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Mosquito Protocol Purpose Scientific Inquiry Abilities To sample, identify and count the number of Identify answerable questions. mosquito larvae in each container at your study Use appropriate mathematics to analyze data site. Develop descriptions and explanations using evidence Overview Recognize and analyze the alternative Students will collect, sort, identify and count the explanations number of mosquito larvae from the Communicate the procedures and the indoor/outdoor containers at your study site. explanations. Student Outcomes Time Students will be able to 1-2 hours to collect samples, count, identify Identify mosquito larvae at your study site and preserve specimens (excluding travel). Understand the importance of representative Time will vary with the abundance and samplings diversity of water containers in each study site. Plot a relative abundance graph of the number of mosquito larvae Level Compare the number of mosquito larvae in Primary and Secondary each species in the different containers Explore relationships between the larvae Frequency species and climatic factors Once a month Collaborate with other GLOBE schools (with your country or other countries) Materials Share observations by submitting data to the Mosquito Field Guide NBIDS website. Mosquito Site Definition Field Guide Mosquito data sheets Science Concepts Equipment used to collect mosquito larvae at Life Science your site: fishnets Organisms have basic needs. Many clear plastic bags and elastic bans Organisms can just only survive in the Mosquito Larvae identification key environments where their needs are met. GPS Protocol Field Guide Earth has many different environments that GPS Protocol Data Sheets support different combinations of Markers organisms. Microscope Humans can change natural environments. Pens/Pencils All organisms must be able to obtain and use the resources while living in a constantly Preparation changing environment. Decide upon study locations and the household All populations are living together and the to be selected. physical factors with which they interact Practice using fishnet constitute an ecosystem. Practice identifying mosquito larvae using The interaction of organisms has evolved mosquito larvae key. together over time. Walailak University 2007: Mosquito 1 Mosquito Protocol- Introduction are willing to work with students. These people can, for example, help identifying The mosquito is a member of the family mosquito up to family level and discussing Culicidae. These insects have a pair of the important indicator species, as well as scaled wings, a pair of haltered, a slender endemic and introducing to organisms body, and long legs. The females of most present in your area. Mosquito Larval keys mosquito species suck blood from man are available in printed manuals and and other warm-blooded animals. Size books. Select and identification key that is varies but is rarely greater than 15 mm (0.6 applicable to your locality. inch). Mosquitoes weigh only about 2 to 2.5 mg. They can fly at about 1.5 to 2.5 Contact local experts in the area to make km/h (0.9 to 1.6 mph). Mosquito have sure that you are not sampling at a site been around for 170 million years. where other people are conducting research or where there are endangered For the Mosquito Protocol, we want to area. You do not want to inadvertently hurt estimate the disease risk area. Most often, a long-term monitoring site or harm it is impossible to count all individuals of endangered area. every species present in the habitat. So we sample mosquito larvae in the habitat, and To have the students become familiar with calculate the Container Index (CI), House mosquito larval identification before you Index (HI), and Breteau Index (BI). Each go to the field, students can bring in larval index can tell us about the number mosquito larvae from their neighborhoods of mosquitoes in our study area, so we can to identify in class. use these data to echelon significance in diseased control. Site Definition and Mapping Students use the stratified random Background sampling technique to sampling the You have to read mosquito fact in households in their study sites. Students Appendix. collect mosquito larvae from both in the indoor and outdoor containers. The indoor Teacher Support containers are categorized into two categories: (1) inside the bathroom such as small earthen jar, large earthen jar, cement Advanced Preparation tank and plastic container and (2) outside Many teachers and students have a little the bathroom such as ant-guard, vase, background in the identification of refrigerator with plate and water plant pot. mosquito larvae, and may be reluctant to For outdoor containers, there are two begin such a class project. This is not a categories: (1) artificial containers and (2) problem, since students find the critters so natural containers. Artificial containers fascinating, they will be teaching consist of small earthen jar, large earthen themselves and each other. jar, cement tank and plastic container, old can, plastic bottle, metal box, plant plate, There are many local experts to call on. plant pot, animal pan, preserved areca jar Often, local mosquito monitoring groups Walailak University 2007: Mosquito 2 and discarded tire. Natural containers different tasks. For example, two students consist of areca husk, banana tree, coconut can hold the fish nets, one student can hold shell, tree hole and clump. Mosquito a plastic bag, one student can read the larvae are collected from all outdoor instructions aloud, and etc. containers within 15 meters around the house. The most time-consuming tasks are the sorting and identifying the mosquito larvae. To save times, we can divide When to Go Sampling students into two teams. Students from You should collect the mosquito larvae Team I do the sorting, counting, and once a month. identifying the mosquito larvae using the Sorting, Identifying and Counting the Supporting Protocols mosquito larvae Protocol Lab Guide. Atmosphere Protocol: Students can Students from team II can be collecting a explore some relationship between some second sample. atmospheric data (i.e. the maximum/minimum temperature, relative After the students collect mosquito larvae, humidity, the number of rainy day, and the teachers look at the jars of sorted mosquito amount of rainfalls) and types of mosquito larvae to verify that all students identify larvae found at the study site. mosquito larvae correctly. If not, gather the students and have them discuss the Hydrology Protocol: Students can explore differences and identify mosquito species some association between some hydrology correctly. data (i.e. water pH, temperature, transparency, the amount of alkalinity, and After all mosquito larvae are sorted and dissolved oxygen) and types of mosquito combined from the teams in separate jars larvae found at the study site. for each species, have a committee of students and yourself look at the larvae to make sure that you all agree on mosquito Preparing for the Field identification. Then, we proceed by This is a sampling method. It would be a counting mosquito larvae and report the good idea to select a study site before the data on one set of data sheets and day of sampling. annihilate all of them. All students in the field should have a baseball cap, sneaker, and wear warm suit. Measurement Procedures Do not sample containers (container, If available, you can take folding tables or puddle, and etc.) that cannot be reached seat desks for the students to handle and safely. If your students sample from count their samples in the field. multiple habitats, you should determine which habitats can be sampled safely and Managing Students in the Field evaluate the percentage of coverage of If you have a large class, you should have each accessible habitat. Record the students work in multiple teams. Students habitats which could not be sampled. in each team can be responsible for Walailak University 2007: Mosquito 3 Students should only sort and count the Helpful Hints number of mosquito larvae. Tadpole, small As scientists do, students should keep field fish and other organism should be notes of your procedures to report what removed from the samples and returned to you did and if there are any deviations the water. from your plans. Make a photo journal of your trip, and bring parents or older We only count the number of live GLOBE students to mentor. Enjoy mosquito larvae. To sort mosquito larvae learning about mosquito species in the in each genus, we use a small plastic spoon world around you!! to collect mosquito larvae, sort them up in genus level (i.e. Aedes, Anopheles, and Having the students work in teams will Culex spp.) and place them in small plastic make sample collections, sorting and cups. We sort Aedes mosquito larvae up to identifying quicker. To work in groups, a species level in the laboratory by using though requires more equipment such as microscope. We discarded all mosquito fishnet, plastic bags, trays, and mosquito larvae after we are done. larval identical keys, can be more fun. Voucher specimens are not required, but The Questions Frequently Asked. they may help with teaching the students 1. What is the life cycle of how to properly identify the mosqito mosquitoes? larvae before going into the field. By A: Adult eggs (2 -3 days) collecting voucher specimens each time, larvae (4 -5 days) pupae (1- 2 the specimens can be compared to make days) sure that identifications are being done correctly each time. 2. How do you identify which one is the Anopheles, Aedes or Culex Equipment Use and Maintenance larvae (identify with eyes)? All of the sampling materials are available A: We can see the characteristics of commercially, but students can also enjoy mosquito larvae: Anopheles making them using the instructions larvae cling parallel with water provided in the Instrument Construction surface. On the other hand, section. You can also buy some parts and Aedes and Culex larvae cling in make others. For example, one can buy a angle of 45° with bank of 0.5 mm-mesh replacement net for a D-net container. Aedes larvae have and make the pole. This is less expressive short siphon but Culex larvae than buying the whole device. have long siphon. 3. What does the mosquito male feed Student Assessments on? The student should collect, identify the A: Male mosquitoes eat nectar mosquito larvae and calculate the House from flower. Index, Container Index and Breteau Index. These indices indicate the risk of DHF transmission. Walailak University 2007: Mosquito 4 4. At what seasons of the year are A: We should use a small fishnet to greater percentages of mosquito collect mosquito larvae from larvae found? discarded tires. If there are few A: We found in rainy season more amount of water in the discarded than in winter and summer seasons. tire, we pour water in large buckets and collect mosquito larvae from 5. What kind of containers that large buckets. female mosquitoes prefer to lay eggs? Questions for Further Investigation A: The preferred containers are 1. Are there any relationships among water jar, cement tank, and areca mosquito larvae and your climatic jar. measurements? 6. How can we collect mosquito 2. Are there seasonal variations to the larvae from discarded-tires? number and type of mosquito larvae at your study site? If so, suggest some possible reason why? Walailak University 2007: Mosquito 5 squito P Mos Protocol d e Field Guide Task the o Count t number of mosquito larvae in e c each water container y larvae up to species lev Identify mosquito l o vel d What You Need nets Fishn tic Plast bags ber Rubb bans manent mark Perm kers eomicroscop Stere pe Mosq ification Ke quito Identi ey Mosq Sheets quito Data S Fishnet In the Field water contai 1. Locate all w d e ool. iners in and around the house/scho wn ner 2. Write dow a contain ID on the water container (students c ray can use spr or a m). rite me er permanent marker to mark them Then wr the sam containe ID on yo Data our Sheet. mosquito lar 3. If we find m er rs, em rvae in wate container we collect all of the with a fishnet. 3.1. La c t ore 5 arge water containers are water containers that can sto water 500 L or reater such as water jar water poo cement tank and etc We samp large gr r, ol, t c. ple water contain w ners by dip et ng pping the ne in the water, startin at the top of the ontainer, co co o m ontinuing to the bottom in a swir n rling motion and samp pling all dges of the container (F ed c Figure 1). re c ampling tec Figur 1. Large container sa motion. chnique in a swirling m k 007: Mosquito Walailak University 20 o 6 mall water containers are comp 3.2. Sm s f es, posed of flower vase plastic bottles, oconut shell and etc. W empty water out thr co ls We w ishnet (Figu 2). rough the fi ure e Figure 2. Small ccontainer sam hnique. mpling tech 4. Place mosq e me quito larvae with som small am w ner mount of water from the contain in a t bber ban wit some air in the bag. plastic bag and close it with a rub th n ner g 5. Write down a Contain ID on a plastic bag with a per rmanent ma w arker that we found arvae. Then record the container ID in Data Sheet. mosquito la n D S 6. Bring mosq e fy quito larvae to identif up to sp pecies level in the lab oratory by using a oscope and Mosquito Id stereomicro on n dentificatio Key. Record data on a Data She eet. k 007: Mosquito Walailak University 20 o 7 Learning Activity How to Use a Microscope Purpose Time Students understand how to use a For 3 hours for practice. stereomicroscope for mosquito larval identification. Level All Overview Students collect mosquito larvae from study Materials and Tools sites, and identify mosquito larvae up to Equipment is listed on Activity Sheets species level by using stereomicroscope and Mosquito Data Sheet mosquito identification key. Microscope Student Outcomes Preparation Students will learn how to use Learn about a stereomicroscope as you can. stereomicroscope for mosquito larval identification. Science Concepts Earth and Space Science Mosquitoes lay their eggs in many types of water containers both indoor and outdoor around a house. Mosquito larvae breed in stagnant water filled containers. Students can identify mosquito larva species by themselves. Background magnification. However, compound Stereomicroscopes allow magnified microscopes of the time suffered from images of illuminated specimens to be chromatic and spherical aberrations in the viewed using 2 lenses (an objective and an lens, which give single lens microscopes eyepiece lens). Microscopes that use two an advantage both in clarity and lenses are called compound microscopes. magnifying power. In the 19th century, Single lens microscopes, of which this however, these problems with the antique created by Leeuwenhoek (1632- compound microscope are successfully 1723) is an example, use only a single lens resolved. Microscopes have been designed to magnify the specimen. Compound based on experience and the design had no microscopes were first invented in the late scientific grounds. A German named Ernst 16th century in Holland, when Zacharias Abbe (1840-1905), however, established a Janssen and his father discovered that method to design microscopes utilizing using two lenses greatly aided in logical calculations. Since then, Walailak University 2007: Mosquito 8 microscope design has progressed rapidly upward). Move it as far as it will go to the present day, with a wide array of without touching the slide! advanced microscopes now available depending on specimen and research 5. Now, look through the eyepiece and purpose. (http://www.coolscope.com/eng/ adjust the illuminator (or mirror) and tech/ 1-0-1.aspx) diaphragm for the greatest amount of light. Teacher Support 6. Slowly turn the coarse adjustment so Advanced Preparation that the objective lens goes up (away from A teacher should be explaining about a the slide). Continue until the image comes microscope in class for 2-3 hours. into focus. Use the fine adjustment, if available, for fine focusing. If you have a What to Do and How to Do It microscope with a moving stage, then turn Ask students about their knowledge of the coarse knob so the stage moves mosquitoes. Begin with questions such as: downward or away from the objective • What are mosquito species in the lens. area? • What are diseases caused by 7. Move the microscope slide around so mosquitoes as vectors that you that the image is in the center of the field know? of view and readjust the mirror, Divide the students into two or three illuminator or diaphragm for the clearest groups. Each group is composed of 4-5 image. students. Identify mosquito larvae by using a microscope as the instrument. 8. Use the fine adjustment, if available. If you cannot focus on your specimen, repeat 1. When moving your microscope, you steps 4 through 7 with the higher power always carry it with both hands, grasp the objective lens in place. arm with one hand and place the other hand under the base for support. 9. The proper way to use a monocular microscope is to look through the eyepiece 2. Turn the revolving nosepiece so that the with one eye and keep the other eye open lowest power objective lens is "clicked" (this helps avoid eye strain). into position. 10. Do not touch the glass part of the 3. Your microscope slide should be lenses with your fingers. Use only special prepared with a cover glass over the lens paper to clean the lenses. specimen. 11. When finished, lower the stage, click 4. Look at the objective lens and the stage the low power lens into position and from the side and turn the coarse focus remove the slide. knob so that the objective lens moves downward (or the stage, if it moves, goes 12. Always keep your microscope covered when not in use. Walailak University 2007: Mosquito 9 Example of the number of mosquito larvae in various water containers. Study site: Muang district, Nakhon Si Thammarat. Other House Container No. of Ae. Ae. Culex Anopheles mosquito ID type containers aegypti albopictus spp. spp. species 1 Flower vase 1 0 0 0 0 0 Flower pot 1 0 0 0 0 0 plates Pot plants 1 3 0 0 0 0 Cement tank 1 10 0 3 20 12 2 Pot plants 1 0 0 0 0 0 Cement tank 1 20 0 5 5 15 Ant guard 1 0 3 0 0 0 Ant guard 2 0 5 0 0 0 Ant guard 3 0 0 0 0 0 Ant guard 4 0 0 0 0 4 Flower pot 3 1 0 0 0 0 0 plates Water jar 1 5 0 0 0 3 Tire 1 0 3 0 12 0 Tire 2 0 5 0 5 3 4 Cement tank 1 12 0 8 6 8 Water jar 1 8 0 0 0 10 Tire 2 0 6 0 10 0 5 Tire 1 0 0 0 3 0 Tire 2 0 7 0 8 3 Flower vase 1 0 0 0 0 1 Flower vase 2 0 0 0 0 5 Water jar 1 10 0 0 0 8 Pot plants 1 2 0 0 0 0 In the table, the numbers that were shown in Italic style represent the number of mosquito identified in the laboratory. Walailak University 2007: Mosquito 10 How to Use a Microscope Activity Sheet Materials Microscope Plastic cups Plastic pools Plate dishes Beakers Droppers Data Sheets What to Do 1. Place the mosquito larva to be identified into a petri dish or other small tray (not the channeled sorting tray as it is too difficult to light the animal correctly). 2. Cover the mosquito larva with enough water so that the incident light does not cause glaring bright patches on the animal (as this makes it difficult to see the key features) but not so much liquid that the mosquito larva floats away every time you let go. 3. Good lighting is imperative, keep the incident light on full unless the view becomes too shiny. The transmitted light is useful for discrimination of setae and antennae. Some dark beetles will also require side lighting to see the grooves in hard dark surfaces of the elytra. 4. For identification of the mosquito larva, the key generally includes characters of the whole animal in dorsal or ventral view. 5. Be sure the animal is well focused at all times and that the particular part of the animal is in focus and being viewed from the correct angle 6. You MUST interact with the microscope and the mosquito larva. Zoom in and out to find the best perspective of the feature in relation to the rest of the mosquito larva. Walailak University 2007: Mosquito 11 Learning Activity Practice Identifying Mosquito Larva Activity Purpose All organisms must be able to obtain and use Let students learn about collecting and resources while living in a constantly identifying the mosquito larvae in water changing environment. containers in each study site. All populations living together and the physical factors with which they interact Overview constitute an ecosystem. Students will collect mosquito larvae from The interaction of organisms has evolved indoor/outdoor containers at their study sites. together over time. Student Outcomes Scientific Inquiry Abilities Students will be able to Identify answerable questions. - identify the mosquito larvae at their site; Use appropriate mathematics to analyze data. - understand the importance of Develop descriptions and explanations using representative sampling; evidence. - explore relationships between the number Recognize and analyze alternative of mosquito larvae and climatic factors; explanations. - collaborate with other GLOBE schools Communicate procedures and explanations. (with your country or other countries); - share observations by submitting data to Time the NBIDS website. 6 hours; 3 hours for sample collecting and another 3 hours for mosquito larval Science Concepts identification Life Science Organisms have basic needs. Level Organisms can only survive in environments Varies with the protocol where their needs are met. Earth has many different environments that Materials support different combinations of Practicing identify the mosquito larvae organisms. activity Humans can change natural environments. Protocol Field Guides Equipment is listed on Activity Sheets for specific protocol to be done. Prerequisites It would be helpful for the class to have seen the collection demonstrated. Teacher can use the power point to demonstrate the key points. Walailak University 2007: Mosquito 12 Practice Identifying Mosquito Larvae Activity Sheet The number of mosquito larvae may indicates the risk level of the vector borne disease. Students can practice to collect, sort and identify mosquito larvae. Materials Clear plastic cup Pen and Pencil Marker Mosquito larvae in plastic bags Stereomicroscope Plastic spoons What to Do 1. Review the characteristics of mosquito larvae. 2. Pour the mosquito larvae from a plastic bag into a clear plastic cup. 3. Separate the mosquito larvae from the clear plastic cup into three cups based on the type of mosquitoes: cup 1: Anopheles, cup 2: Aedes and cup 3: Culex spp. 4. Count the number of mosquito larvae in each cup. 5. Fill the number of mosquito larvae in the table below The number of mosquito larvae The number of Container type Ae. Ae. Culex Anopheles Other mosquito aegypti albopictus spp. spp. species Flower vases Flower pot plates Pot plants Water jars Cement tanks Tires Ant guards Walailak University 2007: Mosquito 13 Learning Activity Key Breeding Site of Mosquito Larvae Purpose Time Let students understand the number of Field trip time plus 2-3 class periods mosquito larvae in each water container and the type of mosquito larvae that Level prefer in each water container. All Overview Materials and Tools Student will study and visit the mosquito Equipment is listed on Activity Sheets study site, and conduct a questionnaire Mosquito Data Sheet and larval survey. Preparation Student Outcomes Find study sites which are appropriate for Student will learn: mosquito larval survey. To use questionnaire and larval survey. To analyze data. Prerequisites To interpret results. Simple statistics calculations and results’ interpretation. Science Concepts Earth and Space Science Mosquitoes lay their eggs in many types of water containers both indoor and outdoor containers around the house. Mosquito larvae breed in stagnant water filled containers. Background (Ae.), those of rodents, monkeys, and There are currently 412 mosquito species humans can only be transmitted by recognized from Thailand, but most of Anopheles (An.). The epidemiology of these are of little practical significance in malaria, as with all vector borne diseases, disease transmission, either because they is shaped by the habitats of the vector. In are not biologically susceptible to human Thailand, the primary vectors are An. pathogens or, more usually, do not have dirus, An. minimus and An. maculates. habits that bring them into sufficient Since An. dirus is a forest dwelling contact with man. The major mosquito- mosquito, it inhabits in areas covered borne diseases of Thailand are Malaria, either with natural forests, orchards, or tree Dengue, Japanese encephalitis virus and plantations. It prefers to bite humans, Filariasis (Rattanarithikul and Panthusiri avoids contact with DDT and fenitrothion, 1994). and is long-lived; all of which characteristics make it a very efficient Although the malaria parasites of birds can vector in spite of generally low population be transmitted by Culex (Cx.) and Aedes Walailak University 2007: Mosquito 14 densities (Rattanarithikul and Panthusiri 2000, Harrington and Edman 2001, 1994). Thavara et al. 2001, Dieng et al. 2002, Guzm′an and Kourí 2002, Hoeck et al. An epidemic of DHF occurred in Southern 2003). Because preventative care is an Thailand (e.g. Samui Island in 1966 and increasingly important part of the strategy, 1967 (Winter et al. 1968)) where Ae. social factors that influence its use must be aegypti and Ae. albopictus were abundant, more closely investigated (Benjamins and and widespread (Gould et al. 1968, Russell Brown 2004). et al. 1968, 1969, Thavara et al. 2004). Ae. albopictus is capable of breeding in a wide Japanese encephalitis (JE) is less common range of container types and water-holding than dengue and is most prevalent in rural habitats. In Thailand, Ae. albopictus has areas in the northern regions of Thailand. been found in forested habitats ranging in Unlike the epidemiology of dengue, elevation from 450 to 1,800 m as well as animal reservoirs, especially domestic in a variety of other habitats in rural and pigs, play an important part in the suburban areas (Scanlon and Esah 1965, transmission of JE. A number of mosquito Gould et al. 1970, Thavara et al. 1996, species, mostly Culex spp., have been 2004). Ubiquitous breeding sites, such as found naturally infected with JE. Cx. tree holds, coconut shells, fruit peels, tritaeniorhynchus, which breeds in great water jars, unused and discarded tires, and numbers in the pools left in rice paddy boats holding water have been found to fields toward the end of the harvest, is contain Ae. albopictus larvae (Thavara et generally considered the principal vector, al. 2004). but there is also evidence that Cx. fuscocephala, Cx. gelidus, Cx. vishnui and There are several factors affecting DHF Cx. pseudovishnui (Burke and Leake incidence including water storage, climatic 1988). and vector factors. Container factors comprise of shape, type, the size of water Filariasis is caused by the helminths surface, purpose for which the water used, Wuchereria bancrofti and Brugia malayi. type of materials, lids and water Adult worms live in the human lymphatic consumption characteristics (Tinker 1964, system and produce microfilariae that O’Meara et al. 1992, Kittayapong and circulate in the blood, where they can be Strickman 1993a, Focks et al. 1994, ingested by mosquitoes. The principal Luemoh et al. 2003). Climatic factors vectors of B. malayi in southern Thailand comprise of the amount of monthly are the Mansonia species. The vectors’ rainfall, vapour pressure, and maximum, larval habitats are susceptible to control by minimum, and mean temperature (Hales et public sanitation measures. The larvae and al. 2002). Vector factors comprise of the pupae of Mansonia remain submerged in strain of the virus, mosquito density, large ponds until exclusion occurs. mosquito behavior and mosquito Transmission of filariasis along the competence, food level, duration of northern border takes place in the forest development, size at emergence, flight and the principal vector may be a member range, survival and biting activity (Rigau- of the Niveus Subgroup of Aedes, all of Pérez et al. 1998, McBride and Ohmann Walailak University 2007: Mosquito 15 which develop in natural containers Collect the data by using mosquito (Rattanarithikul and Panthusiri 1994). protocol Identify the mosquito larvae by using Teacher Support “Learning Activity: Identify mosquito larvae” Advance Preparation Discuss with students about the importance of the disease causes by Further Investigations mosquito vectors. 1. Plot the number of monthly DHF incidence at your area. Are there any indications of climatic factor What to Do and How to Do It (i.e. the amount of monthly Ask students about their mosquito rainfall, mean, maximum and knowledge. Begin with the questions such minimum temperature) that may as: correlate with the number of DHF • Are there mosquitoes in your incidences in our area? house/school that you live? 2. Compare the number of mosquito • What are the diseases cause by larvae between seasons, such as mosquito that you know? between rainy and summer Divide students into two groups. For each seasons. Seasons may have some group have 1 teachers to take care the influence on the number of students. mosquito larvae in your area. Looking at a local map to identify 3. Think about other factors that may mosquito sites: affect to the number of mosquito Divide a local map into grids. larvae such as water pH, Group 1: select 10% of grids to study transparency, the amount of Group 2: select 10% of grids to study dissolved oxygen and etc. For each, collect 1-2 houses per grid. Walailak University 2007: Mosquito 16 Table. The number of mosquito larvae at Muang district, Nakhon Si Thammarat. Culex Anopheles House Container type No. container Ae. aegypti Ae. albopictus Other spp. spp. 1 Flower vases 1 0 0 0 0 0 Flower pot plates 1 0 0 0 0 0 Pot plants 1 3 0 0 0 0 Cement tanks 1 10 0 3 20 12 2 Pot plants 1 0 0 0 0 0 Cement tanks 1 20 0 5 5 15 Ant guards 1 0 3 0 0 0 Ant guards 2 0 5 0 0 0 Ant guards 3 0 0 0 0 0 Ant guards 4 0 0 0 0 4 3 Flower pot plates 1 0 0 0 0 0 Water jars 1 5 0 0 0 3 Tires 1 0 3 0 12 0 Tires 2 0 5 0 5 3 4 Cement tanks 1 12 0 8 6 8 Water jars 1 8 0 0 0 10 Tires 2 0 6 0 10 0 5 Tires 1 0 0 0 3 0 Tires 2 0 7 0 8 3 Flower vases 1 0 0 0 0 1 Flower vases 2 0 0 0 0 5 Water jars 1 10 0 0 0 8 Pot plants 1 2 0 0 0 0 Table: Mean (± S.D.) numbers of mosquito larvae. Other Container type Ae. aegypti Ae. albopictus Culex spp. Anopheles spp. mosquito species Flower vases Flower pot plates Pot plants Water jars Cement tanks Tires Ant guards Walailak University 2007: Mosquito 17 Calculating a mean and standard deviation of the number of mosquito larvae Step 1 First, students should calculate a mean number of mosquito larvae in each water container using Eq. (1). n Eq. (1) ∑ xi x = i =1 n Step 2 Calculate a standard deviation of the number of mosquito larvae in each water container using Eq. (2). n Eq. (1) ∑ ( xi − x ) 2 SD = i =1 n −1 Table: Mean (± S.D.) numbers of mosquito larvae. Other Anopheles Container type Ae. aegypti Ae. albopictus Culex spp. mosquito spp. species Flower vases 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 2.00±0.71 Flower pot plates 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 Pot plants 1.67±1.53 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 Water jars 7.67±2.52 0.00±0.00 0.00±0.00 0.00±0.00 7.00±3.61 Cement tanks 14.00±5.29 0.00±0.00 5.33±2.52 10.33±8.39 11.67±3.51 Tires 0.00±0.00 4.20±2.77 0.00±0.00 7.60±3.65 1.20±1.64 Ant guards 0.00±0.00 2.00±2.45 0.00±0.00 0.00±0.00 1.00±2.00 Calculating Larval Indices Step 1 First calculate larval indices using the following formula: number of positive houses House Index HI = ×100 number of selected houses number of positive containers Container Index CI = ×100 number of selected containers number of positive containers Breteau Index BI = ×100 number of selected houses Walailak University 2007: Mosquito 18 Table: Larval indices at Mueng district, Nakhon Si Thammarat. Culex Anopheles Other mosquito Larval indices Ae. aegypti Ae. albopictus Total spp. spp. species House Index (HI) Container Index (CI) Breteau Index (BI) The number of selected houses = 5 The number of positive houses = 5 The number of selected containers = 23 The number of positive containers = 17 number of positive houses House Index HI = ×100 number of selected houses 5 = ×100 5 = 100 number of positive containers Container Index CI = ×100 number of selected containers 17 = × 100 23 = 73.91 number of positive containers Breteau Index BI = ×100 number of selected houses 17 = × 100 5 = 340 Table: Larval indices at Muang district, Nakhon Si Thammarat. Other Culex Anopheles Larval indices Ae. aegypti Ae. albopictus mosquito Total spp. spp. species House Index (HI) 100 80 60 100 100 100 Container Index (CI) 34.78 26.09 13.04 34.78 47.83 73.91 Breteau Index (BI) 160 120 60 160 220 340 Walailak University 2007: Mosquito 19 Key Breeding Sites of Mosquito Larvae Activity Sheet Materials Fishnets Permanent markers Plastic bags Rubber bands Plastic cups Plastic pools Plate dishes Beakers What to Do 1. Review the Mosquito Field Guide. 2. Move to the study site and collect the data by using questionnaire and larval survey. 3. Bring mosquito larvae to a Laboratory to identify mosquito larvae up to species level. 4. Calculate a mean and a standard deviation of the number of mosquito larvae in each container. 5. Calculate mosquito larval indices and fill them in a table. Table 1. Mean (± S.D.) numbers of mosquito larvae Container type Ae. aegypti Ae. albopictus Culex spp. Anopheles spp. Other mosquito species Flower vases Flower pot plates Pot plants Water jars Cement tanks Tires Ant guards Table 2. Larval indices at study sites Ae. Ae. Culex Anopheles Other mosquito Larval indices Total aegypti albopictus spp. spp. species House Index (HI) Container Index (CI) Breteau Index (BI) Walailak University 2007: Mosquito 20 Learning Activity Data Analysis with SPSS Software Purpose Time Let students understand the data analysis Field trip time plus 2-3 class periods with some computer software that is suitable for data processing. Level All Overview Students will study and analyze the data Materials and Tools from a field survey. Equipment is listed on Activity Sheets Mosquito Data Sheet Student Outcomes Students will learn: Preparation To use SPSS software. Install SPSS software for data analysis. To analyze data with SPSS software. To interpret results from SPSS software. Prerequisites Simple statistics calculations and results Science Concepts interpretation. The number of mosquito larvae in each region may be different. Background than 120,000 corporations, academic Data analysis is the act of transforming institutions, healthcare providers, market research companies and government data with the aim of extracting useful agencies—to better focus their operations information and facilitating conclusions. and improve their performance. The Depending on the type of data and the software helps organizations optimize question, this might include application of interactions with their customers, statistical methods, curve fitting, selecting regardless of whether they are patrons, or discarding certain subsets based on employees, patients, students, or citizens, specific criteria, or other techniques. In and ensure that the actions they are taking today will positively affect their ability to contrast to Data mining, data analysis is reach tomorrow's goals. usually more narrowly intended as not aiming to the discovery of unforeseen Teacher Support patterns hidden in the data, but to the verification or disproval of an existing Advance Preparation model, or to the extraction of parameters Discuss with students about the necessary to adapt a theoretical model to importance of the data analysis with SPSS (experimental) reality. software. SPSS is the leader in predictive analytics technologies. For more than 37 years, SPSS has enabled its customers—more Walailak University 2007: Mosquito 21 What to Do and How to Do It Looking at the Mosquito datasheet and Ask students about the number of follow the activity sheet. mosquito larvae in each region. Begin with the questions such as: Further Investigations • Where did the data come from? 1. Represent the number of mosquito • How were the data collected? larvae in a different type of water Design the personal computer for students: containers with a bar chart. one PC for 2 students. 2. Think about other softwares for data analysis. Walailak University 2007: Mosquito 22 s PSS ftware Data Analysis with SP Soft Part I. Introduction S Start the SPSS program by click “Start > All Programs > SPSS Inc > SPSS”. Wh the SPS program starts, it hen SS will show this dialog. Select “Type in data”. n Clic “OK” button, the SPSS ck program will show as the left e. side In this figure, the SPSS program t p ows sho “Data V t. View” sheet Bef ut a fore we pu our data in the ta Dat View” s sheet, we have to fine our var def Vairable riables in “V ew” Vie sheet. st, Firs we cl lick at “V Variable ew” (show at the bot Vie ttom on the left). k 007: Mosquito Walailak University 20 o 23 es We define the variable as follow wings: (1) First vaariable: indo door/outdoorr containners (2) Name: in_ou (3) Define Variable T Type pe ble: Typ of variab Numeric c Width: 8 Dec cimals: 0 (4) Label: Indoor/Out tdoor container (description of a vvariable) s: (5) Values the values of variable that s e in e we inpu e.g. “1” i the Value box, ut ” el “indoor container” in the labe box, and click “OK”. Com data a mplete the d entry as shown d in the left hand side. ck Clic back to “ w” “Data View sheet for data entry. k 0 o Walailak University 2007: Mosquito 24 Analysis Part II. Data A criptive S I. Desc Statistics e a First, we use data weight y “ cases by select “Data > Weight CCases…” Click at “Weight caases by” ct ble(s). and selec the variab In this c elect the case, we se of o number o mosquito larvae Select ze “Analyz > Descripti ive Statisstics > Frequenccies…” k 007: Mosquito Walailak University 20 o 25 ariable(s) Select va OK” Click “O ysis shown as be The results of statistical analy will be s elow. k 007: Mosquito Walailak University 20 o 26 II. Chi-Square test Chi-Square test” by Select “C click “Analyzee > metric Nonparam > Chi- Square…”” For this dialog, se elect the hat ant variable th you wa to test st in the “Tes Variable List”. ple, ct For examp we selec the test s utdoor variable as Indoor/Ou Container. Click “Options” escriptive” Select “De ntinue” and “OK”. Click “Con d k 007: Mosquito Walailak University 20 o 27 b The results will be shown as below. k 007: Mosquito Walailak University 20 o 28 x2 gency tabl III. 2x conting le cy For a table of frequenc data cros d ss-classified according to two categ iables, gorical vari Y, X and Y each of w wo which has tw levels or subcategor r ample, we w ries. For exa t would like to test ationship bet the rela tween conta on a r ner ainer positio (indoor and outdoor container) and contain type n c ). (earthen and plastic container) The hypo othesis is: H0: There are no assocciation betw ns ween container position and conta ainer types (no nship). relation H1: There are some as between con ssociation b ntainer posit ontainer types tions and co nship). (relation ve s Select “Analyze > Descriptiv Statistics sstabs…” > Cros ct Selec the variab ble(s) After that, click “Statistics” r ” ct are” Selec “Chi-squa n, ontinue” Then click “Con k 007: Mosquito Walailak University 20 o 29 Click “Ceells…” Observed” an “Expected” Select “O and ck ue” Then clic “Continu and “OK K” lts hown as below. The resul will be sh he e P=0.000). So we reject H 0, it mean that there The results show th Pearson Chi-Square = 4.029 (P o ns me ion n was som associati between container p nd positions an container types. k 007: Mosquito Walailak University 20 o 30 ata IV. Da Visualization ot We can plo graphs between the n m rvae =from indoor/outd number of mosquito lar door then/plastic containers. containers and eart . S phs cy Select “Grap > Legac > Bar…” ” S stered” and “Define” Select “Clus I on In the sectio of Bars Represent, select “ “Other stati mean)” istic (e.g. m S o Select the variables into the box: C Category Ax xes: C Container tyype D ster Define Clus by: Mos ae squito larva s species A And then click “OK” k 007: Mosquito Walailak University 20 o 31 T hown as th left The results will be sh he f figure. W it h e We can edi the graph by double click a h. at the graph k ng Click at the bar for changin a bar r. color ou If yo finish ch r hanging bar colors, art click close “Cha Editor”. om changing bar The results fro rs n t color are shown on the left side. k 0 o Walailak University 2007: Mosquito 32 mple Linear Regres V. Sim alysis ssion Ana e Prepare data as described above. In this exammple, l we will examine th he ation betwee the associa en um minimu and max ximum tempera mount ature, the am of monnthly rainfall and mber of DHF the num nces. inciden “Analyze > Select “ Regress ear”. sion > Line The dis splay will be shown he as in th left figuree. k 007: Mosquito Walailak University 20 o 33 Ch ndependent hoose the in ariables into Independe va o ent(s) ox bo and the d v dependent variables nto ent in Depende box. Click “OK”. ow w. The results will sho as below his shows the variables Th figure s which are the inputs of the m model. he Th relations n ship between the ndependent a the dep in and pendent ariables wer shown as in the va re s left figure. From the re ficients of variables and the variab that are suitable esults, we get the coeff d ble s in the m is model. In thi case, the maximum t e del temperature is suitable for the mod (t = 2.3333, 2). ear on 96X P=0.022 The line regressio model is Ydhf = 5.09 max temp -342.070, F3 = 6.152, P = 3,80 , 2 0.001, R = 0.187. k 007: Mosquito Walailak University 20 o 34 dition, we can plot a sc In add c am catter diagra that onship betw shows the relatio ween the max ximum tempe r erature and the number of DHF ences. incide ct ialogs > Selec “Graphs > Legacy Di er/Dot…” Scatte Then the display will be sho y d own in the dialog. ct ple Selec the “Simp Scatter” and click on ine”. “Defi ct bles. Selec the variab dhf” variable into Y Ax “d xis maxt” into X Axis “m ck Clic “OK” button. k 0 o Walailak University 2007: Mosquito 35 re e ot t This figur shows the scatter plo between the m re maximum temperatur and the n D number of DHF incidences. e aph ble n We can edit this gra by doub click on the graph. t f aight line. Click on the dot for fitting a stra Click at “A Fit Lin at Total”.. Add ne Click on “Fit Line” and select “ n “Linear”. n And then click “Apply”. This figu shows the regressi line wit R2 ure t ion th can = 0.143, that mean this model c predict data 14.3%. k 0 o Walailak University 2007: Mosquito 36 Learning Activity Time Series Analysis with Mathematica Purpose Time Let students understand series data using Field trip time plus 2-3 class periods a time series analysis. Level Overview All Students will study series data and construct a time series model and forecast Materials and Tools the data. Equipment is listed on Activity Sheets Mosquito Data Sheet Student Outcomes Student will learn: Preparation To use Mathematica software. Install Mathematica software with Time To develop a time series model with Series application for a time series Mathematica software. analysis. Science Concepts Prerequisites Series data of the number of mosquito Time series analysis calculations and larvae in each region may be different. results’ interpretation. Background , ,… is called a scalar or A discrete time series is a set of time- univariate time series. If at each time t ordered data , ,…, ,…, several related quantities are observed, is a real vector and , ,… obtained from observations of some corresponds to a vector or multivariate phenomenon over time. Throughout this time series. manual, we will assume, as is commonly done, that the observations are made at The fundamental aim of time series equally spaced time intervals. This analysis is to understand the underlying assumption enables us to use the interval mechanism that generates the observed between two successive observations as data and, in turn, to forecast future values the unit of time and, without any loss of of the series. Given the unknowns that generality, we will denote the time series affect the observed values in time series, it by , ,… ,… . The subscript t can is natural to suppose that the generating now be referred to as time, so is the mechanism is probabilistic and to model observed value of the time series at time t. time series as stochastic processes. By this The total number of observations in a time we mean that the observation is presumed series (represent as n) is called the length to be a realized value of some random of the time series (or the length of the variable ; the time series { }, a single data). We will also assume that the realization of a stochastic process (i.e., a observations result in real numbers. So if a sequence of random variables) { }. In the single quantity is observed at each time t, following we will use the term time series the resulting is a real number and Walailak University 2007: Mosquito 37 to refer both to the observed data and to • Where did the data come from? the stochastic process; however, X will • How were the data collected? denote a random variable and x a Assign the personal computer for students: particular realization of X. one PC for 2 students. Looking at the number of DHF incidences Teacher Support in the activity sheet. Advance Preparation Discuss with students about the Further Investigations importance of the time series analysis with 1. Plot the number of mosquito larvae Mathematica software with Time Series in different types of water application. containers with bar charts. 2. Think about other computer software for data analysis. What to Do and How to Do It Ask students about the number of DHF incidence in each region. Begin with the questions such as: Time Series Analysis with Mathematica software Stationary Time Series Models In this section, the commonly used linear time series models (AR, MA, and ARMA models) are defined and the objects that represent them in this package are introduced. Functions that check for stationarity and invertibility of a given ARMA model and that expand a stationary model as an approximate MA model and an invertible model as an approximate AR model are then defined. Autoregressive Moving Average Models The fundamental assumption of time series modeling is that the value of the series at time t, , depends only on its previous values (deterministic part) and on a random disturbance (stochastic part). Furthermore, if this dependence of on the previous p values is assumed to be linear, we can write =∅ +∅ + ⋯+ ∅ + , (1) Where ∅ , ∅ , … , ∅ are real constants. is the disturbance at time t, and it is usually modeled as a linear combination of zero-mean, uncorrelated random variables or a zero-mean white noise process = + + + ⋯+ (2) ( is a white noise process with mean 0 and variance if and only if = 0, = for all t, and = 0 if s≠t, where E denotes the expectation.) is often referred to as the random error or noise at time t. The constants ∅ , ∅ , … , ∅ and , ,…, are called autoregressive (AR) coefficients and moving average (MA) Walailak University 2007: Mosquito 38 coefficients, respectively, for the obvious reason that (1) resembles a regression model and (2) a moving average. Combining (1) and (2) we get −∅ −∅ − ⋯− ∅ = + + + ⋯+ (3) This defines a zero-mean autoregressive moving average (ARMA) process of orders p and q, or ARMA(p, q). In general, a constant term can occur on the right-hand side of (3) signaling a nonzero mean process. However, any stationary ARMA process with a nonzero mean μ can be transformed into one with mean zero simply by subtracting the mean from the process. Start the program “Start > Program > Mathematica” We load the package first. Import Data: Import ["D:\PhD_Study\DataDHF\DHF4Regions_update.csv"]; The first thing to do in analyzing time series data is to plot them since visual inspection of the graph can provide the first clues to the nature of the series: we can "spot" trends, seasonality, and no stationary effects. Often the data are stored in a file and we need to read in the data from the file and put them in the appropriate format for plotting using Mathematica. We provide several examples below. For example: Transformation of Data In order to fit a time series model to data, we often need to first transform the data to render them "well-behaved". By this we mean that the transformed data can be modeled by a zero-mean, stationary ARMA type of process. We can usually decide if a particular time series is stationary by looking at its time plot. Intuitively, a time series "looks" stationary if the time plot of the series appears "similar" at different points along the time axis. Any no constant mean or variability should be removed before modeling. 3000 Seasonally Differencing H1-b12L 1000 The number of DHF incidence 2500 750 2000 500 250 1500 0 1000 -250 500 -500 -750 2003 2004 2005 2006 2007 5 10 15 20 Year Month Fig. 1 The number of DHF incidences in Northern Thailand Fig. 2 Seasonally differencing 2nd order with 12 months of from January 2003-September 2007. DHF incidence in Northern Thailand from January 2003- December 2006. Estimation of Correlation Function and Model Identification As stated in the beginning, given a set of time series data we would like to determine the underlying mechanism that generated the series. In other words, our goal is to identify a Walailak University 2007: Mosquito 39 model that can "explain" the observed properties of the series. If we assume that after appropriate transformations the series is governed by an ARMA type of model, model identification amounts to selecting the orders of an ARMA model. In general, selecting a model (model identification), estimating the parameters of the selected model (parameter estimation), and checking the validity of the estimated model (diagnostic checking) are closely related and interdependent steps in modeling a time series. For example, some order selection criteria use the estimated noise variance obtained in the step of parameter estimation, and to estimate model parameters we must first know the model. Other parameter estimation methods combine the order selection and parameter estimation. Often we may need to first choose a preliminary model, and then estimate the parameters and do some diagnostic checks to see if the selected model is in fact appropriate. If not, the model has to be modified and the whole procedure repeated. We may need to iterate a few times to obtain a satisfactory model. None of the criteria and procedures is guaranteed to lead to the "correct" model for finite data sets. Experience and judgment form necessary ingredients in the recipe for time series modeling. In this section we concentrate on model identification. Since the correlation function is the most telling property of a time series, we first look at how to estimate it and then use the estimated correlation function to deduce the possible models for the series. Other order selection methods will also be introduced. 1 1 0.8 0.8 0.6 0.6 Partial ACF 0.4 0.4 ACF 0.2 0.2 0 0 -0.2 -0.2 5 10 15 20 5 10 15 20 Lag Number Lag Number Fig. 3. ACF of DHF incidence in Northern Thailand between Fig. 4. PACF of DHF incidence in Northern Thailand January 2003-December 2006 (--- represented 95% upper between January 2003-December 2006 (--- represented 95% and lower confidence intervals). upper and lower confidence intervals). Parameter Estimation We first introduce some commonly used methods of estimating the parameters of the ARMA types of models. Each method has its own advantages and limitations. Apart from the theoretical properties of the estimators (e.g., consistency, efficiency, etc.), practical issues like the speed of computation and the size of the data must also be taken into account in choosing an appropriate method for a given problem. Often, we may want to use one method in conjunction with others to obtain the best result. These estimation methods, in general, require that the data be stationary and zero-mean. Failure to satisfy these requirements may result in nonsensical results or a breakdown of the numerical computation. In the following discussion we give brief descriptions of each estimation method in the time series package; for more details the reader is urged to consult a standard time series text. For example we get these models from the data: Model 1: MAModel[{0.757804},194948] Model 2: ARModel[{0.574041},198217] Model 3: ARMAModel[{0.142215},{0.608612},193154] Model 4: ARModel[{0.698411,-0.224706},196561] Walailak University 2007: Mosquito 40 MAModel[{0.732945,0 Model 5: M 0.0886237},200856] ARMAMode Model 6: A el[{-0.0105 696,0.0931435},{0.753489},1925551] ARMAMode Model 7: A 322},{0.663 el[{0.07283 251},200812 322,0.04282 2] ARMAMode Model 8: A 64999},2003 el[{-0.0455 539,0.0779581},{0.775963,0.036 373] Diagnoostic Check king g et t ss he s After fitting a model to a given se of data, the goodnes of fit of th model is usually examined to see if it is ind deed an ap m he is ppropriate model. If th model i not satis sfactory, cations are made to th model an the whole process of model se modific he nd arameter election, pa ion, and dia estimati ecking must be repeated until a sat agnostic che t model is foun tisfactory m nd. al Residua Testing various way of check There are v ys odel is satisfactory. Th common used king if a mo he nly ch approac to diagn nostic check examine the residuals. There are several alt king is to e e ternative ons definitio of the r residuals an here we define the residuals to be the noi calculat from nd o ise ted mated mode the estim el. The res d siduals are calculated first. Since the re e red at es. esiduals are also order in time, we can trea them as a time serie As in the anal s ostic test in examining residuals is to plot lysis of the time series itself, the first diagno s s them as a function of time to see if it app ears to be a stationary random seq quence. The correlaation functio is plotted along with the bounds 2/√ on d h on f om Fig. 5. (b) The correlatio function of residuals fro SARIMA (2,0,1)(0,2,0)12 model (-- represented 95% upper and lower -- d confidence inntervals). looking at the correlati function of the resi Instead of l ion n iduals , at each k, we can ok rst also loo at the fir h correla s a he orrelations are zero ation values together and test if th first h co he he (H0). Th portmanteau test is based on th statistic = +2 ∑ , (4) which has an asympto w otic stribution with h-p-q degrees of freedom. If dis w d ∝ dequacy of the model i rejected at level α. , the ad is a k 007: Mosquito Walailak University 20 o 41 e, c In this case Portmante statistic eau 8644 and χ = 8.98 . ; 7.5871 (P>0 = 27 0.05). So ept we acce H0; There was no different b H0: T o siduals and zero between res There was di H1: T ween residu and zero ifferent betw uals o c o s hat The graphic analysis of residuals showed th the resid duals in the model appeared to te y ro fluctuat randomly around zer with no o obvious trend in variattion as the p ncidence predicted in (Fig. 5). Th indicate s that the most suitab model f predictin DHF values increased ( his m ble for ng incidence in North nd hern Thailan was the S SARIMA(2, ,0,1)(0,2,0)12 model Forecasting Now that w have exp N we plored meth hods to esti imate the parameters o an appro p of opriately e ain es chosen model we turn to one of the ma purpose of time series analy asting or ysis, foreca predicti the futu values of a series. I this secti we disc ing ure o In ion forecasting methods cuss some fo m n es e ar r commonly used in time serie analysis . We first present the best linea predictor and its derivati in the in ion ple nfinite samp limit. TThen we der proximate b rive the app p best linear predictor used to spee up the calculation. We show how to write a prog often u ed . w w gram to update the ion a w predicti formula when new data are a nd available an also intrroduce the simple exp ponential t smoothing forecast procedure. Suppose tha the statio at onary time s series model that is fitte to the da ed ata , ,…, is uld known and we wou like to predict the f p es future value of the ser ries , ,…, based me p n d on the realization of the tim series up to time n. The time n is called the origin of the n t forecast and h the l lead time. Here we dissplay the ne 8 values of the series predicted from the e ext s d estimated SARIMA along with t last 48 observed da points. model a the o ata ig. mber of DHF in Fi 6 The num N and ncidences in Northern Thaila from Janua ary 20 -September 2007. ─ repr 003 r esented predictted resented actual data, --- repre ata. da k 007: Mosquito Walailak University 20 o 42 Examples of Student Research Students at Walailak University investigated the effect of seasons, topographical areas, and mosquito species on the number of mosquito larvae in different types of water containers in Nakhon Si Thammarat province, Thailand. Data were collected by using a stratified simple random sampling technique with a total sample size of 300 households in dry season and re- sampled again in wet season in 2006. Forming a hypothesis A number of mosquito larvae, mostly Culex spp., have been found naturally infected with Japanese encephalitis (JE) which causes substantial human diseases. Various factors affect mosquito abundance and their species distribution including climatic factors, %vegetation cover, breeding sites, season, topography and faith-based communities. Colleting and Data Analysis A structured questionnaire and larval survey were conducted in Nakhon Si Thammarat province in March-November 2006 covering 300 households in three topographical areas (i.e. mangrove, rice paddy and mountainous areas with 100 households per topographical area): 300 households in dry season. These 300 households were re-sampled again in wet season. Data were collected by using a stratified simple random sampling technique. Data were analyzed using t-test and three-way ANOVA tests. Communicating Results The results showed that in wet season, there were higher numbers of mosquito larvae in indoor earthen jars, outdoor cement tanks, outdoor plastic containers, and outdoor metal containers than in dry season. In mangrove area, there were higher numbers of mosquito larvae in outdoor earthen jars than in rice paddy area. Culex larvae were found higher in outdoor earthen jars than Aedes larvae (Table 1 and Figure 1). Walailak University 2007: Mosquito 43 8 8 8 The number of mosquito larvae The number of mosquito larvae The number of mosquito larvae 6 6 6 4 4 4 2 2 2 EJI CTI PCI MCI EJO CTO PCO MCO ECO NC EJI CTI PCI MCI EJO CTO PCO MCO ECO NC EJI CTI PCI MCI EJO CTO PCO MCO ECO NC Container types Container types Container types (a) mangrove area in wet season (b) rice paddy area in wet season (c) mountainous area in wet season 8 8 8 The number of mosquito larvae The number of mosquito larvae The number of mosquito larvae 6 6 6 4 4 4 2 2 2 EJI CTI PCI MCI EJO CTO PCO MCO ECO NC EJI CTI PCI MCI EJO CTO PCO MCO ECO NC EJI CTI PCI MCI EJO CTO PCO MCO ECO NC Container types Container types Container types (d) mangrove area in dry season (e) rice paddy area in dry season (f) mountainous area in dry season Figure 1. Mosquito larva abundance (□: Aedes, : Culex) in three topographical areas. Indoor containers: EJI = earthen jars, CTI = cement tanks, PCI = plastic containers, MCI = metal containers. Outdoor containers: EJO = earthen jars, CTO = cement tanks, PCO = plastic containers, MCO = metal containers, ECO = earthen containers, NC = natural containers. Walailak University 2007: Mosquito 44 Table 1. The number of mosquito larvae in water containers in wet and dry seasons at Nakhon Si Thammarat in 2006. Statistical test Container type S T M SxT SxM TxM SxTxM Indoor container Earthen jar F1;1788=4.685 F2;1788=1.625 F1;1788= 1.346 F2;1788=0.554 F1;1788=0.504 F2;1788=2.046 F2;1788=1.040 Cement tank F1;1788=0.499 F2;1788=0.499 F1;1788= 0.499 F2;1788=0.499 F1;1788=0.499 F2;1788=0.499 F2;1788=0.499 Plastic container Metal container F1;1788=1.455 F2;1788=1.455 F1;1788= 1.455 F2;1788=1.455 F1;1788=1.455 F2;1788=1.455 F2;1788=1.455 Outdoor container Earthen jar F1;3588=0.276 F2;3588=1.199 F1;3588=0.155 F2;3588=0.590 F1;3588=1.858 F2;3588=0.742 F2;3588=1.175 Cement tank F1;3588=12.506*** F2;3588=3.251 F1;3588=8.580** F2;3588=3.420* F1;3588=9.389** F2;3588=2.943 F2;3588=2.562 Plastic container F1;7188=10.934*** F2;7188=0.815 F1;7188=0.024 F2;7188=0.455 F1;7188=0.182 F2;7188=0.189 F2;7188=0.068 Metal container F1;5388=4.011* F2;5388=1.868 F1;5388=3.227 F2;5388=2.070 F1;5388=0.087 F2;5388=1.438 F2;5388=0.061 Earthen container F1;5388=3.964 F2;5388= 5.477** F1;5388=4.952* F2;5388=1.465 F1;5388=2.906 F2;5388=2.723 F2;5388=2.021 Natural container F1;5388=0.000 F2;5388=0.525 F1;5388=0.471 F2;5388=0.417 F1;5388=0.915 F2;5388=0.100 F2;5388=0.075 Container types, season (S), topographical area (T), and mosquito larva species (M) factors. *P<0.05, **P<0.01, ***P<0.001. Walailak University 2007: Mosquito 45

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