1
NAMRU-3
TITLE: Eco-geographical Distribution and Insecticide Susceptibility of Malaria Vectors in Cross River
State, Nigeria
PROPOSAL STATUS: PRE-PROPOSAL APPROVED
PROPOSAL DATES: 10/01/2014 - 09/30/2015
(First Year Proposal)
PRINCIPAL INVESTIGATOR: Bassaly, Hala
Hala.Bassaly.eg@med.navy.mil
CO-PI(s): Tageldin, Reham. Mrs. Reham needs to have edit access reham.tageldin.eg@med.navy.mil
LT Diclaro, Joseph
Dr. Foley, Desmond (5 %)
Dr. Ezedinachi, Emmanuel
REQUESTED BUDGET: FY2015 Budget: $ 165K
PROJECT SUMMARY
PUBLIC HEALTH ISSUE/GAP AND RELEVANCE TO RFP: The President’s Malaria Initiative
currently estimates that one-quarter of all malaria cases in Africa occur in Nigeria. Understanding the
geographic and ecologic distributions of Nigerian malaria vector species is crucial for intelligent design
and targeting of control strategies in Nigeria, and for estimation of the risk of vector incursion to other
regions in the world. This study combines Nigerian vector and ecologic surveillance efforts that will
provide actionable outputs for public health interventions to prevent or control malaria. Also evaluating the
malaria vector insecticide susceptibility profile can be highly correlated with the reported disease. A
parallel DoS funded project (“Building laboratory capacity for integrated malaria surveillance in Cross
River State”) makes use of the same equipment and supplies thereby reducing the cost of this proposal and
supports the establishment of a sustainable research program as well as fostering joint military-to-military
engagement between NAMRU-3 and the Nigerian Armed Forces in Cross River State. Data generated from
the study will be lodged in the GEIS-funded VectorMap database and will be provided to Roll Back
Malaria in Calabar and State MOH to aid vector control in Nigeria.
OBJECTIVES:
1- Provision of refresher training and mentoring exercises in field trapping and laboratory methodologies.
2- Determination of the potential spatial distribution of the Plasmodium transmitting vectors in specific
areas and generate digital mapping for those areas using ArcGIS 10.
3- Determination of malaria vector species composition, infection rates and their feeding preferences
through blood meal analysis.
4- Monitor susceptibility of Anopheles gambiae adults to 4 insecticide groups using WHO Bioassays.
5- Characterization of the knockdown resistance (kdr) mutation from the bioassay population.
6- Provide Walter Reed Biosystematics Unit with NAMRU-3 VBRP vector collection data to be added to
VectorMap. If second year of funding is awarded, predicting distribution of major malaria vectors based on
ecological niche and environmental influences.
METHODS SUMMARY: Our proposed work will be conducted in collaboration with Calabar Institute of
Tropical Disease Research and Prevention (CITDRP) and as a mil to mil collaboration with Navy and
Army base in Calabar.
- Mosquito surveillance will be conducted in 4 regions; Akpabuyo, Yakurrr (Ugep), Ogoja and Calabar.
- Outdoor mosquito adults will be collected using lure and CO2 baited traps every month for 5 consecutive
nights while knock-down insecticide will be used to collect adult indoor resting mosquitoes (Spray catch).
- Anopheles immature stages will be collected and reared. First generation of adults will be used in the
WHO Insecticide susceptibility assays using 0.75% permethrin, 4% DDT, 0.1% bendiocarb and 5%
malathion. Anopheles gambiae s.s. (named ‘AGIB’) maintained in the laboratory insectaries at the Nigerian
Institute of Medical Research, Lagos will be used as a reference strain, i.e. “susceptible strain”.
- All coordinates for trapping sites will be projected on a map using ArcGIS (Version 10).
- Mosquito sorting and identification using specific morphological keys for the area, will take place in the
CITDRP lab. A subset of the catch will be sent to NAMRU-3 lab for confirmation.
- Females Anopheles heads and thoraces will be tested for the presence of circumsporozoite antigens of
Plasmodium species ELISA (Burkot et al. 1984). Members of the Anopheles gambiae or funestus complex
1
2
will be identified using the PCR technique described by Scott et al. (1993) with minor modifications.
Anopheles will be tested for insecticide resistance, by molecular techniques (Weill et al., 2004).
- Data generated from collections will be used in ecologic niche modeling efforts in the 2nd year to predict
species distribution in sampled and unsampled regions.
GOALS
Surveillance and Response
Training and Capacity Building
PRIORITY SURVEILLANCE CONDITIONS
Febrile and Vector-borne Infection (FVBI)
APPROACH
BACKGROUND/COMMENTS
According to a report by The President’s Malaria Initiative (PMI) in 2012, there are more deaths due to
malaria in Nigeria than in any other country, and according to the World Health Organization (WHO)
report in 2011, 100% of the population are at risk of malaria. Ninety five percent of malaria infections in
Nigeria are caused by Plasmodium falciparum and five percent by Plasmodium malariae (Gallup and
Sachs 2001). Malaria transmission not only varies on a continental scale across Africa but also can vary
dramatically between adjacent communities (Carter et al. 2000) depending on environmental conditions
that affect vector mosquito distributions, such as temperature, precipitation, humidity, and land cover
(Ernst et al. 2006). This variability is further complicated by differences among the Anopheles vectors in
their capacity to transmit malaria parasite and in vectors’ responses to control strategies. Nigeria is diverse
in climate and topography with mangrove, forest, forest savanna mosaic in the south and Guinea-savannah
zone in the central region, while the northern part consists of open woodland (Sudan) and arid (Sahel)
savannah. This diversity affects the abundance and behavior of malaria vectors as well their ability to
transmit Plasmodium parasites (Nigeria Malaria Indicator Survey 2010. Abuja, Nigeria: NPC, NMCP, and
ICF International; 2012).
Thirty three Anopheles species are reported from Nigeria (Systematic Catalog of Culicidae,
www.wrbu.org). Of these 33 species, the dominant vector species are members of the Anopheles gambiae
complex and the An. funestus group (Sinka et al. 2010) with some other species playing a minor or local
role like An. coustani, An. hancocki, An. leesoni, An. nili, An. moucheti, An. rivulorum and An. wellcomei
(Okorie et al. 2011).
An accurate understanding of the geographic distribution of these species, especially those
belonging to species complexes that contain vector and non-vector species, would assist a host of healthrelated actions, including predeployment counselling for prophylaxis; the choice of health messages during
deployment; postdeployment evaluation of health risk exposures; the efficient planning of strategies for
targeted control; the choice of vector identification tools; identification of the likely vector for a region;
and management or quarantine of invasive vector and parasite species (Foley et al. 2008).
The few studies on malaria transmission in Nigeria are limited to the north (Olayemi et al. 2011) and southwest of the country (Adeleke et al. 2010, Awolola et al. 2003). The most extensive data available were
compiled by Okorie et al. (2011) from a national database of the Anopheles malaria vectors of Nigeria,but
only 4.3% of studies were carried out in the south-south zone. There is still little to no information on
malaria transmission, vector species distribution and composition in Cross River State, south south region,
which has ideal conditions to support malaria vectors: high humidity and a mean temperature of 35o C.
In recent years, increasing effort has been invested in estimating ecological requirements of species
and using those estimates to identify distributional areas through ecological niche modeling (ENM) (Saupe
et al. 2012). ENM encompasses a suite of tools that relate known occurrences of species or phenomena to
geographic information system (GIS) raster layers that summarize variation in multiple environmental
dimensions. The result is an objective and quantitative picture of how what is known about a species or
phenomenon relates to environmental variation across a landscape, which in turn helps in the
understanding of disease ecology (Peterson 2006).
Data generated from the study will be lodged in the public domain web resource VectorMap
(http://www.vectormap.org/) to be utilized by medical entomologists, vector disease control staff and
health planners to determine what species have been collected and from which sites (Foley et al., 2010).
2
3
Collection data through Ecological niche models derived from this study will enable a comparison and
external validation of other models of vector species available in VectorMap. ‘Fine-tuning’ for improving
the biological reality of vector distribution models is an ongoing process that will improve estimates of
vector-borne disease risk, providing actionable outputs that help public health interventions to prevent or
control malaria. For example, The Mal-area calculator (MAC – under construction) can be used to quantify
the distributions of Anopheles vectors, parasite distribution models obtained from the Malaria Atlas Project
and human population density maps. The MAC can be used to produce maps and an index of malaria risk
for any area of interest in Nigeria, but is sensitive to the accuracy of the distribution layers used as input.
Reports of insecticide resistance are more abundant in West Africa where different levels of
resistance have been found even within short distances and during different seasons (Diabate et al., 2004).
Pyrethroid insecticide resistance in the malaria vector Anopheles gambiae Giles is mainly associated with
reduced target site sensitivity arising from a single point mutation in the sodium channel gene, often
referred to as knockdown resistance (kdr). This resistance mechanism is widespread in West Africa and
was reported for the first time in Nigeria in 2002 (Awolola et al., 2007). Awolola noted that their research
in the last 10 years has shown clearly that there is a lot of resistance to public health insecticides used for
malaria vector control in Nigeria. Due to continuous use of the four classes of chemical insecticides, the
mosquitoes were subjected to a lot of selective pressure and over the years, they have now become resistant
to the insecticides.
Funded by the U.S. Department of State Biosecurity Engagement Program for FY13, NAMRU-3
started a collaborative study with the Calabar Institute of Tropical Diseases Research and Prevention
(CITDRP), located at the University of Calabar Teaching Hospital. The goal of this partnership is to build
laboratory and human diagnostic capacity to face vector-borne disease threats in Cross River State focusing
on malaria establishing a malaria reference lab at CITDRP. In March 2013, NAMRU-3 Vector Biology
Research Program (VBRP) and Bacterial and Parasitic Disease Research Program (BPDRP) provided
training for CITDRP staff to detect Plasmodium, by ELISA in vectors and by PCR in human blood
samples, and to identify malaria vectors down to species level by microscopy and by molecular techniques.
All major equipment needed to run a vector and parasitology molecular lab has been purchased. A follow
up visit was scheduled for September 2013, to install equipment and provide on-site training to local
laboratory technicians to ensure system sustainability.
Collaboration with CITDRP has a synergistic effect on other public health institutions in the
country including the Nigerian armed forces based at Calabar Eburutu Military Barrack (Brigade/Gen Dr.
Simeon Ekanem, HOD of Preventive Medicine) fostering joint military-to-military engagement between
NAMRU-3 and the Nigerian Armed Forces. With the addition of the Nigerian Army’s Preventive
Medicine/ Public Health Department’s malaria vector surveillance sites, the surveillance network in Cross
River State is extended to areas that CITDRP would not normally have access. Also this Mil to Mil
collaboration allows force health protection capacity building for the Nigerian Army Preventive Medicine
team, through additional vector surveillance equipment, training, and the establishment of a network for
advanced testing of field collected specimens at CITDRP.
APPROACH
Study areas
Nigeria is a West African country grouped into six geopolitical zones (northwest, northeast, north central,
southwest, southsouth and southeast). The south-south zone is further divided into six states one of which
is Cross River State. The prevailing climatic feature in the south consists of two distinct seasons: wet
season that normally occur from March/April to October/November (with a short break in July) with mean
annual rainfall of 200 cm and dry season usually between November and March (Coluzzi et al. 1979).
Cross River State is a coastal state of 20,156km2 bordering Cameroon to the east. Its capital is at Calabar,
and it is named for the Cross River, which passes through the state. The study will be conducted at 4
localities in Cross River State; Calabar (Ebrutu Barracks of the Nigerian Army), Akpabuyo, Yakurrr
(Ugep) and Ogoja (See attached map)
Ogoja belongs to the northern region with population of 171,901 and area of 972 km2. It is a suburban area with mean annual rainfall varying from 2000 – 4000 mm with most rainfall concentrated from
3
4
May to September. There is observed local climate modification and change in this area (Eneji et al.
2011). Micro climatic conditions and variations in ecological systems within the region have generated
very complex weather and climatic conditions due to the traditional gardening practice that exist here. This
area is inland and has rivers, stream and swamp sites, encouraging market gardening activities. The area
has more savannah vegetation than the Southern Cross River State. There has been a steady increase in the
mean annual temperature from 43.4 oC in 2005 to 54.8 oC in 2009 during the dry season when the people
engage in market gardening. Concurrently, there has also been an increase in the mean annual temperature
during the rainy season from 40.70 C in 2005 to 50.20 C in 2009. The marked increase in temperature is as
a result of farming activities (deforestation) practiced in this area (Eneji et al. 2011).
Yakurr belongs to the central region with population of 196,670 and area of 620 km2. It is a suburban settlement with Yakurr Local Government Area Headquarters as the administrative headquarters
located 135km from Calabar. The Ekori clan of Yajurr is made up of fourteen traditional villages
historically recognized as the second largest native community in Cross River State (Effiom 2004). The
town is endowed with basic amenities of a town, (water and electricity). Yakurr has tropical rainforest
vegetation and has two major seasons per year. The rainy season begins in April and ends in November
while the dry season starts in November and terminates in March. Yakurr is not a coastal area and is not
mountainous. The annual rainfall is 2800mm, relative humidity averages 80% and the temperature range is
28 – 33oC. Marshy swamps and rice fields provide good breeding sites for malaria vectors. The marshy
rice fields permit the persistence of anopheline larval development year round (Fontenille et al. 1997).
Akpabuyo occurs in the southern region and has a population of 271,395 and an area of 1,241km2 ,
sharing a boundary with Cameroon. It is a coastal area located in the tropical rain forest belt where malaria
infection is holoendemic and perennial, with intense transmission throughout the year. It has a Local
Government Headquarters located in a village-like community with water and electricity. It has a forested
environment with average humidity at 80% and a high temperature of 35oC (Ezedinachi et. al. 1999). Part
of Akpabuyo is mangrove.
Eburutu Military Barrack is situated in a sub-urban environment at the outskirts of Calabar
Municipal Council about 6 km off Calabar Township with an area of 142km2 and a population of 179,392.
It is in the rainforest belt; but it is surrounded by low vegetation a result of the rapid growth of Calabar
town. The camp terrain has a hilly topography. Annual rainfall is between 1270-2000mm and temperature
range is 25-300C with humidity of 77 - 84%. Eburutu is close to a small settlement called Bacoco which is
inundated with large gullies caused by natural land slides. Rubber plantations are close by.
Mosquito sampling: Outdoor trapping will be conducted monthly for 5 consecutive nights at selected field
sites using CO2 baited CDC light traps. Traps will be set in the evening around 17:00 h and collected the
following morning around 08:00h for approximately 12 h. trap periods. The pyrethrum spray catch (PSC)
method for indoor resting mosquito collection will be used during 0600–0730 hrs (Afolabi et al. 2006) for
an average of six houses per area. Selected rooms are those with at least one person overnight. Prior to
spraying, the floors will be covered with clean white sheets. Pyrethroid will be sprayed with doors closed.
There is a 15 min interval before the removal of the sheets. These are inspected for Anopheles species,
collected using mechanical aspirator and transferred to lab for identification.
Sampling for mosquito larvae will take place at each field site where standing water is observed to include:
puddles, ditches, wells, and swamps. All mosquito larvae will be reared to adults for identification.
GPS readings (Garmin Oregon 550t handheld Global Positioning System) and a text description of each
collection site will be recorded. All coordinates for trapping sites will be projected on a map using ArcGIS
10. These points will also be mapped using an excel mapper that will be constructed for this project for the
areas of interest in Nigeria by Dr. Desmond Foley (see http://www.vectormap.org/resources.htm)
Data will be compiled using the downloadable excel collection sheets for mosquitoes available at
http://www.mosquitomap.org/contribute.htm
Morphological identification of mosquitoes: Anopheline mosquitoes will be identified using the
morphological keys of Gilles and De Meillon (1968). A selected number of representative specimens from
each species collected will be withheld from testing and submitted as voucher specimens for archiving at
4
5
NAMRU-3 and at Walter Reed Biosystematics Center. A reference collection of mosquitoes will be kept at
CITDRP to assist with identification.
Mosquito processing: The head and thorax of all adult females will be separated from the abdomen to be
used for sporozoite ELISA. Abdomens, wings, and legs of dried female Anopheles will be removed to
reduce the risk of detection of circumsporozoite (CS) antigen from parts of the body other than the salivary
glands. Abdomens of blood fed will be used for blood meal analysis. The wings and legs will be used for
Anopheles gambiae/ funestus complex identification following DNA extraction, while male Anopheles will
be tested for insecticide resistance.
Anopheles gambiae complex identification: Genomic DNA is extracted using the QIAGEN DNA Mini
Kit (QIAGEN, Valencia, CA) following the manufacturer’s instructions. For differential identification of
sibling species in the A. gambiae complex including simultaneous separation of the M and S molecular
forms within A. gambiae sensu stricto, a PCR-RFLP method is used according to Fanello et al. 2002. This
method involves combination of the protocols established by Scott et al. (1993) and Favia et al. (1997).
Anopheles funestus complex identification: We will use a PCR assay based on species-specific primers
in the ITS2 region on the rDNA to identify five of the most commonly found members of the A. funestus
group: A. funestus, A. vaneedeni, A. parensis, A. leesoni, and A. rivulorum according to Koekemoer et al.
2002.
Plasmodium falciparum sporozoite rate: The heads & thoraces of individual mosquitoes will be tested to
detect Plasmodium falciparum, P. vivax-210, and P. vivax-247 circumsporozoite (CS) proteins by ELISA
as recommended and modified by Wirtz et al. 1992. The monoclonal and peroxidase-conjugated antibodies
are obtained from CDC, Atlanta, Georgia. A sandwich ELISA is carried out on dried mosquitoes.
Sporozoite rate is determined as the percentage of mosquitoes carrying P. falciparum CSP antigen.
Identification of blood meal origin: Engorged field specimens of female Anopheles species will be
collected by pyrethrum spray catch. DNA will be isolated from only engorged abdomens to be used in the
blood meal diagnostic analysis. A multiplex PCR targeting Cytochrome b is used according to Kent and
Norris (2005). This assay can identify the blood meals from five mammal species: cow, human, pig, goat
and dog.
Insecticide susceptibility bioassays: Insecticide susceptibility assays will be performed on adult nonblood fed mosquitoes one- to three-day old that will be reared from field-collected larvae as described
above or on F1s of field-collected adult mosquitoes. The tests will be carried out using the diagnostic doses
of different insecticides recommended by WHO. The assay will be carried out according to the
accompanying instructions. Briefly, for each of the insecticides tested, mosquitoes will be divided into
batches between 15–25 mosquitoes and will be exposed to insecticide-treated papers for 1 h. Insecticide
knockdown effects will be recorded every 15 min. At the end of the exposure period, mosquitoes will be
transferred into tubes with untreated papers and allowed a 24 h recovery period after which mortality will
be recorded. Tests will be accompanied by control tests where mosquitoes will be exposed to papers treated
only with silicone oil for 1 h. Bioassays will be also carried out on the An. gambiae susceptible strain
(insectary Strain). Mortality will be noted 24 h post exposure as defined in the criteria for determining
resistance or susceptibility to diagnostic doses of insecticide. All mosquitoes will be supplied with a 10%
glucose meal during the 24 h recovery period.
Knockdown resistance (kdr) gene analysis: The presence of the knockdown resistance (kdr) will be
tested using standard diagnostic PCR assays. Both the L1014S (leucine-serine) kdr allele and the L1014F
(leucine-phenylalanine) kdr allele (Martinez-Torres 1998) will be assayed for using field-collected
specimens. DNA from a proportion of specimens will be also directly sequenced in a double-blind assay to
re-confirm the presence of the kdr mutation. Sequencing will be performed using the ABI 3700 sequencer
following DNA amplification and purification using a Qiagen purification kit at CDC, Atlanta Georgia.
Environmental Variables. Environmental data used to build the model will be downloaded from
WorldClim database (http://www.worldclim.org/) in an ESRI grid format (30 arc-seconds, ~1 km
resolution). In addition to altitude, bioclimatic variables will include: Annual Mean Temperature, Mean
Diurnal Range, Isothermality, Temperature Seasonality, Max Temperature of Warmest Month, Min
Temperature of Coldest Month, Temperature Annual Range, Mean Temperature of Wettest Quarter, Mean
Temperature of Driest Quarter, Mean Temperature of Warmest Quarter, Mean Temperature of Coldest
5
6
Quarter, Annual Precipitation, Precipitation of Wettest Month, Precipitation of Driest Month, Precipitation
Seasonality, Precipitation of Wettest Quarter, Precipitation of Driest Quarter, Precipitation of Warmest
Quarter, Precipitation of Coldest Quarter. In addition, the hydrological layers slope, angle, aspect,
topoindex will be downloaded from Hydro1K Elevation derivative database with a native resolution 1km x
1km (http://gcmd.nasa.gov/records/GCMD_HYDRO1k.html). Also, soil taxonomy suborders of the world
will
be
obtained
from
the
USDA
National
Soils
Conservation
Service
(http://soils.usda.gov/use/worldsoils/mapindex/order.html). Satellite data, such as the Normalized
Difference Vegetation Index (NDVI), will also be derived from the Moderate Resolution Imaging
Spectroradiometer (MODIS) satellite (National Aeronautics and Space Administration, NASA
(http://modis.gsfc.nasa.gov/)
Vector data going into the model will integrate available collection records from VectorMap and direct
collections executed during this project to help generate continent-wide models that will then be projected
to the area of interest. The environmental data will be clipped and exported to ASCII raster files for use in
maximum Entropy (MaxEnt) program. The mask layer for Cross River State will be created in ArcMap®
for use in Maxent. Predicted distribution maps will be generated for each complex species in Maxent
software. Field effort will be accomplished to externally validate the model and to assess the potential for
using the above mentioned environmental data in models of various Anopheles spp.
Modeling Procedure: Amongst those new modeling techniques, the MaxEnt method selected for this
study (Phillips et al. 2006; Phillips & Dudik 2008) performs particularly well (Elith et al., 2006). It has
been previously used to predict the spatial distribution of malaria vectors (Foley et al., 2010). It does not
require absence data and can be transferred to large areas with sparse or no species sampling records.
MaxEnt’s predictive performance is consistently competitive with the highest performing methods (Elith et
al., 2006). Seventy five percent of the data records for each species will be randomly selected as training
points, used in model building. The remaining 25% of the records will be used in model validation as test
points. Preliminary validation of models will involve a comparison of extrinsic omission rates (i.e.
proportion of test localities falling outside the prediction for each algorithm) coupled with calculations of
the area under the curve (AUC) of the receiver operating characteristic (ROC). In order to determine
which variables contribute most to the model development, the jackknife procedure will be used to produce
three different types of models: (1) models created with one variable at a time excluded and all other
variables included, (2) models created with only one variable included, and (3) a model created with all
variables. Variables that are most important to model development are those that decrease the training gain
when removed from the model and show gain when the model is developed with only one variable.
While the models are validated internally by the Maxent program, it is desirable to conduct field
collections to externally validate the model to allow for further refinement if necessary. Mosquito
collection data and distribution models will be entered into VectorMap.
HUMAN / ANIMAL USAGE: N/A
COUNTRIES: Nigeria
MILESTONES FISCAL YEAR 2015
QUARTER 1
1. Develop protocol and submit to NAMRU-3 SRB.
2. Get host county approvals and develop contract between CITDR&P and NAMRU
3. Purchasing minor equipment and supplies after fund availability.
QUARTER 2
1- Establish 4 mosquito collecting sites.
2- Training for CITDR&P and Nigerian Army Preventive Medicine team personnel involved in
mosquito collection, identification, ELISA and PCR techniques.
3- Field collection of an average of 2000 mosquito samples per month followed by identification
down to species level by the CITDRP team (subsamples to be confirmed by NAMRU-3 mosquito
certified team and a reference collection of mosquitoes will be kept at CITDRP to assist with
identification).
4- Laboratory processing and testing of Anopheles spp. mosquitoes.
5- Start an Anopheles gambaie colony for CITDR&P
6
7
6- Mapping of collection locations for Plasmodium transmitting vectors using ArcGIS 10.
QUARTER 3
1- Field collection of an average of 2000 mosquito samples per month followed by identification
down to species level by the CITDRP team (subsamples to be confirmed by NAMRU-3 mosquito
certified team).
2- Laboratory processing and testing of Anopheles spp. mosquitoes.
3- Run mosquito samples (larvae and adults) to determine insecticide resistance level to different
insecticides groups.
4- Updating map of Plasmodium transmitting vectors
5- Provide WRBU with available data to update VectorMap
QUARTER 4
1- Field collection of an average of 2000 mosquito samples per month followed by identification
down to species level by the CITDRP team (subsamples to be confirmed by NAMRU-3 mosquito
certified team).
2- Laboratory processing and testing of Anopheles spp. mosquitoes.
3- Run mosquito samples (larvae and adults) to determine insecticide resistance level to different
insecticides groups.
4- Updating map of Plasmodium transmitting vectors.
5- Data analysis.
6- Ecological niche modeling (using the program Maxent, occurrence data, and selected
environmental variables available online) to predict the distribution of major malaria vectors.
7- Upload data to update VectoMap.
8- Manuscript preparation.
FISCAL YEAR 2015 BUDGET
PROJECTED COST BY EOR
NAMRUCivilian Pay – Benefits
$0K
3($k)
Civilian Pay (U.S. Government Employees/FSNs)
$20k
Travel
$35k
Transportation of Things
$5k
Rent/Communications
$0k
Printing/Reproduction
$0k
Supply/Materials
$17k
Equipment
$15k
Indirect Cost (25%) + seat rate (5%)
$38.1k
Purchased Services [Including Contracts]
Contract Type
Trapping, sample collection and lab processing
Contract Description
TOTAL REQUEST BUDGET FROM
CONTRACTS
TOTAL REQUEST BUDGET
FROM GEIS
Amount
$35k
$165.1K
BUDGET JUSTIFICATION
1. Salary justification: Salaries include scientist/ 3 technicians for lab work and training /
administrative assistant for logistics.
2. A trip at the beginning of the study for sites selection (2 persons; 1 civilian and 1 military) and
training (2 persons). Each trip will cost approximately $7,000/person/8 days. A night has to be spent
in Lagos or Abuja before flying to Calabar because connecting flights are only available mornings.
The same on the way back to Cairo. Our entomologist at Ghana detachment (Mr. Mba Mosore) will
be involved in the follow up visits every other month for trapping and identification to reduce travel
costs.
7
8
3. Transportation of Things. This includes shipping cost of minor lab surveillance equipment and
supplies to Nigeria, and shipping of samples from Nigeria to Egypt.
4. Supplies as molecular kits, ELISA reagents, WHO kits, insecticides, stereo dissecting microscope.
5. Only minor lab equipment will be purchased as traps, batteries and chargers, GPS and aspirators.
6. CITDR&P contracts: We anticipate contracting 4 CITDR&P personnel for trap setting in 4 sites,
mosquito collection, and 2 other personnel for ELISA, insecticide resistance assays and molecular
lab work. Furthermore, we would be using CITDR&P facilities (part of the contract) for our study.
REFERENCES
Adeleke MA, Mafianaa CF, Idowu AB, Sam-Wobo SO, Idowu OA (2010): Population dynamics of
indoor sampled mosquitoes and their implication in disease transmission in Abeokuta, south-western
Nigeria. Vector Borne Dis, 47: 33–38
Afolabi BM, Amajor CN, Salako LA (2006): Seasonal and Temporal variations in the population and
biting habits of mosquitoes on the Atlantic coast of Lagos, Nigeria. Med Prin Pract, 15: 200–208.
Awolola TS, Ibrahim K, Okorie T, Koekemoer LL, Hunt RH, Coetzee M (2003): Species composition
and biting activities of anthropophilic Anopheles mosquitoes and their role in malaria transmission in a
holoendemic area of south western Nigeria. Afr Entomol, 11(2): 227–232.
Awolola TS, Oduola AO, Oyewole IO, Obansa JB, Amajoh CN, Koekemoer LL, Coetzee M (2007):
Dynamics of knockdown pyrethroid insecticide resistance alleles in a field population of Anopheles
gambiae s.s. in southwestern Nigeria. J Vector Borne Dis.,44(3):181-188.
Carter R, Mendis KN, Roberts DR (2000): Spatial targeting of interventions against malaria. Bulletin of
the World Health Organization, 78: 1401–1411.
Coluzzi M, Sabatini A, Petrarca V, Di Deco MA (1979): Chromosomal differentiation and adaptation to
human environments in the Anopheles gambiae complex. Tran R Soc Trop Med Hyg, 73: 483-497.
Diabate A, Brengues C, Baldet T, Dabiré KR, Hougard JM, Akogbeto M, Kengne P, Simard F, Guillet
P, Hemingway J, Chandre F (2004): The spread of the Leu-Phe kdr mutation through Anopheles gambiae
complex in Burkina Faso: genetic introgression and de novo phenomena. Trop Med Int Health, 9(12):12671273.
Effiom, OE (2004): Ecology of malaria vectors in Ekori, Yakurr Local Government Area of Cross River
State of Nigeria. MSc Thesis, University of Calabar.
Elith J, Graham H, Anderson P, Dudı´k M, Ferrier S, et al. (2006): Novel methods improve prediction
of species’ distributions from occurrence data. Ecography, 29: 129–151.
Eneji VCO, Ben CB, Headboy P, Okongor – En O, Zembo A A, Mubi MA and Oko PE (2011):
Ecological Implication of Market gardening in the Old Ogoja zone of Nigeria. International Journal of
Physical Sciences, 6 (22): 5309 – 53416
Ernst K, Adoka S, Kowuor D, Wilson M, John C (2006): Malaria hotspot areas in a highland Kenya site
are consistent in epidemic and non-epidemic years and are associated with ecological factors. Malaria
Journal, 5: 78.
Ezedinachi ENU, Ekanem OJ, Chukwuani CM, Meremiukwu MM, Ojar EA, Alaribe AAA, Umotong
AB, Haller L (1999): Efficacy and tolerability of a low dose mefloquine- sulfadoxine- pyremethamine
combination compared with chloroquine in the treatment of acute malaria infection in a population with
multiple drug-resistant Plasmodium falciparum. American Journal of Tropical Medicine and Hygiene,
6(1): 114-119
Fanello C, Santolamazza F, della Torre A (2002): Simultaneous identification of species and molecular
forms of the Anopheles gambiae complex by PCR-RFLP. Med Vet Entomol., 16(4):461-464.
Favia G, della Torre E, Bagayoko M, Lanfrancotti A, Sagnon N, Toré YT, Coluzzi M (1997):
Molecular identification of sympatric chromosomal forms of Anopheles gambiae and further evidence of
their reproductive isolation. Insect Molecular Biology, 6: 377-383.
Foley DH, Wilkerson R. MosquitoMap Website. Available at: http://www.mosquitomap.org/ index.htm.
Accessed February 27, 2009.
Foley DH, Klein TA, Kim HC, Wilkerson RC, Rueda LM (2008): Malaria risk assessment for the
Republic of Korea based on models of mosquito distribution. US Army Med Dep J, 46–53.
Foley DH, Wilkerson RC, Birney I, Harrison S, Christensen J, Rueda LM (2010): MosquitoMap and the
8
9
Mal-area calculator: new web tools to relate mosquito species distribution with vector borne disease. Int J
Health Geogr., 9:11
Fontenille D, Lochonuran L, Diagne N, Sokhna C, Lemasson JJ, Diara M, Konate L, Faye F, Rogior C,
Trape JF (1997): High Annual and seasonal variations in malaria transmission by Anopheles and vector
species composition in Dielmo, a holoendemic area in Senegal. American Journal of Medicine and
Hygiene, 56: 237-253
Gallup JL, Sachs JD (2001): The economic burden of malaria. Am J Trop Med Hyg, 64:85-96.
Gillies MT, De Meillon B (1968): The Anophelinae of Africa south of the Sahara (Ethiopian
Zoogeographical Region). Publ S Afr Inst Med Res, 54: 343.
Kent RJ, Norris DE (2005): Identification of mammalian blood meals in mosquitoes by a multiplexed
polymerase chain reaction targeting cytochrome B. Am J Trop Med Hyg., 73(2):336-342.
Koekemoer LL, Kamau L, Hunt RH, Coetzee M (2002): A cocktail polymerase chain reaction assay to
identify members of the Anopheles funestus (Diptera: Culicidae) group. Am J Trop Med Hyg., 66(6):804811.
Martinez-Torres D, Chandre F, Williamson MS, Darriet F, Berge JB, Devonshire AL, Guillet P, Pasteur
N, Pauron D. 1998. Molecular characterization of pyrethroid knockdown resistance (kdr) in the major
Malaria vector Anopheles gambiae s.s. Insect Molecular Biology 7: 179-184.
Okorie PN, McKenzie FE, Ademowo, OG, Bockarie M, Kelly-Hope L (2011): Nigeria Anopheles
vector database: An overview of 100 years’ research. PLoS ONE 6(12): e28347.
Olayemi IK, Ande AT, Chita S, Ibemesi G, Ayanwale VA, Odeyemi OM (2011): Insecticide
susceptibility profile of the principal malaria vector, Anopheles gambiae s.l. (Diptera: Culicidae), in northcentral Nigeria. J Vector Borne Dis, 48: 109–112
Peterson AT (2006): Ecologic niche modeling and spatial patterns of disease transmission. Emerging
Infectious Diseases, 12 (12): 1822-1825.
Phillips S, Anderson R, Schapire R (2006): Maximum entropy modeling of species geographic
distributions. Ecol Modell, 190: 231–259.
Phillips S, Dudik M (2008): Modelling of species distributions with Maxent: new extensions and a
comprehensive evaluation. Ecography, 31: 161–175.
Saupe EE, Barve V, Myers CE, Soberón J, Barve N, Hensz CM,. Peterson AT, Owens HL, LiraNoriega A (2012): Variation in niche and distribution model performance: The need for a priori assessment
of key causal factors. Ecological Modelling, 237– 238 : 11– 22
Scott JA, Brogdon WG, Collins FH (1993): Identification of single specimens of the Anopheles
gambiae complex by polymerase chain reaction. Am J Trop Med Hyg., 49:520-529.
Sinka ME, Bangs MJ, Manguin S, Coetzee M, Mbogo CM, et al. (2010): The dominant Anopheles
vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and
bionomic pre´cis. Parasit Vectors, 3: 117.
Wirtz RA, Sattabongkot J, Hall T, Burkot TR, Rosenberg R (1992): Development and evaluation of an
enzyme-linked immunosorbent assay for Plasmodium vivax-VK247 sporozoites. J Med Entomol.,
29(5):854-857.
ATTACHMENTS
Attachment_1 is the map
9