[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7EMMZYAY60FQNHASHF2GSKY":3,"skill-same:01M7EMMZYAY60FQNHASHF2GSKY":47},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":25,"commitSha":26,"skillDirectory":27,"forksCount":28,"repositoryHtmlUrl":29,"repositoryHomepageUrl":6,"ownerHtmlUrl":30,"ownerBlogUrl":31,"ownerDescription":6,"updatedAt":32,"install":33,"resources":46},"01M7EMMZYAY60FQNHASHF2GSKY","contact-tracing-analytics",null,"Analyzing contact tracing data — network reconstruction, transmission chains, and evaluating tracing performance.","TanNhatCMS\u002Fjarvis","TanNhatCMS","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F113796420?v=4","01M753MV17KG5CD3152G3WQEQY","KietNT","01M7EKEDYVYQ6PWVJJMZ911VMX",0,"MIT","https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis\u002Fblob\u002Fmain\u002Fskills\u002Fams-contact-tracing-analytics\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: contact-tracing-analytics\ndescription: Analyzing contact tracing data — network reconstruction, transmission chains, and evaluating tracing performance.\ncategory: scientific\n---\n\n## Overview\n\ncontact-tracing-analytics covers the quantitative side of contact tracing: reconstructing\ntransmission networks from case-contact data, estimating key parameters (secondary attack rates,\nserial intervals), identifying superspreading, and evaluating whether tracing programs actually\nwork. It bridges field epidemiology and network science.\n\n## When to use\n\n- Analyzing case-contact datasets: completeness, timeliness, yield.\n- Estimating secondary attack rates by setting and contact type.\n- Reconstructing transmission chains and networks.\n- Identifying superspreading events and high-risk settings.\n- Evaluating tracing performance: speed, coverage, forward vs backward tracing.\n- Digital tracing data: app-based proximity, privacy-preserving analytics.\n\n## Core concepts\n\n- **The tracing cascade.** Exposed → identified as contact → reached → tested\u002Fquarantined →\n  positive → their contacts traced. Measure yield and delay at each step — programs usually fail\n  at reach and speed, not at epidemiology. Report the full cascade, not just cases found.\n- **Timeliness is effectiveness.** Tracing that reaches contacts after their infectious period\n  prevents nothing. Key metrics: time from index case symptom onset to contact quarantine;\n  proportion of contacts reached within 48 hours. Model the relationship explicitly — each day of\n  delay costs a quantifiable fraction of prevented transmissions.\n- **Secondary attack rate (SAR).** Proportion of contacts who become cases, stratified by setting\n  (household, workplace, social), contact type, and index-case characteristics. SAR by setting is\n  the evidence base for targeted interventions — household SARs are typically an order of\n  magnitude above casual-contact SARs.\n- **Forward vs backward tracing.** Forward tracing finds who the case may have infected\n  (prevents onward transmission); backward tracing finds who infected the case (finds clusters\n  and superspreading events). Backward tracing is disproportionately valuable for overdispersed\n  pathogens — finding one superspreading event beats finding ten isolated transmissions.\n- **Overdispersion and superspreading.** The dispersion parameter k quantifies transmission\n  heterogeneity; small k means most transmission comes from few individuals\u002Fevents. Tracing data\n  can estimate k from the offspring distribution — and it argues for cluster-focused (backward)\n  strategies when k is small.\n- **Network reconstruction.** Cases as nodes, transmission links as edges (inferred from\n  contact + timing + genomics). Analyze: chain lengths, cluster sizes, setting-specific\n  subnetworks. Genomic data resolves ambiguous links — integrate where available.\n- **Serial interval.** Time between symptom onsets in infector-infectee pairs, estimated from\n  traced pairs with known links. Needed for Rt estimation; watch for truncation bias (recent\n  pairs incomplete) and recall bias in onset dates.\n- **Digital tracing.** Bluetooth proximity apps generate exposure notifications at scale but\n  with noisy distance\u002Fduration proxies. Evaluate: adoption rates (network effects dominate —\n  low adoption ≈ no effect), positive predictive value of notifications, and equity (smartphone\n  access). Privacy-preserving designs (decentralized) trade analytic visibility for trust.\n\n## Practical workflow\n\n1. **Assemble data.** Case-contact line lists with dates (onset, test, interview, quarantine),\n   contact types, settings, outcomes. Link records deterministically where possible.\n2. **Cascade analysis.** Compute yield and delay at each step; stratify by demographics and\n   geography to find inequities.\n3. **Parameter estimation.** SAR by setting\u002Ftype; serial interval from linked pairs (adjust for\n   truncation); offspring distribution and k.\n4. **Network analysis.** Reconstruct chains; identify clusters and superspreading events;\n   characterize high-risk settings.\n5. **Performance evaluation.** Timeliness metrics, coverage (contacts identified per case vs\n   expected), prevented-transmission estimates via modeling.\n6. **Report.** Cascade figures, SAR tables, timeliness distributions, equity stratifications,\n   and concrete operational recommendations (the point is improving the program).\n\n## Common pitfalls\n\n- Counting traced cases without measuring delay (slow tracing = theater).\n- Forward-only tracing for overdispersed pathogens (missing the cluster-finding value).\n- SARs without setting stratification (meaningless averages).\n- Serial intervals from unlinked pairs or with truncation bias.\n- Ignoring the denominator: contacts never identified.\n- Digital-tracing evaluations without adoption-rate context.\n- Equity-blind analysis (tracing works worst where it's needed most).\n- Overdispersion ignored — forward-only tracing designed as if spread were homogeneous.\n- Contacts' contacts never followed, missing second-generation transmission chains.\n","## Overview\n\ncontact-tracing-analytics covers the quantitative side of contact tracing: reconstructing\ntransmission networks from case-contact data, estimating key parameters (secondary attack rates,\nserial intervals), identifying superspreading, and evaluating whether tracing programs actually\nwork. It bridges field epidemiology and network science.\n\n## When to use\n\n- Analyzing case-contact datasets: completeness, timeliness, yield.\n- Estimating secondary attack rates by setting and contact type.\n- Reconstructing transmission chains and networks.\n- Identifying superspreading events and high-risk settings.\n- Evaluating tracing performance: speed, coverage, forward vs backward tracing.\n- Digital tracing data: app-based proximity, privacy-preserving analytics.\n\n## Core concepts\n\n- **The tracing cascade.** Exposed → identified as contact → reached → tested\u002Fquarantined →\n  positive → their contacts traced. Measure yield and delay at each step — programs usually fail\n  at reach and speed, not at epidemiology. Report the full cascade, not just cases found.\n- **Timeliness is effectiveness.** Tracing that reaches contacts after their infectious period\n  prevents nothing. Key metrics: time from index case symptom onset to contact quarantine;\n  proportion of contacts reached within 48 hours. Model the relationship explicitly — each day of\n  delay costs a quantifiable fraction of prevented transmissions.\n- **Secondary attack rate (SAR).** Proportion of contacts who become cases, stratified by setting\n  (household, workplace, social), contact type, and index-case characteristics. SAR by setting is\n  the evidence base for targeted interventions — household SARs are typically an order of\n  magnitude above casual-contact SARs.\n- **Forward vs backward tracing.** Forward tracing finds who the case may have infected\n  (prevents onward transmission); backward tracing finds who infected the case (finds clusters\n  and superspreading events). Backward tracing is disproportionately valuable for overdispersed\n  pathogens — finding one superspreading event beats finding ten isolated transmissions.\n- **Overdispersion and superspreading.** The dispersion parameter k quantifies transmission\n  heterogeneity; small k means most transmission comes from few individuals\u002Fevents. Tracing data\n  can estimate k from the offspring distribution — and it argues for cluster-focused (backward)\n  strategies when k is small.\n- **Network reconstruction.** Cases as nodes, transmission links as edges (inferred from\n  contact + timing + genomics). Analyze: chain lengths, cluster sizes, setting-specific\n  subnetworks. Genomic data resolves ambiguous links — integrate where available.\n- **Serial interval.** Time between symptom onsets in infector-infectee pairs, estimated from\n  traced pairs with known links. Needed for Rt estimation; watch for truncation bias (recent\n  pairs incomplete) and recall bias in onset dates.\n- **Digital tracing.** Bluetooth proximity apps generate exposure notifications at scale but\n  with noisy distance\u002Fduration proxies. Evaluate: adoption rates (network effects dominate —\n  low adoption ≈ no effect), positive predictive value of notifications, and equity (smartphone\n  access). Privacy-preserving designs (decentralized) trade analytic visibility for trust.\n\n## Practical workflow\n\n1. **Assemble data.** Case-contact line lists with dates (onset, test, interview, quarantine),\n   contact types, settings, outcomes. Link records deterministically where possible.\n2. **Cascade analysis.** Compute yield and delay at each step; stratify by demographics and\n   geography to find inequities.\n3. **Parameter estimation.** SAR by setting\u002Ftype; serial interval from linked pairs (adjust for\n   truncation); offspring distribution and k.\n4. **Network analysis.** Reconstruct chains; identify clusters and superspreading events;\n   characterize high-risk settings.\n5. **Performance evaluation.** Timeliness metrics, coverage (contacts identified per case vs\n   expected), prevented-transmission estimates via modeling.\n6. **Report.** Cascade figures, SAR tables, timeliness distributions, equity stratifications,\n   and concrete operational recommendations (the point is improving the program).\n\n## Common pitfalls\n\n- Counting traced cases without measuring delay (slow tracing = theater).\n- Forward-only tracing for overdispersed pathogens (missing the cluster-finding value).\n- SARs without setting stratification (meaningless averages).\n- Serial intervals from unlinked pairs or with truncation bias.\n- Ignoring the denominator: contacts never identified.\n- Digital-tracing evaluations without adoption-rate context.\n- Equity-blind analysis (tracing works worst where it's needed most).\n- Overdispersion ignored — forward-only tracing designed as if spread were homogeneous.\n- Contacts' contacts never followed, missing second-generation transmission chains.\n",{"name":5,"category":24,"description":7},"scientific","main","af388a77c2fe5e0b4fd879cca4300a0ffa9a6579","skills\u002Fams-contact-tracing-analytics",1,"https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis","https:\u002F\u002Fgithub.com\u002FTanNhatCMS","https:\u002F\u002Fmrkiet.name.vn","2026-11-07 14:23:08",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add TanNhatCMS\u002Fjarvis --skill contact-tracing-analytics",{"tool":39,"command":40},"bunx","bunx skills add TanNhatCMS\u002Fjarvis --skill contact-tracing-analytics",{"tool":42,"command":43},"pnpm","pnpm dlx skills add TanNhatCMS\u002Fjarvis --skill contact-tracing-analytics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis\u002Fblob\u002Fmain\u002Fskills\u002Fams-contact-tracing-analytics\u002FSKILL.md",true,[],[48,55,63,70,77,84],{"skillNo":49,"name":50,"nameZh":6,"description":51,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":52,"categories":53,"keywords":54,"publishedAt":19},"01M7EWYJR0J8DARY7E59Z6Y9MV","web-scrape","Turn web pages into LLM-ready markdown via the Firecrawl API (renders JS, anti-bot handling). Use when browser_open\u002Ffetch fails on JS-heavy pages, or when you need clean markdown\u002Fstructured extraction from a site. Requires FIRECRAWL_API_KEY.","https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis\u002Fblob\u002Fmain\u002Fskills\u002Fweb-scrape\u002FSKILL.md",[],[],{"skillNo":56,"name":57,"nameZh":6,"description":58,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":59,"sourceHtmlUrl":60,"categories":61,"keywords":62,"publishedAt":19},"01M7EWYBN5P6F2AJG3FR24T9BP","design-taste-frontend","tasteskill v2, the anti-slop frontend skill. Art direction for websites, distilled from a study of 247 curated gallery sites (One Page Love, Awwwards Sites of the Day). Deals researched directions (idea, composition, type, palette, photographs, section blueprints) from a library, enforces a strict no-slop house style with a real-browser inspector, and runs a look-and-fix loop on screenshots. Use this whenever you build, design, redesign, restyle, polish or review a landing page, marketing site, portfolio, product page, one-pager, hero or any public-facing web page, in any stack (HTML\u002FCSS, React, Next.js, Vue, Svelte, Astro, Tailwind) — also when the user says a page looks generic, templated, \"AI-made\", sloppy, or asks to make it look designed, premium, award-level or less like every other site. Not for dashboards or app screens.","https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis\u002Fblob\u002Fmain\u002Fskills\u002Ftaste-design\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis\u002Fblob\u002Fmain\u002Fskills\u002Ftaste-design\u002FSKILL.md",[],[],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":19},"01M7EWY8FQFNQ2GB7NZZWGSSDX","test-driven-development","Use when implementing any feature or bugfix, before writing implementation code","https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis\u002Fblob\u002Fmain\u002Fskills\u002Fsuperpowers-tdd\u002FSKILL.md",[],[],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M7EWY5SHBFRH1HDHVWM9K7TV","systematic-debugging","Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes","https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis\u002Fblob\u002Fmain\u002Fskills\u002Fsuperpowers-debugging\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M7EWY3NM0Z0JAKS7HDZ24K66","requesting-code-review","Use when completing tasks, implementing major features, or before merging to verify work meets requirements","https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis\u002Fblob\u002Fmain\u002Fskills\u002Fsuperpowers-code-review\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M7EWY0WRMWX4M9ZFY0WKC593","brainstorming","You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).","https:\u002F\u002Fgithub.com\u002FTanNhatCMS\u002Fjarvis\u002Fblob\u002Fmain\u002Fskills\u002Fsuperpowers-brainstorming\u002FSKILL.md",[],[]]