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      An Environmental Data Set for Vector-Borne Disease Modeling and Epidemiology

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          Abstract

          Understanding the environmental conditions of disease transmission is important in the study of vector-borne diseases. Low- and middle-income countries bear a significant portion of the disease burden; but data about weather conditions in those countries can be sparse and difficult to reconstruct. Here, we describe methods to assemble high-resolution gridded time series data sets of air temperature, relative humidity, land temperature, and rainfall for such areas; and we test these methods on the island of Madagascar. Air temperature and relative humidity were constructed using statistical interpolation of weather station measurements; the resulting median 95 th percentile absolute errors were 2.75°C and 16.6%. Missing pixels from the MODIS11 remote sensing land temperature product were estimated using Fourier decomposition and time-series analysis; thus providing an alternative to the 8-day and 30-day aggregated products. The RFE 2.0 remote sensing rainfall estimator was characterized by comparing it with multiple interpolated rainfall products, and we observed significant differences in temporal and spatial heterogeneity relevant to vector-borne disease modeling.

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          Most cited references28

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          Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti.

          Most studies on the ability of insect populations to transmit pathogens consider only constant temperatures and do not account for realistic daily temperature fluctuations that can impact vector-pathogen interactions. Here, we show that diurnal temperature range (DTR) affects two important parameters underlying dengue virus (DENV) transmission by Aedes aegypti. In two independent experiments using different DENV serotypes, mosquitoes were less susceptible to virus infection and died faster under larger DTR around the same mean temperature. Large DTR (20 °C) decreased the probability of midgut infection, but not duration of the virus extrinsic incubation period (EIP), compared with moderate DTR (10 °C) or constant temperature. A thermodynamic model predicted that at mean temperatures 18 °C, larger DTR reduces DENV transmission. The negative impact of DTR on Ae. aegypti survival indicates that large temperature fluctuations will reduce the probability of vector survival through EIP and expectation of infectious life. Seasonal variation in the amplitude of daily temperature fluctuations helps to explain seasonal forcing of DENV transmission at locations where average temperature does not vary seasonally and mosquito abundance is not associated with dengue incidence. Mosquitoes lived longer and were more likely to become infected under moderate temperature fluctuations, which is typical of the high DENV transmission season than under large temperature fluctuations, which is typical of the low DENV transmission season. Our findings reveal the importance of considering short-term temperature variations when studying DENV transmission dynamics.
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            Spatial epidemiology: an emerging (or re-emerging) discipline.

            Spatial epidemiology is the study of spatial variation in disease risk or incidence. Several ecological processes can result in strong spatial patterns of such risk or incidence: for example, pathogen dispersal might be highly localized, vectors or reservoirs for pathogens might be spatially restricted, or susceptible hosts might be clumped. Here, we briefly describe approaches to spatial epidemiology that are spatially implicit, such as metapopulation models of disease transmission, and then focus on research in spatial epidemiology that is spatially explicit, such as the creation of risk maps for particular geographical areas. Although the spatial dynamics of infectious diseases are the subject of intensive study, the impacts of landscape structure on epidemiological processes have so far been neglected. The few studies that demonstrate how landscape composition (types of elements) and configuration (spatial positions of those elements) influence disease risk or incidence suggest that a true integration of landscape ecology with epidemiology will be fruitful.
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              A climate-based distribution model of malaria transmission in sub-Saharan Africa.

              Malaria remains the single largest threat to child survival in sub-Saharan Africa and warrants long-term investment for control. Previous malaria distribution maps have been vague and arbitrary. Marlies Craig, Bob Snow and David le Sueur here describe a simple numerical approach to defining distribution of malaria transmission, based upon biological constraints of climate on parasite and vector development. The model compared well with contemporary field data and historical 'expert opinion' maps, excepting small-scale ecological anomalies. The model provides a numerical basis for further refinement and prediction of the impact of climate change on transmission. Together with population, morbidity and mortality data, the model provides a fundamental tool for strategic control of malaria.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                22 April 2014
                : 9
                : 4
                : e94741
                Affiliations
                [1]Institute for Disease Modeling, Intellectual Ventures, Bellevue, Washington, United States of America
                Tulane University School of Public Health and Tropical Medicine, United States of America
                Author notes

                Competing Interests: The authors have the following interests. All authors are employed by Intellectual Ventures Laboratory. The authors have filed a provisional patent application titled LAND-TEMP INTERPOLATION PROCEDURE ACCORDING TO AN EMBODIMENT, number 61/667831. The authors have been issued the following U.S. Utility Patent Applications: 13/665,883 - DETERMINING PORTIONS OF MULTIPLE SIGNALS ACCORDING TO RESPECTIVE ALGORTHMS; 13/665,888 - INTERPOLATING A PORTION OF A SIGNAL IN RESPONSE TO MULTIPLE COMPONENTS OF THE SIGNAL; 13/665,889 - INTERPOLATING A PORTION OF A SIGNAL IN RESPONSE TO A COMPONENT OF ANOTHER SIGNAL; 13/665,894 - INTERPOLATING A PORTION OF A SIGNAL IN RESPONSE TO A COMPONENT OF ANOTHER SIGNAL; and 13/665,896 - INTERPOLATING A PORTION OF A SIGNAL IN RESPONSE TO A COMPONENT OF THE SIGNAL AND A COMPONENT OF ANOTHER SIGNAL. There are no further patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

                Conceived and designed the experiments: GCC KRN PAE. Performed the experiments: GCC. Analyzed the data: GCC. Contributed reagents/materials/analysis tools: GCC. Wrote the paper: GCC.

                Article
                PONE-D-12-23511
                10.1371/journal.pone.0094741
                3995884
                24755954
                8068e016-9dbc-4050-8699-ca188f9f92ec
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 1 August 2012
                : 19 March 2014
                Page count
                Pages: 17
                Funding
                Intellectual Ventures Laboratory funded this study. The funder provided support in the form of salaries for all authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the “author contributions” section.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Population Modeling
                Infectious Disease Modeling
                Computer and Information Sciences
                Computerized Simulations
                Earth Sciences
                Atmospheric Science
                Meteorology
                Medicine and Health Sciences
                Epidemiology
                Disease Informatics
                Disease Surveillance
                Disease Vectors
                Infectious Disease Epidemiology
                Infectious Diseases
                Parasitic Diseases
                Public and Occupational Health
                Disease Ecology
                Global Health
                Tropical Diseases
                Neglected Tropical Diseases
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Statistics (Mathematics)
                Statistical Methods

                Uncategorized
                Uncategorized

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