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.

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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

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.

Dengue virus is one of the most important arboviral pathogens and the causative agent of dengue fever, dengue hemorrhagic fever, and dengue shock syndrome. It is transmitted between humans by the mosquitoes Aedes aegypti and Aedes albopictus, and at least 2.5 billion people are at daily risk of infection. During their lifecycle, mosquitoes are exposed to a variety of microbes, some of which are needed for their successful development into adulthood. However, recent studies have suggested that the adult mosquito's midgut microflora is critical in influencing the transmission of human pathogens. In this study we assessed the reciprocal interactions between the mosquito's midgut microbiota and dengue virus infection that are, to a large extent, mediated by the mosquito's innate immune system. We observed a marked decrease in susceptibility to dengue virus infection when mosquitoes harbored certain field-derived bacterial isolates in their midgut. Transcript abundance analysis of selected antimicrobial peptide genes suggested that the mosquito's microbiota elicits a basal immune activity that appears to act against dengue virus infection. Conversely, the elicitation of the mosquito immune response by dengue virus infection itself influences the microbial load of the mosquito midgut. In sum, we show that the mosquito's microbiota influences dengue virus infection of the mosquito, which in turn activates its antibacterial responses.

Equations of a generic arbovirus transmission dynamic model including the effect of a mosquito repellent.

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BackgroundOne major consequence of economic development in South-East Asia has been a rapid expansion of rubber plantations, in which outbreaks of dengue and malaria have occurred. Here we explored the difference in risk of exposure to potential dengue, Japanese encephalitis (JE), and malaria vectors between rubber workers and those engaged in traditional forest activities in northern Laos PDR.Methodology/Principal findingsAdult mosquitoes were collected for nine months in secondary forests, mature and immature rubber plantations, and villages. Human behavior data were collected using rapid participatory rural appraisals and surveys. Exposure risk was assessed by combining vector and human behavior and calculating the basic reproduction number (R0) in different typologies. Compared to those that stayed in the village, the risk of dengue vector exposure was higher for those that visited the secondary forests during the day (odds ratio (OR) 36.0), for those living and working in rubber plantations (OR 16.2) and for those that tapped rubber (OR 3.2). Exposure to JE vectors was also higher in the forest (OR 1.4) and, similar when working (OR 1.0) and living in the plantations (OR 0.8). Exposure to malaria vectors was greater in the forest (OR 1.3), similar when working in the plantations (OR 0.9) and lower when living in the plantations (OR 0.6). R0 for dengue was >2.8 for all habitats surveyed, except villages where R0≤0.06. The main malaria vector in all habitats was Anopheles maculatus s.l. in the rainy season and An. minimus s.l. in the dry season.Conclusions/SignificanceThe highest risk of exposure to vector mosquitoes occurred when people visit natural forests. However, since rubber workers spend long periods in the rubber plantations, their risk of exposure is increased greatly compared to those who temporarily enter natural forests or remain in the village. This study highlights the necessity of broadening mosquito control to include rubber plantations.

Assumes repellent has 100% initial efficacy after application and a half-life of 7 hours, with two applications per day.

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

Kaplan-Meier analysis to determine mean protection time of dUDL and PMD in three treatment formats.