WO2015132208A1

WO2015132208A1 - Method for the profiling of pests and for the determination and prediction of associated risks and means for adapted pest control

Info

Publication number: WO2015132208A1
Application number: PCT/EP2015/054320
Authority: WO
Inventors: Guy HENDRICKX; Els DUCHEYNE; Veerle VERSTEIRT
Original assignee: Avia-Gis
Priority date: 2014-03-03
Filing date: 2015-03-02
Publication date: 2015-09-11

Abstract

The present invention relates to an integrated precision pest management system to reduce the risks associated with vector-borne diseases in a given area. The method is applicable to any tropical or non-tropical setting requiring the prevention of disease transmission through pest control or the reduction of the nuisance pests that may affect any defined group of people, animal or plants within any given natural, private or public area. In particular, the method of the invention protects visiting travellers as well as resident personnel living in tropical resorts or in camps or villages in the surrounding area.

Description

METHOD FOR THE PROFILING OF PESTS AND

FOR THE DETERMINATION AND PREDICTION OF ASSOCIATED RISKS AND MEANS FOR ADAPTED PEST CONTROL

Technical field of the invention

The technical fields of the present invention relate to pestology, public health, veterinary, agronomy, environmental technology, meteorology, space-time information systems (STIS), geographical information systems (GIS), sensors technology, remote sensing, and information and communications technology.

Technical background of the invention

Vector-borne diseases are infections transmitted by the bite of infected arthropod species, such as mosquitoes, ticks, triatomine bugs, sandflies, blackflies, and many others [1 ]. Disease transmission depends on the interplay of several factors of which the most important are: (i) presence, density, and activity of the vector species; (ii) pathogen circulation, replication in vector, host, and reservoir; (iii) frequency of the contact between vector and host. Transmission can thus be restricted by controlling the vector through its elimination from the area or by keeping its population level below a certain threshold. A review of the methods to control malaria vectors is for instance provided by Walker (2002) [2]. Walker describes how interventions at various factors contribute to the control of these vectors.

Environmental management, which involves a physical change in the environment through the prevention, reduction, or elimination of potential breeding sites, is considered a long-term sustainable solution. Keiser et al. (2005) [3] performed a systematic literature review and emphasise that environmental management is highly effective in reducing morbidity and mortality.

Larvicidal treatment by means of biological (Bacillus thuringiensis israeliensis - Bti and Bacillus sphaericus - Bs) compounds is a second modus operandus for the control of vectors. Unfortunately, many environmental factors can reduce the efficacy or effective life span of Bti and Bs products. Natural breakdown or inactivation processes are accelerated by heat, ultraviolet light, and water with high organic matter (Consoli et al. 1995 [4]; Lacey & Lacey 1990 [5]). Bti and Bs products may also fail to control anopheline larvae due to the tendency of spores to sink below the surface level where larval feeding occurs (Kroeger et al. 1995 [6]; Orduz et al. 1995 [7]). At present, weekly applications are often recommended to deal with the problem of their short effective life spans, although such frequent applications may not be economically feasible in some circumstances (Kumar et al. 1995 [8]). Improved slow-release formulations may help to solve this problem, but researchers are also exploring the possibility of genetically modifying other bacteria common in mosquito breeding sites to produce Bti or Bs toxins (Orduz et al. 1995 [7]). A potential negative side effect of larvicidal treatment is on the biodiversity of larger animals such as birds. Poulin (2012) [9] and Poulin et al. (2010) [10] indicated that the use of Bti negatively impacts breeding bird populations in the Camargue, South-France. Similarly, Kamata et al (2010) [1 1 ] demonstrated the adverse impact of chemical treatment on avian eggs reproduction. Phytotoxines are an alternative to the use of biological treatment of habitats but are currently not applicable on an operational scale (Ghosh et al, 2012) [12].

Adulticidal treatment through chemical compounds is often performed through the use of pyrethroids applied to netting. Unfortunately, resistance to these chemical compounds is increasing (Butler, 201 1 [13]; Ranson, 201 1 [14]) and this may result in an increase of malaria incidence in the near future. Alternative measures that do not involve chemical treatment include for instance mosquito and insect bite resistant nonwoven fabrics as claimed in patent application WO2013/009769 (Nanosyntex, Inc).

Whereas the quest for new pesticides continues, spatio-temporal modelling approaches can provide useful information to optimise the existing control strategies. Yang et al. (2008) [15] describe the impact of environmental factors on the abundance of tropical insects in Northern Australia and model the dynamics through a growth function. Similar models have been developed for other disease vector or nuisance species (Hanafi-Bojd, et al., 2012 [16]). Eisen & Lozano-Fuentes (2009) [17] indicate that many different models have been developped, yet these are mainly confined to the realm of the academic institutions and still need to find their way into operational decision support tools.

A major obstacle is the data collection feeding these spatio-temporal models. The use of sensors within the domain of agriculture has many applications (Wang, Zhang, & Wang, 2006 [18]). An example of pest monitoring can be found in patent application WO2002/069234 (Mafra-Neto Agenor) but does not describe a system for disease or nuisance vector population dynamics monitoring and beyond into the control of these vectors.

WO2001/95163 (AG-Chem Equipment Company, Inc.) discloses a system and method for creating application maps for site-specific farming.

WO2003/084320 A1 (Ecolab Inc.) discloses a method and apparatus for automatic pest trap report generation and for recording additional trap parameters. The claimed method basically counts pests collected in to the trap and generates a report therefrom. The method of WO2003/084320 A1 does not involve a registration of the micro-environmental key pest markers that will impact the abundance of the pest species nor uses this information as a pest marker index for control activities to reduce population size below nuisance level.

WO201 1/150319 A2 (Geostellar, LLC) discloses a system and method for geomatic modelling of a diverse resource base across broad landscapes.

US2003/0040895 A1 (Wisconsin Alumni Research Foundation) discloses a method and system for calculating the spatio-temporal effects of climate and other environmental conditions on animals. In particular, the method is directed to specific individual temperature-dependent growth models for the preservation of endangered and rare animal species that assess the impact of temperature changes due to global change on their physiology and how these can be used to improve their wellbeing. Since the methods of US2003/0040895 A1 are limited to temperature markers, they are not able to provide a system that can discriminate the key markers affecting the abundance level of pest species causing nuisance. Consequently, the methods of US2003/0040895 A1 are not able to provide control activities to reduce population size below nuisance level.

US6853328 B1 (Guice et al.) discloses a method and system for monitoring and controlling airborne harmful biota, particularly insects.

The state of the art methods for the determination and prediction of pest-associated risks are based on the limited data of presence and absence of a given pest. Fewer methods collect pest abundance data and make forecasts therefrom.

To the best of the knowledge of the inventors, there is no integrated system in the state of the art for the profiling of pests, for the determination and prediction of associated risks, and for an adapted vector control plan, that combines in situ ground measurement and remote sensing, which is more precise, requires less toxic treatments like the use of pesticides, optimally protecting the health of humans and animals in a given area, particularly increasing the environmental safety of tropical areas.

Summary of the invention

An objective of the present invention relates to profiling the pest population in a given space and for a given time span.

An objective of the present invention relates to providing the phenotypic and optionally the genotypic profile of a pest population in a given space and for a given time span.

An objective of the present invention relates to providing a system for the determination and prediction of risks associated with pests in a given space and for a give time span, which system is based on spatio-temporal data collected from in situ ground measurements and remote sensing. An objective of the present invention relates to providing an integrated method for the determination, prediction, and control of risks associated with pests in a given space and for a given time span.

An objective of the present invention relates to any one of the systems or methods mentioned above that acquire spatio-temporal data continuously.

An objective of the present invention relates to providing a method for the collection of spatio-temporal data from pests and pest markers in a given space, which collecting method is cost-effective and easy to implement.

Another objective of the present invention relates to providing a system for the determination and prediction of risks associated with pests in a given space and for a given time span, which system is robust.

Another objective of the present invention relates to providing a system for the determination and prediction of risks associated with pests in a given space and for a give time span, which benefits from data integration from other spaces.

Another objective of the present invention aims at providing a system for the profiling of pest and for the determination and prediction of risks associated with said pests in a given space and for a given time span, which systems uses advanced modeling.

Another objective of the present invention aims at preventing, stopping, or minimizing the increase of insecticide resistance in pest populations.

Another objective of the present invention relates to providing tailored or adapted pest control measures for a specific space in a given time span in order to decrease the risks associated with pests at non-significant levels.

Another objective of the present invention relates to providing tailored or adapted pest control measures for a specific space in a given time span in order to decrease the risks associated with pests, these control measures being ecologically friendly or with minimal environmental impact, environmentally cost-effective, or economically cost-effective.

Another objective of the present invention relates more specifically to providing tailored or adapted pest control measures for a specific space by treating the right spot within the right time window.

The present invention relates to a method of profiling one or more pests in a geographical volume comprising:

a) fine-scale characterization of the landscape elements of a geographical volume by integrating data from remote and ground sensors, wherein fine-scale refers to a spatial resolution equal to or less than 30 meters;

b) stratifying said landscape elements of said geographical volume into relevant pest habitat sites; c) sampling pest habitat sites within the strata obtained from step b); d) obtaining spatio-temporal data of pests and pest markers in said geographical volume; wherein the pest data are obtained from pest specimens collected, by means of traps or any other device, from the sampled pest habitat sites of step c), which pests are analyzed for phenotypic and optionally genotypic characterisation, thereby resulting in said pest data; wherein the pest marker data are obtained from ground sensors obtaining information from the sampled pest habitat sites of step c), from remote sensors, from forecasts, or any combination thereof;

e) processing the spatio-temporal data from pest and pest markers obtained in step d);

f) computing one or more sets of models using the processed data from step e), thereby obtaining a profile of said one or more pests and optionally a prediction of said one or more pest population dynamics.

In one embodiment, the method of the present invention further comprises the determination and optionally the prediction of the risks associated with the profiled pest(s) by means of computing one or more sets of models using the profile data of said one or more pests and optionally the prediction data of said one or more pest population dynamics.

In one embodiment, the method of the present invention further comprises establishing a pest prevention or treatment plan, said plan comprising applying biological means, chemical means, physical means, or any combination thereof, to reduce the pest population under a defined threshold.

The present invention further relates to a system for profiling one or more pests in a geographical volume and optionally determining or predicting the risks associated with the profiled pest(s), the system comprising at least one processor and an associated storage medium containing a program executable by means of said at least one processor, said system comprising configured different software code portions, which when executed provide a profile of one or more pests in a geographical volume and optionally determine or predict the risks associated with the profiled pest(s).

The present invention further relates to a computer program product executable on a computer device and comprising software code for executing the methods according to present invention, when run on said computer device.

The present invention allows to continuously and remotely monitor the profile of one or more pests, based on the monitoring of key pest markers. With the retrieved information, control activities can be designed remotely and executed locally. The methods of the present invention, by integration of data from different geographical volumes, is fine-tuned on a frequent basis, and is able to provide improved treatment solutions to these different geographical volumes.

Brief description of the drawings

Figure 1 shows an integrated scheme of the methods according to the present invention for profiling the pests in a geographical volume, predicting the pest population dynamics, determining the risks associated with the profiled pests, and establishing a target pest control plan to reduce the risks associated with the profiled pests under an acceptable threshold. SAT refers to satellite imagery, and UAV refers to unmanned airborne vehicle.

Description of the invention

b) stratifying said landscape elements of said geographical volume into relevant pest habitat sites;

c) sampling pest habitat sites within the strata obtained from step b);

d) obtaining spatio-temporal data of pests and pest markers in said geographical volume; wherein the pest data are obtained from pest specimens collected, by means of traps or any other device, from the sampled pest habitat sites of step c), which pests are analyzed for phenotypic and optionally genotypic characterisation, thereby resulting in said pest data; wherein the pest marker data are obtained from ground sensors obtaining information from the sampled pest habitat sites of step c), from remote sensors, from forecasts, or any combination thereof;

In one embodiment, the method of the present invention further comprises the subsequent monitoring of the profiled pest(s) by re-sampling pest habitat sites by means of reallocating the sampling sites; by modifying the number of pest habitat sites, pest data, or pest marker data to be measured; or by modifying the measurement frequency. In one embodiment, the method of the present invention further comprises the determination and optionally the prediction of the risks associated with the profiled pest(s) by means of computing one or more sets of models using the profile data of said one or more pests and optionally the prediction data of said one or more pest population dynamics.

In one embodiment, the method of the present invention further comprises the integration with other geographical volumes by means of shared anonymous data collation of geographical volumes with similar pests or pest markers.

In one embodiment, in any one of the methods of the present invention, any one of the geographical volumes is selected from any discrete volume in earth, preferably a geographical volume in tropical latitudes, more preferably a tropical resort.

In one embodiment, in any one of the methods of the present invention, the remote sensors are carried upon satellites, Manned Areal Vehicles including fixed wing aircrafts and helicopters, Unmanned Areal Vehicles or drones including fixed wing crafts and helicopters, areal balloons, any other suitable carrier enabling a bird perspective of the measured geographical volume, or any combination thereof.

In one embodiment, in any one of the methods of the present invention, the ground sensors are selected from biosensors, mechanical sensors, optical sensors, the human eye, acoustical sensors, moisture sensors, temperature sensors, pressure sensors, chemical composition sensors, radioactivity sensors, or any other sensor allowing the biological, chemical, or physical measurement of a pest marker, and any combination thereof.

In one embodiment, in any one of the methods of the present invention, the pest habitat site is a natural or man-made element that is relevant to the lifecycle and population dynamics of said one or more pests, and may be selected from topographic features selected from slope, altitude, agricultural or natural land cover type; ecotones; soil type; permanent or semi-permanent water elements like ponds, ditches, troughs, drainage lines, streams, rivers, canals, trenches, furrows; man-made constructions; biological organisms selected from hosts, competitors, predators, pollinators, prey; and any combination thereof.

In one embodiment, in any one of the methods of the present invention, the pest marker is selected from temperature profiles, tide profiles, rainfall, humidity, wind velocity and direction, air pressure, air pollutants including pesticides, solar radiation, infrared radiation, remotely sensed derived indices, water quality indices, occasional infection of animal hosts, epidemic breakthrough, infestation of crops, and any combination thereof. The present invention further relates to a system for profiling one or more pests in a geographical volume and optionally determining or predicting the risks associated with the profiled pest(s), the system comprising at least one processor and an associated storage medium containing a program executable by means of said at least one processor, said system comprising:

- first software code portions configured for characterization of the fine-scale landscape elements of a geographical volume by integrating data from remote and ground sensors, which when executed characterize fine-scale landscape elements of a geographical volume by integrating data from remote and ground sensors, wherein fine-scale refers to a spatial resolution equal to or less than 30 meters;

- second software code portions configured for stratifying said landscape elements of said geographical volume into relevant pest habitat sites, which when executed stratify said landscape elements of said geographical volume into relevant pest habitat sites;

- third software code portions configured for sampling pest habitat sites within the strata obtained from the executed second software code portions, which when executed sample said pest habitat sites within the strata obtained from the executed second software code portions;

- fourth software code portions configured for obtaining spatio-temporal data of pests and pest markers in said geographical volume; wherein the pest data are obtained from pest specimens collected, by means of traps or any other device, from the sampled pest habitat sites from the executed third software portions, which are analyzed for phenotypic and optionally genotypic characterisation, thereby resulting in said pest data; wherein the pest marker data are obtained from ground sensors obtaining information from the sampled pest habitat sites from the executed third software portions, from remote sensors, from forecasts, or any combination thereof; which when executed obtain said spatio-temporal data of pests and pest markers in said geographical volume;

- fifth software code portions configured for processing the spatio-temporal data from pest and pest markers from the executed fourth software code portions, which when executed process the spatio-temporal data from pest and pest markers from the executed fourth software code portions;

- sixth software code portions configured for computing one or more set of models using the processed data from the fifth software code portions, which when executed provide a profile of said one or more pests and optionally a prediction of said one or more pest population dynamics; and

- optionally seventh software code portions configured for computing one or more set of models using the computed data from the sixth software code portions, which when executed provide a determination or prediction of the risks associated with the profiled pest(s).

In one embodiment, the system of the present invention comprises one of the following: a personal computer, a portable computer, a laptop computer, a netbook computer, a tablet computer, a smartphone, a digital still camera, a video camera, a mobile communication device, a personal digital assistant, a scanner, or a multi-function device.

The present invention further relates to a non-transient storage medium on which a computer program product is stored comprising software code portions in a format executable on a computer device and configured for performing the following steps when executed on said computer device:

c) sampling pest habitat sites within the strata obtained from step b);

d) obtaining spatio-temporal data of pests and pest markers in said geographical volume; wherein the pest data are obtained from pest specimens collected from the sampled pest habitat sites of step c) which are analyzed for phenotypic and optionally genotypic characterisation, thereby resulting in said pest data; wherein the pest marker data are obtained from ground sensors obtaining information from the sampled pest habitat sites of step c), from remote sensors, from forecasts, or any combination thereof;

f) computing one or more set of models using the processed data from step e), thereby obtaining a profile of said one or more pests and optionally a prediction of said one or more pest population dynamics; and

g) optionally determining or predicting the risks associated with the profiled pest(s). The present invention further relates to a computer program product executable on a computer device and comprising software code for executing the method according to any one of the embodiments of the present invention, when run on said computer device.

In one embodiment relating to the non-transient storage medium or another embodiment relating to the computer program product presented herein, the computer device is selected from a personal computer, a portable computer, a laptop computer, a netbook computer, a tablet computer, a smartphone, a digital still camera, a video camera, a mobile communication device, a personal digital assistant, a scanner, and a multi-function device.

Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure, in one or more embodiments. Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention, and form different embodiments, as would be understood by those in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.

In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration only of specific embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims.

Unless otherwise defined, all terms used in disclosing the invention, including technical and scientific terms, have the meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. By means of further guidance, definitions for the terms used in the description are included to better appreciate the teaching of the present invention.

The terms "comprising", "comprises" and "comprised of" as used herein are synonymous with "including", "includes" or "containing", "contains", and are inclusive or open-ended and do not exclude additional, non-recited members, elements or method steps. The terms "comprising", "comprises" and "comprised of" also include the term "consisting of".

The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges, as well as the recited endpoints.

Unless otherwise apparent from the context or implied meaning of a passage, the term "or" is inclusive, referring both to "and" and "or".

The term "profiling" refers to phenotypic characterization, genotypic characterization, taxonomic identification including pest species, variants, and strains thereof, presence, abundance, distribution, pathogen infection rates, resistance, vectorial capacity, nuisance capacity, population dynamics, and the like.

The term "pest" refers to a plant or animal detrimental to man or to his interests. Pests include, but are not limited to, insects, pathogens, weeds, molluscs, birds, mammals, fish, nematodes, and microbes. Pests of frequent concern include various types of insects and rodents. Subterranean termites are a particularly troublesome type of pest with the potential to cause severe damage to wooden structures. Likewise, other insects, such as bedbugs, are problematic.

The term "vector" refers to any agent (person, animal or microorganism) that carries and transmits an infectious pathogen from one living organism, referred to as a "host", to another. Example of disease-carrying vectors are mosquitoes (e.g. malaria, yellow fever, dengue, viral encephalitis, filariasis), houseflies (e.g. diarrhea, dysentery, conjunctivitis, typhoid fever, trachoma), cockroaches (e.g. cholera, salmonellosis, diarrhea, dysentery), lice (e.g. endemic typhus, pediculosis, relapsing fever, trench fever), bedbugs (e.g. severe skin inflammation), ticks (e.g. Rickettsial fever, tularaemia, relapsing fever, borreliosis, viral encephalitis), mites and fleas carried by rodents (e.g. bubonic plague, endemic typhus, scrub typhus, Rickettsial pox), and rodents (e.g. rat bite fever, leptospirosis, salmonellosis, melioidosis).

The malaria parasite is in particular transmitted by the Anopheles mosquitoes, the dengue virus by the Aedes mosquitoes, mainly Aedes aegypti, but also Aedes albopictus, Ae. polynesiensis or Ae. scutellaris. The chikungunya virus is transmitted by Ae. aegypti and Ae. albopictus.

The term "geographical volume" refers to any space with an animal, human or crop that needs to be protected against vector-borne diseases, and expanded to its surroundings. This geographical volume is selected from any discrete volume in earth, preferably a geographical volume in tropical latitudes, more preferably a tropical resort. In a preferred embodiment, the geographical volume is located in a tropical tourist resort, or places of economic value like farms, dense regions, industry vulnerable to vector-borne diseases like the food industry, and the like.

The term "fine-scale" refers to a spatial resolution equal to or less than 30 meters.

The term "landscape element" refers to anything belonging to a landscape, either natural or human-made. It includes natural elements like vegetation elements, geological elements including rocks, land-forms such as (ice-capped) mountains, hills, water bodies such as rivers, lakes, ponds, the sea, agricultural land, as well as man-made elements such as retaining walls, buildings, bridges, sidewalks, driveways, patios, pools, ponds, and the like. The term "integrating data" refers to combining data residing in different sources and providing a unified view of these data. Integrating data may be performed by a variety of procedures including object recognition, data fusion, mosaicking, up- and downscaling, pixel aggregation, pixel un-mixing, and the like.

The term "remote sensor" refers to any type of photographic analogue or digital camera, analogue or digital video camera, or any combination thereof, carried on board satellites, manned areal vehicles including fixed-wing aircrafts and helicopters, unmanned areal vehicles or drones including fixed-wing crafts and helicopters, areal balloons, and any other carrier or platform enabling a bird perspective of the measured geographical volume.

An alternative to remote sensing is through web-based data exchange protocols with in situ users.

The term "ground sensor" includes, without being limited to, biosensors, mechanical sensors, optical sensors, the human eye, acoustical sensors, moisture sensors, temperature sensors, pressure sensors, chemical composition sensors, radioactivity sensors, and any combination thereof.

The term "stratifying" refers to the process of dividing members of a population into homogeneous subgroups before sampling. The resulting strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded. Then, according to the method of the present invention, simple random sampling or systematic sampling is applied within each stratum. This often improves the representativeness of the sample by reducing sampling error. It can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population. Applied to the present invention, stratifying includes i) identifying relevant pest habitat sites like ecotone, water, grass, trees, and the like; and ii) assigning weights to each of these landscape mapping elements.

The term "pest habitat site" refers to a natural or man-made element that is relevant to the lifecycle and population dynamics of said one or more pests, and may be selected from topographic features selected from slope, altitude, agricultural, and natural land cover type; ecotones; soil type; permanent or semi-permanent water elements like ponds, ditches, troughs, drainage lines, streams, rivers, canals, trenches, or furrows; man-made constructions; biological organisms selected from and without being limited to hosts, competitors, predators, pollinators, prey; any other suitable pest habitat site known by the person skilled in the art; and any combination thereof.

The pest habitat sites are characterized by a set of pest markers.

The term "sampling" refers to the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. The step of sampling according to the present invention includes selecting an optimal quantity and an optimal distribution of pest habitat sites according to cost-efficiency criteria by assigning a number of sampling sites per element (like density), and randomly distributing these sampling sites (sampling sites include for example the traps and the environmental sampling means). For each type of pest, the life cycle is evaluated and tailored sampling sites (like traps) are selected. For each pest marker, different parameters are evaluated with ground sensors and remote sensors, and the adapted sensors are selected.

The term "spatio-temporal data" refers to data associated or referring to space and time.

In one embodiment, the spatial data are taken from a geographical volume or space of 1 km² to 100 km², preferably from 1 ha. to 1000 ha., more preferably from 0.5 ha. to 500 ha.

In one embodiment, the temporal data are taken at a time interval selected from 1 hour, 2 hours, 3 hours, 4 hours, 6 hours, 8 hours, 12 hours, 24 hours, 2 days, 3 days, 4 days, 5 days 7 days, 2 weeks, 3 weeks, 4 weeks, 6 weeks, 2 months, 3 months, 4 months, 5 months, 6 months, 1 year, 18 months, 2 years, 30 months, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 1 1 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years, 18 years, 19 years, and 20 years.

The term "pest marker" refers to a measured characteristic, which is used as an indicator of some biological state or condition related to a pest. Examples of pest markers include, without being limited to, temperature profiles, tide profiles, rainfall, humidity, wind velocity and direction, pressure, air pollutants including pesticides, solar radiation, infrared radiation, remotely sensed derived indices, water quality indices, occasional infection of animal hosts, epidemic breakthrough, infestation of crops, and the like, and any combination thereof.

Selection of the pest markers is usually based on their importance as a predictive factor for a given risk. The pest markers are selected from intrinsic and extrinsic markers.

Intrinsic markers include, without being limited to, insecticide resistance, pathogen presence, vectorial capacity, infection rate, incubation time, and population dynamics.

Extrinsic markers include, without being limited to, i) environmental variables measured using remote sensors, ground sensors, weather forecasts like wind direction, temperature, barometric pressure, rain conditions, and the like, as well as ii) information on hosts, flora, fauna, day length or photoperiod, agricultural practices, land composition, presence of attractants and repellants, and the like.

The term "pest specimen" refers to a sample of one or more pests, usually collected with a trap. The pest can be alive or dead, and of at any live-cycle stage (e.g. egg, larvae, nymph, imago, to name a few). The term "traps or any other device" refers to a device used to catch and possibly kill one or more pests, as defined herein.

Insect traps of various types are well known. A particularly common trap type, particularly for flying insects, comprises an insect attractant means, such as, for example a UV light source and an insect trapping means, such as, for example a non-return cage, an adhesive board or paper contained in a housing. The flying insects are attracted to the trap, enter the housing through openings and get caught on or in the trapping means. To maintain efficiency of capture, the non-return cage, adhesive board or paper, need to be regularly replaced and the trap cleaned. The non-return cage, adhesive board or paper also needs to be inspected and records kept. The lights also need to be cleaned as insects get "welded" to the bulbs and in any case, the lights have a limited life span. The lights require a power supply through rechargeable batteries, or connection to (a) solar cell(s), power generator or power grid.

Further examples for trapping insects include sticky bait stations, high voltage electrocutes, electrostatic powders, magnetic powders, powder paralysing powders.

Live traps are designed to trap a rodent, typically a mouse, within an enclosure without having to poison or immediately kill the rodent. Such traps typically include a trap mechanism in the form of a ramp and a trap door that closes behind the rodent as the rodent moves over the ramp. The housing is designed to inhibit non-targeted animals (e.g., dogs, cats) and unauthorized individuals (e.g., children) from accessing the interior of the trap. Some traps may also have a glue board or other adhesive on the floor of the trap to restrict movement of the rodent once the rodent is trapped therein, or a spring-loaded bar, which can be held in tension by means of a lever that is activated by the rodent stepping onto the trap to eat bait.

Traps may be connected to devices, which automatically monitor trap entry and/ or identify pest types/ species entering the trap. Such monitoring can be recorded or automatically transmitted by any means to a remote observer.

The term "forecasts" or "forecasting" refers to the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example may be the estimation of some variable of interest at some specified future date.

The term "processing" refers to data clean up, data aggregation, data extraction, data interpolation, splining, processing of satellite image time series, and the like.

The term "computing" refers to any goal-oriented activity requiring, or benefiting from computers. For example, computing includes designing, developing and building hardware and software systems; processing, structuring, and managing various kinds of information; doing scientific research on and with computers; making computer systems behave intelligently; and creating and using communications. Computing includes data relevance assessment, data interpolation, data extrapolation, data mining, statistical modelling, and the like.

The term "models" include any statistical or non-statistical method including methods from data mining and artificial intelligence and any combination thereof that allow inferring a causal relationship between the pest and the pest markers and therefore allowing the i) determination of the profile of a pest, ii) the prediction of the profile of the pest on sites where there is no observation, iii) the prediction of the pest population dynamics, iv) the prediction of the risks associated with the profiled pest. The quality of the models is assessed through accuracy indices calculated on an independent pest marker and pest data set. These can be sensitivity, specificity, kappa index of agreement, area under the curve of the receiver operation characteristic, percentage of correctly classified pixels, producer's accuracy, user's accuracy and the like. Preferably, the accuracy of the output model for presence of a given pest is at least 0.7 AUC (area under the curve).

In one embodiment, an ensemble of a generalized linear model and a linear discriminant analysis may be used as a model.

The term "prediction" refers to either i) the prediction of an event within the geographic volume at a different place from that one or those ones where a measurement was conducted (i.e. filling gaps in maps), or ii) a statement about what will happen or might happen in the future under specific conditions.

The term "pest population dynamics" refers to the study of short-term and long-term changes in the size and age composition of a pest population, and the biological and environmental processes influencing those changes. One common mathematical model for population dynamics is the exponential growth model. With the exponential model, the rate of change of any given population is proportional to the already existing population.

The term "risk" refers to, in the context of pest vectors, to any one of a risk to transmit disease to humans, animals or plants, a risk to be a nuisance for humans or animals (causing annoyance, irritation, allergies, and the like), a risk to destroy crops, a risk to destroy 'ornamental' plants, and any one known by the person skilled in the art.

For each risk there is a threshold or cut-off value under which the risk is 'acceptable' for a specific condition. In this regard, identification of the pest population dynamics is advantageous, i.e. to keep population peaks under a specific or defined threshold.

The term "defined threshold" refers to an agreed value under which the performed pest control is cost efficient; or has an acceptable environmental impact; sufficiently reduces or eliminates nuisance; sufficiently reduces or eliminates disease transmission.

The term "shared anonymous data collation" refers to the assembly of information in a standardized way. The main advantage of the shared anonymous data collation is that it allows using collected pest and pest marker data from volumes that have the same pests or that are similar in a k-dimensional pest marker space. The said collated data is used to improve the computation as defined in step f) of the method according to the present invention. The said collated data is used to improve the computation as defined in step f) of the method according to the present invention.

One way to implement the present invention would for instance start by performing a Baseline Landscape Mapping, which includes all natural and man-made elements relevant to the lifecycle of the pest of interest. Technical means for performing this baseline landscape mapping include satellite information (RS): high and very-high satellite imagery with which landscape elements and categories are recognized automatically (pixel or object-based) using state of the art tools and software; locally a drone (UAV) may be used to take centimeter-resolution pictures and refine or replace material obtained from satellite imagery. A ground survey may be also conducted to validate and correct the results obtained using RS and UAV; and any other relevant available or newly developed method or technique may be used to further fine-tune the obtained results.

Next step would be performing a Baseline Pest Habitat Mapping for the pest species of interest. This mapping includes the selection, categorization, and ranking of landscape natural and man-made elements relevant to the lifecycle of the pest of interest. The selection, categorization and ranking of the elements may be performed for instance according to risk of occurrence of the pest; risk of occasional exposure by visitors; and risk of occupational exposure by workers.

Following, a Baseline Pest Survey of the pest of interest would be performed based on the baseline landscape and pest habitat maps. As a result of this survey, an integrated sampling strategy is designed. This integrated sampling strategy takes into consideration the minimum number of samples required to capture landscape variability and to enable the development of spatial risk models. The integrated sampling strategy in practice results in a selection of a subset of each relevant landscape category. Locally state of the art trapping techniques are used to trap the pest of interest at planned trapping sites. The trapped pest is identified and analyzed. The end result is a Baseline Map of Observed Pest Occurrence.

Then, based on the Baseline Map of Observed Pest Occurrence, spatial distribution and abundance models for the pest species of interest are developed using state of the art spatial modeling techniques and a database of explanatory spatial variables. Since only a subset of the landscape (map objects or pixels) has been sampled, the models aim at predicting the occurrence and abundance of each pest in non-sampled map objects or pixels, i.e. gaps are filled in the observed pest distribution maps. The outcome is a Baseline Spatial Pest Risk Model for the pest of interest which quantifies pest presence, absence, and abundance for each map object or pixel. Further, based on the computed spatial pest risk model, a Baseline Pest Control Plan is designed for the pest of interest. This control plan takes into consideration the optimal use of different measures including state of the art control products, state of the art dispensing methods and tools, environmental efficiency (the most adequate product, at the most adequate quantity or dose, in the most convenient administration method, in the most adequate location and convenient frequency); integrating specific control activities of the different contracted pests based on historical data of successes and drawbacks; and quality or efficacy test of control measurements, like how effective was the control and possible improvements.

It is usually unlikely that in any given situation the local eradication of a pest can be achieved, e.g. due to permanent reinvasion pressure. Therefore the aim of the Baseline Pest Control plan is to reduce pest population levels below thresholds at which they are a nuisance or a risk for transmitting diseases. Part of this plan may also be to prevent contact with a given pest.

Continuous habitat sensing may be further implemented in order to maintain pest populations below transmission risk or nuisance level. Therefore, a continuous monitoring is established of pest population dynamics and the environmental factors which may trigger the increase and fluctuations in pest populations. Based on the previous data of the Baseline Pest Habitat Maps, Observed Pest Occurrence Maps, and Pest Risk Models, there is a selection of a cost-efficient network of in situ sampling points to monitor on a regular basis environmental factors affecting the pest of interest. These may include and are not limited to temperature, air humidity, soil moisture, rainfall, evaporation, transpiration, level of water in water bodies, measures of water quality such as pH and turbidity, vegetation activity, and so forth. The continuous habitat sensing makes also use of a variety of techniques for the in situ and remote monitoring of key environmental variables including weather forecasts, meteorological satellites, and any other relevant available or newly developed method or technique enabling the measurement of relevant environmental factors.

Continuous Pest Surveillance may be further implemented in order to maintain pest populations below transmission risk and/or nuisance level. Therefore, there is a continuous monitoring of pest population dynamics and the environmental factors, which may trigger the increase and fluctuations in pest populations. Based on the previous data of the Baseline Pest Habitat Maps, Observed Pest Occurrence Maps, and Pest Risk Models, there is a selection of a cost-efficient network of sampling points to monitor the population dynamics of the pest of interest. In this process care is taken to monitor all relevant live stages as applicable for each pest, e.g. for mosquitoes: egg positioning, larval development, adult occurrence. Pest populations are monitored using state of the art trapping techniques adapted to the pest species of interest. Further modeling may be applied, like for instance Generic Spatio-temporal Pest Dynamics Modeling, which combines in situ and remote data comprising expert knowledge on population dynamics of pests, existing published knowledge on population dynamics of pests, in situ and remotely sensed pest habitat monitoring, and in situ pest population dynamics monitoring, which combines these into a powerful model using state of the art modeling techniques. These models describe pest activity in space and time according to varying environmental and seasonal settings.

Additionally, Pest Population Dynamics Forecasts may be predicted based on the outcome of the Generic Spatio-Temporal Pest Dynamics Model with the timely information on Habitat Sensing and Pest Surveillance gathered in specific situations to forecast pest population dynamics in each specific setting. Since different pest stages respond differently to specific environmental conditions for their development, it is possible for the present invention to for example forecast whether massive hatching of mosquito adults, which may cause a nuisance peak, is to be expected given specific environmental settings, e.g. draught period followed by rain showers.

Consequently, a Sequential Pest Control Plan may be developed and applied based on the Pest Population Dynamics Forecasts. Whilst the Baseline Pest Control Plan mainly takes into consideration spatial aspects, this plan also includes a strong temporal component: in addition to 'where' and 'how' to conduct 'which' type of control, also and most importantly 'when' and 'how frequent' to do it to achieve the best results and maintain pest population below critical levels. This control plan takes into consideration the optimal use of different measures including state of the art control products, state of the art dispensing methods and tools, environmental efficiency (the most adequate product, at the most adequate quantity or dose, in the most convenient administration method, in the most adequate location and convenient frequency); integrating specific control activities of the different contracted pests; and based on historical data of successes and drawbacks.

The following example is intended to illustrate the present invention in further detail and should not be interpreted as limiting it thereto.

Example

During the month of April, a tropical 18-hole golf resort in the lowlands of the Rift Valley in Kenya is visited. This resort is known to be in a malaria-risk area, a tropical disease transmitted by Anopheles mosquitoes. Malaria epidemics are usually reported in April (onset of the rainy season) but transmission occurs year-round. The female mosquitoes bite usually at dusk and during the night. The resort currently provides bed nets to its customers to avoid the clients being bitten at night, and repellents as protection during the day. In March, one month prior to the field visit, a Pleiades satellite image of the holiday resort with a spatial resolution of 0.5 m is ordered and processed. The satellite image is classified into the different land cover classes using unsupervised clustering to detect spectrally different classes. The resulting classified image consists of:

- Built-up elements:

o Man-made building

o Road

- Vegetation elements:

o Grass

o Bushes

o Tree savannah

- Water bodies:

o Swamps

o Ditches and drains

o Natural pools and puddles

o Man-made pools

The classified image is used to select ground validation sampling sites. Three strata are defined: Built-up (15% of image), Vegetation (50%) and Water bodies (35%). The optimal number of validation sites for the first baseline survey map amounts to 50 sample sites distributed proportionally according to the area covered by the three strata: 8 sampling sites in the built-up stratum, 25 sampling sites in the vegetation stratum, and 17 sampling sites near water bodies.

One entomologist travels from the research headquarters (HQ) to the resort in April. The entomologist brings (i) a habitat surveying kit containing three WXT520 weather stations connected to a data logger and transmitter for measuring temperature, rainfall, relative air humidity, wind speed and direction, and barometric pressure; (ii) a trapping kit containing 10 adult mosquito traps and 10 larval dippers of 350 ml; (iii) a morphological identification kit containing a stereoscopic binocular and a determination key; and (iv) a larval bioassay kit.

On day one the entomologist visits all identified land cover ground validation sites. Date, location, land cover type and a picture in all wind cardinal directions are stored using a dedicated "SensRiZK" app for Android. Survey data are synchronized to HQ through GPRS (General Packet Radio Service). Until synchronization is possible, these are stored on the smartphone. At HQ, the collected ground validation data are used to create the final supervised classification landscape map. The overall accuracy of the supervised

classification is 92% and the overall Kappa index of agreement 0.86. Based on the final land cover map, the mosquito sampling strategy is generated: the ten adult and larval traps are all distributed proportionally to the updated landscape map: 2 sites in the built-up stratum, 3 in the vegetation stratum, and 5 near the water bodies.

On day two, the entomologist starts the training of the local resort staff members. These local resort staff members will form the core of the Pest Control Team. The Pest Control Team is trained for trap placement and operation, trap content collection and storage of the samples for both adult and larval trapping techniques, and the placement and operation of the weather station.

On day three, all adult traps are placed according to the mosquito-sampling scheme by the trained local staff under supervision of the entomologist. The adult traps are active for 48 hours. One weather station is placed near an adult trap. Automatic data transmission from the weather station is set up and tested.

On day four, the larval sites are visited and dipped using the standardised dipper. Two weather stations are placed near larval sites. Automatic data transmission from the weather station is set up and tested.

The samples are analysed and the outcome shows that small water bodies exposed to direct sun have the most abundant population of larvae. The entomologist carries out a bioassay for the assessment of knockdown susceptibility on the day of collection using the mosquito larvae obtained from the collection sites. D-T₈₀-allethrin (pyrethroid) is used for the test. The larvae collected from each collection site are briefly identified on the day of collection, and fourth instar larvae of Anopheles gambiae s.l. are used for the susceptibility test. Each larva is individually placed in a glass vial with 20 ml of water. An emulsifiable concentrate of 90% d-T₈o-allethrin is diluted with water to obtain a 250-ppm solution. After releasing the larva, 32 or 8 μΙ of the solution is added in each vial to obtain a concentration of 0.4 and 0.1 ppm, respectively. Regardless of the total number of larvae collected, a maximum of 20 larvae from each site are used for each concentration regime. Knockdown of the larvae is observed for 30 min. Larvae that sink to the bottom of the glass vial and cannot swim, float, or are paralyzed are judged as knocked down larvae; the time to knockdown is recorded for each larva. After the test, each larva is placed in a 1 .5-ml plastic vial containing ethanol solution for identification at a later time. The median knockdown times (KT₅₀s), i.e., the time required for 50% knockdown, are scored according to the following 6 categories: 1 , <5 min; 2, 5-10 min; 3, 10-15 min; 4, 15-20 min; 5, 20-30 min; and 6, >30 min. The susceptibility index is calculated as the product of the scores at 0.1 and 0.4 ppm. Thus, mosquito larvae with susceptibility index of 1 are considered to be the most susceptible, and those with susceptibility index of 6 are considered to be the least susceptible to d-allethrin. Moribund larvae (presenting tremors, rigidity or inability to reach water surface) are considered as dead. The tested larvae show a significant decreased susceptibility to d-T₈₀-allethrin. The mortality range is only 10% indicating a high resistance level to pyrethroids in the onsite Anopheles population.

On day five, the adult traps are emptied and the mosquito samples taken from the selected sites are morphologically identified on site using the morphological identification kit. This results in positive sites for Anopheles gambiae in the following way:

o 0 out of 2 built-up adult sampling sites are positive;

o 2 out of 3 vegetation adult sampling sites are positive, more specifically open grass within 600 m. around water bodies in open sun;

o 5 out of 5 water body adult sampling sites are positive.

Based on the adult and larval trap results, the pest habitat zone is delineated as a buffer with a radius of 600 m around the water bodies.

At HQ, a cost efficient sampling strategy to detect variability in the pest habitat sites and to develop landscape models is generated. This sampling strategy is communicated to the field.

On day six and seven, the training of the local resort staff members continues. The HQ staff member sends all adult samples to a partner lab for molecular pathogen detection.

On day seven, the three weather stations are reallocated to three random locations within the pest habitat zone next to high risk production sites and start registering the meteorological parameters as described in the sampling strategy, generated at HQ. From now on, 30% of the highest risk water body type is monitored through larval dipping. This ends the field visit.

One week after return from the field, the spatial pest risk model is developed at HQ using the measured values from the sensor network in situ and the Pleiades image. The three most important variables contributing towards the risk are the number of small water bodies, the normalised vegetation index, and rainfall. Distance to open water bodies is positively correlated to the observed abundance: more small water bodies results in a higher abundance. Normalised differencing vegetation index are negatively correlated with the presence and abundance of the Anopheles gambiae s.l.: sites in direct sunlight (without vegetation cover) have higher abundance. Rainfall in the six days prior to the sampling is positively correlated with abundance.

Based on all the available information the detailed Pest Control Plan is generated. The aim is to keep the mosquito population below the thresholds at which they are a nuisance or a risk for transmitting malaria. The small water bodies will be treated with Vectobac GR, as the bioassay results indicated that the larvae are highly insensitive to treatment with pyrethroids. The Pest Control Plans are communicated from HQ to the Pest Control Teams using a web-based data exchange platform. The web-based data exchange platform also provides a description of all procedures for operating the traps and the weather stations.

From now on, the Pest Control Stage starts. The Pest Control Team implements the baseline control plan. A local contracted and trained Partner Company provides back up. This company provides equipment, maintenance, and pest control products.

Using the continuous habitat sensing through the locally installed sensor network, the environment is monitored. The two monitored factors are temperature and rainfall. The sensor network transmits the parameters on a daily basis to HQ. The Pest Control Team performs larval dipping on a weekly basis. They share the dipping results via a web exchange platform with HQ.

At HQ the relationship between the monitored parameters and the Anopheles population dynamics is continuously monitored. This indicates that larval sites should be treated when temperature is higher than 24 degrees Celsius for 7 days and the accumulated rainfall during the past 5 days is at least 400 mm. Data from the field is used in a feedback loop to continuously refine this relationship.

The pest population dynamics are forecasted using the sensed variables from the resort in combination with data collected from other yet similar resorts. When the

temperature and rainfall threshold is exceeded, HQ informs the local PCT to treat the high- risk zones according to the updated Pest Control Plan. After one year, larval samples are analysed towards their resistance against pyrethroids to verify if the current treatment scheme is still applicable. Resistance test indicate that the treatment strategy can be continued.

List of journal references

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[2] Walker, K. (2002). A Review of Control Methods for African Malaria Vectors. Washington: Office of Health, Infectious Diseases and Nutrition, Bureau for Global Health, U.S. Agency for International Development, under EHP Project 26568/CESH.OPR.MAL.LIT

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Claims

1 . A method of profiling one or more pests in a geographical volume comprising:

c) sampling pest habitat sites within the strata obtained from step b);

2. The method according to claim 1 , further comprising the subsequent monitoring of the profiled pest(s) by re-sampling pest habitat sites by means of reallocating the sampling sites; by modifying the number of pest habitat sites, pest data, or pest marker data to be measured; or by modifying the measurement frequency.

3. The method according to any one of claims 1 -2, further comprising the determination and optionally the prediction of the risks associated with the profiled pest(s) by means of computing one or more sets of models using the profile data of said one or more pests and optionally the prediction data of said one or more pest population dynamics.

4. The method according to any one of claims 1 -3 further comprising establishing a pest prevention or treatment plan, said plan comprising applying biological means, chemical means, physical means, or any combination thereof, to reduce the pest population under a defined threshold.

5. The method according to claim 1 , further comprising the integration with other geographical volumes by means of shared anonymous data collation of geographical volumes with similar pests or pest markers.

6. The method according to any one of claims 1 -5, wherein any one of the geographical volumes is selected from any discrete volume in earth, a geographical volume in tropical latitudes, and a tropical resort.

7. The method according to any one of claims 1 -6, wherein the remote sensors are carried upon satellites, Manned Areal Vehicles including fixed wing aircrafts and helicopters, Unmanned Areal Vehicles or drones including fixed wing crafts and helicopters, areal balloons, any other suitable carrier enabling a bird perspective of the measured geographical volume, or any combination thereof.

8. The method according to any one of claims 1 -7, wherein the ground sensors are selected from biosensors, mechanical sensors, optical sensors, the human eye, acoustical sensors, moisture sensors, temperature sensors, pressure sensors, chemical composition sensors, radioactivity sensors, and any combination thereof.

9. The method according to any one of claims 1 -8, wherein the pest habitat site is a natural or man-made element that is relevant to the lifecycle and population dynamics of said one or more pests, and may be selected from topographic features selected from slope, altitude, agricultural or natural land cover type; ecotones; soil type; permanent or semipermanent water elements like ponds, ditches, troughs, drainage lines, streams, rivers, canals, trenches, furrows; man-made constructions; biological organisms selected from hosts, competitors, predators, pollinators, prey; and any combination thereof.

10. The method according to any one of claims 1 -9, wherein the pest marker is selected from temperature profiles, tide profiles, rainfall, humidity, wind velocity and direction, pressure, air pollutants including pesticides, solar radiation, infrared radiation, remotely sensed derived indices, water quality indices, occasional infection of animal hosts, epidemic breakthrough, infestation of crops, and any combination thereof.

1 1 . A system for profiling one or more pests in a geographical volume and optionally determining or predicting the risks associated with the profiled pest(s), the system comprising at least one processor and an associated storage medium containing a program executable by means of said at least one processor, said system comprising:

12. The system of claim 1 1 , comprising one of the following: a personal computer, a portable computer, a laptop computer, a netbook computer, a tablet computer, a smartphone, a digital still camera, a video camera, a mobile communication device, a personal digital assistant, a scanner, or a multi-function device.

13. A non-transient storage medium on which a computer program product is stored comprising software code portions in a format executable on a computer device and configured for performing the following steps when executed on said computer device: a) fine-scale characterisation of the landscape elements of a geographical volume by integrating data from remote and ground sensors, wherein fine-scale refers to a spatial resolution equal to or less than 30 meters;

c) sampling pest habitat sites within the strata obtained from step b);

e) processing the spatio-temporal data from pest and pest markers obtained in step d); f) computing one or more set of models using the processed data from step e), thereby obtaining a profile of said one or more pests and optionally a prediction of said one or more pest population dynamics; and

g) optionally determining or predicting the risks associated with the profiled pest(s).

14. A computer program product executable on a computer device and comprising software code for executing the method according to any one of claims 1 -10, when run on said computer device.

15. The non-transient storage medium according to claim 13 or the computer program product of claim 14, wherein the computer device is selected from a personal computer, a portable computer, a laptop computer, a netbook computer, a tablet computer, a smartphone, a digital still camera, a video camera, a mobile communication device, a personal digital assistant, a scanner, and a multi-function device.