25:["$","title",null,{"children":"Within-Host Models | epichef | Observable"}] 4b:T564,{ var t = ex_sol.time var S = ex_sol.SI.map((x)=>{return x[0]}) // console.log(S.slice(-1)) var Iw = ex_sol.SI.map((x)=>{return x[1]}) var Iz = ex_sol.SI.map((x)=>{return x[2]}) var strace = { type: "scatter", mode: "step", name: 'S', x: t, y: S, line: {color: 'blue'} } var iwtrace = { type: "scatter", mode: "step", name: 'Iw', x: t, y: Iw, line: {color: 'red'} } var iztrace = { type: "scatter", mode: "step", name: 'Iz', x: t, y: Iz, line: {color: 'purple'} } var data = [strace,iwtrace, iztrace]; var layout = { width: width }; var r1 = Math.round((betaw/(g*(1-epsilon) + g_prime*epsilon ))*100)/100; var r2 = Math.round((betaz/(g))*100)/100; var s_star = Math.round(S.slice(-1)[0]*100)/100; var iw_star = Math.round(Iw.slice(-1)[0]*100)/100; var iz_star = Math.round(Iz.slice(-1)[0]*100)/100; // var brn = tex.block`R_0` var layout = { title: `Exclusive Infection Model
R0w = ` + r1 +`, R0z = ` + r2 + `, Endemic state: (S*, Iw*, Iz*) = ` +`(` + s_star + ` , ` + iw_star +`, ` + iz_star + `)`, xaxis: { title: 'Time', titlefont: { size: 18, } }, yaxis: { title: 'Number', titlefont: { size: 18, } }}; const div = DOM.element('div'); Plotly.newPlot(div, data, layout); return div; } 4c:T566,{ var t = re_sol.time var S = re_sol.SI.map((x)=>{return x[0]}) // console.log(S.slice(-1)) var Iw = re_sol.SI.map((x)=>{return x[1]}) var Iz = re_sol.SI.map((x)=>{return x[2]}) var strace = { type: "scatter", mode: "step", name: 'S', x: t, y: S, line: {color: 'blue'} } var iwtrace = { type: "scatter", mode: "step", name: 'Iw', x: t, y: Iw, line: {color: 'red'} } var iztrace = { type: "scatter", mode: "step", name: 'Iz', x: t, y: Iz, line: {color: 'purple'} } var data = [strace,iwtrace, iztrace]; var layout = { width: width }; var r1 = Math.round((betaw/(g*(1-epsilon) + g_prime*epsilon ))*100)/100; var r2 = Math.round((betaz/(g))*100)/100; var s_star = Math.round(S.slice(-1)[0]*100)/100; var iw_star = Math.round(Iw.slice(-1)[0]*100)/100; var iz_star = Math.round(Iz.slice(-1)[0]*100)/100; // var brn = tex.block`R_0` var layout = { title: `Replacement Infection Model
R0w = ` + r1 +`, R0z = ` + r2 + `, Endemic state: (S*, Iw*, Iz*) = ` +`(` + s_star + ` , ` + iw_star +`, ` + iz_star + `)`, xaxis: { title: 'Time', titlefont: { size: 18, } }, yaxis: { title: 'Number', titlefont: { size: 18, } }}; const div = DOM.element('div'); Plotly.newPlot(div, data, layout); return div; } 4d:T57c,{ var t = uni_sol.time var S = uni_sol.SI.map((x)=>{return x[0]}) // console.log(S.slice(-1)) var Iw = uni_sol.SI.map((x)=>{return x[1]}) var Iz = uni_sol.SI.map((x)=>{return x[2]}) var strace = { type: "scatter", mode: "step", name: 'S', x: t, y: S, line: {color: 'blue'} } var iwtrace = { type: "scatter", mode: "step", name: 'Iw', x: t, y: Iw, line: {color: 'red'} } var iztrace = { type: "scatter", mode: "step", name: 'Iz', x: t, y: Iz, line: {color: 'purple'} } var data = [strace,iwtrace, iztrace]; var layout = { width: width }; var r1 = Math.round((betaw/(g*(1-epsilon) + rho*epsilon + g_prime*epsilon ))*100)/100; var r2 = Math.round((betaz/(g))*100)/100; var s_star = Math.round(S.slice(-1)[0]*100)/100; var iw_star = Math.round(Iw.slice(-1)[0]*100)/100; var iz_star = Math.round(Iz.slice(-1)[0]*100)/100; // var brn = tex.block`R_0` var layout = { title: `Unidirectional Conversion Model
R0w = ` + r1 +`, R0z = ` + r2 + `, Endemic state: (S*, Iw*, Iz*) = ` +`(` + s_star + ` , ` + iw_star +`, ` + iz_star + `)`, xaxis: { title: 'Time', titlefont: { size: 18, } }, yaxis: { title: 'Number', titlefont: { size: 18, } }}; const div = DOM.element('div'); Plotly.newPlot(div, data, layout); return div; } 4e:T6f6,function changingtreatment(len, maxtreat) { let e = []; let r = []; let u = []; let i; const d = 0; const treatment = linspace(0,maxtreat, len) for (i =0; i{return x[1]})) let solr = simulate(replacement, 0.01, [1-I0W-I0Z,I0W, I0Z], 0, tmax) let solu = simulate(unidirectional, 0.01, [1-I0W-I0Z,I0W, I0Z], 0, tmax) // console.log(sole.SI.map((x)=>{return x[1]}).slice(-1)[0]) e.push([sole.SI.map((x)=>{return x[1]}).slice(-1)[0], sole.SI.map((x)=>{return x[2]}).slice(-1)[0]]) r.push([solr.SI.map((x)=>{return x[1]}).slice(-1)[0], solr.SI.map((x)=>{return x[2]}).slice(-1)[0]]) u.push([solu.SI.map((x)=>{return x[1]}).slice(-1)[0], solu.SI.map((x)=>{return x[2]}).slice(-1)[0]]) // console.log(i) } return {Ex:e, Re:r, Uni:u}; }4f:[] 47:{"id":"093aa175a7caffda","slug":"within-host-models","title":"Within-Host Models","thumbnail":null,"access_level":"public","create_time":"$D2018-10-03T14:42:10.338Z","update_time":"$D2018-10-03T14:42:10.338Z","view_time":null,"first_public_time":"$D2018-10-03T14:45:17.209Z","has_unpublished_edits":null,"fork":{"id":"5f34c6a929423b5c","slug":"within-host-models","title":"Within-Host Models","owner":{"id":"ce5816d279104ed1","login":"ersouthall","name":""}},"stars":0,"starred":false,"owner":{"type":"team","id":"1cd33c417ff0d018","login":"epichef","name":"epichef","avatar_url":"https://avatars-next.observableusercontent.com/70dc97d7ff2150f8107b41e625ea31c76fcb3002082314c845e579febc189bf7","tier":"starter_2024","bio":"A cookbook of epidemiological models @epirecipes","followed_by_you":false,"follows_you":false},"version":897,"publish_version":897,"role":null,"authored":false,"authors":[{"type":"individual","id":"a88dcd8f4bc6daf3","login":"epichef","name":"epichef","avatar_url":"https://avatars-next.observableusercontent.com/70dc97d7ff2150f8107b41e625ea31c76fcb3002082314c845e579febc189bf7","tier":null,"bio":"A cookbook of epidemiological models @epirecipes","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"a88dcd8f4bc6daf3","login":"epichef","name":"epichef","avatar_url":"https://avatars-next.observableusercontent.com/70dc97d7ff2150f8107b41e625ea31c76fcb3002082314c845e579febc189bf7","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Within-Host Models","cells":[{"id":0,"value":"md`# Within-Host Models`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":834,"value":"md`Author: Emma Southall @ersouthall\n\nDate: 2018-10-03`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":489,"value":"md`Models taken from Spicknall et al, doi: [10.1093/aje/kwt017](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3736756/). Each model summarised has different mechanisms for how antibiotic resistance effects disease transmission. In particular, models developed for the two competiting strains of infection: Iz (antibioitic-resistant strain) and Iw (antibioitic-sensitive strain).`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":487,"value":"md`## Exclusive Infection`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":493,"value":"tex.block`\\frac{dS}{dt} = -\\beta_w S I_w - \\beta_z S I_z + (\\gamma (1 - \\varepsilon) + \\gamma' \\varepsilon)I_w + \\gamma I_z , \\\\\n\\frac{dI_w}{dt} = \\beta_w S I_w - (\\gamma(1-\\varepsilon) + \\gamma' \\varepsilon)I_w , \\\\\n\\frac{dI_z}{dt} = \\beta_z SI_z - \\gamma I_z `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":556,"value":"md`## Replacement Infection`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":554,"value":"tex.block`\\frac{dS}{dt} = -\\beta_w S I_w - \\beta_z S I_z + (\\gamma (1 - \\varepsilon) + \\gamma' \\varepsilon)I_w + \\gamma I_z , \\\\\n\\frac{dI_w}{dt} = \\beta_w S I_w - (\\gamma(1-\\varepsilon) + \\gamma' \\varepsilon)I_w -(\\beta_z - \\beta_w)I_z I_w - , \\\\\n\\frac{dI_z}{dt} = \\beta_z SI_z - \\gamma I_z - +(\\beta_z - \\beta_w) I_z I_w `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":586,"value":"md`## Unidirectional Conversion Model`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":585,"value":"tex.block`\\frac{dS}{dt} = -\\beta_w S I_w - \\beta_z S I_z + (\\gamma (1 - \\varepsilon) + \\gamma' \\varepsilon)I_w + \\gamma I_z , \\\\\n\\frac{dI_w}{dt} = \\beta_w S I_w - (\\gamma(1-\\varepsilon) + \\gamma' \\varepsilon)I_w - \\rho \\epsilon I_w , \\\\\n\\frac{dI_z}{dt} = \\beta_z SI_z - \\gamma I_z - +\\rho \\varepsilon I_w`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":170,"value":"md`## Plots - Comparison of within-host models`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":173,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":570,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":580,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":612,"value":"md`# Figure 4 of Spicknall et al.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":813,"value":"md`Plots show which strain outcompetes as the percentage of individuals recieving treatment changes. When both strains are non-zero, coexistance occurs. Initially run with parameters from Spicknall et al.
Note: reason why the two plots may not match Spicknall et al., could be due to running the system to endemic state.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":820,"value":"tex.block`\\beta_z = 0.015, \\\\ \\beta_w = 0.04, \\\\ \\gamma = 0.01, \\\\ \\gamma' = 0.1, \\\\ \\rho = 0.5, \\\\ 0< \\varepsilon < 0.15`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":701,"value":"{var treatment = linspace(0,30,100);\n var exclusive = change.Ex\n let Iw_change = exclusive.map(function(value, index) {return value[0];})\n let Iz_change = exclusive.map(function(value, index) {return value[1];})\n var iwtrace = {\n type: \"scatter\",\n mode: \"step\",\n name: 'Iw',\n x: treatment,\n y: Iw_change,\n line: {color: 'red'}\n }\n var iztrace = {\n type: \"scatter\",\n mode: \"step\",\n name: 'Iz',\n x: treatment,\n y: Iz_change,\n line: {color: 'purple'}\n }\n var data = [iwtrace, iztrace];\n \n var layout = {\n width: width\n };\n \n var layout = {\n title: `Changing Treatment: Exclusive Infection
Doesn't match Spicknall et al.
for initial parameters (expect change of stability at 5%) `,\n xaxis: {\n title: 'Percentage of Treatment (%)',\n titlefont: {\n size: 18,\n }\n },\n yaxis: {\n title: 'Number',\n titlefont: {\n size: 18,\n }\n }};\n const div = DOM.element('div');\n Plotly.newPlot(div, data, layout);\n return div;\n\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":781,"value":"{var treatment = linspace(0,30,100);\n var replacement = change.Re\n let Iw_change = replacement.map(function(value, index) {return value[0];})\n let Iz_change = replacement.map(function(value, index) {return value[1];})\n var iwtrace = {\n type: \"scatter\",\n mode: \"step\",\n name: 'Iw',\n x: treatment,\n y: Iw_change,\n line: {color: 'red'}\n }\n var iztrace = {\n type: \"scatter\",\n mode: \"step\",\n name: 'Iz',\n x: treatment,\n y: Iz_change,\n line: {color: 'purple'}\n }\n var data = [iwtrace, iztrace];\n \n var layout = {\n width: width\n };\n \n var layout = {\n title: `Changing Treatment: Replacement Infection
Doesn't match Spicknall et al.
for initial parmateters expect stability change at 7.5%`,\n xaxis: {\n title: 'Percentage of Treatment (%)',\n titlefont: {\n size: 18,\n }\n },\n yaxis: {\n title: 'Number',\n titlefont: {\n size: 18,\n }\n }};\n const div = DOM.element('div');\n Plotly.newPlot(div, data, layout);\n return div;\n\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":784,"value":"{var treatment = linspace(0,30,100);\n var uni = change.Uni\n let Iw_change = uni.map(function(value, index) {return value[0];})\n let Iz_change = uni.map(function(value, index) {return value[1];})\n var iwtrace = {\n type: \"scatter\",\n mode: \"step\",\n name: 'Iw',\n x: treatment,\n y: Iw_change,\n line: {color: 'red'}\n }\n var iztrace = {\n type: \"scatter\",\n mode: \"step\",\n name: 'Iz',\n x: treatment,\n y: Iz_change,\n line: {color: 'purple'}\n }\n var data = [iwtrace, iztrace];\n \n var layout = {\n width: width\n };\n \n var layout = {\n title: `Changing Treatment: Unidirectional Conversion Infection
Matches Spicknall et al. `,\n xaxis: {\n title: 'Percentage of Treatment (%)',\n titlefont: {\n size: 18,\n }\n },\n yaxis: {\n title: 'Number',\n titlefont: {\n size: 18,\n }\n }};\n const div = DOM.element('div');\n Plotly.newPlot(div, data, layout);\n return div;\n\n}\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"md`### Parameters`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":65,"value":"viewof g = slider({\n value : 0.01,\n min: 0,\n max: 1,\n precision: 2,\n description: \"Recovery Rate (without treatment)\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":497,"value":"viewof g_prime = slider({\n value : 0.1,\n min: 0,\n max: 1,\n precision: 2,\n description: \"Recovery Rate (with treatment)\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":86,"value":"viewof betaw = slider({\n value : 0.04,\n min: 0,\n max: 5,\n precision: 2,\n description: \"Infectivity of sensitive-strain parameter\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":504,"value":"viewof betaz = slider({\n value : 0.015,\n min: 0,\n max: 5,\n precision: 3,\n description: \"Infectivity of resistant-strain parameter\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":506,"value":"viewof epsilon = slider({\n value : 0.15,\n min: 0,\n max: 0.5,\n precision: 2,\n description: \"Proportion of Individuals treated\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":596,"value":"viewof rho = slider({\n value : 0.5,\n min: 0,\n max: 0.5,\n precision: 2,\n description: \"rate that resistance-strain outcompetes sensitive-strain\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":228,"value":"viewof I0Z = slider({\n value : 0.01,\n min: 0,\n max: 1,\n precision: 3,\n description: \"Initial proportion of Infecteds (Iz)\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":513,"value":"viewof I0W = slider({\n value : 0.3,\n min: 0,\n max: 1,\n precision: 2,\n description: \"Initial proportion of Infecteds (Iw)\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"N =1000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":406,"value":"tmax = 3000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"md`### Model Code`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"function exclusive(dydt, y, t) {\n dydt[0] = -betaw*y[0]*y[1] - betaz*y[0]*y[2] + (g*(1-epsilon) + g_prime*epsilon)*y[1] + g*y[2];\n dydt[1] = betaw*y[0]*y[1] - (g*(1-epsilon) + g_prime*epsilon)*y[1];\n dydt[2] = betaz*y[0]*y[2] - g*y[2] ;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":573,"value":"function replacement(dydt, y, t) {\n dydt[0] = -betaw*y[0]*y[1] - betaz*y[0]*y[2] + (g*(1-epsilon) + g_prime*epsilon)*y[1] + g*y[2] ;\n dydt[1] = betaw*y[0]*y[1] - (g*(1-epsilon) + g_prime*epsilon)*y[1] - (betaz- betaw)*y[1]*y[2] ;\n dydt[2] = betaz*y[0]*y[2] - g*y[2]+ (betaz-betaw)*y[1]*y[2] ;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":600,"value":"function unidirectional(dydt, y, t) {\n dydt[0] = -betaw*y[0]*y[1] - betaz*y[0]*y[2] + (g*(1-epsilon) + g_prime*epsilon)*y[1] + g*y[2] ;\n dydt[1] = betaw*y[0]*y[1] - (g*(1-epsilon) + g_prime*epsilon)*y[1] - rho*epsilon*y[1];\n dydt[2] = betaz*y[0]*y[2] - g*y[2]+ rho*epsilon*y[1];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":126,"value":"ex_sol = simulate(exclusive, 0.01, [1-I0W-I0Z,I0W, I0Z], 0, tmax)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":576,"value":"re_sol = simulate(replacement, 0.01, [1-I0W-I0Z,I0W, I0Z], 0, tmax)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":603,"value":"uni_sol = simulate(unidirectional, 0.01, [1-I0W-I0Z,I0W, I0Z], 0, tmax)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":759,"value":"change = changingtreatment(100, 0.3)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":314,"value":"md`### Utility Functions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"function simulate(func, step, initial, t0, maxtime){\n var integrator = rk4( initial, func, t0, step )\n var time = []\n var SI = []\n time.push(t0)\n SI.push(initial)\n var t = t0\n while(t=0;i--) { ret[i] = (i*b+(n-i)*a)/n; }\n return ret;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"md`### Dependencies`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":119,"value":"rk4 = require(`https://bundle.run/ode-rk4`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":159,"value":"Plotly = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":220,"value":"import {slider} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":328,"value":"md`### Test + Benchmark`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":415,"value":"{let times = 0;\n let i;\n const tmax = 100;\n for (i = 0; i < 1; i++) {\n const start = new Date();\n let sii_e = simulate(exclusive, 0.01, [1-I0W-I0Z,I0W, I0Z], 0,tmax );\n const elapsed = new Date() - start;\n times += elapsed;\n }\n return 'Average time elapsed over 1 replicates for exclusive model: ' + times/1 + ' ms';\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":839,"value":"{let times = 0;\n let i;\n const tmax = 100;\n for (i = 0; i < 1; i++) {\n const start = new Date();\n let sii_r = simulate(replacement, 0.01, [1-I0W-I0Z,I0W, I0Z], 0, tmax);\n const elapsed = new Date() - start;\n times += elapsed;\n }\n return 'Average time elapsed over 1 replicates for replacement model: ' + times/1 + ' ms';\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":842,"value":"{let times = 0;\n let i;\n const tmax = 100;\n for (i = 0; i < 1; i++) {\n const start = new Date();\n let sii_u = simulate(unidirectional, 0.01, [1-I0W-I0Z,I0W, I0Z], 0, tmax);\n const elapsed = new Date() - start;\n times += elapsed;\n }\n return 'Average time elapsed over 1 replicates for unidirectional model: ' + times/1 + ' ms';\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":479,"value":"md`Fix parameters as given by Spicknall et al.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":853,"value":"tex.block`\\beta_z = 0.015, \\\\ \\beta_w = 0.04, \\\\ \\gamma = 0.01, \\\\ \\gamma' = 0.1, \\\\ \\rho = 0.5, \\\\ \\varepsilon = 0.15 \\\\ (I_w(0),I_z(0)) = (0.3,0.01) \\\\ \\text{max time} = 100`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":469,"value":"{const betaw= 0.04;\n const betaz = 0.015;\n const g = 0.01;\n const g_prime = 0.1;\n const rho = 0.5;\n const epsilon = 0.15;\n const I0W = 0.3;\n const I0Z = 0.01;\n const tmax = 100;\n function exclusive(dydt, y, t) {\n dydt[0] = -betaw*y[0]*y[1] - betaz*y[0]*y[2] + (g*(1-epsilon) + g_prime*epsilon)*y[1] + g*y[2];\n dydt[1] = betaw*y[0]*y[1] - (g*(1-epsilon) + g_prime*epsilon)*y[1];\n dydt[2] = betaz*y[0]*y[2] - g*y[2] ;\n}\n const test_sol_e = simulate(exclusive, 0.01,[1-I0W-I0Z, I0W, I0Z], 0, tmax)\n return `Final proportion of sensitive-strain infections (exclusive model): ` + test_sol_e.SI.map((x)=>{return x[1]}).slice(-1)}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":878,"value":"{const betaw= 0.04;\n const betaz = 0.015;\n const g = 0.01;\n const g_prime = 0.1;\n const rho = 0.5;\n const epsilon = 0.15;\n const I0W = 0.3;\n const I0Z = 0.01;\n const tmax = 100;\nfunction replacement(dydt, y, t) {\n dydt[0] = -betaw*y[0]*y[1] - betaz*y[0]*y[2] + (g*(1-epsilon) + g_prime*epsilon)*y[1] + g*y[2] ;\n dydt[1] = betaw*y[0]*y[1] - (g*(1-epsilon) + g_prime*epsilon)*y[1] - (betaz- betaw)*y[1]*y[2] ;\n dydt[2] = betaz*y[0]*y[2] - g*y[2]+ (betaz-betaw)*y[1]*y[2] ;\n}\n const test_sol_r = simulate(replacement, 0.01,[1-I0W-I0Z, I0W, I0Z], 0, tmax)\n return `Final proportion of sensitive-strain infections (replacement model): ` + test_sol_r.SI.map((x)=>{return x[1]}).slice(-1)} ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":885,"value":"{const betaw= 0.04;\n const betaz = 0.015;\n const g = 0.01;\n const g_prime = 0.1;\n const rho = 0.5;\n const epsilon = 0.15;\n const I0W = 0.3;\n const I0Z = 0.01;\n const tmax = 100;\nfunction unidirectional(dydt, y, t) {\n dydt[0] = -betaw*y[0]*y[1] - betaz*y[0]*y[2] + (g*(1-epsilon) + g_prime*epsilon)*y[1] + g*y[2] ;\n dydt[1] = betaw*y[0]*y[1] - (g*(1-epsilon) + g_prime*epsilon)*y[1] - rho*epsilon*y[1];\n dydt[2] = betaz*y[0]*y[2] - g*y[2]+ rho*epsilon*y[1];\n}\n const test_sol_u = simulate(unidirectional, 0.01,[1-I0W-I0Z, I0W, I0Z], 0, tmax)\n return `Final proportion of sensitive-strain infections (unidirectional model): ` + test_sol_u.SI.map((x)=>{return x[1]}).slice(-1)} ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4f","stdlib":"1"}} 24:null