From Related Genomes to Transmission Histories: What Pathogen Sequencing Can Establish Codex (AI) | Commissioned review v1 | 2026-10-22 Pathogen genomes constrain outbreak histories but do not directly reveal who infected whom. Within-host diversity, incomplete sampling and limited temporal signal create multiple transmission histories compatible with one phylogeny. This review develops explicit sampling and clock models, using outbreak sequencing studies to define the boundary between genetic relatedness and epidemiological attribution. A genealogy is not a contact history A phylogenetic tree describes ancestry among sampled genetic lineages. A transmission tree describes infection events among hosts. Their nodes and times need not coincide because lineages can coexist within a host and only some pass to another host. Treating these trees as identical can assign unwarranted certainty to a proposed transmission route, especially when many infections were never sampled. Sequencing and epidemiological inference Gire and colleagues used genomic surveillance to investigate Ebola-virus emergence and transmission during the West African outbreak. Quick and colleagues demonstrated portable sequencing in an outbreak setting. Didelot and colleagues developed inference for partially sampled and ongoing outbreaks. These contributions concern observational epidemiology and computational reconstruction. None makes genomic similarity alone sufficient evidence of direct transmission between named individuals. References: ef-10-1, ef-10-2, ef-10-3 A slow clock can leave many ties When expected substitutions over an interval are small, several transmission events can occur without a consensus-sequence difference. Identical sequences then support recent relatedness but do not determine the order of hosts. Conversely, sequencing error or within-host diversity can create differences that are not separate transmission steps. The useful temporal resolution depends on the clock, sampling interval and uncertainty in the sequence calls. Missing hosts create hidden paths A sampled pair may be linked through one or more unobserved infections. The probability of seeing a complete chain declines quickly when sampling is incomplete. A method that forbids unsampled intermediates can force a direct link even when an indirect path is more plausible. Sampling probability should be represented explicitly and varied when the surveillance system does not support a precise estimate. Dates provide constraints rather than certainty Collection dates differ from infection dates, and symptom onset has its own uncertainty. Contact information can exclude some routes while leaving others possible. Combining these data with genomes narrows the set of histories, but errors shared across data sources can remain. A robust reconstruction should retain alternative paths and explain which observations exclude them. Consensus sequences discard variation A consensus sequence compresses a within-host population into one representative. Low-frequency variants may contain additional information, but their interpretation depends on reproducible detection and the transmission bottleneck. Shared minor variants can arise through error or recurrent mutation. More sensitive sequencing therefore creates both opportunities and new calibration requirements; it does not automatically resolve directionality. Geography can enter twice Spatial proximity may influence both who gets sampled and the prior probability of transmission. If those effects are not separated, a model can appear to discover geographic structure that was partly imposed by surveillance and prior assumptions. Validation should examine how inferred routes change when sampling intensity varies by location, and should distinguish a spatially plausible history from a uniquely identified one. The appropriate output is an uncertainty set Operationally useful inference can identify clusters, rule out implausible links and prioritize missing information without producing one definitive chain. Posterior probabilities are conditional on the model and available data, not a certificate of individual responsibility. This review concerns aggregate scientific inference and does not provide pathogen modification, laboratory propagation or sequence-design instructions. A transmission tree contains unobserved people A phylogeny relates sampled genomes; a transmission tree relates infection events among hosts. Unsampled infections can lie between two sampled hosts, and within-host diversity can produce several lineages. A proposed inference should therefore allow unsampled intermediates and uncertainty in infection times. Epidemiological contact information can constrain alternatives, but a reported contact is not proof of transmission and an absent reported contact is not proof of no contact. The output should distinguish strongly constrained relationships from those supported only by a convenient sampling pattern. Confidence in a sequence alignment does not transfer automatically to confidence in who infected whom. Sampling policy can create an apparent cluster If sequencing is concentrated in one hospital or one period, the resulting tree can appear to identify a geographically or temporally coherent outbreak even when the pattern partly reflects observation. A sensitivity analysis would vary the inclusion of densely sampled groups and compare inferred links with the known testing policy. A prospective validation could freeze the model, then evaluate predictions against later sequences and independently collected timing information. Such validation tests predictive coherence rather than retrospectively rewarding a narrative that fits every available observation. The Poisson example in this review illustrates limited genetic resolution; it is not a complete transmission likelihood. A model that can be challenged The four equations below are a compact analytical model, not a joint fit to all the cited papers. Their interpretation is as follows. A simple clock gives substitution count K over genome length L and elapsed time; rate variation and recurrent substitutions can invalidate it. Zero observed substitutions remain likely when expected clock change is small; identity does not prove direct transmission. For m infections independently sampled with probability q, this gives complete-chain observation; real surveillance can be correlated. A schematic posterior for transmission history T uses genomic G and epidemiological E evidence; factorization requires appropriate conditional assumptions. The observational target is sets of transmission histories compatible with genomes and dates. The model deliberately isolates a tractable mechanism; its purpose is to reveal which measurements would distinguish that mechanism from an alternative. Parameter values in the figures are declared illustrative inputs. A curve produced by those values is a mathematical consequence, not an estimate of an unmeasured biological or physical quantity. A discriminating validation design The primary comparison should test calibrated recovery of known aggregate transmission features. The unit of independent replication is independent outbreak with a documented sampling scheme; repeated readings from the same unit improve precision but do not create independent replication. A strong validation would hold out different surveillance intensities before choosing thresholds or fitting nuisance corrections. In particular, unsampled infections and within-host diversity must be measured, constrained or varied in a sensitivity analysis. Report both the principal estimate and how it changes when that assumption is relaxed. A model that succeeds only after choosing the most favorable exclusion rule has answered a different question from one evaluated with a locked rule. Worked numerical analysis For an expected clock change of 0.5 substitutions, the probability of no change is exp(-0.5), about 0.607. This illustrative number is not an Ebola mutation-rate estimate. If each of five infections is sampled independently with probability 0.7, complete observation has probability 0.168. The figures vary expected clock change and sampling coverage separately, showing why an apparently precise tree may leave many host-level histories unresolved. These calculations can be reconstructed from the companion Python file and its parameter table. Axis labels retain the model's units, and the caption distinguishes the quantities varied from those held fixed. The numerical examples were selected to expose a dependency, not to reproduce the appearance of a published figure. A useful independent check is to evaluate a limiting case and then recompute an intermediate point without the plotting code. Agreement validates the arithmetic within the declared model; it does not establish that nature follows that model. Evidence, transfer and next steps The scope of the conclusion is limited by unsampled infections and within-host diversity. Transfer to different surveillance intensities requires evidence beyond fitting the original observations. A future replication should preserve raw measurements, calibration records, exclusions and a machine-readable link between each result and the corresponding analysis. The evidence table below identifies what each selected primary study contributes. The comparison is a focused narrative synthesis, not a systematic review or a claim that all subsequent evidence has been surveyed. The proposed study has not been performed here. Its value is that a negative result would constrain an explicit explanation rather than merely lower a benchmark score. Equations (1) K\sim\operatorname{Poisson}(\mu L\Delta t) A simple clock gives substitution count K over genome length L and elapsed time; rate variation and recurrent substitutions can invalidate it. (2) P(K=0)=e^{-\mu L\Delta t} Zero observed substitutions remain likely when expected clock change is small; identity does not prove direct transmission. (3) P(\mathrm{all\ sampled})=q^m For m infections independently sampled with probability q, this gives complete-chain observation; real surveillance can be correlated. (4) P(T\mid G,E)\propto P(G\mid T)P(E\mid T)P(T) A schematic posterior for transmission history T uses genomic G and epidemiological E evidence; factorization requires appropriate conditional assumptions. Analytical figures Figure 1. An unchanged genome need not imply no transmission A Poisson count with mean lambda has zero-count probability exp(-lambda). Identical sampled genomes alone do not establish direction or a direct transmission event. figures/genomic-outbreak-inference-01.csv Figure 2. Sampling gaps compound across a chain All five cases must be sampled independently. Selection correlated with symptoms, place or time invalidates this simple expression. figures/genomic-outbreak-inference-02.csv Comparison Evidence or design choice | What it identifies | What remains unresolved Source 1 | Genomic surveillance contributed to reconstruction of an Ebola outbreak. | Genetic ancestry and direct host-to-host transmission differ. Source 2 | Portable sequencing enabled outbreak-time genomic observation. | Operational speed does not eliminate sampling or sequence uncertainty. Source 3 | Inference methods represented partially sampled ongoing outbreaks. | Results remain conditional on transmission and sampling models. Primary observable | sets of transmission histories compatible with genomes and dates | unsampled infections and within-host diversity Independent test | calibrated recovery of known aggregate transmission features | Requires new measurements Transfer set | different surveillance intensities | Not validated by the constructed numerical example Proposed validation protocol 1. Specify sets of transmission histories compatible with genomes and dates before fitting. 2. Retain independent identifiers for each independent outbreak with a documented sampling scheme. 3. Separate different surveillance intensities from model development. 4. Measure or bound unsampled infections and within-host diversity. 5. Record units, calibration and the observation operator. 6. Compare the primary model with a named rival explanation. 7. Prespecify the test: calibrated recovery of known aggregate transmission features. 8. Archive raw data, exclusions, parameter files and executable analysis. Limitations 1. Genetic ancestry and direct host-to-host transmission differ. 2. Operational speed does not eliminate sampling or sequence uncertainty. 3. Results remain conditional on transmission and sampling models. 4. Companion figures are constructed analytical examples, not new experimental results. 5. Transfer to different surveillance intensities has not been demonstrated by this review. Research questions 1. Which additional dates most reduce transmission ambiguity? 2. How sensitive are inferred links to unsampled intermediates? 3. When does within-host variation add calibrated information? Source evidence ef-10-1: Genomic surveillance contributed to reconstruction of an Ebola outbreak. Boundary: Genetic ancestry and direct host-to-host transmission differ. Locator: Abstract / selected primary-source sections; see linked bibliographic record. ef-10-2: Portable sequencing enabled outbreak-time genomic observation. Boundary: Operational speed does not eliminate sampling or sequence uncertainty. Locator: Abstract / selected primary-source sections; see linked bibliographic record. ef-10-3: Inference methods represented partially sampled ongoing outbreaks. Boundary: Results remain conditional on transmission and sampling models. Locator: Abstract / selected primary-source sections; see linked bibliographic record. References ef-10-1. Stephen K. Gire, Augustine Goba, Kristian G. Andersen, Rachel S. G. Sealfon, Daniel J. Park, Lansana Kanneh, Simbirie Jalloh, Mambu Momoh, Mohamed Fullah, Gytis Dudas, Shirlee Wohl, Lina M. Moses, Nathan L. Yozwiak, Sarah Winnicki, Christian B. Matranga, Christine M. Malboeuf, James Qu, Adrianne D. Gladden, Stephen F. Schaffner, Xiao Yang, Pan-Pan Jiang, Mahan Nekoui, Andres Colubri, Moinya Ruth Coomber, Mbalu Fonnie, Alex Moigboi, Michael Gbakie, Fatima K. Kamara, Veronica Tucker, Edwin Konuwa, Sidiki Saffa, Josephine Sellu, Abdul Azziz Jalloh, Alice Kovoma, James Koninga, Ibrahim Mustapha, Kandeh Kargbo, Momoh Foday, Mohamed Yillah, Franklyn Kanneh, Willie Robert, James L. B. Massally, Sinéad B. Chapman, James Bochicchio, Cheryl Murphy, Chad Nusbaum, Sarah Young, Bruce W. Birren, Donald S. Grant, John S. Scheiffelin, Eric S. Lander, Christian Happi, Sahr M. Gevao, Andreas Gnirke, Andrew Rambaut, Robert F. Garry, S. Humarr Khan, Pardis C. Sabeti. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science (2014). https://doi.org/10.1126/science.1259657 ef-10-2. Joshua Quick, Nicholas J. Loman, Sophie Duraffour, Jared T. Simpson, Ettore Severi, Lauren Cowley, Joseph Akoi Bore, Raymond Koundouno, Gytis Dudas, Amy Mikhail, Nobila Ouédraogo, Babak Afrough, Amadou Bah, Jonathan H. J. Baum, Beate Becker-Ziaja, Jan Peter Boettcher, Mar Cabeza-Cabrerizo, Álvaro Camino-Sánchez, Lisa L. Carter, Juliane Doerrbecker, Theresa Enkirch, Isabel García- Dorival, Nicole Hetzelt, Julia Hinzmann, Tobias Holm, Liana Eleni Kafetzopoulou, Michel Koropogui, Abigael Kosgey, Eeva Kuisma, Christopher H. Logue, Antonio Mazzarelli, Sarah Meisel, Marc Mertens, Janine Michel, Didier Ngabo, Katja Nitzsche, Elisa Pallasch, Livia Victoria Patrono, Jasmine Portmann, Johanna Gabriella Repits, Natasha Y. Rickett, Andreas Sachse, Katrin Singethan, Inês Vitoriano, Rahel L. Yemanaberhan, Elsa G. Zekeng, Trina Racine, Alexander Bello, Amadou Alpha Sall, Ousmane Faye, Oumar Faye, N’Faly Magassouba, Cecelia V. Williams, Victoria Amburgey, Linda Winona, Emily Davis, Jon Gerlach, Frank Washington, Vanessa Monteil, Marine Jourdain, Marion Bererd, Alimou Camara, Hermann Somlare, Abdoulaye Camara, Marianne Gerard, Guillaume Bado, Bernard Baillet, Déborah Delaune, Koumpingnin Yacouba Nebie, Abdoulaye Diarra, Yacouba Savane, Raymond Bernard Pallawo, Giovanna Jaramillo Gutierrez, Natacha Milhano, Isabelle Roger, Christopher J. Williams, Facinet Yattara, Kuiama Lewandowski, James Taylor, Phillip Rachwal, Daniel J. Turner, Georgios Pollakis, Julian A. Hiscox, David A. Matthews, Matthew K. O’ Shea, Andrew McD. Johnston, Duncan Wilson, Emma Hutley, Erasmus Smit, Antonino Di Caro, Roman Wölfel, Kilian Stoecker, Erna Fleischmann, Martin Gabriel, Simon A. Weller, Lamine Koivogui, Boubacar Diallo, Sakoba Keïta, Andrew Rambaut, Pierre Formenty, Stephan Günther, Miles W. Carroll. Real-time, portable genome sequencing for Ebola surveillance. Nature (2016). https://doi.org/10.1038/nature16996 ef-10-3. Xavier Didelot, Christophe Fraser, Jennifer Gardy, Caroline Colijn. Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks. Molecular Biology and Evolution (2017). https://doi.org/10.1093/molbev/msw275 Provenance A focused narrative analysis of the named primary studies, not a systematic literature search or a claim to comprehensive coverage through 2026. Source-based research review and original mathematical analysis written by Codex (AI). Cited experiments belong to the cited research teams. Concept cover created with the built-in ImageGen tool. Quantitative figures are explicit constructed analyses, with code and numerical data; they are not measurements from the cited papers. This local draft has no independent peer-review or journal-acceptance record.