Disease Prevention versus Data Privacy: Using Landcover Maps to Inform Spatial Epidemic Models
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{"title"=>"Disease Prevention versus Data Privacy: Using Landcover Maps to Inform Spatial Epidemic Models", "type"=>"journal", "authors"=>[{"first_name"=>"Michael J.", "last_name"=>"Tildesley", "scopus_author_id"=>"12766351300"}, {"first_name"=>"Sadie J.", "last_name"=>"Ryan", "scopus_author_id"=>"10039124600"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"23133352", "sgr"=>"84870676094", "scopus"=>"2-s2.0-84870676094", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "doi"=>"10.1371/journal.pcbi.1002723", "pui"=>"366216203", "issn"=>"1553734X"}, "id"=>"e8ea0cf6-c4c4-34b3-9353-32747ca71614", "abstract"=>"The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.", "link"=>"http://www.mendeley.com/research/disease-prevention-versus-data-privacy-using-landcover-maps-inform-spatial-epidemic-models", "reader_count"=>35, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>14, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>2, "Student > Master"=>2, "Lecturer"=>2, "Professor"=>2, "Unspecified"=>1, "Other"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>14, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>2, "Student > Master"=>2, "Lecturer"=>2, "Professor"=>2, "Unspecified"=>1, "Other"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>3, "Medicine and Dentistry"=>3, "Agricultural and Biological Sciences"=>16, "Veterinary Science and Veterinary Medicine"=>2, "Business, Management and Accounting"=>1, "Psychology"=>1, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Psychology"=>{"Psychology"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>16}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>3}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"Sweden"=>1, "United States"=>4, "United Kingdom"=>2}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/549149"], "description"=>"<p>95% confidence intervals are also shown for each data set. In both (a) and (b) results are shown for the random data (black line), LC1 (magenta line), LC2 (green line), LC3 (blue line) and LC4 (red line). In (b), spatial clustering for the recorded data is indicated by the cyan line.</p>", "links"=>[], "tags"=>["synthetic", "recorded", "cumbria", "radius", "varied", "farms", "averaged", "realisations"], "article_id"=>219635, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Michael J. Tildesley", "Sadie J. Ryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002723.g005", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphs_showing_a_the_root_mean_square_error_RM_between_the_synthetic_data_and_the_recorded_data_for_Cumbria_as_the_radius_is_varied_and_b_the_mean_density_of_farms_per_km_2_within_a_given_radius_of_all_other_farms_averaged_over_all_realisations_of_each_s/219635", "title"=>"Graphs showing (a) the root mean square error (RM) between the synthetic data and the recorded data for Cumbria as the radius is varied and (b) the mean density of farms (per km<sup>2</sup>) within a given radius of all other farms, averaged over all realisations of each synthetic data set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-01 02:40:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/549384"], "description"=>"<p>The subclass number, the corresponding aggregate class (AC) number and the land types are listed. The final column indicates which of the subclasses are included in the generation of the data sets LC1–LC4. All ACs and SCs that are considered to be correlated with livestock farm locations are highlighted in bold.</p>", "links"=>[], "tags"=>["listing", "subclasses", "aggregate", "classes", "lcm", "2000"], "article_id"=>219876, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Michael J. Tildesley", "Sadie J. Ryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002723.t001", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Table_listing_all_subclasses_SC_and_aggregate_classes_AC_in_the_1_km_2_LCM_2000_database_/219876", "title"=>"Table listing all subclasses (SC) and aggregate classes (AC) in the 1 km<sup>2</sup> LCM 2000 database.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-01 02:44:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/549039"], "description"=>"<p>Land cover classes that are assumed to host livestock farms are colored in shades of green, whilst all other land cover classes are colored in shades of grey according to LC1 (top left panel), LC2 (top right panel), LC3 (bottom right panel) and LC4 (bottom left panel). Figure provided courtesy of the Centre for Ecology and Hydrology (NERC (CEH)).</p>", "links"=>[], "tags"=>["parcels", "10", "aggregate", "classes", "25", "subclasses"], "article_id"=>219534, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Michael J. Tildesley", "Sadie J. Ryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002723.g004", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Land_cover_parcels_for_the_10_aggregate_classes_top_two_panels_and_25_subclasses_bottom_two_panels_according_to_Land_Cover_Map_2000_/219534", "title"=>"Land cover parcels for the 10 aggregate classes (top two panels) and 25 subclasses (bottom two panels) according to Land Cover Map 2000.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-01 02:38:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/549341"], "description"=>"<p>The optimal ring cull radius is defined to be the radius which minimises the mean Epidemic Impact (<i>EI</i>). The mean Epidemic Impact (<i>EI</i>) is listed for the recorded farm location data (RD), whilst for the simulated data sets, the mean (averaged over all 100 data sets) ring cull radius <i>R<sub>RC</sub></i> (in km) and the mean Epidemic Impact (<i>EI</i>) and the range of values of optimal ring cull radii (<i>R<sub>RC(min)</sub></i>-<i>R<sub>RC(max)</sub></i>) and Epidemic Impacts (<i>EI<sub>min</sub>-EI<sub>max</sub></i>) across each group of 100 data sets are shown. The maximum increase in epidemic impact, Δ<i>EI<sub>max</sub></i>, if the optimal ring cull radius as determined on each of the 100 generated data sets in each group was carried out on the recorded farm location data for that county (as opposed to a ring cull at optimal radius for the recorded farm location data) is quoted in brackets. The number of farms and area of each county (in square kilometers) are also shown.</p>", "links"=>[], "tags"=>["optimal", "cull", "radius", "epidemics", "simulated", "recorded", "generated", "sets", "lc3", "lc4", "aberdeenshire", "clwyd", "farms"], "article_id"=>219823, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Michael J. Tildesley", "Sadie J. Ryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002723.t002", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Table_showing_the_optimal_ring_cull_radius_R_RC_in_km_for_epidemics_simulated_on_the_recorded_farm_location_data_and_the_generated_data_sets_Random_LC1_LC2_LC3_and_LC4_for_Cumbria_Devon_Aberdeenshire_and_Clwyd_with_the_number_of_farms_and_size_of_the_cou/219823", "title"=>"Table showing the optimal ring cull radius <i>R<sub>RC</sub></i> (in km) for epidemics simulated on the recorded farm location data and the generated data sets “Random”, LC1, LC2, LC3 and LC4 for Cumbria, Devon, Aberdeenshire and Clwyd (with the number of farms and size of the county).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-01 02:43:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/548859"], "description"=>"<p>The lower panels show the mean epidemic impact (solid line) and the raw outputs (black dots) against the radius of ring culling for the three data sets shown in the upper panels.</p>", "links"=>[], "tags"=>["panels", "cumbria", "census", "realisation", "assuming", "locations", "farms", "located", "lc3"], "article_id"=>219347, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Michael J. Tildesley", "Sadie J. Ryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002723.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_upper_panels_show_farm_location_data_for_Cumbria_according_to_the_agricultural_census_left_panel_one_realisation_assuming_random_locations_central_panel_and_one_realisation_assuming_farms_are_located_according_to_LC3_right_panel_/219347", "title"=>"The upper panels show farm location data for Cumbria according to the agricultural census (left panel), one realisation assuming random locations (central panel) and one realisation assuming farms are located according to LC3 (right panel).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-01 02:35:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/293775", "https://ndownloader.figshare.com/files/293798", "https://ndownloader.figshare.com/files/293819", "https://ndownloader.figshare.com/files/293852", "https://ndownloader.figshare.com/files/293900", "https://ndownloader.figshare.com/files/293933", "https://ndownloader.figshare.com/files/293980", "https://ndownloader.figshare.com/files/294027", "https://ndownloader.figshare.com/files/294129", "https://ndownloader.figshare.com/files/294184"], "description"=>"<div><p>The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.</p> </div>", "links"=>[], "tags"=>["landcover", "maps", "spatial", "models"], "article_id"=>117846, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Michael J. Tildesley", "Sadie J. Ryan"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002723.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002723.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002723.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002723.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002723.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002723.s006", "https://dx.doi.org/10.1371/journal.pcbi.1002723.s007", "https://dx.doi.org/10.1371/journal.pcbi.1002723.s008", "https://dx.doi.org/10.1371/journal.pcbi.1002723.s009", "https://dx.doi.org/10.1371/journal.pcbi.1002723.s010"], "stats"=>{"downloads"=>18, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Disease_Prevention_versus_Data_Privacy_Using_Landcover_Maps_to_Inform_Spatial_Epidemic_Models__/117846", "title"=>"Disease Prevention versus Data Privacy: Using Landcover Maps to Inform Spatial Epidemic Models", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-11-01 02:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/548966"], "description"=>"<p>The results for the randomly selected data sets in each category are given by the red, green, yellow, magenta and cyan lines. The raw outputs for the recorded farm location data are shown by the blue dots. The black circles on each line indicate the radius which minimises the Epidemic Impact in each case. Each line is the mean of 20,000 model simulations and epidemics are seeded in Cumbria. (c) The number of doses of vaccine used as the vaccination radius varies for Cumbria (solid line), Devon (dashed line), Aberdeenshire (dash-dot line) and Clwyd (dotted line).</p>", "links"=>[], "tags"=>["radius", "cumbria", "recorded", "randomly", "sets", "lc3", "culling"], "article_id"=>219460, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Michael J. Tildesley", "Sadie J. Ryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002723.g003", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphs_showing_the_mean_Epidemic_Impact_against_the_radius_of_ring_culling_vaccination_for_Cumbria_only_for_the_recorded_farm_location_data_blue_line_and_for_five_randomly_selected_data_sets_from_LC3_for_a_ring_culling_and_b_vaccination_/219460", "title"=>"Graphs showing the mean Epidemic Impact against the radius of ring culling/vaccination for Cumbria only, for the recorded farm location data (blue line) and for five randomly selected data sets from LC3 for (a) ring culling and (b) vaccination.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-01 02:37:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/549289"], "description"=>"<p>The rows in the table are the same as for <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002723#pcbi-1002723-t002\" target=\"_blank\">table 2</a>.</p>", "links"=>[], "tags"=>["optimal", "vaccination", "radius", "epidemics", "simulated", "recorded", "generated", "sets", "lc3", "lc4", "aberdeenshire"], "article_id"=>219770, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Michael J. Tildesley", "Sadie J. Ryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002723.t003", "stats"=>{"downloads"=>5, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Table_showing_the_optimal_vaccination_radius_R_V_in_km_for_epidemics_simulated_on_the_recorded_farm_location_data_RD_and_the_generated_data_sets_Random_LC1_LC2_LC3_and_LC4_for_Cumbria_Devon_Aberdeenshire_and_Clwyd_/219770", "title"=>"Table showing the optimal vaccination radius <i>R<sub>V</sub></i> (in km) for epidemics simulated on the recorded farm location data (RD) and the generated data sets “Random”, LC1, LC2, LC3 and LC4 for Cumbria, Devon, Aberdeenshire and Clwyd.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-01 02:42:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/548744"], "description"=>"<p>Each set of simulations was run for a different method of deriving the underlying farm data: In (a) CSM, mean cow and sheep numbers on farms of that category, EQU, equal numbers of cows and sheep on every farm, RAN, spatially randomized locations, SHU, farms shuffled between locations, and TRU, the recorded data (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002723#s2\" target=\"_blank\">materials and methods</a> for details). In (b) the different treatments are for derived land covers LC1–LC4, as described in the <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002723#s2\" target=\"_blank\">materials and methods</a> section, using coarse and fine resolution land cover classification to describe potential farm land. The boxes represent the range of mean values for each of the 100 datasets used to run the model, and the whiskers show the 95% Prediction Interval (PI) for 1000 runs on each of the 100 generated datasets. NB: the methods TRU and EQU have no range of mean values.</p>", "links"=>[], "tags"=>["plots", "contacts", "infected", "premises", "duration", "outbreak", "cumbria", "simulated", "outbreaks"], "article_id"=>219228, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Michael J. Tildesley", "Sadie J. Ryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002723.g001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_These_plots_show_the_number_of_dangerous_contacts_DC_top_panel_infected_premises_IP_middle_panel_and_duration_of_outbreak_T_bottom_panel_for_each_of_four_counties_Aberdeen_Clywd_Cumbria_and_Devon_in_simulated_outbreaks_of_FMD_/219228", "title"=>"These plots show the number of dangerous contacts (DC, top panel), infected premises (IP, middle panel) and duration of outbreak (T, bottom panel), for each of four counties: Aberdeen, Clywd, Cumbria and Devon, in simulated outbreaks of FMD.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-01 02:33:48"}

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Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Organisms", "average_usage"=>[331, 557, 677, 777, 868, 960, 1050, 1136, 1223, 1307, 1390, 1466, 1536, 1603, 1673, 1741, 1814, 1889, 1954, 2028, 2096, 2164, 2233, 2305, 2362]}, {"subject_area"=>"/Biology and life sciences/Veterinary science", "average_usage"=>[368, 635, 753, 861, 975, 1103, 1205, 1302, 1411, 1512, 1630, 1714, 1767, 1840, 1918, 1992, 2095, 2196, 2254, 2308, 2385, 2438, 2520, 2604, 2682]}, {"subject_area"=>"/Medicine and health sciences/Public and occupational health", "average_usage"=>[327, 554, 673, 780, 874, 968, 1060, 1133, 1222, 1304, 1376, 1463, 1536, 1602, 1663, 1742, 1796, 1853, 1931, 1985, 2044, 2108, 2170, 2248, 2305]}]}
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