Estimating Air Temperature and Its Influence on Malaria Transmission across Africa
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{"title"=>"Estimating Air Temperature and Its Influence on Malaria Transmission across Africa", "type"=>"journal", "authors"=>[{"first_name"=>"Tini", "last_name"=>"Garske", "scopus_author_id"=>"15135817600"}, {"first_name"=>"Neil M.", "last_name"=>"Ferguson", "scopus_author_id"=>"7103246319"}, {"first_name"=>"Azra C.", "last_name"=>"Ghani", "scopus_author_id"=>"7006814439"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"23437143", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0056487", "pui"=>"368400279", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84874274867", "sgr"=>"84874274867"}, "id"=>"4e76362d-9a31-3a72-ac58-9c1121bdf245", "abstract"=>"Malaria transmission is strongly influenced by climatic conditions which determine the abundance and seasonal dynamics of the Anopheles vector. In particular, water temperature influences larval development rates whereas air temperature determines adult longevity as well as the rate of parasite development within the adult mosquito. Although data on land surface temperature exist at a spatial resolution of approximately 1 km globally with four time steps per day, comparable data are not currently available for air temperature. In order to address this gap and demonstrate the importance of using the right type of temperature data, we fitted simple models of the relationship between land-surface and air temperature at lower resolution to obtain a high resolution estimate of air temperature across Africa. We then used these estimates to calculate some crucial malaria transmission parameters that strongly depend on air temperatures. Our results demonstrate substantial differences between air and surface temperatures that impact temperature-based maps of areas suitable for transmission. We present high resolution maps of the malaria transmission parameters driven by air temperature and their seasonal variation. The fitted air temperature datasets are made publicly available alongside this publication.", "link"=>"http://www.mendeley.com/research/estimating-air-temperature-influence-malaria-transmission-across-africa", "reader_count"=>62, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Librarian"=>1, "Researcher"=>15, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>2, "Other"=>2, "Student > Master"=>10, "Student > Bachelor"=>3, "Lecturer"=>5, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Librarian"=>1, "Researcher"=>15, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>2, "Other"=>2, "Student > Master"=>10, "Student > Bachelor"=>3, "Lecturer"=>5, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Agricultural and Biological Sciences"=>22, "Business, Management and Accounting"=>1, "Veterinary Science and Veterinary Medicine"=>2, "Computer Science"=>2, "Earth and Planetary Sciences"=>2, "Economics, Econometrics and Finance"=>1, "Environmental Science"=>11, "Materials Science"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>8, "Sports and Recreations"=>1, "Physics and Astronomy"=>1, "Social Sciences"=>4, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>8}, "Social Sciences"=>{"Social Sciences"=>4}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>11}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>22}, "Computer Science"=>{"Computer Science"=>2}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"United States"=>1, "Brazil"=>1, "United Kingdom"=>2, "Australia"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/957459"], "description"=>"<p>Locations as marked in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056487#pone-0056487-g001\" target=\"_blank\">Figure 1:</a> L1 left (panels A and D), L2 middle (panels B and E) and L3 right (panels C and F), respectively, for night time (top: panels A, B, C) and day time (bottom: panels D, E, F). Red lines for difference between fitted and air temperatures, black lines for difference between surface and air temperatures.</p>", "links"=>[], "tags"=>["temperatures"], "article_id"=>627614, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Differences_between_surface_and_air_temperatures_over_time_for_three_selected_locations_/627614", "title"=>"Differences between surface and air temperatures over time for three selected locations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:06:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/957456"], "description"=>"<p>Locations L1, L2 and L3 at latitudes 19.5, 0, and −25.5 and longitudes 0. 19.5 and 25.5, respectively, give the locations for the time series shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056487#pone-0056487-g002\" target=\"_blank\">Figure 2</a>.</p>", "links"=>[], "tags"=>["differences", "observed"], "article_id"=>627611, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.g001", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Annual_mean_differences_between_observed_surface_and_air_temperature_during_night_left_and_day_right_/627611", "title"=>"Annual mean differences between observed surface and air temperature during night (left) and day (right).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:06:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1004951"], "description"=>"<p>x indicates interaction terms not considered.</p><p>– indicates variables not included in the final model.</p>", "links"=>[], "tags"=>["interactions", "variances", "fitted"], "article_id"=>665575, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Coefficients_of_time_series_and_location_based_variables_interactions_between_these_as_well_as_variances_of_the_random_effects_for_the_model_fitted_to_day_time_air_temperatures_/665575", "title"=>"Coefficients of time series and location based variables, interactions between these as well as variances of the random effects for the model fitted to day time air temperatures.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 01:32:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/957465"], "description"=>"<p>(A) average mosquito life span, (B) mean extrinsic incubation period (cut off at 60 days), (C) mean extrinsic infectious period, (D) mean biting rate, (E) mean number of infectious bites per infected mosquito and (F) temperature suitability index.</p>", "links"=>[], "tags"=>["malaria"], "article_id"=>627620, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.g004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Maps_of_the_mean_annual_malaria_transmission_parameters_/627620", "title"=>"Maps of the mean annual malaria transmission parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:07:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/957467"], "description"=>"<p>(A) Daily minimum (solid lines) and maximum (dashed lines) air temperatures, (B) extrinsic incubation period, (C) extrinsic infectious period, (D) daily biting rate, (E) average number of infectious bites per infected mosquito and (F) temperature suitability index. Locations as marked in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056487#pone-0056487-g001\" target=\"_blank\">Figure 1:</a> Black, red and green lines for L1, L2 and L3, respectively.</p>", "links"=>[], "tags"=>["temperatures", "malaria"], "article_id"=>627622, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Seasonality_of_temperatures_as_well_as_several_malaria_transmission_parameters_for_three_selected_locations_/627622", "title"=>"Seasonality of temperatures as well as several malaria transmission parameters for three selected locations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:07:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/957463"], "description"=>"<p>Mean temperatures (A and D) as well as the amplitudes of the annual (B and E) and biannual (C and F) modes of the Fourier transform for night (top) and day time (bottom), respectively.</p>", "links"=>[], "tags"=>["extrapolated"], "article_id"=>627618, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.g003", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fitted_air_temperature_in_176_C_extrapolated_to_a_0_1_176_grid_/627618", "title"=>"Fitted air temperature in °C extrapolated to a 0.1° grid.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:06:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1004895"], "description"=>"<p>Variances of the random effects of the locations excluded from the 90% dataset for the model fitted to all locations, and the model fitted to 90% of locations, extrapolated to the remaining 10% of locations.</p>", "links"=>[], "tags"=>["locations", "excluded", "dataset", "fitted", "extrapolated"], "article_id"=>665518, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.t005", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variances_of_the_random_effects_of_the_locations_excluded_from_the_90_dataset_for_the_model_fitted_to_all_locations_and_the_model_fitted_to_90_of_locations_extrapolated_to_the_remaining_10_of_locations_/665518", "title"=>"Variances of the random effects of the locations excluded from the 90% dataset for the model fitted to all locations, and the model fitted to 90% of locations, extrapolated to the remaining 10% of locations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 01:31:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1004876"], "description"=>"<p>Fitted random effects are obtained from the full model, whereas extrapolated random effects are from the validation model extrapolated to the excluded 10% of locations.</p>", "links"=>[], "tags"=>["cis", "1000", "bootstrap", "fitted", "extrapolated"], "article_id"=>665499, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.t004", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlations_95_CIs_from_1000_bootstrap_samples_between_the_fitted_and_extrapolated_random_effects_for_models_fitted_to_night_and_day_time_temperatures_/665499", "title"=>"Correlations (95% CIs from 1000 bootstrap samples) between the fitted and extrapolated random effects for models fitted to night and day time temperatures.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 01:31:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1004933"], "description"=>"<p>x indicates interaction terms not considered.</p><p>– indicates variables not included in the final model.</p>", "links"=>[], "tags"=>["interactions", "variances", "fitted"], "article_id"=>665561, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.t001", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Coefficients_95_CIs_of_time_series_and_location_based_variables_interactions_between_these_as_well_as_variances_of_the_random_effects_for_the_model_fitted_to_night_time_air_temperatures_/665561", "title"=>"Coefficients (95% CIs) of time series and location based variables, interactions between these as well as variances of the random effects for the model fitted to night time air temperatures.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 01:32:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/957506", "https://ndownloader.figshare.com/files/957530", "https://ndownloader.figshare.com/files/957534", "https://ndownloader.figshare.com/files/957536", "https://ndownloader.figshare.com/files/957538", "https://ndownloader.figshare.com/files/957539", "https://ndownloader.figshare.com/files/957540", "https://ndownloader.figshare.com/files/957541"], "description"=>"<div><p>Malaria transmission is strongly influenced by climatic conditions which determine the abundance and seasonal dynamics of the <i>Anopheles</i> vector. In particular, water temperature influences larval development rates whereas air temperature determines adult longevity as well as the rate of parasite development within the adult mosquito. Although data on land surface temperature exist at a spatial resolution of approximately 1 km globally with four time steps per day, comparable data are not currently available for air temperature. In order to address this gap and demonstrate the importance of using the right type of temperature data, we fitted simple models of the relationship between land-surface and air temperature at lower resolution to obtain a high resolution estimate of air temperature across Africa. We then used these estimates to calculate some crucial malaria transmission parameters that strongly depend on air temperatures. Our results demonstrate substantial differences between air and surface temperatures that impact temperature-based maps of areas suitable for transmission. We present high resolution maps of the malaria transmission parameters driven by air temperature and their seasonal variation. The fitted air temperature datasets are made publicly available alongside this publication.</p> </div>", "links"=>[], "tags"=>["estimating", "malaria", "africa"], "article_id"=>627652, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056487.s001", "https://dx.doi.org/10.1371/journal.pone.0056487.s002", "https://dx.doi.org/10.1371/journal.pone.0056487.s003", "https://dx.doi.org/10.1371/journal.pone.0056487.s004", "https://dx.doi.org/10.1371/journal.pone.0056487.s005", "https://dx.doi.org/10.1371/journal.pone.0056487.s006", "https://dx.doi.org/10.1371/journal.pone.0056487.s007", "https://dx.doi.org/10.1371/journal.pone.0056487.s008"], "stats"=>{"downloads"=>8, "page_views"=>33, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Estimating_Air_Temperature_and_Its_Influence_on_Malaria_Transmission_across_Africa__/627652", "title"=>"Estimating Air Temperature and Its Influence on Malaria Transmission across Africa", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-02-20 02:07:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1004919"], "description"=>"<p>Results are shown only for the 10% of locations excluded from the validation dataset.</p><p>A lower root mean squared difference indicates a better fit.</p>", "links"=>[], "tags"=>["squared", "differences", "observed", "aggregated", "64", "points", "validation", "smoothed", "versions"], "article_id"=>665537, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.t006", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Root_mean_squared_differences_between_the_raw_observed_temperature_time_series_aggregated_to_64_points_per_year_and_the_estimated_time_series_from_both_the_full_fit_and_the_validation_fit_as_well_as_smoothed_versions_of_the_raw_data_full_fit_and_validati/665537", "title"=>"Root mean squared differences between the raw observed temperature time, series aggregated to 64 points per year, and the estimated time series from both the full fit and the validation fit, as well as smoothed versions of the raw data, full fit and validation fit.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 01:32:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/1004972"], "description"=>"<p>Data are based on the time series between 2003 and 2006 aggregated to 64 time points per year, showing correlations (95% confidence intervals based on 1000 bootstrap samples) between time series and between means of the time series for night and day time.</p><p>– for correlations of a dataset with itself.</p>", "links"=>[], "tags"=>["ranges", "correlations", "temperatures"], "article_id"=>665593, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Tini Garske", "Neil M. Ferguson", "Azra C. Ghani"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056487.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Temperature_ranges_and_correlations_between_air_and_surface_temperatures_across_Africa_/665593", "title"=>"Temperature ranges and correlations between air and surface temperatures across Africa.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 01:33:13"}

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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Organisms", "average_usage"=>[281, 484, 611, 728, 835, 934, 1030, 1123, 1214, 1299, 1383, 1464]}, {"subject_area"=>"/Biology and life sciences/Population biology", "average_usage"=>[269, 448, 558, 658, 744, 830, 914, 995, 1068, 1139, 1214, 1284, 1349]}, {"subject_area"=>"/Medicine and health sciences/Parasitic diseases", "average_usage"=>[320, 619, 774, 903, 1012, 1118, 1224, 1333, 1442, 1518, 1600, 1702, 1779]}, {"subject_area"=>"/People and places/Demography", "average_usage"=>[253, 440, 552, 650, 735, 820, 902, 976, 1042, 1112, 1192, 1271, 1337]}]}
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