Context

Dengue fever is a mosquito-borne disease that occurs in tropical and sub-tropical parts of the world. In mild cases, symptoms are similar to the flu: fever, rash, and muscle and joint pain. In severe cases, dengue fever can cause severe bleeding, low blood pressure, and even death.

Because it is carried by mosquitoes, the transmission dynamics of dengue are related to climate variables such as temperature and precipitation. Although the relationship to climate is complex, a growing number of scientists argue that climate change is likely to produce distributional shifts that will have significant public health implications worldwide.

In recent years dengue fever has been spreading. Historically, the disease has been most prevalent in Southeast Asia and the Pacific islands. These days many of the nearly half billion cases per year are occurring in Latin America.

Accurate dengue predictions would help public health workers ... and people around the world take steps to reduce the impact of these epidemics. But predicting dengue is a hefty task that calls for the consolidation of different data sets on disease incidence, weather, and the environment, an understanding of the relationship between climate and dengue dynamics can improve research initiatives and resource allocation to help fight life-threatening pandemics.

https://www.chathampublichealth.com/wp-content/uploads/2012/10/Dengue-Fever-Outbreak-on-World-Map1.jpg" alt="image">

Content

The goal is to predict the total_cases label for each (city, year, weekofyear) in the test set. There are two cities, San Juan and Iquitos, with test data for each city spanning 5 and 3 years respectively. You will make one submission that contains predictions for both cities. The data for each city have been concatenated along with a city column indicating the source: sj for San Juan and iq for Iquitos. The test set is a pure future hold-out, meaning the test data are sequential and non-overlapping with any of the training data. Throughout, missing values have been filled as NaNs.

The data includes the following set of information on a (year, weekofyear) timescale:

(Where appropriate, units are provided as a _unit suffix on the feature name.)

City and date indicators

• city – City abbreviations: sj for San Juan and iq for Iquitos
• week_start_date – Date given in yyyy-mm-dd format

NOAA's GHCN daily climate data weather station measurements

• station_max_temp_c – Maximum temperature
• station_min_temp_c – Minimum temperature
• station_avg_temp_c – Average temperature
• station_precip_mm – Total precipitation
• station_diur_temp_rng_c – Diurnal temperature range

PERSIANN satellite precipitation measurements (0.25x0.25 degree scale)

precipitation_amt_mm – Total precipitation

NOAA's NCEP Climate Forecast System Reanalysis measurements (0.5x0.5 degree scale)

• reanalysis_sat_precip_amt_mm – Total precipitation
• reanalysis_dew_point_temp_k – Mean dew point temperature
• reanalysis_air_temp_k – Mean air temperature
• reanalysis_relative_humidity_percent – Mean relative humidity
• reanalysis_specific_humidity_g_per_kg – Mean specific humidity
• reanalysis_precip_amt_kg_per_m2 – Total precipitation
• reanalysis_max_air_temp_k – Maximum air temperature
• reanalysis_min_air_temp_k – Minimum air temperature
• reanalysis_avg_temp_k – Average air temperature
• reanalysis_tdtr_k – Diurnal temperature range

Satellite vegetation - Normalized difference vegetation index (NDVI) - NOAA's CDR Normalized Difference Vegetation Index (0.5x0.5 degree scale) measurements

• ndvi_se – Pixel southeast of city centroid
• ndvi_sw – Pixel southwest of city centroid
• ndvi_ne – Pixel northeast of city centroid
• ndvi_nw – Pixel northwest of city centroid

Acknowledgements

This data collected by various U.S. Federal Government agencies—from the Centers for Disease Control and Prevention to the National Oceanic and Atmospheric Administration in the U.S. Department of Commerce

Inspiration

• The relationship between climate and dengue dynamics
• predict the number of dengue fever cases reported each week in San Juan, Puerto Rico and Iquitos, Peru

These data represent mosquito trap site results in the District of Columbia from 2016 to 2018. Trap locations are considered approximate address and/or the “nearest” street address or block to the stated coordinates in the data. Visit Fight the Bite: Protecting the District of Columbia from Mosquitoes- a collection of the 2016-2018 Arbovirus Surveillance Program conducted annually by DC Health, Health Regulation & Licensing Admin., Animal Services Div.Mosquitoes have the potential to spread harmful diseases. During the annual mosquito season in Washington DC, usually from April – October, DC Health deploys surveillance and mitigation methods to control the mosquito population in the District. DC Health (also known as the D.C. Department of Health or formerly DOH) has been trapping and testing mosquitoes for West Nile virus (WNV) for well over a decade. Starting in 2016, and in response to the Zika outbreak in Latin America and the Caribbean, DC Health substantially increased mosquito monitoring activities across the city. There were a total of 28 sites and 36 traps across the 8 wards. Data was submitted to the Centers for Disease Control MoquitoNet Portal.Note: the 2017 analysis does not include data for October. This is because October of 2017 would have skewed the results far too much based on a few variables that occurred. For example, the number of traps which had failed by the end of the season.Mosquito species in Washington, D.C.:Culex Pipiens, Salinarius and Culex Restuan: spread West Nile VirusAedes aegypti : according to the Centers for Disease Control (CDC), health experts have determined this species to be the most competent vector, capable of transmitting Zika to the human population. To date, none of the Aedes aegypti trapped in Washington, D.C. have been found to carry the Zika virus.Aedes albopictus: capable of spreading Zika to people. However, health experts are still learning whether it is likely to do so as it appears at this time, it is not as competent a vector for transmitting Zika as is the Aedes aegypti. Just because a mosquito can carry the virus does not mean that it will cause disease. So far, none of the Aedes albopictus trapped in Washington, D.C. have been found to carry the Zika virus.Aedes japonicus: normally found in South Florida, is present in D.C. in small numbers. Presently there is no indication that they are competent vectors for spreading Zika to the human population.

Mosquito-borne diseases affect millions of people and cause thousands of deaths yearly. Vaccines have been hitherto insufficient to mitigate them, which makes mosquito control the most viable approach. But vector control depends on correct species identification and geographical assignment, and the taxonomic characters of mosquitoes are often inconspicuous to non-taxonomists, which are restricted to a life stage and/or even damaged. Thus, geometric morphometry, a low cost and precise technique that has proven to be efficient for identifying subtle morphological dissimilarities, may contribute to the resolution of these types of problems. We have been applying this technique for more than 10 years and have accumulated thousands of wing images with their metadata. Therefore, the aims of this work were to develop a prototype of a platform for the storage of biological data related to wing morphometry, by means of a relational database and a web system named “WingBank.” In order to build the WingBank prototype, a multidisciplinary team performed a gathering of requirements, modeled and designed the relational database, and implemented a web platform. WingBank was designed to enforce data completeness, to ease data query, to leverage meta-studies, and to support applications of automatic identification of mosquitoes. Currently, the database of the WingBank contains data referring to 77 species belonging to 15 genera of Culicidae. From the 13,287 wing records currently cataloged in the database, 2,138 were already made available for use by third parties. As far as we know, this is the largest database of Culicidae wings of the world.

Dengue is a mosquito-borne viral disease which infects millions of people every year, specially in developing countries. Some of the main challenges facing the disease are reporting risk indicators and rapidly detecting outbreaks. Traditional surveillance systems rely on passive reporting from health-care facilities, often ignoring human mobility and locating each individual by their home address. Yet, geolocated data are becoming commonplace in social media, which is widely used as means to discuss a large variety of health topics, including the users' health status. In this dataset paper, we make available two large collections of dengue related labeled Twitter data. One is a set of tweets available through the Streaming API using the keywords dengue and aedes from 2010 to 2016. The other is the set of all geolocated tweets in Brazil during the year of 2015 (available also through the Streaming API). We detail the process of collecting and labeling each tweet containing keywords related to dengue in one of 5 categories: personal experience, information, opinion, campaign, and joke. This dataset can be useful for the development of models for spatial disease surveillance, but also scenarios such as understanding health-related content in a language other than English, and studying human mobility.

For each parameter, we include its representative symbol, a description, the standard value used, and the range of values considered. For parameters with a value given, we used that value during the fitting process, but performed univariate sensitivity. For parameters marked fitted, the range listed under variation is the constraint when fitting using MatLab function fmincon.

The original parameter choice was a = 0.0043 and b = 1.61. We then consider a = 0.001 and a = 0.01, each with b = 1.61, as well as b = 1 and b = 2, each with a = 0.0043.

Context

Dengue, Zika, and Chikungunya are global diseases that affect millions of people every year. Dengue alone causes 50 million infections annually and more than 2,5 billion people are at direct risk of getting the infection.

These diseases, which I am going to call vector-borne diseases for simplicity, are all transmitted by a mosquito: Aedes spp. Therefore, vector biology has a major role in transmission. This mosquito has specific conditions that have to be met to proliferate, such as specific minimum temperatures and a place to lay eggs in the water, such as ponds and water tanks. These data imply that precipitation and temperature must have some kind of relation with incidence of these vector-borne disease.

Content

The dataset is composed of data from two Brazilian governmental institutions, the INMET and DATASUS/SINAN, and about 5 cities of a region (Zona da Mata) of Minas Gerais State. There are two types of data, climate data, and disease data.

For context, climate data are collected using automatic stations that are spread throughout the country. They are unevenly spread, then only a few cities have this kind of data, in this specific context only 5 cities in the area have one.

On the other hand, disease data is largely available through DATASUS/SINAN, even though data can be untrustworthy or with lost sequences of time.

Dengue is the far most reported disease, as it is also the oldest, and together with the others, it has a cycle that is attributed to the mosquito. In the summers usually, there is an increase in the reported cases, following the idea that the mosquito procreates intensively in this period because of the higher temperatures and more frequent rain.

Further, I intend to add socioeconomic data from IBGE

Context

• Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes.
• It is preventable and curable.
• In 2018, there were an estimated 228 million cases of malaria worldwide.
• The estimated number of malaria deaths stood at 405 000 in 2018.
• Children aged under 5 years are the most vulnerable group affected by malaria;
• in 2018, they accounted for 67% (272 000) of all malaria deaths worldwide.
• The WHO African Region carries a disproportionately high share of the global malaria burden.
• In 2018, the region was home to 93% of malaria cases and 94% of malaria deaths.

Data

The dataset contains 2 folders

• Test
• Train

In Those two folder we have 2 folders - Infected - Uninfected

• there are total 550 images

Inspiration

Save humans by detecting and deploying Image Cells that contain Malaria or not!

preview of data

https://www.mdpi.com/asi/asi-04-00082/article_deploy/html/images/asi-04-00082-g001-550.jpg" alt="">

This dataset contains positive cases of West Nile virus found in humans by county of residence, 2006-present. Humans usually become infected with West Nile virus by being bitten by an infected mosquito. Viruses carried in the mosquito’s saliva enter the blood stream and local tissues where they infect immune cells. Most of the people who do become sick during a WNV infection develop what is referred to as “West Nile fever.” A small percentage of people will develop a much more serious illness called West Nile neuroinvasive disease (WNND). Positive cases in this dataset include both West Nile fever and West Nile neuroinvasive disease.

Context

The database contains wav recordings from the same optical sensor inserted in-turn into six insectary boxes containing only one mosquito species of both sexes (about 200-300 flying mosquitoes in each cage). As the mosquitoes fly randomly through the sensor their wingbeat partially occludes the light from the transmitter to the receiver. The light fluctuation recorded is modulated by the wingbeat of the insect. The resulting signal is pseudo-acoustic, meaning that it sounds exactly like a microphone recording but has been acquired using optical means (however, not vision based). Insect Biometrics, in the context of our work, is a measurable behavioral characteristic of flying insects. Biometric identifiers are related to the shape of the body (main body size, wing shape, wingbeat frequency, pattern movement of the wings). Biometric identification methods use biometric characteristics or traits to verify species/sex identities when insects access endpoint traps following a bait.

Content

• 279,566 wingbeat recordings correctly labeled

• 6 mosquito species (Ae. aegypti, Ae. albopictus, An. arabiensis, An. gambiae, Cu. pipiens, Cu. quinquefasciatus)

• 3 genera of mosquito species (Aedes, Anopheles, Culex)

Acknowledgements

The data have been recorded at the premises of Biogents, Regensburg, Germany (https://www.biogents.com/) and with the help of IRIDEON SA, Spain (http://irideon.eu/ ). The data have been recorded using the device published in:

Potamitis I. and Rigakis I., "Large Aperture Optoelectronic Devices to Record and Time-Stamp Insects’ Wingbeats," in IEEE Sensors Journal, vol. 16, no. 15, pp. 6053-6061, Aug.1, 2016. doi: 10.1109/JSEN.2016.2574762

The REMOSIS project that supported the creation of the database has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 691131.

We gratefully acknowledge the support of NVIDIA Corporation with the donation of a TITAN-X GPU used for training the deep learning networks used to classify mosquitoes’ spectra.

Inspiration

The point of having such recordings is to eventually embed optoelectronic sensors in automatic traps that will report counts, species and sex identity of captured mosquitoes. All species of this dataset can be dangerous as they are potential vectors of pathogens that cause serious illnesses. A widespread network of traps for insects of economic importance such as fruit flies and of hygienic importance such as mosquitoes allows the automatic creation of spatiotemporal maps and cuts down significantly the manual cost of visiting the traps. The creation of historical data can lead to the prediction of outbreaks and risk assessment in general.

We provide code to read the data and extract the power spectral density signature of each wingbeat. We also extract Mel-scaled, filter-bank features. How about wavelets and time-varying autoregressive models? The starter code using top-tier shallow classifiers achieves a mean accuracy of 81-84%. Deep-learning performs better. Can you classify genus, perform clustering, apply transfer learning to spectral data?

Come aboard and help humanity against killer mosquitoes!

Context:

Understanding the distribution of anopheline vectors of malaria is an important prelude to the design of national malaria control and elimination programmes. A single, geo-coded continental inventory of anophelines using all available published and unpublished data has not been undertaken since the 1960s. We present the largest ever geo-coded database of anophelines in Africa representing a legacy dataset for future updating and identification of knowledge gaps at national levels. The geo-coded and referenced database is made available with the related publication as a reference source for African national malaria control programmes planning their future control and elimination strategies. Information about the underlying research studies can be found at http://kemri-wellcome.org/programme/population-health/.

Content:

Geocoded info on anopheline inventory. See key below.

Acknowledgements:

KEMRI-Wellcome Trust assembled the data and distributed it on Dataverse.

Inspiration:

• Where have malarial mosquito populations grown or decreased?
• Can you predict mosquito population growth trends?
• Do you seen any correlation between mosquito populations and malaria deaths from this dataset?
• Is the banner image mosquito capable of carrying malaria?

Malaria is a disease caused by a parasite. The parasite is spread to humans through the bites of infected mosquitoes. People who have malaria usually feel very sick with a high fever and shaking chills. While the disease is uncommon in temperate climates, malaria is still common in tropical and subtropical countries

@article{owidmalaria, author = {Max Roser and Hannah Ritchie}, title = {Malaria}, journal = {Our World in Data}, year = {2019}, note = {https://ourworldindata.org/malaria} }

Created on 29 Sep 2024 - 16:28 by Lee HainesThe project describes a potential method for reducing the spread of diseases carried by insects, specifically mosquitoes that transmit malaria. The method involves using drugs that make the blood of animals or humans toxic to insects that feed on blood. The key point is that a specific enzyme called 4- hydroxyphenylpyruvate dioxygenase, HPPD, which is important for blood-feeding insects like mosquitoes, can be targeted. A drug called nitisinone, which is already approved by the FDA to treat rare human diseases linked to tyrosine metabolism, can inhibit this enzyme.We further characterise nitisinone's activity - when mosquitoes were fed human blood containing nitisinone, it killed both young and old mosquitoes, and those resistant to other insecticides. In side by side comparisons to another similar drug named ivermectin, nitisinone had a better killing profile for mosquitoes. Additionally, people with a rare genetic condition called alkaptonuria, who therapeutically ingest a low daily dose of nitisinone (2 mg/day), have blood that kills mosquitoes. This suggests that using nitisinone to inhibit this enzyme could be a new way to help control malaria.

BackgroundArthropod-borne viruses are important emerging pathogens world-wide. Viruses transmitted by mosquitoes, such as dengue, yellow fever, and Japanese encephalitis viruses, infect hundreds of millions of people and animals each year. Global surveillance of these viruses in mosquito vectors using molecular based assays is critical for prevention and control of the associated diseases. Here, we report an oligonucleotide DNA microarray design, termed ArboChip5.1, for multi-gene detection and identification of mosquito-borne RNA viruses from the genera Flavivirus (family Flaviviridae), Alphavirus (Togaviridae), Orthobunyavirus (Bunyaviridae), and Phlebovirus (Bunyaviridae).Methodology/Principal FindingsThe assay utilizes targeted PCR amplification of three genes from each virus genus for electrochemical detection on a portable, field-tested microarray platform. Fifty-two viruses propagated in cell-culture were used to evaluate the specificity of the PCR primer sets and the ArboChip5.1 microarray capture probes. The microarray detected all of the tested viruses and differentiated between many closely related viruses such as members of the dengue, Japanese encephalitis, and Semliki Forest virus clades. Laboratory infected mosquitoes were used to simulate field samples and to determine the limits of detection. Additionally, we identified dengue virus type 3, Japanese encephalitis virus, Tembusu virus, Culex flavivirus, and a Quang Binh-like virus from mosquitoes collected in Thailand in 2011 and 2012.Conclusions/SignificanceWe demonstrated that the described assay can be utilized in a comprehensive field surveillance program by the broad-range amplification and specific identification of arboviruses from infected mosquitoes. Furthermore, the microarray platform can be deployed in the field and viral RNA extraction to data analysis can occur in as little as 12 h. The information derived from the ArboChip5.1 microarray can help to establish public health priorities, detect disease outbreaks, and evaluate control programs.

Background:

• Malaria is caused by small germs carried by mosquitoes. People can get malaria if an infected mosquito bites them. Malaria destroys red blood cells and reduces oxygen in the blood. Most malaria is mild, but severe malaria kills at least 660,000 people each year. About 75% of these are children in Sub-Saharan Africa, most under age 5. Researchers want to find a safe vaccine that helps prevent malaria.

  Objectives:

• To see if a new malaria vaccine is well tolerated and effective.

  Eligibility:

• Healthy adults 18 35 years old who are not pregnant and live in Mali.

  Design:

  • Participants will be screened with medical history, physical exam, and blood test. They will also have an ECG. Soft electrodes will be stuck to the skin. A machine will record the heart s electrical signals.

  • Study participation will last about 1 year.

  • Participants will be randomly placed in 5 groups. Some will get 2 doses of the PfSPZ vaccine weeks apart; some will get 3 or 5 doses of vaccine; some will get 3 or 5 doses of placebo.

  • Doses will be given through a needle in the arm directly into the bloodstream. Then participants must stay at the clinic for 2 hours.

  • After each dose, participants will return to the clinic several times for blood tests and physical exam.

  • A week before the first dose and 2 weeks after the last, participants will take a full course of anti-malaria drugs.

  • If a participant gets malaria during the study, they will take another course of anti-malaria drugs.

The data shows the number of Malaria Cases in children of age 0-5 years in yearly distributions in different states of India . Note:-(1)Malaria is a disease caused by a parasite. The parasite is spread to humans through the bites of infected mosquitoes. People who have malaria usually feel very sick with a high fever and shaking chills.

BuzzBGone Reviews BuzzBGone Reviews – Check BuzzBGone Consumer Reports Here These superbugs also are getting smarter each day. The new invention advanced to diminish their malice seems to paintings for a while after which destroy down due to one reason or every other. You can not assume a eating place, as an example, to have a mosquito net, can you? That would no longer attraction to the customers trying to revel in their night at all. https://apnews.com/283413c30ea9cbe98995d2c213436f88

This data set contains data from the MEGABITESS 2019 cohort including ovitrap locations and characteristics, egg count data, and demographic data.Below are definitions and descriptions for the columns of dataNo - Unique IDSite ID Alpha - yy-site## where yy is the two digit year, site is a 4 digit school/teacher ID, ## is the number 1-10 for each siteSite ID - yy-site-## where yy is the two digit year, site is a 4 digit school/teacher ID, ## is the number 1-10 for each siteInformal Name - school name, teacher name, site number (1-10)School - school nameEducator - Teacher nameTrap Number - Each school has 10 traps, this is the number of the trap at each schoolElevation - Elevation where the trap was located in feetTotalPopulation - the total number of people in that census tractChildUnder18 - number of children in that census tract that are under 18DateStaratedISO - date in the format yyyymmddDate Started - The day egg traps were set out for the weekDateCollectedISO - date in the format yyyymmddDate Collected - The day the egg traps were collected during the weekEggs_Counted_by - the initials of the person at UTK who counted the eggsDataEntry - Initials of the person at UTK who entered the dataInformal_ID - school name, teacher name, site number (1-10)Calendar Week - The calendar week that the egg traps were set out (1-52)Study Week - Each school set up traps for 10 weeks; this number is a number 1-10Aedes hatched - the number of eggs on the germination paper that had hatchedAedes Embryonating - the number of eggs on the germination paper that had not hatchedOther - the number of eggs of a different speciesTotal - the total number of eggs and embryonating eggsComments - comments about the egg countingAdults_Identified_by - Initials of the person at UTK who coutned the adultsData_Entered_by - Initials of the person at UTK who entered the dataAdult Female mosquitoes - number of adult female mosquitoes that hatchedAdult Male mosquitoes - number of adult male mosquitoes that hatchedIdentification NotesLand Cover -primary land cover where the ovitrap was locatedOther - Land Cover - primary land cover if other was selectedShade Covered - how much shade was at the ovitrap locationNotes Or Comments - notes or comments about the trap locationShade Type - what is causing the shadeOther-Shade Type - cause of shade if otherWater Adjacency - is the ovitrap adjacent to a water sourceLatitude - latitude in decimal degreesLongitude - longitude in decimal degrees-9999 = no data; be sure to filter out the no data values when running any statistics

Arboviral disease transmission by Aedes mosquitoes poses a major challenge to public health systems in Ecuador, where constraints on health services and resource allocation call for spatially informed management decisions. Employing a unique dataset of larval occurrence records provided by the Ecuadorian Ministry of Health, we used ecological niche models (ENMs) to estimate the current geographic distribution of Aedes aegypti in Ecuador, using mosquito presence as a proxy for risk of disease transmission. ENMs built with the Genetic Algorithm for Rule-Set Production (GARP) algorithm and a suite of environmental variables were assessed for agreement and accuracy. The top model of larval mosquito presence was projected to the year 2050 under various combinations of greenhouse gas emissions scenarios and models of climate change. Under current climatic conditions, larval mosquitoes were not predicted in areas of high elevation in Ecuador, such as the Andes mountain range, as well as the eastern portion of the Amazon basin. However, all models projected to scenarios of future climate change demonstrated potential shifts in mosquito distribution, wherein range contractions were seen throughout most of eastern Ecuador, and areas of transitional elevation became suitable for mosquito presence. Encroachment of Ae. aegypti into mountainous terrain was estimated to affect up to 4,215 km2 under the most extreme scenario of climate change, an area which would put over 12,000 people currently living in transitional areas at risk. This distributional shift into communities at higher elevations indicates an area of concern for public health agencies, as targeted interventions may be needed to protect vulnerable populations with limited prior exposure to mosquito-borne diseases. Ultimately, the results of this study serve as a tool for informing public health policy and mosquito abatement strategies in Ecuador.

Malaria affects 300 million people worldwide every year and is endemic in 22 countries in the Americas where transmission occurs mainly in the Amazon Region. Most malaria cases in the Americas are caused by Plasmodium vivax, a parasite that is almost impossible to cultivate in vitro, and Anopheles aquasalis is an important malaria vector. Understanding the interactions between this vector and its parasite will provide important information for development of disease control strategies. To this end, we performed mRNA subtraction experiments using A. aquasalis 2 and 24 hours after feeding on blood and blood from malaria patients infected with P. vivax to identify changes in the mosquito vector gene induction that could be important during the initial steps of infection. A total of 2,138 clones of differentially expressed genes were sequenced and 496 high quality unique sequences were obtained. Annotation revealed 36% of sequences unrelated to genes in any database, suggesting that they were specific to A. aquasalis. A high number of sequences (59%) with no matches in any databases were found 24 h after infection. Genes related to embryogenesis were down-regulated in insects infected by P. vivax. Only a handful of genes related to immune responses were detected in our subtraction experiment. This apparent weak immune response of A. aquasalis to P. vivax infection could be related to the susceptibility of this vector to this important human malaria parasite. Analysis of some genes by real time PCR corroborated and expanded the subtraction results. Taken together, these data provide important new information about this poorly studied American malaria vector by revealing differences between the responses of A. aquasalis to P. vivax infection, in relation to better studied mosquito-Plasmodium pairs. These differences may be important for the development of malaria transmission-blocking strategies in the Americas.