover is conceptualized in different ways by different individuals and agencies [130,131]. Fritz and See [132] addressed this when comparing the MODIS land cover products MOD12Q1 and GLC2000 (see Table 2), which use different land cover definitions. They used fuzzy logic and expert opinion to harmonize the class legend of the two maps and to identify areas of uncertainty. Ambiguity is an important issue to consider when making comparisons between studies. We need to be clear whether EO data with the same label really represent the same quantity.

Uncertainty in prediction receives substantial attention in disease mapping studies; however, the uncertainty in the EO data is not usually considered. Of the 40 papers identified (S2 Text), 32 did not consider uncertainty in EO data, and the remaining eight gave only a partial assessment. We recommend further research to identify uncertainty in NTD studies that is associated with uncertainty in EO data, including the choice of EO data products.

Spatial data quality

The quality of EO data can influence the results of epidemiological analyses. An overview of spatial data quality is provided by Morrison and Veregin [133]. The International Organization for Standardization (ISO) defines data quality elements and procedures for evaluating the quality of geographic data. ISO 19157 [134] defines five quantitative SDQ elements: completeness, logical consistency, positional accuracy, temporal accuracy, and thematic (attribute) accuracy. Completeness refers to omission (missing data) and commission (additional data), and logical consistency refers to the adherence to rules governing the structure of the data [134–136]. Quantitative SDQ elements can be evaluated directly. For example, thematic accuracy can be evaluated against a reference dataset [134]. The quality evaluation may differ between the data producer and the user [135]. The producer evaluates the SDQ elements and determines whether the data meet their specified criteria. The user may have different criteria and may even wish to evaluate the SDQ elements against a different reference dataset.

Quality relates to the “totality of characteristics of a product that bear on its ability to satisfy stated and implied needs” [137]. Hence, to evaluate whether a dataset is fit-for-use, the user (the epidemiologist) needs to evaluate the above data quality elements together with the data specification (including support and extent) and information about the lineage, purpose, and usage. The provision of this information is supported by standards for metadata (ISO standard 19115 [138]) [135,136] as well as its technical implementation (ISO standard 19139 [139]). Lineage, purpose, and usage are often discussed in the context of SDQ [133] and were included as overview elements in earlier ISO standards [137,140], although ISO now consider these part of metadata. These may be used for indirect data quality evaluation based on external knowledge or experience. Historically, metadata standards have been provided by national agencies, although many are now transitioning to the ISO standards. For example, the US Federal Geographic Data Committee (FGDC) now encourages transitioning from the United States Content Standard for Digital Geospatial Information (CSDGM) to ISO 19115 [141].

Standard SDQ metadata have been criticized for being overly complicated, inaccessible, and insufficiently informative to enable a potential user to make a choice about the suitability of a given dataset for their application [37,142,143]. This situation may be exacerbated when the user is not an expert in geoinformation [130,131]. Users tend to use less formal information, such as availability, reputation, cost, and popularity, when making choices about datasets [37,143]. Herbreteau et al. [1] noted the same phenomenon when choices are made about which EO products to include in epidemiological studies, and advocated making choices on more scientific grounds. Tools to help users properly interpret SDQ information include software that allow users to visualize uncertainty and to explore different quality elements [37,144]. Searchable free-text descriptions, including reports from other users, have also been proposed [37,130,143].

Yang et al. [37] proposed that metadata should be organized hierarchically to describe different aspects of the data at different levels of spatial detail. Such an approach is adopted for the MODIS EO products [74], where there is a detailed validation and accuracy assessment that applies to the product as a whole. Furthermore, each individual image has its own specific quality evaluation, as illustrated in Fig 1. Bastin et al. [126] proposed a system that allows documented uncertainty to be propagated through subsequent analysis. Such a system would track processing of the data, including aggregation and disaggregation. Although challenging to implement in the NTD domain, this could bring benefits, including a clear and open documentation of processing steps, which is often lacking in spatial epidemiology papers, and a fuller assessment of uncertainty in epidemiological analyses. Furthermore, we could reason backwards to identify which uncertain EO data and which modelling choices an epidemiological analysis is most sensitive to [126,127]. This could also help to identify the utility of EO products for operational NTD healthcare management [32].

On a more basic level, we recommend that EO datasets and their processing should be clearly described by authors and that a check on this should be part of the peer-review process. Considering the 40 identified articles (S2 Text), the origin of the EO data was not clearly described in 21 articles, and the processing of those data was not clearly described in 22 articles. This journal already requires authors of observational studies in epidemiology to adhere to the STROBE (strengthening the reporting of observational studies in epidemiology [145,146]) statement. A proposal to extend this to include geospatial data was provided by Aimone et al. [35], although that requires further investigation. Finally, we found that the quality of EO data is given little attention in the papers that we reviewed. Of the 40 articles identified (S2 Text), 20 did not discuss the quality of the EO data, and only three papers discussed it thoroughly.

Clements et al. [32] stated that optimal use of EO is restricted by the expertise of the potential user and the difficulty of identifying potentially useful EO data. Restrictions of this nature could be addressed by augmenting widely used datasets, such as those given in Table 2, with user-centred SDQ metadata documenting their suitability for addressing standard questions for specific NTDs. Such an approach would require initial research investment but would benefit operational use in the long term. When an NTD project has specific requirements, an alternative would be to involve geoinformation experts in projects [3,8,34], either as technical consultants or research partners. Finally, there is an increasing demand for VFR RS data [5,32]; however, such data are expensive. We propose that the cost of VFR EO data should be justified in the context of the whole project cost.

Interaction between uncertainty in EO and NTD data

A full treatment of uncertainty in infection data lies outside the scope of this paper; however, we consider briefly how both the uncertainty in EO and NTD data may interact. We consider two examples concerning scale and positional uncertainty.

Schur et al. [76] and Schur et al. [55] mapped schistosomiasis prevalence in young people at a resolution of 5 × 5 km in west and east Africa, respectively. They then aggregated these maps to estimate endemicity for different administrative units [147]. Aggregation to different administrative units showed different patterns of endemicity and implied different intervention approaches. These studies emphasize three points: first, it is necessary to consider the appropriate spatial resolution for analysis (this was not addressed explicitly); second, there is a MAUP effect, where aggregating to different supports may show different patterns in the data (this was demonstrated by aggregating to different administrative units); finally, the organization of administrative and decision-making units may influence the final map and have consequences for intervention planning. A possible consequence is that localized areas of high endemicity may not be addressed properly.

Cressie and Kornak [148] presented two models of positional uncertainty. Under the coordinate-positioning (CP) model, position is determined in advance but the actual measurement is taken at a different location, for example, due to the use of an imprecise positioning instrument. Under the feature-positioning (FP) model, the attribute is recorded first and a location is assigned later. CP and FP both lead to the response variable being linked to the wrong environmental covariate values [149] but require different solutions [148]. Cressie and Kornak [148] demonstrated a significant effect on geostatistical estimation and prediction and proposed a model to adjust for CP. They did not address FP.

Positional uncertainty has received some attention with respect to species distribution modelling (SDM) in ecology. Here, FP is relevant because animal species are first observed and then later assigned a location. Osborne and Leitao [150] investigated the effect of positional uncertainty in the covariate and the response variable. They introduced a random error into the location of the response variable but a systematic error into the location of the covariate layers. They found that the SDM accuracy was more sensitive to error in the response variable, although they noted that the nature of the errors was quite different. Furthermore, the magnitude of the random error was larger than the systematic error. Naimi et al. [151] concluded that the effect of positonal uncertainty is largest where the range of spatial auto correlation in the covariates is more than three times the standard deviation of the positional uncertainty. Naimi et al. [152] used local indicators of spatial autocorrelation to identify locations where positional uncertainty had a strong effect on species distribution modelling. As with the ecology example, the FP model is relevant in the infectious disease case. This may be a particular problem for historic datasets when precise location data were not gathered and the location was inferred later [65,102]. Additional complications arise because the assigned location (e.g., a home or school) may not be the same as the location where an individual or a group of individuals is exposed to infection [65]. We could not find studies that investigated the effect of positional uncertainty on infectious disease modelling, and we concluded that simulation studies to investigate this effect would be worthwhile.