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Data from: A multi-year case study highlighting the influence of hydrological conditions on epidemic dynamics in a natural plant pathosystem
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Little Appleton Pasteuria epidemic dataset
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Heather nectar extracts reduce within-colony epidemics of the bumblebee parasite <em>Crithidia bombi</em>
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Data for: Age structure eliminates the impact of coinfection on epidemic dynamics in a freshwater zooplankton system
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The first arriving virus shapes within-host viral diversity during natural epidemics
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COVID-19 epidemic in Fiji
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Branching Trees from standard Epidemic Aftershock Sequences (ETAS) Model (no time, no space)
<p>All files licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)</p> <p>###############<br> 0. SUMMARY<br> ###############</p> <p>1. DESCRIPTION</p> <p>2. INPUT PARAMETERS</p> <p>3. TYPES OF FILES<br> 3.1. Raw data<br> 3.2. List of trees<br> 3.3. Tree-size frequencies</p> <p>4. LIST OF FILES<br> 4.1. Raw data<br> 4.2. List of trees<br> 4.3. Tree-size frequencies<br> 4.4 Known missing/broken files</p> <p>###############<br> 1. DESCRIPTION<br> ################<br> Simulation results of an standard ETAS model as a branching process. Using a two seed version of the RANDU linear congruential pseudorandom number generator. The offspring number is a Poisson number given the rate n(M) (see below). Details of simulation procedure can be found in reference [1]: 'Topological properties of epidemic aftershock processes', by J. Baró submitted to J. of Geophysical Research - Solid Earth (JGR-B)</p> <p>##############<br> 2. INPUT PARAMETERS<br> ##############<br> The input parameters (see reference for details) for each raw and processes data-file are indicated in the prefix of the file: "ETASbranch_b(b)r(r)N(nb)*"</p> <p>- M0(= 1) = magnitude of completeness (arbitrary for the study of topological properties of trees)<br> - (b) = b-value (arbitrary for the study of topological properties of trees)<br> - (nb) = average branching ratio<br> - (r) = ratio a/b</p> <p>The b-value defines the distribution of event-magnitudes: P(M) = 10^(b*(M-M0)) . The nb and a define the productivity law: n(M) = (nb*(b-a)/b)*10^(a*(M-M0))</p> <p><br> #################<br> 3. TYPES OF FILES<br> #################</p> <p>3.1. Raw data:<br> --------------</p> <p>42 x "*.Seq" files with input b=0.50 and different nb, r values. Raw data from simulation code. (all cases, simulated with 10^5 background events)<br> Each row represents an individual event in the point process, or element of the simulated branching forest.<br> Columns description (9 columns x data point):<br> c0:Time (arbitrary, used here as id.)<br> c1:Magnitude of the event<br> c2:Identification number of the cluster or tree<br> c3:Depth of the event in the tree structure (background events have Depth = 0)<br> c4:Time of the direct parent of the event (set to -1 for background event)<br> c5:Magnitude of the direct parent of the event (set to -10 if background event)<br> c6:Time of the event initiating the tree (set to own time if background)<br> c7:Magnitude of the event initiating the tree (set to own magnitude if background)<br> c8:N or offspring number of the event. (Events are leafs if N=0)</p> <p><br> 3.2. List of trees<br> ------------------</p> <p>53 x "*TopoTrees.dat" files obtained from simulations (after processing of *.Seq files. Files ending with "N0.99", "N0.50" obtained from 10^5 background events from files above. Files ending with "N0.500*" obtained from 10^7 simulations)<br> Each row represents an individual tree constituted by one or several causally connected events of the simulated branching forest. Files used to generate fig. 4 of ref. [1]</p> <p>Columns description (8 columns x data point):<br> c0:Maximum Depth of the tree<br> c1:Number of events in the tree<br> c2:Average depth of leaves<br> c3:total number of leaves<br> c4:Sum of the depth of all leaves (=c2*c3)<br> c5:(=0) not used<br> c6:Magnitude of root<br> c7:Maximum magnitude of an event inside the tree</p> <p><br> 4.3. Tree-size frequencies<br> --------------------------</p> <p>18 x "*TopoTrees.FK" files obtained from "*TopoTrees.dat". Contains the frequencies of tree-sizes. Each raw number correspond to a size. Each value corresponds to number of incidences of that size divided by total number of events (10^5 in all cases). Notice that last point is missing at size = max-length, freq.= 1.0 / total number of events. Files used to generate fig. 3 of ref. [1]</p> <p><br> ################<br> 4. LIST OF FILES<br> ################</p> <p>(copy of this text)<br> readme.txt<br> md5:2073cdbb4a0afd8ab96f8bfaef20579f 13 Kb</p> <p>4.1. Raw data (42 files)<br> ------------------------</p> <p>ETASbranch_b0.50r0.00N0.50.Seq<br> md5:bae21678db4fe575b06b69a5f32253d2 12.5 Mb<br> ETASbranch_b0.50r0.00N0.99.Seq<br> md5:76ea70ba94aeeed7ba3c358305f4b85b 1.3 Gb<br> ETASbranch_b0.50r0.05N0.50.Seq<br> md5:0ccf5af07efb9dd99d63a9675fd3069f 12.4 Mb<br> ETASbranch_b0.50r0.05N0.99.Seq<br> md5:77a7bfd490af54a5a3d15c4183e9e6f7 1.4 Gb<br> ETASbranch_b0.50r0.10N0.50.Seq<br> md5:f274717039f8f69ce57d5054577777af 12.4 Mb<br> ETASbranch_b0.50r0.10N0.99.Seq<br> md5:e18f4c26acfdff8d79a6223aa86867d2 1.3 Gb<br> ETASbranch_b0.50r0.15N0.50.Seq<br> md5:5f93c14a8d0f88fd021bc6d8a5fc7d6e 12.3 Mb<br> ETASbranch_b0.50r0.15N0.99.Seq<br> md5:906e09ecb6c6c18fdbcfdca3d9dbe225 1.3 Gb<br> ETASbranch_b0.50r0.20N0.50.Seq<br> md5:2467c2c7fafeddaa630074e991cb7767 12.4 Mb<br> ETASbranch_b0.50r0.20N0.99.Seq<br> md5:64c45a47b7f953d7088b10d4badd068c 1.3 Gb<br> ETASbranch_b0.50r0.25N0.50.Seq<br> md5:d65abaa4664b2412707b27b6e9143215 12.4 Mb<br> ETASbranch_b0.50r0.25N0.99.Seq<br> md5:8d7467b0bd80ce7e8cbcaf5dfe2725b7 1.2 Gb<br> ETASbranch_b0.50r0.30N0.50.Seq<br> md5:fd3af9802b3b66a623172bb2aafc0a0b 12.4 Mb<br> ETASbranch_b0.50r0.30N0.99.Seq<br> md5:2ad1b9b9c858638067af6068dfd25d59 1.3 Gb<br> ETASbranch_b0.50r0.35N0.50.Seq<br> md5:8cf36cc90a19f1283c838dee4849626b 12.5 Mb<br> ETASbranch_b0.50r0.35N0.99.Seq<br> md5:6b35085100561865ac9463edfb166d7f 1.4 Gb<br> ETASbranch_b0.50r0.40N0.50.Seq<br> md5:317b154c8ed74262b18c41762597d236 12.3 Mb<br> ETASbranch_b0.50r0.40N0.99.Seq<br> md5:c83dc299035928c9ae8a7f6d101ac9b8 1.2 Gb<br> ETASbranch_b0.50r0.45N0.50.Seq<br> md5:d3baccfbf347e555c30f3dfb1db1d9c0 12.4 Mb<br> ETASbranch_b0.50r0.45N0.99.Seq<br> md5:5bf42163acc2eae1eeae8549b850ccc9 1.1 Gb<br> ETASbranch_b0.50r0.50N0.50.Seq<br> md5:3ad7427725dfcf302c2dc2c519dad8c0 12.4 Mb<br> ETASbranch_b0.50r0.50N0.99.Seq<br> md5:7316fb3661b87215a1a7b1c11a54975b 1.3 Gb<br> ETASbranch_b0.50r0.55N0.50.Seq<br> md5:6dbc9846506675c61d110ae918165a6b 12.4 Mb<br> ETASbranch_b0.50r0.55N0.99.Seq<br> md5:5d4d007bf898512185dd2f87b715715e 1.4 Gb<br> ETASbranch_b0.50r0.60N0.50.Seq<br> md5:569c1c409ef618f518fe6fb6f41493a3 12.7 Mb<br> ETASbranch_b0.50r0.60N0.99.Seq<br> md5:e739613598e8d2530d108ff24e8c045a 1.1 Gb<br> ETASbranch_b0.50r0.65N0.50.Seq<br> md5:e95cead475c1eedb0618bebf1b548ec2 12.3 Mb<br> ETASbranch_b0.50r0.65N0.99.Seq<br> md5:8fe8b042085f261971d94bf12f96dc7e 1 Gb<br> ETASbranch_b0.50r0.70N0.50.Seq<br> md5:48824f1efdbc31c5bb4d4f799bc166d5 12.2 Mb<br> ETASbranch_b0.50r0.70N0.99.Seq<br> md5:2ead803394a4c3b04f1e22e9b8c5ba46 872.9 Mb<br> ETASbranch_b0.50r0.75N0.50.Seq<br> md5:8c5cf4753836076c677a74d831354f33 12.2 Mb<br> ETASbranch_b0.50r0.75N0.99.Seq<br> md5:91ed931aedfb365761799e0882fcfb86 1 Gb<br> ETASbranch_b0.50r0.80N0.50.Seq<br> md5:f93e1779bc5344ffd17021bb387bb373 11 Mb<br> ETASbranch_b0.50r0.80N0.99.Seq<br> md5:6b0fd7fcad20ac4467fecda9502e9954 75.6 Mb<br> ETASbranch_b0.50r0.85N0.50.Seq<br> md5:a5debe21de70204a490c75dd7c68657a 11 Mb<br> ETASbranch_b0.50r0.85N0.99.Seq<br> md5:f9ed3ad6db12ad4a612c3945ccf72f18 48.5 Mb<br> ETASbranch_b0.50r0.90N0.50.Seq<br> md5:4493eef2170b68052cf56636c63091d7 9.7 Mb<br> ETASbranch_b0.50r0.90N0.99.Seq<br> md5:70043ed6c84e3896268c46f77eabefd3 26.7 Mb<br> ETASbranch_b0.50r0.95N0.50.Seq<br> md5:874916faa95c63312aa27aecea45de8f 7.5 Mb<br> ETASbranch_b0.50r0.95N0.99.Seq<br> md5:bbc54464c2e3cbac64401978216ae993 11.7 Mb<br> ETASbranch_b0.50r1.00N0.50.Seq<br> md5:3ff467b894f46a4be0b0747c686edad5 5.8 Mb<br> ETASbranch_b0.50r1.00N0.99.Seq<br> md5:d4d5416d56ed3de6eb2702a8eeb0a0b5 5.8 Mb</p> <p><br> 3.2. List of trees (53 files)<br> -----------------------------</p> <p><br> ETASbranch_b1.00r0.00N0.500TopoTrees.dat<br> md5:a5834b8d047fd3446e6d83488422bb5b 1.8 Gb<br> ETASbranch_b1.00r0.00N0.99TopoTrees.dat<br> md5:34eb899782444475151073ed6fcfc6aa 18.6 Mb<br> ETASbranch_b1.00r0.05N0.50TopoTrees.dat<br> md5:cc5f0edfc31a5f15394444243560e0ad 18.6 Mb<br> ETASbranch_b1.00r0.05N0.99TopoTrees.dat<br> md5:58cc7e7e746051f9efbe510c164a2b09 18.6 Mb<br> ETASbranch_b1.00r0.10N0.500TopoTrees.dat<br> md5:52cb3a9a5aeb1e222743bcdad3ea3b3d 1.8 Gb<br> ETASbranch_b1.00r0.10N0.50TopoTrees.dat<br> md5:a68a69280c0129ee3554d5c97dd0fa47 18.6 Mb<br> ETASbranch_b1.00r0.10N0.99TopoTrees.dat<br> md5:307edf1353af5141f11d89cbab6c4d30 18.6 Mb<br> ETASbranch_b1.00r0.15N0.500TopoTrees.dat<br> md5:52807004c32bdc1a999a2aaf1ff91bb9 1.8 Gb<br> ETASbranch_b1.00r0.15N0.50TopoTrees.dat<br> md5:0bdb45b13ed0fd5c0fa1619310d71670 18.6 Mb<br> ETASbranch_b1.00r0.15N0.99TopoTrees.dat<br> md5:f26e351184921ed9a85c8993404a4718 18.6 Mb<br> ETASbranch_b1.00r0.20N0.500TopoTrees.dat<br> md5:5fd930a98e28d551c9c204d22c6f564a 1.8 Gb<br> ETASbranch_b1.00r0.20N0.50TopoTrees.dat<br> md5:4aaeb952e0ecb7d4fc4bd2fb7822c729 18.6 Mb<br> ETASbranch_b1.00r0.20N0.99TopoTrees.dat<br> md5:a30687662b269f447d7e4cc020bd3773 18.6 Mb<br> ETASbranch_b1.00r0.25N0.500TopoTrees.dat<br> md5:4168e6e70b247977213d2278b94d65f3 1.8 Gb<br> ETASbranch_b1.00r0.25N0.50TopoTrees.dat<br> md5:b360877be00088b52676ba4717377761 18.6 Mb<br> ETASbranch_b1.00r0.25N0.99TopoTrees.dat<br> md5:34d11b64c78c852d4f50de7b1265c9b7 18.6 Mb<br> ETASbranch_b1.00r0.30N0.500TopoTrees.dat<br> md5:8c1a8b07c5f2350646d415a687f84492 1.8 Gb<br> ETASbranch_b1.00r0.30N0.50TopoTrees.dat<br> md5:0c1bfccd8cd141090a0bfb0cc7ae1ccd 18.6 Mb<br> ETASbranch_b1.00r0.30N0.99TopoTrees.dat<br> md5:652f975754241fed317acba066cf339b 18.6 Mb<br> ETASbranch_b1.00r0.35N0.500TopoTrees.dat<br> md5:6bc2a3f7b4f7fd8a92595271d2ea425f 1.8 Gb<br> ETASbranch_b1.00r0.35N0.50TopoTrees.dat<br> md5:d1d4e8a467d8d445cd3f6ca27c3c8af3 18.6 Mb<br> ETASbranch_b1.00r0.35N0.99TopoTrees.dat<br> md5:e56eee0119b193898ea729b75c572617 18.6 Mb<br> ETASbranch_b1.00r0.40N0.500TopoTrees.dat<br> md5:370a2f6c670fd7c9b515e43891417b73 1.8 Gb<br> ETASbranch_b1.00r0.40N0.50TopoTrees.dat<br> md5:9f50c23e33cca983df58e2b17f29f8e5 18.6 Mb<br> ETASbranch_b1.00r0.40N0.99TopoTrees.dat<br> md5:d9971930b46838de09a7ccdc7cd9b459 18.6 Mb<br> ETASbranch_b1.00r0.45N0.500TopoTrees.dat<br> md5:cfef99746043d0718ad19e48ffdfeee4 1.8 Gb<br> ETASbranch_b1.00r0.45N0.50TopoTrees.dat<br> md5:44667f56360024e40b11be4f52ccf9c3 18.6 Mb<br> ETASbranch_b1.00r0.45N0.99TopoTrees.dat<br> md5:09b84c6f3c9dd58331c6f9ab4eec8f58 18.6 Mb<br> ETASbranch_b1.00r0.50N0.500TopoTrees.dat<br> md5:e9e880f24ffc9ba40db7c7f78d0f8d89 1.8 Gb<br> ETASbranch_b1.00r0.50N0.50TopoTrees.dat<br> md5:ce4835d532513bffd54723f285207898 1.9 Mb<br> ETASbranch_b1.00r0.50N0.99TopoTrees.dat<br> md5:4c935d76aee113c88f4a3348197f75f3 18.6 Mb<br> ETASbranch_b1.00r0.55N0.500TopoTrees.dat<br> md5:46cd9e8d508c15009e90a194a141008f 1.8 Gb<br> ETASbranch_b1.00r0.55N0.50TopoTrees.dat<br> md5:8e1cb9c364c2a275d10373b54dab6239 18.6 Mb<br> ETASbranch_b1.00r0.55N0.99TopoTrees.dat<br> md5:304b947c52e826a9b4cb68c38f007443 18.6 Mb<br> ETASbranch_b1.00r0.60N0.500TopoTrees.dat<br> md5:df8b7889e76694188b29f7fc16f6dce2 1.8 Gb<br> ETASbranch_b1.00r0.60N0.50TopoTrees.dat<br> md5:fc6c2110e139290ae9401765e8aae782 18.6 Mb<br> ETASbranch_b1.00r0.60N0.99TopoTrees.dat<br> md5:6af466cc527b7422f1f958bfc6a87d15 18.6 Mb<br> ETASbranch_b1.00r0.65N0.500TopoTrees.dat<br> md5:129abd6de7821da465265a485217156d 1.8 Gb<br> ETASbranch_b1.00r0.65N0.50TopoTrees.dat<br> md5:469fc2ae3768fe9ae0ab0b8fbfaf3051 18.6 Mb<br> ETASbranch_b1.00r0.65N0.99TopoTrees.dat<br> md5:b7148c414b243b1911de8543d25e3d38 18.6 Mb<br> ETASbranch_b1.00r0.70N0.500TopoTrees.dat<br> md5:3a3eed181ae6308c29f274d5e14a97df 1.8 Gb<br> ETASbranch_b1.00r0.70N0.50TopoTrees.dat<br> md5:ca7472f17916e7f8634a97c08ca96c58 18.6 Mb<br> ETASbranch_b1.00r0.70N0.99TopoTrees.dat<br> md5:8c2a4ab3800e8a8178be5d6e856f5b50 18.6 Mb<br> ETASbranch_b1.00r0.75N0.50TopoTrees.dat<br> md5:374796f34a3e25f711601161f8a34ae7 18.6 Mb<br> ETASbranch_b1.00r0.75N0.99TopoTrees.dat<br> md5:591f6ac31545f3e7558b4a5e151c80a5 18.6 Mb<br> ETASbranch_b1.00r0.80N0.50TopoTrees.dat<br> md5:473c6e9ac33cc6e8eabe0ed3c91b40b7 18.6 Mb<br> ETASbranch_b1.00r0.80N0.99TopoTrees.dat<br> md5:81f41fbce89c931f87890133b4c0f9f9 18.6 Mb<br> ETASbranch_b1.00r0.85N0.50TopoTrees.dat<br> md5:2c38db4208898d95c1c5b2ef0a94c930 18.6 Mb<br> ETASbranch_b1.00r0.85N0.99TopoTrees.dat<br> md5:4866f179b52ff5a6191d47e6d8ead5ce 18.6 Mb<br> ETASbranch_b1.00r0.90N0.50TopoTrees.dat<br> md5:f14a7f700c0c73743eb5747399abdcce 18.6 Mb<br> ETASbranch_b1.00r0.90N0.99TopoTrees.dat<br> md5:da4bd00aeefcbd1e67f0d7fe8fb1d8be 18.6 Mb<br> ETASbranch_b1.00r0.95N0.50TopoTrees.dat<br> md5:57b7f0bd901bb47ba3c253f801301716 18.6 Mb<br> ETASbranch_b1.00r0.95N0.99TopoTrees.dat<br> md5:6381354768603c6d2129c99df2a7798a 18.6 Mb</p> <p>4.3. Tree-size frequencies (18 files)<br> -------------------------------------</p> <p>ETASbranch_b1.00r0.00N0.99TopoTrees.FK<br> md5:5b6ccaf1c1218f101ed24c332bfefec3 612 Kb<br> ETASbranch_b1.00r0.10N0.30TopoTrees.FK<br> md5:f9f07bbd7be5377e1589101e85640149 468 B<br> ETASbranch_b1.00r0.20N0.30TopoTrees.FK<br> md5:6c7e40f8b93c7a3e62f2c105f0e7a89b 558 B<br> ETASbranch_b1.00r0.20N0.99TopoTrees.FK<br> md5:158884c9bc4afa10c8ebb2e7b516a421 3.3 Mb<br> ETASbranch_b1.00r0.30N0.30TopoTrees.FK<br> md5:14d837b9763b9a54e64311e832e29f04 846 B<br> ETASbranch_b1.00r0.30N0.99TopoTrees.FK<br> md5:90b8b5a38a83cb4b4c833b605cf6b38e 2.1 Mb<br> ETASbranch_b1.00r0.40N0.30TopoTrees.FK<br> md5:1f198a7c08078066e7fa57ce9ebda8eb 3 Kb<br> ETASbranch_b1.00r0.40N0.99TopoTrees.FK<br> md5:229b837e7950fe85ea1c51be8e3f457e 3.3 Mb<br> ETASbranch_b1.00r0.50N0.30TopoTrees.FK<br> md5:6ead8bff09c3c2687c526648bfec6ab3 16 Kb<br> ETASbranch_b1.00r0.60N0.30TopoTrees.FK<br> md5:38c5cb9f544367ff9bdab766c4bcf03f 112 Kb<br> ETASbranch_b1.00r0.60N0.99TopoTrees.FK<br> md5:2591d4e2dd1c2051c0b7d96f423c526a 106.5 Mb<br> ETASbranch_b1.00r0.70N0.30TopoTrees.FK<br> md5:4f7741314e1a0d0b1c1733532d909277 7.5 Mb<br> ETASbranch_b1.00r0.80N0.30TopoTrees.FK<br> md5:3c4240e79ee19c03afac91d631f38cb8 602 Kb<br> ETASbranch_b1.00r0.80N0.30TopoTrees.FK<br> md5:3c4240e79ee19c03afac91d631f38cb8 602 Kb<br> ETASbranch_b1.00r0.90N0.30TopoTrees.FK<br> md5:914a782fa2e73c0bc5a6e70bbb2afec9 1.3 Mb<br> ETASbranch_b1.00r0.90N0.99TopoTrees.FK<br> md5:cc165045d4d9f25c0f9c30c0ec71759f 864 Kb<br> ETASbranch_b1.00r0.99N0.30TopoTrees.FK<br> md5:a08cfc27a0e42d54f207ea05b7210714 187 Kb<br> ETASbranch_b1.00r0.99N0.99TopoTrees.FK<br> md5:a15681be56f870a95fe62794b11524df 365 Kb</p> <p><br> 4.4 Known missing/broken files<br> ------------------------------</p> <p>ETASbranch_b1.00r0.50N0.50TopoTrees.dat<br> ETASbranch_b1.00r0.10N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.20N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.50N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.70N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.05N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.70N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.75N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.80N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.85N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.90N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.95N0.500TopoTrees.dat<br> </p>
The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic
<p>This repository contains the data of the study "The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic".</p> <p><strong>Epidemiological data</strong></p> <p>The folder <em>zika_USA_weekly_cases_2016.zip </em>contains weekly ZIKV incidence counts reported by the US Centers for Disease Control and Prevention in 2016, by state. Data were extracted from reports made publicly available by the CDC at: <a href="https://zenodo.org/record/584136#.Xk07-RNKjOQ">https://zenodo.org/record/584136#.Xk07-RNKjOQ</a> </p> <p><strong>Web news data</strong></p> <p>The file <em>news_GDELT_data.csv.gz </em>contains all news items extracted from the GDELT platform (<a href="https://www.gdeltproject.org/">https://www.gdeltproject.org/</a>) matching <em>TAX_DISEASE_ZIKA </em>as a Theme, and <em>United_States</em> as a Location in the GDELT platform. </p> <p><strong>TV closed captions</strong></p> <p>The file <em>zika_TV_mentions_dataframe.csv </em>contains all the TV news items of 2016 matching the word ``Zika" in the TV News Archive https://archive.org/details/tv</p> <p><strong>Wikipedia pageview counts</strong></p> <p>Dataset 1: <em>wikipedia_dataset1_zika_daily_pageview_usa.csv</em></p> <p>Content of each line of the dataset: day, pageview_count</p> <p>The dataset contains the daily number of pageview counts of 128 different Wikipedia pages related to the Zika virus (aggregated and summed to total) originated in the United States, from January 1st to December 31st, 2016.</p> <p>Dataset 2: <em>wikipedia_dataset2_zika_daily_pageview_bystate.zip</em></p> <p>Content of each line of the dataset: day, pageview_count, state</p> <p>The dataset contains the daily number of pageview counts of 128 different Wikipedia pages related to the Zika virus (aggregated and summed to total) originated in the United States, disaggregated by state, from January 1st to December 31st, 2016.</p> <p>Dataset 3: <em>wikipedia_dataset3_zika_pagecount_by_city.csv</em></p> <p>Content of each line of the dataset: US_city, pageview_count_Zika,pageview_count_total</p> <p>The dataset contains the total number of pageview counts of 128 different Wikipedia pages related to the Zika virus (pageview_count_Zika) originated in 788 cities (US_city) of the United States with a population larger than 40,000 in 2016.The dataset also contains the total number of pageview counts to all Wikipedia pages (all Wikipedia projects, pageview_count_total) originated in 788 cities (US_city) of the United States with a population larger than 40,000 in 2016."</p>
Data from: Salinity stress increases the severity of ranavirus epidemics in amphibian populations
The stress-induced susceptibility hypothesis, which predicts chronic stress weakens immune defenses, was proposed to explain increasing infectious disease-related mass mortality and population declines. Previous work characterized wetland salinization as a chronic stressor to larval amphibian populations. Thus, we combined field observations with experimental exposures quantifying epidemiological parameters to test the role of salinity stress in the occurrence of ranavirus-associated mass mortality events. Despite ubiquitous pathogen presence (94%), populations exposed to salt runoff had slightly more frequent ranavirus related mass mortality events, more lethal infections, and 117-times greater pathogen environmental DNA. Experimental exposure to chronic elevated salinity (0.8-1.6 g/L Cl-) reduced tolerance to infection, causing greater mortality at lower doses. We found a strong negative relationship between splenocyte proliferation and corticosterone in ranavirus-infected larvae at a moderate elevation of salinity, supporting glucocorticoid-medicated immunosuppression, but not at high salinity. Salinity alone reduced proliferation further at similar corticosterone levels and infection intensities. Finally, larvae raised in elevated salinity had 10-times more intense infections and shed 5-times as much virus with similar viral decay rates, suggesting increased transmission. Our findings illustrate how a small change in habitat quality leads to more lethal infections and potentially greater transmission efficiency, increasing the severity of ranavirus epidemics.
Data and code from: Vector demography, dispersal, and the spread of disease: Experimental epidemics under elevated resource supply
1. The spread of many diseases depends on the demography and dispersal of arthropod vectors. Classic epidemiological theory typically ignores vector dynamics and instead makes the simplifying assumption of frequency-dependent transmission. Yet vector ecology may be critical for understanding the spread of disease over space and time and how disease dynamics respond to environmental change. 2. Here, we ask how environmental change shapes vector demography and dispersal, and how these traits of vectors govern the spatiotemporal spread of disease. 3. We developed disease models parameterized by traits of vectors and fit them to experimental epidemics. The experiment featured a viral pathogen (CYDV-RPV) vectored by aphids (Rhopalosiphum padi) among populations of grass hosts (Avena sativa) under two rates of environmental resource supply (i.e., fertilization of the host). We compared a non-spatial model that ignores vector movement, a lagged dispersal model that emphasizes the delay between vector reproduction and dispersal, and a travelling wave model that generates waves of infections across space and time. 4. Resource supply altered both vector demography and dispersal. The lagged dispersal model fit best, indicating that vectors first reproduced and then dispersed among hosts in the experiment. Elevated resources decreased vector population growth rates, nearly doubled their carrying capacity per host, increased dispersal rates when vectors carried the virus, and homogenized disease risk across space. 5. Together, the models and experiment show how environmental eutrophication can shape spatial disease dynamics – for example, homogenizing disease risk across space – by altering the demography and behavior of vectors.
Datasets to "Piecewise quadratic growth during the 2019 novel coronavirus epidemic"
<pre>This directory contains an index.html file with links to the run directories for Figs.8-11 and idl plotting routines with secondary data for the other figures for the paper "Piecewise quadratic growth during the 2019 novel coronavirus epidemic" by Axel Brandenburg (Nordita) with the URL https://arxiv.org/abs/2002.03638 </pre>
Data from: Epidemic and endemic pathogen dynamics correspond to distinct host population microbiomes at a landscape scale
Infectious diseases have serious impacts on human and wildlife populations, but the effects of a disease can vary, even among individuals or populations of the same host species. Identifying the reasons for this variation is key to understanding disease dynamics and mitigating infectious disease impacts, but disentangling cause and correlation during natural outbreaks is extremely challenging. This study aims to understand associations between symbiotic bacterial communities and an infectious disease, and examines multiple host populations before or after pathogen invasion to infer likely causal links. The results show that symbiotic bacteria are linked to fundamentally different outcomes of pathogen infection: host–pathogen coexistence (endemic infection) or host population extirpation (epidemic infection). Diversity and composition of skin-associated bacteria differed between populations of the frog, Rana sierrae, that coexist with or were extirpated by the fungal pathogen, Batrachochytrium dendrobatidis (Bd). Data from multiple populations sampled before or after pathogen invasion were used to infer cause and effect in the relationship between the fungal pathogen and symbiotic bacteria. Among host populations, variation in the composition of the skin microbiome was most strongly predicted by pathogen infection severity, even in analyses where the outcome of infection did not vary. This result suggests that pathogen infection shapes variation in the skin microbiome across host populations that coexist with or are driven to extirpation by the pathogen. By contrast, microbiome richness was largely unaffected by pathogen infection intensity, but was strongly predicted by geographical region of the host population, indicating the importance of environmental or host genetic factors in shaping microbiome richness. Thus, while both richness and composition of the microbiome differed between endemic and epidemic host populations, the underlying causes are most likely different: pathogen infection appears to shape microbiome composition, while microbiome richness was less sensitive to pathogen-induced disturbance. Because higher richness was correlated with host persistence in the presence of Bd, and richness appeared relatively stable to Bd infection, microbiome richness may contribute to disease resistance, although the latter remains to be directly tested.
Data from: Drought and immunity determine the intensity of West Nile virus epidemics and climate change impacts
The effect of global climate change on infectious disease remains hotly debated because multiple extrinsic and intrinsic drivers interact to influence transmission dynamics in nonlinear ways. The dominant drivers of widespread pathogens, like West Nile virus, can be challenging to identify due to regional variability in vector and host ecology, with past studies producing disparate findings. Here, we used analyses at national and state scales to examine a suite of climatic and intrinsic drivers of continental-scale West Nile virus epidemics, including an empirically derived mechanistic relationship between temperature and transmission potential that accounts for spatial variability in vectors. We found that drought was the primary climatic driver of increased West Nile virus epidemics, rather than within-season or winter temperatures, or precipitation independently. Local-scale data from one region suggested drought increased epidemics via changes in mosquito infection prevalence rather than mosquito abundance. In addition, human acquired immunity following regional epidemics limited subsequent transmission in many states. We show that over the next 30 years, increased drought severity from climate change could triple West Nile virus cases, but only in regions with low human immunity. These results illustrate how changes in drought severity can alter the transmission dynamics of vector-borne diseases.
2014, Nigeria Ebola epidemic data and knowledge
Information about the 2014, Nigeria Ebola epidemic curated from multiple publications and reports. The information is represented in machine-interpretable Apollo-XSD format. The terminology is defined by the Apollo-SV ontology and standard identifiers.
2014, Mali Ebola epidemic data and knowledge
Information about the 2014, Mali Ebola epidemic curated from multiple publications and reports. The information is represented in machine-interpretable Apollo-XSD format. The terminology is defined by the Apollo-SV ontology and standard identifiers.
2014, Democratic Republic of the Congo Ebola epidemic data and knowledge
Information about the 2014, Democratic Republic of the Congo Ebola epidemic curated from multiple publications and reports. The information is represented in machine-interpretable Apollo-XSD format. The terminology is defined by the Apollo-SV ontology and standard identifiers.
2014, Dallas, Texas, United States of America Ebola epidemic data and knowledge
Information about the 2014, Dallas, Texas, United States of America Ebola epidemic curated from multiple publications and reports. The information is represented in machine-interpretable Apollo-XSD format. The terminology is defined by the Apollo-SV ontology and standard identifiers.
2007, Bundibugyo, Uganda Ebola epidemic data and knowledge
Information about the 2007, Bundibugyo, Uganda Ebola epidemic curated from multiple publications and reports. The information is represented in machine-interpretable Apollo-XSD format. The terminology is defined by the Apollo-SV ontology and standard identifiers.
2012, Orientale, Democratic Republic of the Congo Ebola epidemic data and knowledge
Information about the 2012, Orientale, Democratic Republic of the Congo Ebola epidemic curated from multiple publications and reports. The information is represented in machine-interpretable Apollo-XSD format. The terminology is defined by the Apollo-SV ontology and standard identifiers.
2014 - 2017, Sierra Leone Ebola epidemic data and knowledge
Information about the 2014 - 2017, Sierra Leone Ebola epidemic curated from multiple publications and reports. The information is represented in machine-interpretable Apollo-XSD format. The terminology is defined by the Apollo-SV ontology and standard identifiers.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.