31:T669,md`## Delež cepljenih SARI - učinek cepiva **——— Predvideno razmerje ob neučinkovitem cepivu** je izračunano z upoštevanjem starostne strukture sprejetih v bolnišnico [2] in deleža cepljenih v populaciji po starosti [3]
**○ ○ ○ Dejansko razmerje** je izračunano iz [1] kot delež cepljenih od vseh sprejetih v bolnišnico
${Plot.plot({ grid: true, x: { label: null }, y: { label: "delež cepljenih SARI", type: "linear", tickFormat: "p" }, marks: [ Plot.ruleY([0,1]), Plot.line(sari_in_data.map(d => d[0]), {x: "date_to", y:"sim_vax_share", strokeWidth: 2, stroke: clr_cep.all}), Plot.dot(sari_in_data.map(d => d[0]), {x: "date_to", y:"hos_vax_share", strokeWidth: 2, stroke: clr_cep.all}) ] }) } Podatki:
[1] SARI covid-19 sprejemi po cepilnem statusu (NIJZ EPISARI via [Sledilnik:episari-nijz-weekly.csv](https://github.com/sledilnik/data/blob/master/csv/episari-nijz-weekly.csv))
[2] SARI covid-19 sprejemi po starosti (NIJZ EPISARI via [Sledilnik:episari-nijz-weekly.csv](https://github.com/sledilnik/data/blob/master/csv/episari-nijz-weekly.csv))
[3] Cepljeni po starostnih skupinah ([NIJZ / cepimose](https://app.powerbi.com/view?r=eyJrIjoiYWQ3NGE1NTMtZWJkMi00NzZmLWFiNDItZDc5YjU5MGRkOGMyIiwidCI6ImFkMjQ1ZGFlLTQ0YTAtNGQ5NC04OTY3LTVjNjk5MGFmYTQ2MyIsImMiOjl9) via [Sledilnik:vaccination-by_age.csv](https://github.com/sledilnik/data/blob/master/csv/vaccination-by_age.csv)) `32:T47c,text_hos_factor_all = { const lastVal = sari_in_data.slice(-1)[0][0]; const fp = d3.format(".2p"); //return lastVal; return md`Analiza primerja delež sprejetih SARI pacientov, ki so cepljeni, z deležem cepljenih, ki bi ga pričakovali, če bi osebe enake starosti naključno izbrali izmed vseh prebivalcev. Rezultat analize pokaže, da bi, če cepljenje ne bi bilo učinkovito, v bolnišnice sprejeti cepljeni bolniki predstavljali **${fp(lastVal.sim_vax_share)}** vseh sprejemov namesto **${fp(lastVal.hos_vax_share)}**, kolikor znaša ta delež v resnici. To pomeni, da je v bolnišnicah približno **${d3.format(".2m")(lastVal.protection_factor)}-krat** manj cepljenih bolnikov, kot bi jih pričakovali v primeru, da cepivo ne bi učinkovalo (simulacija neučinkovitega cepiva ob istem številu ${lastVal.hos_in_notvaxed} necepljenih predvidi ${Math.round(lastVal.protection_factor * lastVal.hos_in_vaxed_all)} cepljenih namesto dejanskih ${lastVal.hos_in_vaxed_all}). *(op.: Razmerja in deleži v tekstu veljajo za zadnji poročani teden, od ${date_to_iso(lastVal.date_from)} do ${date_to_iso(lastVal.date_to)})* `; }33:T599,chart_hos_vax_share_age = md`### Delež cepljenih SARI glede na starost: 0-64, 65+, vsi ——— pričakovan delež ob neučinkovitem cepivu ○ ○ ○ dejanski delež ${(() => { const both_data = sari_in_data.map(d => d[0]); const data_0_64 = sari_in_data.map(d => d.length>1 ? d[1] : d[0]); const data_64plus = sari_in_data.map(d => d.length>1 ? d[2] : d[0]); //return both_data; return Plot.plot({ grid: true, x: { label: null }, y: { label: "↑ % cepljenih SARI", type: "linear", transform: d => d * 100, //domain: [0, 70] }, marks: [ Plot.ruleY([0,1]), Plot.line(both_data, {x: "date_to", y:"sim_vax_share", strokeWidth: 1.5, stroke: clr_cep.all}), Plot.dot(both_data, {x: "date_to", y:"hos_vax_share", fill: clr_cep.all}), Plot.line(data_0_64, {x: "date_to", y:"sim_vax_share", strokeWidth: 2, stroke: clr_cep.lt}), Plot.dot(data_0_64, {x: "date_to", y:"hos_vax_share", strokeWidth: 2, stroke: clr_cep.lt}), Plot.line(data_64plus, {x: "date_to", y:"sim_vax_share", strokeWidth: 2, stroke: clr_cep.gt}), Plot.dot(data_64plus, {x: "date_to", y:"hos_vax_share", strokeWidth: 2, stroke: clr_cep.gt}), ] }); })()} `34:T4ca,chart_hos_vax_factor_age = md`## Faktor razmerja C:N 0-65, 65+, skupaj Kolikokrat manjše je razmerje med cepljenimi in necepljenimi od pričakovanega ${(() => { const max = 100; return Plot.plot({ grid: true, x: { label: null }, y: { label: "Csim/C [log]", type: "log", domain: [0.5, 30], tickFormat: "1m" }, marks: [ Plot.ruleY([0]), Plot.line(sari_in_data.map(d => d[0]), {x: "date_to", y:"protection_factor", strokeWidth: 2, stroke: clr_cep.all}), Plot.dot(sari_in_data.map(d => d[0]), {x: "date_to", y:"protection_factor", strokeWidth: 0, fill: clr_cep.all}), Plot.dot( sari_in_data.map(d => ({...d[1], protection_factor: Math.min(max, d[1].protection_factor)})), {x: "date_to", y:"protection_factor", strokeWidth: 2, stroke: clr_cep.lt}), Plot.dot( sari_in_data.map(d => ({...d[2], protection_factor: Math.min(max, d[2].protection_factor)})), {x: "date_to", y:"protection_factor", strokeWidth: 2, stroke: clr_cep.gt}), ] }) })()}`35:T587,text_hos_incidence = { const lastVal = sari_in_data.slice(-1)[0][0]; const lt = sari_in_data.slice(-1)[0][1]; const gt = sari_in_data.slice(-1)[0][2]; const fd = d3.format(".2m"); const finc = a => d3.format(".3m")(a*100000); //return lastVal return md` Še druga analiza, s katero lahko pokažemo učinek cepiva na hospitalizacije, je primerjava celotnih skupin cepljenih oz. necepljenih prebivalcev glede na delež izmed njih, ki so sprejeti v bolnišnico. Če pri primerjavi upoštevamo vso populacijo naenkrat, dobimo incidenco **${finc(lastVal.hos_inc_vaxed)} za cepljene** in **${finc(lastVal.hos_inc_notvaxed)} za necepljene** (na 100.000 prebivalcev), oz. **${fd(lastVal.hos_inc_factor)}-krat** večje tveganje pri necepljenih. Z ločeno obravnavo po starosti se učinek cepljenja pokaže še bolj jasno, saj se tako deloma izognemo močnemu učinku starostnih razlik na statistiko - pri mlajših od 65 let cepljenje tveganje zmanjša za **${fd(lt.hos_inc_factor)}-krat** (incidenca **${finc(lt.hos_inc_vaxed)}** oz. **${finc(lt.hos_inc_notvaxed)}** na 100.000 prebivalcev); pri starejših pa za **${fd(gt.hos_inc_factor)}-krat** (incidenca **${finc(gt.hos_inc_vaxed)}** oz. **${finc(gt.hos_inc_notvaxed)}** na 100.000 prebivalcev). *(op.: Razmerja in deleži v tekstu veljajo za zadnji poročani teden, od ${date_to_iso(lt.date_from)} do ${date_to_iso(lt.date_to)})* ` }36:T4c9,chart_icu_vax_share = md`### Delež cepljenih v EIT - učinek cepiva **——— Predviden delež cepljenih ob neučinkovitem cepivu**, izračunano z upoštevanjem starostne strukture sprejetih v EIT in deleža cepljenih v populaciji po starosti
**○ ○ ○ Dejanski delež**: delež cepljenih od vseh sprejetih v EIT
${Plot.plot({ grid: true, x: { label: null }, y: { label: "delež cepljenih ICU", type: "linear", tickFormat: "p" }, marks: [ Plot.ruleY([0,1]), Plot.dot(sari_in_data.map(d => d[0]), {x: "date_to", y: d => d.icu_sim_vax_share || NaN, r: 2, fill: clr_cep.icu, strokeWidth: 0, title: (d) => d3.format(".2p")(d["icu_sim_vax_share"])}), Plot.line(sari_in_data.map(d => d[0]), { x: "date_to", y: d => d.icu_sim_vax_share || NaN, strokeWidth: 2, stroke: clr_cep.icu }), Plot.dot(sari_in_data.map(d => d[0]), {x: "date_to", y: d => d.icu_vax_share || NaN, strokeWidth: 2, stroke: clr_cep.icu}) ] }) }`37:T4ba,text_icu_factor = { const lastVal = sari_in_data.slice(-1)[0][0]; const fp = d3.format(".2p"); //return lastVal; return md`Analiza primerja delež sprejetih SARI EIT pacientov, ki so cepljeni, z deležem cepljenih, ki bi ga pričakovali, če bi osebe enake starosti naključno izbrali izmed vseh prebivalcev. Rezultat analize pokaže, da bi, če cepljenje ne bi bilo učinkovito, v EIT sprejeti cepljeni bolniki predstavljali **${fp(lastVal.icu_sim_vax_share)}** vseh sprejemov namesto **${fp(lastVal.icu_vax_share)}**, kolikor znaša ta delež v resnici. To pomeni, da je v EIT sprejetih približno **${d3.format(".2m")(lastVal.icu_protection_factor)}-krat** manj cepljenih bolnikov, kot bi jih pričakovali v primeru, da cepivo ne bi učinkovalo (simulacija neučinkovitega cepiva ob istem številu ${lastVal.icu_vax_data.icu_all-lastVal.icu_vax_data.icu_vax} necepljenih sprejetih v EIT predvidi ${Math.round(lastVal.icu_protection_factor * lastVal.icu_vax_data.icu_vax)} cepljenih namesto dejanskih ${lastVal.icu_vax_data.icu_vax}). *(op.: Razmerja in deleži v tekstu veljajo za zadnji poročani teden, od ${date_to_iso(lastVal.date_from)} do ${date_to_iso(lastVal.date_to)})* `; }38:T585,chart_icu_hos_vax_factor = md`### Faktor razmerja C:N za H in EIT Kolikokrat manjše je razmerje med cepljenimi in necepljenimi od pričakovanega ${(() => { const max = 100; return Plot.plot({ grid: true, x: { label: null }, y: { label: "(Csim:Nsim) / (C:N)", type: "log", domain: [1, 50], tickFormat: ".0d" }, marks: [ Plot.ruleY([0]), Plot.line(sari_in_data.map(d => d[0]), {x: "date_to", y:"protection_factor", strokeWidth: 2, stroke: clr_cep.hos}), Plot.dot(sari_in_data.map(d => d[0]), {x: "date_to", y:"protection_factor", strokeWidth: 0, fill: clr_cep.hos}), Plot.line(sari_in_data.map(d => d[0]), {x: "date_to", y:"icu_protection_factor", strokeWidth: 2, stroke: clr_cep.icu}), Plot.dot(sari_in_data.map(d => d[0]), {x: "date_to", y:"icu_protection_factor", strokeWidth: 0, fill: clr_cep.icu}), Plot.dot( sari_in_data.map(d => ({...d[1], protection_factor: Math.min(max, d[1].protection_factor)})), {x: "date_to", y:"protection_factor", strokeWidth: 2, stroke: clr_cep.lt}), Plot.dot( sari_in_data.map(d => ({...d[2], protection_factor: Math.min(max, d[2].protection_factor)})), {x: "date_to", y:"protection_factor", strokeWidth: 2, stroke: clr_cep.gt}), ] }) })()}`39:T4b0,chart_icu_pred_vax_factor = md`### Faktor zaščite pred EIT za populacijo F_icu F_icu|H × F_H F_icu|H × F_H<65 F_icu|H × F_H>65 ${(() => { const plot_data = icu_pred_vax_data.slice(22); return Plot.plot({ grid: true, x: { label: null }, y: { label: "", domain: [1, 30], type: "log", tickFormat: "1m" }, marks: [ Plot.ruleY([1]), Plot.line(plot_data, {x: "date_to", y:"icu_protection_factor", strokeWidth: 2, stroke: clr_cep.icu}), Plot.line(plot_data, {x: "date_to", y:"icu_comb_protection_factor", strokeWidth: 2, stroke: clr_cep.hos}), Plot.dot(plot_data, {x: "date_to", y:"icu_comb_protection_factor_lt65", strokeWidth: 2, stroke: clr_cep.lt}), Plot.dot(plot_data, {x: "date_to", y:"icu_comb_protection_factor_gt65", strokeWidth: 2, stroke: clr_cep.gt}), ] }) })()}`3a:T139e,sari_in_data = { const vax_lookup = vaccinated_lookup(); const hosp_age_to_grp = {}; age_groups.forEach(ag => { ag.hosp.forEach(agh => { hosp_age_to_grp[agh] = ag.group }); }); const age_group_lookup = array_to_lookup(age_groups, ag => ag.group); function hos_ages(sari_row, ageprop) { return age_groups.map(grp => { const sari_in = d3.sum(grp.hosp.map(hage =>sari_row[ageprop+hage])); const vax_obj = vax_lookup[date_to_iso(d3.timeWeek.offset(sari_row["episari.date.to"], -2))]; const pop_vax2 = vax_obj ? vax_obj[grp.group]["2nd"] : NaN; const pop = age_group_lookup[grp.group].population; const pop_vax2_share = pop_vax2 / pop; const sim_vax2 = pop_vax2_share * sari_in; return { age: grp.group, in_act: sari_in, population: pop, pop_vaxed2: pop_vax2, pop_vax2_share: pop_vax2_share, in_sim_vax: sim_vax2, in_sim_unvax: sari_in - sim_vax2 }; }); } const hos_wk_data = episari_subset_csv .map((el, i, arr) => { return { date_from: el["episari.date.from"], date_to: el["episari.date.to"], hos_in_all: el["episari.covid.in"], hos_in_vaxed_all: el["episari.covid.in.vacc"], hos_in_notvaxed_all: el["episari.covid.in.notvacc"], hos_in_vaxed_65: el["episari.covid.in.vacc.age.65+"], hos_in_ages: hos_ages(el, "episari.covid.in.age."), hos_icu_all: el["episari.covid.icu.in"], hos_icu_ages: hos_ages(el, "episari.covid.icu.in.age."), icu_vax_data: icu_vax_lookup[i] }; }).map(el => { const ranges = [ {group_name: "all", startGrp: 0, endGrp: 7, hos_in_vaxed: el.hos_in_vaxed_all}, {group_name: "<65", startGrp: 0, endGrp: 4, hos_in_vaxed: el.hos_in_vaxed_all - number_or_nan(el.hos_in_vaxed_65)}, {group_name: "65+", startGrp: 5, endGrp: 7, hos_in_vaxed: number_or_nan(el.hos_in_vaxed_65)} ]; const icu_vax_share = el.icu_vax_data.icu_vax_share.fully; const known_vax_share = (el.hos_in_notvaxed_all + el.hos_in_vaxed_all)/el.hos_in_all; //return known_vax_share; return ranges.map(range => { let sari_in = 0; let population = 0; let pop_vaxed1 = 0; let pop_vaxed2 = 0; let sari_in_sim_vax = 0; let sari_in_sim_unvax = 0; let icu_in = 0; let icu_in_sim_vax = 0; let icu_in_sim_unvax = 0; el.hos_in_ages.slice(range.startGrp, range.endGrp+1).forEach(d => { sari_in += d.in_act; population += d.population; pop_vaxed2 += d.pop_vaxed2; sari_in_sim_vax += d.in_sim_vax; sari_in_sim_unvax += d.in_sim_unvax; }); el.hos_icu_ages.slice(range.startGrp, range.endGrp+1).forEach(d => { icu_in += d.in_act; icu_in_sim_vax += d.in_sim_vax; icu_in_sim_unvax += d.in_sim_unvax; }); const hos_in_notvaxed = (sari_in * known_vax_share) - range.hos_in_vaxed; const icu_in_notvaxed = icu_in * (1 - icu_vax_share); return { ...el, ...range, hos_in_notvaxed: hos_in_notvaxed, hos_inc_vaxed: range.hos_in_vaxed / pop_vaxed2, hos_inc_notvaxed: hos_in_notvaxed / (population - pop_vaxed2), known_vax_share: known_vax_share, hos_in: sari_in, population: population, pop_vaxed2: pop_vaxed2, hos_in_sim_vax: sari_in_sim_vax, hos_in_sim_unvax: sari_in_sim_unvax, hos_in_sim_unvax_chk: sari_in - sari_in_sim_vax, sim_vax_share: sari_in_sim_vax/(sari_in_sim_vax + sari_in_sim_unvax), hos_vax_share: range.hos_in_vaxed/(range.hos_in_vaxed + hos_in_notvaxed), hos_vax_norm_unvax: hos_in_notvaxed * (sari_in_sim_vax / sari_in_sim_unvax), icu_in_vaxed: icu_in * icu_vax_share, icu_vax_share: icu_vax_share, icu_in_sim_vax: icu_in_sim_vax, icu_in_sim_unvax: icu_in_sim_unvax, icu_in_sim_unvax_chk: icu_in - icu_in_sim_vax, icu_sim_vax_share: icu_in_sim_vax/(icu_in_sim_vax + icu_in_sim_unvax), icu_vax_norm_unvax: icu_in_notvaxed * (icu_in_sim_vax / icu_in_sim_unvax), } }).map(d => ({ ...d, hos_inc_effectiveness: 1 - d.hos_inc_vaxed / (d.hos_inc_notvaxed || NaN), hos_inc_factor: (d.hos_inc_notvaxed / (d.hos_inc_vaxed || NaN)), sim_unvax_share: 1 - d.sim_vax_share, hos_unvax_share: 1 - d.hos_vax_share, hos_vs_sim: d.hos_vax_share/d.sim_vax_share, protection_factor: d.hos_vax_norm_unvax/d.hos_in_vaxed, ratio: d.hos_in_vaxed/d.hos_vax_norm_unvax, effectiveness: 1 - d.hos_in_vaxed/d.hos_vax_norm_unvax, icu_protection_factor: d.icu_vax_norm_unvax/d.icu_in_vaxed, })); }); return hos_wk_data; }3b:T814,icu_pred_vax_data = sari_in_data.map((lastD, idx, arr) => { if (idx < 0) { return null; } const prevD = arr[idx]; const vaxH = prevD[0].hos_in_ages.map(age => { const protFact = age.age < "65-74" ? prevD[1].protection_factor : prevD[2].protection_factor; const pred_vaxed = age.pop_vax2_share / protFact; const pred_unvaxed = 1 - age.pop_vax2_share; const pred_sh_vaxed = pred_vaxed / (pred_vaxed + pred_unvaxed); return { protection_factor: protFact, pred_sh_vaxed: pred_sh_vaxed, in_pred_vax: age.in_act * pred_sh_vaxed, in_pred_unvax: age.in_act * (1 - pred_sh_vaxed), ...age } }); const vaxICU = lastD[0].hos_icu_ages.map((age, icuIdx) => { const vaxHRef = vaxH[icuIdx]; return { vax_ref: vaxHRef, icu_pred_vax: age.in_act * vaxHRef.pred_sh_vaxed, icu_pred_unvax: age.in_act * (1 - vaxHRef.pred_sh_vaxed), ...age } }); const pred_vax_icu_all = d3.sum(vaxICU.map(a => a.icu_pred_vax)); const pred_unvax_icu_all = d3.sum(vaxICU.map(a => a.icu_pred_unvax)); const pred_vax_icu_nc = pred_unvax_icu_all/pred_vax_icu_all; const vax_icu_nc = (1 - lastD[0].icu_vax_share) / lastD[0].icu_vax_share; const icu_h_protection_factor = vax_icu_nc / pred_vax_icu_nc; return { //prevD: prevD, //lastD: lastD, //vaxH: vaxH, //vaxICU: vaxICU, ...lastD[0], pred_vax_icu_all: pred_vax_icu_all, pred_unvax_icu_all: pred_unvax_icu_all, pred_vax_icu_share: pred_vax_icu_all/(pred_vax_icu_all + pred_unvax_icu_all), vax_icu_share: lastD[0].icu_vax_share, pred_vax_icu_NC: pred_vax_icu_nc, vax_icu_nc: vax_icu_nc, icu_h_protection_factor: icu_h_protection_factor, icu_h_VE: 1 - pred_vax_icu_nc / vax_icu_nc, icu_comb_protection_factor: prevD[0].protection_factor * icu_h_protection_factor, icu_comb_protection_factor_lt65: prevD[1].protection_factor * icu_h_protection_factor, icu_comb_protection_factor_gt65: prevD[2].protection_factor * icu_h_protection_factor }; }).filter(a => !(a ===null));3c:T547,icu_vax_lookup = { function isNum(a, defaultVal) { if (a===0 || a) { return true; } return false; } function castNum(a) { if (a===0 || a) { return a; } return -1; } const filtered = icu_csv.filter(d => isNum(d["state.icu.in.vaccinated"])); return episari_subset_csv.map(d => { const icuVals = filtered.filter(a => a.date >= d["episari.date.from"] && a.date <= d["episari.date.to"]); const icuVaxed = icuVals.length ? d3.sum(icuVals, a => castNum(a["state.icu.in.vaccinated"])) : NaN; const icuVaxedPart = icuVals.length ? d3.sum(icuVals, a => castNum(a["state.icu.in.vaccinatedpartially"])) : NaN; const icuRecovered = icuVals.length ? d3.sum(icuVals, a => castNum(a["state.icu.in.recovered"])) : NaN; const icuOther = icuVals.length ? d3.sum(icuVals, a => castNum(a["state.icu.in.other"])) : NaN; const icuAll = icuVals.length ? d3.sum(icuVals, a => castNum(a["state.icu.in"])) : NaN; const icuVaxData = ({ recovered: icuRecovered, partially: icuVaxedPart, fully: icuVaxed, other: icuOther }); return ({ date_from: d["episari.date.from"], date_to: d["episari.date.to"], icu_vax: icuVaxData, icu_all: icuAll, icu_vax_share: objectMap(icuVaxData, cnt => cnt / icuAll), icuVals: icuVals }) }); }3d:[] 2f:{"title":"EPISARI podatki","cells":[{"id":0,"value":"md`# EPISARI podatki\n- [Učinek cepljenja z uporabo starostne porazdelitve sprejetih SARI](#cell-750)\n- [Učinek cepljenja s primerjavo incidence v populaciji](#cell-1852)\n- [Učinek cepljenja v enotah intenzivne terapije](#cell-1857)\n- [Starostna struktura](#cell-1864)\n- [Ostali podatki](#cell-563)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":335,"value":"md`\n### Povprečna starost COVID bolnikov v bolnišnici: sprejeti, EIZ, umrli\n*(podatki EPISARI NIJZ)*\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":204,"value":"{\n const dFilt = episari_weekly_csv.filter(a => a['episari.covid.in.age.mean']);\n \n return vl.markLine().config({\"font\": \"IBM Plex Sans\"})\n .data(prepareData(dFilt, 'episari.date.to', [\n 'episari.covid.in.age.mean',\n 'episari.covid.icu.in.age.mean',\n 'episari.covid.deceased.age.mean'\n ]).map(a => ({...a, Kategorija: a.category.substring(6, a.category.length-5), \"povp starost\": a.y >= 0.01 ? a.y : null}))\n )\n .encode(\n vl.x().fieldT(\"datum\"),\n vl.y().fieldQ(\"povp starost\").scale({\"type\": \"linear\", \"nice\": false, \"domain\": [35, 90]}).axis({\"format\": \".2d\"}),\n vl.color().fieldN(\"Kategorija\")\n )\n .width(width-160)\n .height(width * 2/5)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":750,"value":"md`# Učinek cepljenja z uporabo starostne porazdelitve sprejetih SARI`","pinned":false,"hidden":false,"mode":"ojs"},{"id":725,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1777,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1224,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1791,"value":"text_hos_factor_age = {\n const lt = sari_in_data.slice(-1)[0][1];\n const gt = sari_in_data.slice(-1)[0][2];\n const fp = d3.format(\".2m\");\n const fd = d3.format(\".2m\");\n //return lt;\n return md`Če analizo ponovimo ločeno po dveh starostnih skupinah - za mlajše oz. starejše od 65 let - dobimo podobne rezultate: pri mlajših od 65 let bi ob neučinkovitem cepljenju pričakovali delež cepljenih **${fp(lt.sim_vax_share)}** (dejanski znaša **${fp(lt.hos_vax_share)}**), pri starejših od 65 let pa **${fp(gt.sim_vax_share)}** (dejanski delež sprejetih cepljenih v tej starostni skupini je **${fp(gt.hos_vax_share)}**). Če cepljenje ne bi bilo učinkovito, bi pri mlajših od 65 let pričakovali **${fd(lt.protection_factor)}-krat** , pri starejših od 65 let pa **${fd(gt.protection_factor)}-krat** večje število cepljenih pacientov.\n\n*(op.: Razmerja in deleži v tekstu veljajo za zadnji poročani teden, od ${date_to_iso(lt.date_from)} do ${date_to_iso(lt.date_to)})*\n`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1035,"value":"md`## Razmerje med dejanskimi in predvidenimi cep. v H\n**\n0-65,\n65+,\nskupaj\n**\n(ob predpostavki, da število necepljenih v H ostane enako)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1013,"value":"Plot.plot({\n grid: true,\n x: {\n label: null\n },\n y: {\n label: \"1 - C/simC\",\n type: \"linear\",\n tickFormat: \"p\",\n domain: [0.35, 1]\n },\n marks: [\n Plot.ruleY([0,1]),\n Plot.dot(sari_in_data.map(d => d[1] || NaN), {x: \"date_to\", y:\"effectiveness\", strokeWidth: 1.8, stroke: clr_cep.lt}),\n Plot.dot(sari_in_data.map(d => d[2] || NaN), {x: \"date_to\", y:\"effectiveness\", strokeWidth: 1.8, stroke: clr_cep.gt}),\n Plot.dot(sari_in_data.map(d => d[0]), {x: \"date_to\", y:\"effectiveness\", strokeWidth: 2, fill: clr_cep.all}),\n Plot.line(sari_in_data.map(d => d[0]), {x: \"date_to\", y:\"effectiveness\", strokeWidth: 2, stroke: clr_cep.all})\n ]\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1126,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1852,"value":"# Učinek cepljenja s primerjavo incidence v populaciji","pinned":false,"hidden":false,"mode":"md"},{"id":1814,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1426,"value":"md`## Razmerje incidenc c/nec 0-65, 65+, skupaj\n\n\nIc: cepljeni SARI na 100.000 cepljenih preb. |\nIn: necepljeni SARI na 100.000 necepljenih preb.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1430,"value":"Plot.plot({\n grid: true,\n x: {\n label: null\n },\n y: {\n label: \"1 - Ic/In\",\n tickFormat: \"p\",\n domain: [0, 1]\n },\n marks: [\n Plot.ruleY([1]),\n Plot.dot(sari_in_data.map(d => d[0] || NaN), {x: \"date_to\", y:\"hos_inc_effectiveness\", fill: clr_cep.all}),\n Plot.line(sari_in_data.map(d => d[0] || NaN), {x: \"date_to\", y:\"hos_inc_effectiveness\", stroke: clr_cep.all}),\n Plot.dot(sari_in_data.map(d => d[1] || NaN), {x: \"date_to\", y:\"hos_inc_effectiveness\", strokeWidth: 2, stroke: clr_cep.lt}),\n Plot.dot(sari_in_data.map(d => d[2] || NaN), {x: \"date_to\", y:\"hos_inc_effectiveness\", strokeWidth: 1, stroke: clr_cep.gt}),\n ]\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1556,"value":"chart_hos_inc_factor = md`## Faktor incidence SARI necep./cep. 0-65, 65+, skupaj ${\nPlot.plot({\n grid: true,\n x: {\n label: null\n },\n y: {\n label: \"Inec/Ic [log]\",\n type: \"log\",\n tickFormat: \"1m\",\n domain: [0.5, 25]\n },\n marks: [\n Plot.ruleY([0]),\n Plot.dot(sari_in_data.map(d => d[0] || NaN), {x: \"date_to\", y:\"hos_inc_factor\", fill: clr_cep.all}),\n Plot.line(sari_in_data.map(d => d[0] || NaN), {x: \"date_to\", y:\"hos_inc_factor\", stroke: clr_cep.all}),\n Plot.dot(sari_in_data.map(d => d[1] || NaN), {x: \"date_to\", y:\"hos_inc_factor\", strokeWidth: 2, stroke: clr_cep.lt}),\n Plot.dot(sari_in_data.map(d => d[2] || NaN), {x: \"date_to\", y:\"hos_inc_factor\", strokeWidth: 1, stroke: clr_cep.gt}),\n ]\n })\n}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1857,"value":"# Učinek cepljenja v enotah intenzivne terapije","pinned":false,"hidden":false,"mode":"md"},{"id":1702,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1878,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":1714,"value":"chart_icu_vax_factor = md`### Faktor razmerja C:N za EIT\nKolikokrat manjše je razmerje med cepljenimi in necepljenimi od pričakovanega ${(() => {\n const max = 100;\n return Plot.plot({\n grid: true,\n x: {\n label: null\n },\n y: {\n label: \"(Csim:Nsim) / (C:N) [log]\",\n type: \"log\",\n domain: [0.5, 40],\n tickFormat: \"1m\"\n },\n marks: [\n Plot.ruleY([0]),\n Plot.dot(sari_in_data.filter(d => d[0].icu_protection_factor).map(d => d[0]), {x: \"date_to\", y:\"icu_protection_factor\", strokeWidth: 2, stroke: clr_cep.icu})\n ]\n })\n})()}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1744,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":1917,"value":"chart_icu_pred_vax_share = md`### Delež cepljenih v EIT glede na cepljene v H (osnutek)\n\n— pričakovan % ob neučinkovitem cepivu \n— pričakovan % glede na zaščito v H \n○ dejanski %\n ${(() => {\n const max = 100;\n const plot_data = icu_pred_vax_data.slice(22);\n return Plot.plot({\n grid: true,\n x: {\n label: null\n },\n y: {\n label: \"% C v EIT\",\n tickFormat: \"1p\"\n },\n marks: [\n Plot.ruleY([0,1]),\n Plot.line(plot_data, {x: \"date_to\", y:\"icu_sim_vax_share\", strokeWidth: 2, stroke: clr_cep.icu}),\n Plot.dot(plot_data, {x: \"date_to\", y:\"icu_vax_share\", strokeWidth: 2, fill: clr_cep.icu}),\n Plot.line(plot_data, {x: \"date_to\", y:\"pred_vax_icu_share\", strokeWidth: 2, stroke: clr_cep.hos}),\n ]\n })\n})()}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2232,"value":"md`## Zaščita pred EIT za sprejete v H` ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1987,"value":"chart_icu_pred_factor = md`### F_icu|H — faktor zaščite pred EIT za H paciente\n${(() => {\n return Plot.plot({\n grid: true,\n x: {\n label: null\n },\n y: {\n label: \"Cp:Np/C:N [log]\",\n type: \"log\",\n domain: [0.35, 5],\n tickFormat: \"1m\"\n },\n marks: [\n Plot.ruleY([0,1]),\n Plot.line(icu_pred_vax_data.slice(22), {x: \"date_to\", y:\"icu_h_protection_factor\", strokeWidth: 2, stroke: clr_cep.icu}),\n Plot.dot(icu_pred_vax_data.slice(22), {x: \"date_to\", y:\"icu_h_protection_factor\", strokeWidth: 0, r: 2, fill: clr_cep.icu})\n ]\n })\n})()}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1970,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":1462,"value":"clr_cep = ({\n all: \"rgb(205, 130, 40)\",\n lt : \"#189A73\",\n gt: \"#B01C83\",\n hos: 'rgb(190, 122, 42)',\n icu: 'rgb(251, 106, 74)',\n deceased: 'rgb(109, 91, 128)'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":590,"value":"age_groups = {\n const aggrp = [{ group: \"00-24\", vax: [\"0-11\", \"12-17\", \"18-24\"], hosp: [\"00-04\",\"05-14\",\"15-24\"]}];\n for (let i = 25; i <= 75; i += 10) {\n aggrp.push({ group: i + \"-\" + (i+9), vax: [i + \"-\" + (i + 4), (i + 5) + \"-\" + (i + 9)], hosp: [i + \"-\" + (i+9)]});\n }\n aggrp.push({group: \"85+\", vax: [\"85-89\", \"90+\"], hosp: [\"85+\"]});\n \n return add_pop(aggrp);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":625,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":1076,"value":"function add_pop(aggrp) {\n return aggrp.map(ag => {\n let sumM = 0;\n let sumF = 0;\n ag.hosp.forEach(aId => {\n sumM += age_population[aId].male;\n sumF += age_population[aId].female;\n });\n return {\n ...ag,\n population: sumF + sumM,\n female: sumF,\n male: sumM\n };\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1912,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":1693,"value":"episari_subset_csv = episari_weekly_csv.slice(48).filter(a => a[\"episari.covid.in.notvacc\"]>0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1663,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":2256,"value":"({icu: icu_csv[580], episari: episari_subset_csv[32]})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1069,"value":"function vaccinated_lookup(age_grouping = age_groups) {\n const vax_lookup = {};\n vax_age_csv.forEach(vac => {\n const vRec = {date: vac.date};\n age_grouping.forEach(agrp => {\n let sumVac1 = 0;\n let sumVac2 = 0;\n agrp.vax.forEach(agv => {\n sumVac1 += vac[\"vaccination.age.\"+agv+\".1st.todate\"];\n sumVac2 += vac[\"vaccination.age.\"+agv+\".2nd.todate\"];\n });\n vRec[agrp.group] = {\n \"1st\": sumVac1,\n \"1st.share\": sumVac1/agrp.population,\n \"2nd\": sumVac2,\n \"2nd.share\": sumVac2/agrp.population\n };\n });\n vax_lookup[date_to_iso(vac.date)] = vRec;\n });\n return vax_lookup;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1080,"value":"janez_groups = add_pop([\n { group: \"00-24\", vax: [\"0-17\",\"18-24\"], hosp: [\"00-04\",\"05-14\",\"15-24\"] },\n { group: \"25-44\", vax: [\"25-29\", \"30-34\", \"35-39\", \"40-44\"], hosp: [\"25-34\",\"35-44\"] },\n { group: \"45-64\", vax: [\"45-49\",\"50-54\", \"55-59\",\"60-64\"], hosp: [\"45-54\",\"55-64\"]},\n { group: \"65-74\", vax: [\"65-69\",\"70-74\"], hosp: [\"65-74\"] },\n { group: \"75+\", vax: [\"75-79\",\"80-84\",\"85-89\",\"90+\"], hosp: [\"75-84\",\"85+\"]}\n])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1088,"value":"janez_shares=Object.values(vaccinated_lookup(janez_groups))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1864,"value":"# Starostna struktura","pinned":false,"hidden":false,"mode":"md"},{"id":1621,"value":"md`## Umrli, ICU in sprejeti v H po starosti\nPodatki NIJZ EPISARI, tedensko`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1606,"value":"Plot.plot({\n width: width,\n height: width*0.9,\n y: {\n grid: true,\n label: \"^ Count\"\n },\n facet: {\n data: episari_wk_counts,\n y: \"state\",\n marginRight: 60\n },\n color: {\n type: \"categorical\",\n range: ['#FFEEBA80','#FFDA6B80','#E9B82580','#AEEFDB80','#52C4A280','#33AB8780','#189A7380','#F4B2E080','#D559B080','#B01C8380']\n },\n marks: [\n Plot.areaY(episari_wk_counts,\n Plot.stackY({y: \"count\", \n x: \"date_to\",\n fill: \"age\",\n order: \"age\",\n title: \"age\"}))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2167,"value":"{\n const clrs = ['#FFEEBA80','#FFDA6B80','#E9B82580'];\n const ages = [\"00-04\",\"05-14\",\"15-24\"];\n const dta = episari_wk_counts.filter(a => a.state == \"in\" && ages.includes(a.age));\n\nreturn Plot.plot({\n width: width,\n height: width*0.25,\n y: {\n grid: true,\n label: \"^ Count\"\n },\n color: {\n type: \"categorical\",\n range: clrs\n },\n marks: [\n Plot.areaY(dta,\n Plot.stackY({y: \"count\", \n x: \"date_to\",\n fill: \"age\",\n order: \"age\",\n title: \"age\"}))\n ]\n})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2182,"value":"{\n const clrs = ['#AEEFDB80','#52C4A280','#33AB8780','#189A7380'];\n const ages = [\"25-34\",\"35-44\",\"45-54\",\"55-64\"];\n const dta = episari_wk_counts.filter(a => a.state == \"in\" && ages.includes(a.age));\n\nreturn Plot.plot({\n width: width,\n height: width*0.25,\n y: {\n grid: true,\n label: \"^ Count\"\n },\n color: {\n type: \"categorical\",\n range: clrs\n },\n marks: [\n Plot.areaY(dta,\n Plot.stackY({y: \"count\", \n x: \"date_to\",\n fill: \"age\",\n order: \"age\",\n title: \"age\"}))\n ]\n})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2186,"value":"{\n const clrs = ['#F4B2E080','#D559B080','#B01C8380'];\n const ages = [\"65-74\",\"75-84\",\"85+\"];\n const dta = episari_wk_counts.filter(a => a.state == \"in\" && ages.includes(a.age));\n\nreturn Plot.plot({\n width: width,\n height: width*0.25,\n y: {\n grid: true,\n label: \"^ Count\"\n },\n color: {\n type: \"categorical\",\n range: clrs\n },\n marks: [\n Plot.areaY(dta,\n Plot.stackY({y: \"count\", \n x: \"date_to\",\n fill: \"age\",\n order: \"age\",\n title: \"age\"}))\n ]\n})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2321,"value":"{\n const maxs = {};\n age_classes.forEach(acls => { maxs[acls] = d3.max(episari_wk_counts.filter(wk => wk.age == acls).map(wk => wk.count)); });\n const dta = episari_wk_counts.filter(a => a.state == \"in\").map(wk => ({...wk, rel_count: wk.count/maxs[wk.age]}));\n // return maxs;\n return Plot.plot({\n width: width,\n height: width*0.9,\n y: {\n grid: true,\n label: \"^ Count\"\n },\n facet: {\n data: dta,\n y: \"age\",\n marginRight: 60\n },\n color: {\n type: \"categorical\",\n range: ['#FFEEBA80','#FFDA6B80','#E9B82580','#AEEFDB80','#52C4A280','#33AB8780','#189A7380','#F4B2E080','#D559B080','#B01C8380']\n },\n marks: [\n Plot.areaY(dta,{y: \"rel_count\", \n x: \"date_to\",\n fill: \"age\",\n order: \"age\",\n title: \"age\"})\n ]\n});\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1624,"value":"md`## Umrli, ICU in sprejeti v H - delež po starosti\nPodatki NIJZ EPISARI, tedensko`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1617,"value":"Plot.plot({\n width: width,\n height: width*0.9,\n y: {\n grid: true,\n label: \"^ Share\",\n },\n facet: {\n data: episari_wk_counts,\n y: \"state\",\n marginRight: 60\n },\n color: {\n type: \"categorical\",\n range: ['#FFEEBA80','#FFDA6B80','#E9B82580','#AEEFDB80','#52C4A280','#33AB8780','#189A7380','#F4B2E080','#D559B080','#B01C8380']\n },\n marks: [\n Plot.areaY(episari_wk_counts,\n Plot.stackY({y: \"count\", \n x: \"date_to\",\n offset: \"expand\",\n fill: \"age\",\n order: \"age\",\n title: \"age\"}))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":563,"value":"md`# Ostali podatki`","pinned":false,"hidden":false,"mode":"ojs"},{"id":47,"value":"{\n const dFilt = episari_weekly_csv.slice(1);\n \n return vl.markLine().config({\"font\": \"IBM Plex Sans\"})\n .data(prepareData(dFilt, 'episari.date.from', [\n 'episari.sari.in',\n 'episari.tested.in',\n 'episari.covid.in',\n 'episari.covid.in.notsari'\n ]).map(a => ({...a, Kategorija: a.category, \"število\": a.y >= 0.01 ? a.y : null}))\n )\n .encode(\n vl.x().fieldT(\"datum\"),\n vl.y().fieldQ(\"število\").scale({\"type\": \"linear\", \"nice\": false, \"domain\": [1, 900]}).axis({\"format\": \"d\"}),\n vl.color().fieldN(\"Kategorija\")\n )\n .width(width-160)\n .height(width/2)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"{\n const dFilt = episari_weekly_csv.slice(1);\n \n return vl.markLine().config({\"font\": \"IBM Plex Sans\"})\n .data(prepareData(dFilt, 'episari.date.from', [\n 'episari.sari.in',\n 'episari.tested.in',\n 'episari.covid.in',\n 'episari.covid.icu.in',\n 'episari.covid.out',\n 'episari.covid.deceased', \n 'episari.covid.in.notsari',\n 'episari.covid.discoveredinhospital',\n 'episari.covid.acquiredinhospital'\n ]).map(a => ({...a, Kategorija: a.category, \"število\": a.y >= 0.01 ? a.y : null}))\n )\n .encode(\n vl.x().fieldT(\"datum\"),\n vl.y().fieldQ(\"število\").scale({\"type\": \"log\", \"nice\": false, \"domain\": [1, 1000]}).axis({\"format\": \"g\"}),\n vl.color().fieldN(\"Kategorija\")\n )\n .width(width-160)\n .height(width/2)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"function prepareData(arr, dateProp, props) {\n let dta = [];\n props.forEach(prop => {\n dta = dta.concat(arr.map(\n a => ({datum: a[dateProp],\n category: prop.substring(8),\n y: 0.0000001 + a[prop]}))\n )\n });\n return dta;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"import { vl } from '@vega/vega-lite-api'","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"import {icu_csv, episari_weekly_csv, stats_weekly_csv, episari_age_csv, data_daily, vax_age_csv, age_population} from '@mkadunc/sledilnik-data'","pinned":false,"hidden":false,"mode":"ojs"},{"id":634,"value":"date_to_iso = d3.timeFormat(\"%Y-%m-%d\");","pinned":true,"hidden":false,"mode":"ojs"},{"id":215,"value":"Plot = require(\"@observablehq/plot@0.1.0/dist/plot.umd.min.js\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":5,"value":"d3 = require('d3')","pinned":false,"hidden":false,"mode":"ojs"},{"id":131,"value":"stats_off = 0;","pinned":false,"hidden":false,"mode":"ojs"},{"id":67,"value":"hosp_in_wk = data_daily.slice(7 + stats_off).map((d, idx) => ({\n datum: data_daily[idx].date,\n Kategorija: 'h.in',\n \"število\": null_if_small(0.01,\n d[\"state.in_hospital.todate\"] - ((idx+stats_off >= 0)\n ? (data_daily[idx+stats_off][\"state.in_hospital.todate\"] || 0)\n : 0)\n )\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"{\n const dFilt = episari_weekly_csv.slice(1).map(a => ({\n ...a,\n \"episari.in + notsari\": a['episari.covid.in'] + a['episari.covid.in.notsari'] + a['episari.covid.discoveredinhospital'] + a['episari.covid.acquiredinhospital']\n }));\n \n return vl.markLine().config({\"font\": \"IBM Plex Sans\"})\n .data(prepareData(dFilt, 'episari.date.from',\n [\n 'episari.covid.in',\n //'episari.covid.in.notsari',\n 'episari.in + notsari'\n /*,\n 'episari.notcovid.in',\n 'episari.nottested.in'*/\n ]).map(a => ({...a, Kategorija: a.category, \"število\": null_if_small(0.01, a.y)}))\n .concat(hosp_in_wk)\n )\n .encode(\n vl.x().fieldT(\"datum\"),\n vl.y().fieldQ(\"število\").scale({\"type\": \"log\", \"nice\": false, \"domain\": [1, 1000]}).axis({\"format\": \"d\"}),\n vl.color().fieldN(\"Kategorija\")\n )\n .width(width-160)\n .height(width/2)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":95,"value":"icu_in_wk = data_daily.slice(7+stats_off).map((d, idx) => ({\n datum: data_daily[idx].date,\n Kategorija: 'icu.in',\n \"število\": null_if_small(0.01, d[\"state.icu.todate\"] - (\n (idx+stats_off >= 0)\n ? data_daily[idx+stats_off][\"state.icu.todate\"]\n : 0)\n )\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":93,"value":"{\n const dFilt = episari_weekly_csv.slice(1);\n \n return vl.markLine().config({\"font\": \"IBM Plex Sans\"})\n .data(prepareData(dFilt, 'episari.date.from', ['episari.covid.icu.in'])\n .map(a => ({...a, Kategorija: a.category, \"število\": null_if_small(0.01, a.y)}))\n .concat(icu_in_wk)\n )\n .encode(\n vl.x().fieldT(\"datum\"),\n vl.y().fieldQ(\"število\").scale({\"type\": \"linear\", \"nice\": false, \"domain\": [0, 140]}).axis({\"format\": \"d\"}),\n vl.color().fieldN(\"Kategorija\")\n )\n .width(width-160)\n .height(width/2)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":107,"value":"hosp_deceased_wk = data_daily.slice(7+stats_off).map((d, idx) => ({\n datum: data_daily[idx].date,\n Kategorija: 'hosp.deceased',\n \"število\": null_if_small(0.01, d[\"state.deceased.hospital.todate\"] - ((idx + stats_off) >= 0\n ? data_daily[idx+stats_off][\"state.deceased.hospital.todate\"]\n : 0))\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":119,"value":"{\n const dFilt = episari_weekly_csv.slice(1);\n \n return vl.markLine().config({\"font\": \"IBM Plex Sans\"})\n .data(prepareData(dFilt, 'episari.date.from',['episari.covid.deceased'])\n .map(a => ({...a, Kategorija: a.category, \"število\": null_if_small(0.01, a.y)}))\n .concat(hosp_deceased_wk)\n )\n .encode(\n vl.x().fieldT(\"datum\"),\n vl.y().fieldQ(\"število\").scale({\"type\": \"linear\", \"nice\": false, \"domain\": [0, 260]}).axis({\"format\": \"d\"}),\n vl.color().fieldN(\"Kategorija\")\n )\n .width(width-160)\n .height(width/2)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1601,"value":"episari_wk_counts = {\n const age_cls_src = [\"00-04\", \"05-14\", \"15-24\", \"25-34\", \"35-44\", \"45-54\", \"55-64\", \"65-74\", \"75-84\", \"85+\"]\n const ret = [];\n episari_weekly_csv.slice(31).forEach(row => {\n ['in','icu.in','deceased'].forEach(state => {\n age_cls_src.forEach(age => {\n const val = row['episari.covid.'+state+'.age.'+age];\n if (val || val === 0) {\n ret.push({\n \"date_from\": row['episari.date.from'],\n \"date_to\": row['episari.date.to'],\n \"state\": state,\n \"age\": (age=='0-4' ? '00-04' : age=='5-14' ? '05-14' : age),\n \"count\": val\n });\n }\n });\n });\n });\n return ret;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":534,"value":"md`## Delež umrlih, ICU in sprejetih v H po starosti\n## NEAŽURNI PODATKI - DO 2021-09
\nPodatki NIJZ EPISARI, 28d glajenje`","pinned":false,"hidden":false,"mode":"ojs"},{"id":505,"value":"{\n const episari_age_counts = d3.flatGroup(episari_counts, d=>d.date_from, d=>d.date_to, d=>d.state, d=>d.age)\n .map(grp => {\n return ({\n date_from: grp[0],\n date_to: grp[1],\n state: grp[2],\n age: grp[3],\n count: d3.sum(grp[4].map(d => d.count))\n });\n });\n //return episari_age_counts;\n return Plot.plot({\n width: width,\n height: width*0.9,\n y: {\n grid: true,\n label: \"^ Count\",\n },\n facet: {\n data: episari_age_counts,\n y: \"state\",\n marginRight: 60\n },\n color: {\n type: \"categorical\",\n range: ['#FFEEBA80','#FFDA6B80','#E9B82580','#AEEFDB80','#52C4A280','#33AB8780','#189A7380','#F4B2E080','#D559B080','#B01C8380']\n },\n marks: [\n Plot.areaY(episari_age_counts,\n Plot.stackY({y: \"count\", \n x: \"date_to\",\n offset: \"expand\",\n fill: \"age\",\n order: \"age\",\n title: \"age\"}))\n ]\n});\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":530,"value":"md`## Delež umrlih, ICU in sprejetih v H po starosti in spolu
\n## NEAŽURNI PODATKI - DO 2021-09
\nPodatki NIJZ EPISARI, 28d glajenje`","pinned":false,"hidden":false,"mode":"ojs"},{"id":217,"value":"Plot.plot({\n width: width,\n height: width*0.9,\n y: {\n grid: true,\n label: \"^ Count\",\n },\n facet: {\n data: episari_counts,\n y: \"state\",\n marginRight: 60\n },\n color: {\n type: \"categorical\",\n range: ['#FFEEBA80','#FFDA6B80','#E9B82580','#AEEFDB80','#52C4A280','#33AB8780','#189A7380','#F4B2E080','#D559B080','#B01C8380','#FFEEBA80','#FFDA6B80','#E9B82580','#AEEFDB80','#52C4A280','#33AB8780','#189A7380','#E0B2F480','#B059D580','#831CB080']\n },\n marks: [\n Plot.areaY(episari_counts,\n Plot.stackY({y: \"count\", \n x: \"date_to\",\n offset: \"expand\",\n fill: \"sex_age\",\n order: sex_age_order,\n title: \"sex_age\"}))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":222,"value":"age_classes = [\"00-04\", \"05-14\", \"15-24\", \"25-34\", \"35-44\", \"45-54\", \"55-64\", \"65-74\", \"75-84\", \"85+\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":292,"value":"sex_age_order = {\n const age_cls_adj = age_classes;\n return [\n ...age_cls_adj.map(a => 'f.'+a),\n ...age_cls_adj.slice().reverse().map(a => 'm.'+a)];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":383,"value":"age_colors = {\n const arr = ['#FFEEBA','#FFDA6B','#E9B825','#AEEFDB','#52C4A2','#33AB87','#189A73','#F4B2E0','#D559B0','#B01C83'].map(c => d3.hsl(c).darker(2).formatHex());\n const ret = {};\n arr.forEach((a, i) => ret[age_classes[i]] = a);\n return ret;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":218,"value":"episari_counts = {\n const age_cls_src = [\"0-4\", \"5-14\", \"15-24\", \"25-34\", \"35-44\", \"45-54\", \"55-64\", \"65-74\", \"75-84\", \"85+\"]\n const ret = [];\n episari_age_csv.slice(31).forEach(row => {\n ['in','icu.in','deceased'].forEach(state => {\n ['m','f'].forEach(sex => {\n age_cls_src.forEach(age => {\n const val = row['episari.covid.'+state+'.'+sex+'.'+age];\n if (val || val === 0) {\n ret.push({\n \"date_from\": row['episari.date.from'],\n \"date_to\": row['episari.date.to'],\n \"state\": state,\n \"sex\": sex,\n \"age\": (age=='0-4' ? '00-04' : age=='5-14' ? '05-14' : age),\n \"sex_age\": sex+'.'+(age=='0-4' ? '00-04' : age=='5-14' ? '05-14' : age),\n \"count\": val\n });\n }\n });\n });\n });\n });\n return ret;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":331,"value":"episari_counts_15_34 = episari_counts.filter(a => a.age == '15-24' || a.age == '25-34')","pinned":true,"hidden":false,"mode":"ojs"},{"id":256,"value":"Reducers = ({sum: 'sum', count: 'count', proportion: 'proportion'});","pinned":true,"hidden":false,"mode":"ojs"},{"id":254,"value":"Plot.groupX({y: \"count\"}, {x: \"date_to\", fill: \"sex\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":87,"value":"function null_if_small(threshold, value) {\n return value >= threshold ? value : null;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1296,"value":"function number_or_nan(a) {\n return (a || a===0) ? a : NaN;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":684,"value":"function array_to_lookup(arr, keyFun, valFun = (a => a)) {\n const ret = {};\n arr.forEach(a => {\n ret[keyFun(a)] = valFun(a)\n })\n return ret;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2275,"value":"// returns a new object with the values at each key mapped using mapFn(value)\nfunction objectMap(obj, mapFn) {\n const result = {};\n for (const [key, value] of Object.entries(obj)) {\n result[key] = mapFn(value, key);\n }\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":59,"value":"style = html`\n\n\n\n`","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3d","stdlib":"1","titling":"auto"}