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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>
Data from: Adaptive estimation for epidemic renewal and phylogenetic skyline models
Open the record for dataset details and reuse information.
Data and code of Covid-19 SIRDS model with fuzzy transitions between epidemic periods
<p>Repository for code and data of project that implement SIRDS model with fuzzy transitions between epidemic periods.</p>
A code implementing the unified activities-centered approach to the modelling of viral epidemics
<p>A new approach to formulating mathematical models of increasing complexity to describe the dynamics of viral epidemics is proposed. Unifying the compartmental and stochastic approaches, it focuses on daily communicative activities of different groups of population (commuting, work, shopping, socializing) viewing them as the channels through which infection spreads. In order to describe these activities, we introduce a map of social interactions characterizing the structure of the population to which the model is applied and the patterns of behaviour typical to the social groups it is made of. By employing the mathematics of difference equations, the new approach makes it possible to incorporate the clinical picture of a particular viral infection and the complications it causes directly, in the way this picture is reported by medical professionals. As an illustration of the new approach, we consider the simplest model formulated in its framework and apply it to the ongoing pandemic of SARS-CoV-2 (COVID-19), using the UK as a representative country, to assess the impact of non-pharmaceutical measures of social distancing imposed to control its course. Although the purpose of this application is merely to illustrate the approach which is open to further development, the simplest model nevertheless allows one to make some predictions and an a posteriori assessment of the measures already taken.</p>
Data for: Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model
<p>Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. </p>
Data from: Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models
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A code implementing the unified activities-centered approach to the modelling of viral epidemics
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A conceptual disease cycle model to link the size of past and future epidemics
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Data for: Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model
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On the use of real-time mortality data in modelling and analysis during an epidemic outbreak - underlying data
<p>This project contains the primary data set analyzed in the paper "On the use of real-time mortality data in modelling and analysis during an epidemic outbreak ". The data was generated by downloading the <em>D<sup>T</sup></em>-series daily from the Public Health Agency of Sweden between 2020-04-02 and 2020-07-09. </p> <p>This project contains the following files:</p> <ul> <li>FHM_Covid_Download.zip. (Zip-archive of raw downloaded files with Swedish deaths data.)</li> <li>swedish_covid_deaths_data.csv. (Swedish deaths data collated from the raw data files in a .csv format.)</li> <li>swedish_covid_deaths_data.xlsx. (Swedish deaths data collated from the raw data files in a .xlsx format.)</li> </ul>
Data from: Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance
Non-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stochastic processes with stationary increments which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of beta-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://github.com/mrc-ide/skygrowth.
Data from: Achieving a step change in the tuberculosis epidemic through comprehensive community-wide intervention: A model-based analysis
<p><span><span><span><span><span><span><span><span><span><span><span><b>Background:</b> Global progress towards reducing tuberculosis (TB) incidence and mortality has consistently lagged behind World Health Organization targets leading to a perception that large reductions in TB burden cannot be achieved. However, several recent and historical trials suggest that intervention efforts that are comprehensive and focused can have substantial epidemiological impact. We aimed to quantify the potential epidemiological impact of an intensive but realistic, community-wide campaign utilizing existing tools, and designed to achieve a "step change" in TB burden.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods:</b> We developed a compartmental model of tuberculosis transmission in a mid-sized city in India, the country with the greatest absolute burden of TB worldwide. We modeled the impact of a campaign comprising one-time community-wide screening with treatment for TB disease and preventive therapy for latent TB infection (LTBI). This one-time intervention was followed by strengthening of tuberculosis-related health system achieved by leveraging the one-time campaign. We estimated the tuberculosis cases and deaths that could be averted over 10 years using this comprehensive approach and assessed the contributions of individual components of the intervention.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results: </b>A campaign that successfully screened 70% of the adult population for active and latent tuberculosis and subsequently reduced diagnostic and treatment delays and unsuccessful treatment outcomes by 50% was projected to avert 7,800 (95% range: 5,450 – 10,200) cases and 1,710 (1,290 – 2,180) tuberculosis-related deaths per 1 million population over 10 years. Of the total averted deaths, 33.5% (28.2 – 38.3) were attributable to inclusion of preventive therapy and 52.9% (48.4 - 56.9) to health system strengthening. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions:</b> A one-time, community-wide mass campaign, comprehensively designed to detect, treat, and prevent tuberculosis with currently existing tools can have meaningful and long-lasting epidemiological impact. Successful treatment of LTBI is critical to achieving this result. Health system strengthening is essential to any effort to transform the TB response.</span></span></span></span></span></span></span></span></span></span></span></p>
The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models
<p>Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March–June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number (R0) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number (RE) varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of a nearly epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.</p>
Data from: Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance
Open the record for dataset details and reuse information.
The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models
Open the record for dataset details and reuse information.
Data from: Achieving a step change in the tuberculosis epidemic through comprehensive community-wide intervention: A model-based analysis
Open the record for dataset details and reuse information.
On the use of real-time mortality data in modelling and analysis during an epidemic outbreak – extended data
<p>This project contains extended data related to the journal article "On the use of real-time mortality data in modelling and analysis during an epidemic outbreak" by Per Liljenberg</p> <p>This project contains the following extended data:</p> <ul> <li>Liljenberg2020_OGR_Appendix.pdf (Appendix to the main article)</li> <li>swedish_covid_deaths_OGR.R. (R-script to generate graphs and nowcasts in the paper.)</li> <li>MDAR author checklist.pdf (Completed MDAR reporting checklist)</li> </ul>
Impact of DAA Uptake in Controlling HCV Epidemic and Modeling Interventions for HCV Elimination Among HIV-infected Persons in San Diego
ClinicalTrials.gov study NCT03551002. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Epidemic Profile of Left Ventricular Diastoic Dysfunction in the Community Elderly and Establishing Prediction Model: the Northern Shanghai Study
ClinicalTrials.gov study NCT03735251. IPD Sharing: Not stated. Countries: 0. Publications: 0.
ScienceDex guides
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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.