A Transferable Learned Temporal Prior for Transmission Reconstruction and Decision-Relevant Uncertainty in Real Outbreak Labels

Md Ahsan Karim

arxiv: 2606.30842 · v1 · pith:27RG5PCGnew · submitted 2026-06-29 · 💻 cs.LG · q-bio.QM

A Transferable Learned Temporal Prior for Transmission Reconstruction and Decision-Relevant Uncertainty in Real Outbreak Labels

Md Ahsan Karim This is my paper

Pith reviewed 2026-07-01 06:22 UTC · model grok-4.3

classification 💻 cs.LG q-bio.QM

keywords transmission reconstructiontemporal prioroutbreak labelsAndes viruslabel uncertaintyparent rankingphylogenetic concordancelogistic regression

0 comments

The pith

A temporal prior learned on eleven disease families and locked before seeing target data improves Andes virus parent ranking over baselines.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper trains a logistic regression model on timing patterns across eleven disease families, freezes every parameter, and applies the fixed model to rank possible transmission sources in a 29-task Andes virus benchmark. It reports higher mean reciprocal rank and top-1 accuracy than the strongest parametric baseline trained on the target data itself. The same work audits real outbreak label reliability with phylogenetic data and shows that keeping uncertain transmission edges alters which cases rise to the top of intervention priority lists. A reader would care because current reconstruction methods treat timing and labels as fixed ground truth, yet the results indicate both can be improved and quantified without domain-specific retraining.

Core claim

A logistic regression temporal prior trained on eleven disease families, with all parameters locked before any Andes virus data is seen, produces mean reciprocal rank 0.571 and top-1 accuracy 37.9 percent on a 29-task parent-ranking benchmark, compared with 0.274 and 13.8 percent for the best source-trained baseline; permutation tests give p less than or equal to 0.0002. An independent phylogenetic audit of 75 New York City mpox pairs finds 54.67 percent genomically unresolved or unsupported. Retaining those uncertain edges in Andes virus and Guangdong Delta graphs changes the Jaccard overlap of top-5 source-priority sets to between 0.429 and 0.667.

What carries the argument

Locked logistic regression temporal prior trained on eleven disease families and applied without refitting

If this is right

Source-priority sets for intervention shift when uncertain transmission edges are retained rather than discarded.
Transmission-label uncertainty can be measured directly from phylogenetic concordance in real outbreak graphs.
A single locked model can be applied across outbreaks without collecting target-specific training data.
Current practice of treating epidemiological timing and labels as deterministic ground truth understates reconstruction error.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same locked prior could be tested on additional virus families to check whether the transfer advantage holds beyond Andes virus.
Decision pipelines that currently drop uncertain edges might instead propagate them as weighted inputs to intervention ranking.
If phylogenetic audits become routine, outbreak graphs could carry explicit uncertainty flags that change which cases receive follow-up resources.

Load-bearing premise

Temporal transmission patterns learned from the eleven disease families are similar enough to Andes virus dynamics that the frozen model transfers without any adjustment.

What would settle it

A new independent outbreak dataset on which the locked prior produces lower mean reciprocal rank than a source-trained parametric baseline would falsify the transfer claim.

Figures

Figures reproduced from arXiv: 2606.30842 by Md Ahsan Karim.

**Figure 1.** Figure 1: Study design and evidence architecture. A source-trained temporal prior is learned on the D1 multi-disease benchmark, locked before target access, and evaluated on the strict ANDV parent-ranking benchmark. Separate MPXV and Guangdong Delta modules quantify transmission-label reliability and graph-level decision uncertainty; these modules support uncertainty analysis rather than prior validation. quiring de… view at source ↗

**Figure 2.** Figure 2: Locked temporal prior and source-trained temporal baselines. Curves show peak-normalized plausibility as a [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: ANDV parent-ranking performance (n = 29 tasks) [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

**Figure 4.** Figure 4: Finite-sample robustness diagnostics. Second, targeted combined-removal analyses were performed for the most influential source group and the orthopoxcoded group. The orthopox-coded group was removed as a combined smallpox/orthopox category because the D1 metadata did not encode separable mpox-only rows. This analysis was included to test whether the external ANDV result could be attributed to an orthopox… view at source ↗

**Figure 5.** Figure 5: NYC mpox inter-host concordance classes (n = 75 pairs). to a single index case. The visualization distinguishes high-confidence directed edges (solid lines) from uncertain edges (dashed lines). Machine-readable graph data were extracted from the underlying asset files cases.json and case_connections_raw.json. After normalization, the extracted graph comprised: • 131 visualization nodes (122 human case node… view at source ↗

**Figure 6.** Figure 6: Source structure under strict and expanded graphs [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗

**Figure 7.** Figure 7: Decision regret across response capacities (k = 2–10). records. The SVD pilot used data from a published surveillance report; no individually identifiable information was extracted or retained. All analyses in this preprint use data derived from previously published outbreak investigations and public resources cited in the text. The present arXiv version reports aggregate benchmark statistics, model-compar… view at source ↗

read the original abstract

Outbreak transmission reconstruction treats epidemiological timing and transmission labels as deterministic ground truth; neither has been systematically evaluated. We trained a logistic regression temporal prior on eleven disease families, locked all parameters before accessing any target outbreak data, and applied it without refitting to a strict Andes virus (ANDV) parent-ranking benchmark of 29 tasks. The locked prior achieved mean reciprocal rank (MRR) 0.571 versus 0.274 and Top-1 accuracy 37.9% versus 13.8% against the best source-trained parametric baseline (permutation p <= 0.0002; 7-8 reversals to lose MRR significance). A phylogenetic concordance audit of 75 NYC mpox inter-host pairs - independent label-reliability evidence rather than a prior validation - found that 54.67% (exact 95% CI: 42.75-66.21%) were genomically unresolved or unsupported. Retaining uncertain edges in ANDV and Guangdong Delta graphs shifted top-5 source-priority sets (Jaccard 0.429-0.667). Transmission-label uncertainty was measurable in the outbreak evidence modules examined, and retaining uncertain links changed which source cases were prioritized for intervention.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Locked multi-family logistic prior beats baseline on ANDV ranking but the transfer claim rests on an untested similarity assumption between disease families.

read the letter

The paper trains a logistic regression on temporal features from eleven disease families, locks every coefficient, and applies the model unchanged to a 29-task Andes virus parent-ranking benchmark. It reports MRR of 0.571 versus 0.274 and top-1 accuracy of 37.9% versus 13.8% against the strongest source-trained baseline, with a permutation test at p ≤ 0.0002. A separate mpox phylogenetic check on 75 pairs finds over half the links genomically unresolved or unsupported, and keeping uncertain edges changes the top-5 source sets.

The concrete protocol—multi-family training, strict locking, and an independent label-reliability audit—is new in this form. The numbers are reported with a clear significance threshold and the uncertainty audit is presented as separate evidence rather than circular validation. That part is useful for anyone who has to decide which cases to prioritize when labels are noisy.

The main weakness is that nothing in the abstract or reported results tests whether the temporal patterns learned from the eleven families actually match Andes virus dynamics. There is no comparison of generation-interval distributions, incubation statistics, or coefficient stability across families. The permutation test only shows the locked model beats the listed baseline; it does not show the baseline would not improve if given Andes-specific timing information. If the families are not representative, the reported gap is an artifact of domain mismatch rather than evidence for a general prior.

This work is aimed at people building transmission-reconstruction tools who already care about label uncertainty. The methods section will need to be checked for feature definitions, exclusion rules, and exact benchmark construction before the numbers can be trusted. It is worth sending to referees so those details and the similarity assumption can be examined directly.

Referee Report

1 major / 2 minor

Summary. The manuscript claims that a logistic regression temporal prior trained on eleven disease families, with all parameters locked before accessing any target data, transfers without refitting to a strict Andes virus (ANDV) parent-ranking benchmark of 29 tasks. It reports improved mean reciprocal rank (0.571 vs. 0.274) and Top-1 accuracy (37.9% vs. 13.8%) over the best source-trained parametric baseline, supported by a permutation test (p ≤ 0.0002). A separate phylogenetic audit of 75 NYC mpox inter-host pairs is presented as evidence of label uncertainty, showing that retaining uncertain edges alters top-5 source-priority sets (Jaccard 0.429-0.667).

Significance. If the transferability claim holds after addressing domain-similarity concerns, the work would offer a practical, locked prior for transmission reconstruction that demonstrably improves ranking performance on real outbreak data while quantifying how label uncertainty affects intervention prioritization. The independent mpox audit and permutation-based significance testing are strengths that support falsifiability of the performance claims.

major comments (1)

[Abstract and ANDV benchmark description] The central transfer result (MRR 0.571 / Top-1 37.9% on the 29 ANDV tasks) rests on the assumption that temporal patterns learned from the eleven source families align with ANDV dynamics sufficiently for locked application. No comparison of generation-interval distributions, incubation-period statistics, or fitted coefficient stability between source families and ANDV is described in the benchmark or methods sections; the permutation test only contrasts against the reported baseline and does not test whether the baseline would improve under ANDV-specific temporal statistics. This assumption is load-bearing for the claim of a generalizable transferable prior.

minor comments (2)

[Results on phylogenetic audit] The mpox phylogenetic audit is explicitly independent of prior validation; the text should clarify whether any of its uncertainty estimates were used to adjust the ANDV graphs or remain strictly separate.
[Methods] Feature construction, exact timing variables, and exclusion rules for the eleven source families and the ANDV benchmark are referenced but not detailed enough for full reproducibility of the locked model.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and for identifying the load-bearing assumption in the transferability claim. We respond to the single major comment below and indicate planned revisions.

read point-by-point responses

Referee: [Abstract and ANDV benchmark description] The central transfer result (MRR 0.571 / Top-1 37.9% on the 29 ANDV tasks) rests on the assumption that temporal patterns learned from the eleven source families align with ANDV dynamics sufficiently for locked application. No comparison of generation-interval distributions, incubation-period statistics, or fitted coefficient stability between source families and ANDV is described in the benchmark or methods sections; the permutation test only contrasts against the reported baseline and does not test whether the baseline would improve under ANDV-specific temporal statistics. This assumption is load-bearing for the claim of a generalizable transferable prior.

Authors: We agree that the manuscript does not include explicit comparisons of generation-interval distributions, incubation-period statistics, or fitted coefficient stability between the eleven source families and ANDV; this omission weakens the supporting evidence for alignment. We will add such comparisons (using available timing metadata from the source families and the ANDV benchmark) to the methods and results sections in revision. At the same time, the primary support for transferability remains the locked-prior performance on the 29 held-out ANDV tasks, which yields a statistically significant gain over the best source-trained parametric baseline under a permutation test that respects the no-target-data constraint. An ANDV-specific version of the baseline would, by definition, require target data and therefore falls outside the locked-prior experimental setting the paper evaluates. The breadth of the eleven source families and the magnitude of the observed improvement (MRR 0.571 vs. 0.274) provide indirect evidence that the learned coefficients capture transferable temporal structure rather than family-specific idiosyncrasies. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper trains a logistic regression temporal prior on eleven source disease families, explicitly locks all parameters before accessing any ANDV target data, and evaluates transfer performance on an independent 29-task parent-ranking benchmark. The reported MRR and Top-1 metrics are computed on held-out target data rather than being fitted or redefined from the source inputs. The phylogenetic concordance audit on NYC mpox pairs is presented separately as label-reliability evidence and does not enter the prior training or ANDV evaluation equations. No self-definitional reduction, fitted-input prediction, or load-bearing self-citation chain appears in the derivation.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the transferability of timing patterns across disease families and on the assumption that the 29-task ANDV benchmark constitutes a fair, strict test; the logistic regression coefficients are the main fitted quantities.

free parameters (1)

logistic regression coefficients
Fitted once on the eleven disease families and then locked; these are the parameters that define the temporal prior.

axioms (1)

domain assumption Temporal patterns of transmission are sufficiently conserved across the eleven disease families to justify transfer to Andes virus without refitting.
Invoked to support locking the model before accessing target data.

pith-pipeline@v0.9.1-grok · 5748 in / 1391 out tokens · 32732 ms · 2026-07-01T06:22:35.796852+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

27 extracted references · 12 canonical work pages

[1]

Wang, Helly Amin, Faten Taki, Nelson De La Cruz, Moinuddin Chowdhury, Tyler Clabby, Erik Kopping, Victoria E

Saymon Akther, Michelle Su, Jade C. Wang, Helly Amin, Faten Taki, Nelson De La Cruz, Moinuddin Chowdhury, Tyler Clabby, Erik Kopping, Victoria E. Ruiz, Mindy Leelawong, Julia Latash, Kimberly Johnson, Jennifer Baumgartner, Marcia Wong, Aaron Olsen, Randal C. Fowler, Jonathan E. Pekar, Jennifer L. Havens, Tetyana I. Vasylyeva, Joel O. Wertheim, Scott Hughe...

work page doi:10.1038/s41467-025-60486-x 2022
[2]

outbreaker2: a modular platform for outbreak reconstruction.BMC bioinformatics, 19(Suppl 11):363, 2018

Finlay Campbell, Xavier Didelot, Rich Fitzjohn, Neil Ferguson, Anne Cori, and Thibaut Jombart. outbreaker2: a modular platform for outbreak reconstruction.BMC bioinformatics, 19(Suppl 11):363, 2018

2018
[3]

Incorporating epidemiological data into the genomic analysis of partially sampled infectious disease outbreaks.Molecular Biology and Evolution, 42(4):msaf083,

Jake Carson, Matt Keeling, Paolo Ribeca, and Xavier Didelot. Incorporating epidemiological data into the genomic analysis of partially sampled infectious disease outbreaks.Molecular Biology and Evolution, 42(4):msaf083,
[4]

doi:10.1093/molbev/msaf083

work page doi:10.1093/molbev/msaf083
[5]

Caroline Colijn, Matthew Hall, and Remco Bouckaert. Taking a BREATH (Bayesian reconstruction and evolu- tionary analysis of transmission histories) to simultaneously infer phylogenetic and transmission trees for partially sampled outbreaks.bioRxiv, July 2024. doi:10.1101/2024.07.11.603095. URL https://doi.org/10.1101/ 2024.07.11.603095. Preprint

work page doi:10.1101/2024.07.11.603095 2024
[6]

Ferguson, Christophe Fraser, and Simon Cauchemez

Anne Cori, Neil M. Ferguson, Christophe Fraser, and Simon Cauchemez. A new framework and software to estimate time-varying reproduction numbers during epidemics.American Journal of Epidemiology, 178(9): 1505–1512, 2013. doi:10.1093/aje/kwt133

work page doi:10.1093/aje/kwt133 2013
[7]

Scotti: efficient reconstruction of transmission within outbreaks with the structured coalescent.PLoS computational biology, 12(9):e1005130, 2016

Nicola De Maio, Chieh-Hsi Wu, and Daniel J Wilson. Scotti: efficient reconstruction of transmission within outbreaks with the structured coalescent.PLoS computational biology, 12(9):e1005130, 2016

2016
[8]

Bayesian reconstruction of transmission within outbreaks using genomic variants.PLoS computational biology, 14(4):e1006117, 2018

Nicola De Maio, Colin J Worby, Daniel J Wilson, and Nicole Stoesser. Bayesian reconstruction of transmission within outbreaks using genomic variants.PLoS computational biology, 14(4):e1006117, 2018

2018
[9]

Bayesian inference of infectious disease transmission from whole-genome sequence data.Molecular biology and evolution, 31(7):1869–1879, 2014

Xavier Didelot, Jennifer Gardy, and Caroline Colijn. Bayesian inference of infectious disease transmission from whole-genome sequence data.Molecular biology and evolution, 31(7):1869–1879, 2014

2014
[10]

Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks.Molecular biology and evolution, 34(4):997–1007, 2017

Xavier Didelot, Christophe Fraser, Jennifer Gardy, and Caroline Colijn. Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks.Molecular biology and evolution, 34(4):997–1007, 2017

2017
[11]

Estimating the generation interval for coronavirus disease (covid-19) based on symptom onset data, march 2020

Tapiwa Ganyani, Cécile Kremer, Dongxuan Chen, Andrea Torneri, Christel Faes, Jacco Wallinga, and Niel Hens. Estimating the generation interval for coronavirus disease (covid-19) based on symptom onset data, march 2020. Eurosurveillance, 25(17):2000257, 2020

2020
[12]

Decision making under deep uncertainty for pandemic policy planning.Health policy, 133:104831, 2023

Sophie Hadjisotiriou, Vincent Marchau, Warren Walker, and Marcel Olde Rikkert. Decision making under deep uncertainty for pandemic policy planning.Health policy, 133:104831, 2023

2023
[13]

Ario, Julie R

Zainah Kabami, Alex R. Ario, Julie R. Harris, Mackline Ninsiima, Sherry R. Ahirirwe, Jane R. Aceng Ocero, Diana Atwine, Henry G. Mwebesa, Daniel J. Kyabayinze, Allan N. Muruta, et al. Ebola disease outbreak caused by the sudan virus in uganda, 2022: A descriptive epidemiological study.The Lancet Global Health, 12(10): e1684–e1692, 2024. doi:10.1016/S2214-...

work page doi:10.1016/s2214-109x(24)00260-2 2022
[14]

Molecular infectious disease epidemiology: survival analysis and algorithms linking phylogenies to transmission trees.PLoS computational biology, 12(4): e1004869, 2016

Eben Kenah, Tom Britton, M Elizabeth Halloran, and Ira M Longini Jr. Molecular infectious disease epidemiology: survival analysis and algorithms linking phylogenies to transmission trees.PLoS computational biology, 12(4): e1004869, 2016

2016
[15]

Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to ebola.Proceedings of the Royal Society B: Biological Sciences, 282(1806), 2015

Aaron A King, Matthieu Domenech de Cellès, Felicia MG Magpantay, and Pejman Rohani. Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to ebola.Proceedings of the Royal Society B: Biological Sciences, 282(1806), 2015. doi:https://doi.org/10.1098/rspb.2015.0347

work page doi:10.1098/rspb.2015.0347 2015
[16]

Allan Komakech, Shannon L. M. Whitmer, Jonathan Izudi, Charles Kizito, Mackline Ninsiima, Sherry R. Ahirirwe, Zainah Kabami, Alex R. Ario, et al. Sudan virus disease super-spreading, uganda, 2022.BMC Infectious Diseases, 24:520, 2024. doi:10.1186/s12879-024-09391-0

work page doi:10.1186/s12879-024-09391-0 2022
[17]

Viral infection and transmission in a large, well-traced outbreak caused by the sars-cov-2 delta variant.Nature communications, 13(1):460, 2022

Baisheng Li, Aiping Deng, Kuibiao Li, Yao Hu, Zhencui Li, Yaling Shi, Qianling Xiong, Zhe Liu, Qianfang Guo, Lirong Zou, et al. Viral infection and transmission in a large, well-traced outbreak caused by the sars-cov-2 delta variant.Nature communications, 13(1):460, 2022

2022
[18]

super-spreaders

Valeria P Martínez, Nicholas Di Paola, Daniel O Alonso, Unai Pérez-Sautu, Carla M Bellomo, Ayelén A Iglesias, Rocio M Coelho, Beatriz López, Natalia Periolo, Peter A Larson, et al. “super-spreaders” and person-to-person transmission of andes virus in argentina.New England Journal of Medicine, 383(23):2230–2241, 2020

2020
[19]

Jahangir Hossain, A.K.M

Birgit Nikolay, Henrik Salje, M. Jahangir Hossain, A.K.M. Dawlat Khan, Hossain M.S. Sazzad, Mahmudur Rahman, Peter Daszak, Emily S. Gurley, et al. Transmission of nipah virus — 14 years of investigations in bangladesh.New England Journal of Medicine, 380(19):1804–1814, 5 2019. doi:10.1056/NEJMoa1805376. URL https://doi.org/10.1056/NEJMoa1805376

work page doi:10.1056/nejmoa1805376 2019
[20]

Moreno, Taylor Brock-Fisher, Lydia A

Ivan Specht, Gage K. Moreno, Taylor Brock-Fisher, Lydia A. Krasilnikova, Brittany A. Petros, Jonathan E. Pekar, Mark Schifferli, Ben Fry, Catherine M. Brown, Lawrence C. Madoff, Meagan Burns, Stephen F. Schaffner, Daniel J. Park, Bronwyn L. MacInnis, Al Ozonoff, Patrick Varilly, Michael D. Mitzenmacher, and Pardis C. Sabeti. JUNIPER: Reconstructing transm...

work page doi:10.21203/rs.3.rs-6264999/v1 2025
[21]

Taube, Paige B

Juliana C. Taube, Paige B. Miller, and John M. Drake. An open-access database of infectious dis- ease transmission trees to explore superspreader epidemiology.PLOS Biology, 20(6):e3001685, 2022. doi:10.1371/journal.pbio.3001685. URLhttps://doi.org/10.1371/journal.pbio.3001685

work page doi:10.1371/journal.pbio.3001685 2022
[22]

Vickers and Elena B

Andrew J. Vickers and Elena B. Elkin. Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making, 26(6):565–574, 2006

2006
[23]

Hannah Waddel, Katia Koelle, and Max S. Y . Lau. ScITree: Scalable bayesian inference of transmis- sion tree from epidemiological and genomic data.PLOS Computational Biology, 21(6):e1012657, 2025. doi:10.1371/journal.pcbi.1012657. URLhttps://doi.org/10.1371/journal.pcbi.1012657

work page doi:10.1371/journal.pcbi.1012657 2025
[24]

How generation intervals shape the relationship between growth rates and reproductive numbers.Proceedings of the Royal Society B: Biological Sciences, 274(1609):599–604, 2007

Jacco Wallinga and Marc Lipsitch. How generation intervals shape the relationship between growth rates and reproductive numbers.Proceedings of the Royal Society B: Biological Sciences, 274(1609):599–604, 2007

2007
[25]

Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures.American Journal of Epidemiology, 160(6):509–516, 2004

Jacco Wallinga and Peter Teunis. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures.American Journal of Epidemiology, 160(6):509–516, 2004. doi:10.1093/aje/kwh255

work page doi:10.1093/aje/kwh255 2004
[26]

Michael Walsh, S. K. Srinathan, Daniel F. McAuley, Marko Mrkobrada, Oron Levine, Christine Ribic, et al. The statistical significance of randomized controlled trial results is frequently fragile: A case for a fragility index. Journal of Clinical Epidemiology, 67(6):622–628, 2014

2014
[27]

Wasserstein and Nicole A

Ronald L. Wasserstein and Nicole A. Lazar. The asa’s statement on p-values: Context, process, and purpose.The American Statistician, 70(2):129–133, 2016. 30

2016

[1] [1]

Wang, Helly Amin, Faten Taki, Nelson De La Cruz, Moinuddin Chowdhury, Tyler Clabby, Erik Kopping, Victoria E

Saymon Akther, Michelle Su, Jade C. Wang, Helly Amin, Faten Taki, Nelson De La Cruz, Moinuddin Chowdhury, Tyler Clabby, Erik Kopping, Victoria E. Ruiz, Mindy Leelawong, Julia Latash, Kimberly Johnson, Jennifer Baumgartner, Marcia Wong, Aaron Olsen, Randal C. Fowler, Jonathan E. Pekar, Jennifer L. Havens, Tetyana I. Vasylyeva, Joel O. Wertheim, Scott Hughe...

work page doi:10.1038/s41467-025-60486-x 2022

[2] [2]

outbreaker2: a modular platform for outbreak reconstruction.BMC bioinformatics, 19(Suppl 11):363, 2018

Finlay Campbell, Xavier Didelot, Rich Fitzjohn, Neil Ferguson, Anne Cori, and Thibaut Jombart. outbreaker2: a modular platform for outbreak reconstruction.BMC bioinformatics, 19(Suppl 11):363, 2018

2018

[3] [3]

Incorporating epidemiological data into the genomic analysis of partially sampled infectious disease outbreaks.Molecular Biology and Evolution, 42(4):msaf083,

Jake Carson, Matt Keeling, Paolo Ribeca, and Xavier Didelot. Incorporating epidemiological data into the genomic analysis of partially sampled infectious disease outbreaks.Molecular Biology and Evolution, 42(4):msaf083,

[4] [4]

doi:10.1093/molbev/msaf083

work page doi:10.1093/molbev/msaf083

[5] [5]

Caroline Colijn, Matthew Hall, and Remco Bouckaert. Taking a BREATH (Bayesian reconstruction and evolu- tionary analysis of transmission histories) to simultaneously infer phylogenetic and transmission trees for partially sampled outbreaks.bioRxiv, July 2024. doi:10.1101/2024.07.11.603095. URL https://doi.org/10.1101/ 2024.07.11.603095. Preprint

work page doi:10.1101/2024.07.11.603095 2024

[6] [6]

Ferguson, Christophe Fraser, and Simon Cauchemez

Anne Cori, Neil M. Ferguson, Christophe Fraser, and Simon Cauchemez. A new framework and software to estimate time-varying reproduction numbers during epidemics.American Journal of Epidemiology, 178(9): 1505–1512, 2013. doi:10.1093/aje/kwt133

work page doi:10.1093/aje/kwt133 2013

[7] [7]

Scotti: efficient reconstruction of transmission within outbreaks with the structured coalescent.PLoS computational biology, 12(9):e1005130, 2016

Nicola De Maio, Chieh-Hsi Wu, and Daniel J Wilson. Scotti: efficient reconstruction of transmission within outbreaks with the structured coalescent.PLoS computational biology, 12(9):e1005130, 2016

2016

[8] [8]

Bayesian reconstruction of transmission within outbreaks using genomic variants.PLoS computational biology, 14(4):e1006117, 2018

Nicola De Maio, Colin J Worby, Daniel J Wilson, and Nicole Stoesser. Bayesian reconstruction of transmission within outbreaks using genomic variants.PLoS computational biology, 14(4):e1006117, 2018

2018

[9] [9]

Bayesian inference of infectious disease transmission from whole-genome sequence data.Molecular biology and evolution, 31(7):1869–1879, 2014

Xavier Didelot, Jennifer Gardy, and Caroline Colijn. Bayesian inference of infectious disease transmission from whole-genome sequence data.Molecular biology and evolution, 31(7):1869–1879, 2014

2014

[10] [10]

Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks.Molecular biology and evolution, 34(4):997–1007, 2017

Xavier Didelot, Christophe Fraser, Jennifer Gardy, and Caroline Colijn. Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks.Molecular biology and evolution, 34(4):997–1007, 2017

2017

[11] [11]

Estimating the generation interval for coronavirus disease (covid-19) based on symptom onset data, march 2020

Tapiwa Ganyani, Cécile Kremer, Dongxuan Chen, Andrea Torneri, Christel Faes, Jacco Wallinga, and Niel Hens. Estimating the generation interval for coronavirus disease (covid-19) based on symptom onset data, march 2020. Eurosurveillance, 25(17):2000257, 2020

2020

[12] [12]

Decision making under deep uncertainty for pandemic policy planning.Health policy, 133:104831, 2023

Sophie Hadjisotiriou, Vincent Marchau, Warren Walker, and Marcel Olde Rikkert. Decision making under deep uncertainty for pandemic policy planning.Health policy, 133:104831, 2023

2023

[13] [13]

Ario, Julie R

Zainah Kabami, Alex R. Ario, Julie R. Harris, Mackline Ninsiima, Sherry R. Ahirirwe, Jane R. Aceng Ocero, Diana Atwine, Henry G. Mwebesa, Daniel J. Kyabayinze, Allan N. Muruta, et al. Ebola disease outbreak caused by the sudan virus in uganda, 2022: A descriptive epidemiological study.The Lancet Global Health, 12(10): e1684–e1692, 2024. doi:10.1016/S2214-...

work page doi:10.1016/s2214-109x(24)00260-2 2022

[14] [14]

Molecular infectious disease epidemiology: survival analysis and algorithms linking phylogenies to transmission trees.PLoS computational biology, 12(4): e1004869, 2016

Eben Kenah, Tom Britton, M Elizabeth Halloran, and Ira M Longini Jr. Molecular infectious disease epidemiology: survival analysis and algorithms linking phylogenies to transmission trees.PLoS computational biology, 12(4): e1004869, 2016

2016

[15] [15]

Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to ebola.Proceedings of the Royal Society B: Biological Sciences, 282(1806), 2015

Aaron A King, Matthieu Domenech de Cellès, Felicia MG Magpantay, and Pejman Rohani. Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to ebola.Proceedings of the Royal Society B: Biological Sciences, 282(1806), 2015. doi:https://doi.org/10.1098/rspb.2015.0347

work page doi:10.1098/rspb.2015.0347 2015

[16] [16]

Allan Komakech, Shannon L. M. Whitmer, Jonathan Izudi, Charles Kizito, Mackline Ninsiima, Sherry R. Ahirirwe, Zainah Kabami, Alex R. Ario, et al. Sudan virus disease super-spreading, uganda, 2022.BMC Infectious Diseases, 24:520, 2024. doi:10.1186/s12879-024-09391-0

work page doi:10.1186/s12879-024-09391-0 2022

[17] [17]

Viral infection and transmission in a large, well-traced outbreak caused by the sars-cov-2 delta variant.Nature communications, 13(1):460, 2022

Baisheng Li, Aiping Deng, Kuibiao Li, Yao Hu, Zhencui Li, Yaling Shi, Qianling Xiong, Zhe Liu, Qianfang Guo, Lirong Zou, et al. Viral infection and transmission in a large, well-traced outbreak caused by the sars-cov-2 delta variant.Nature communications, 13(1):460, 2022

2022

[18] [18]

super-spreaders

Valeria P Martínez, Nicholas Di Paola, Daniel O Alonso, Unai Pérez-Sautu, Carla M Bellomo, Ayelén A Iglesias, Rocio M Coelho, Beatriz López, Natalia Periolo, Peter A Larson, et al. “super-spreaders” and person-to-person transmission of andes virus in argentina.New England Journal of Medicine, 383(23):2230–2241, 2020

2020

[19] [19]

Jahangir Hossain, A.K.M

Birgit Nikolay, Henrik Salje, M. Jahangir Hossain, A.K.M. Dawlat Khan, Hossain M.S. Sazzad, Mahmudur Rahman, Peter Daszak, Emily S. Gurley, et al. Transmission of nipah virus — 14 years of investigations in bangladesh.New England Journal of Medicine, 380(19):1804–1814, 5 2019. doi:10.1056/NEJMoa1805376. URL https://doi.org/10.1056/NEJMoa1805376

work page doi:10.1056/nejmoa1805376 2019

[20] [20]

Moreno, Taylor Brock-Fisher, Lydia A

Ivan Specht, Gage K. Moreno, Taylor Brock-Fisher, Lydia A. Krasilnikova, Brittany A. Petros, Jonathan E. Pekar, Mark Schifferli, Ben Fry, Catherine M. Brown, Lawrence C. Madoff, Meagan Burns, Stephen F. Schaffner, Daniel J. Park, Bronwyn L. MacInnis, Al Ozonoff, Patrick Varilly, Michael D. Mitzenmacher, and Pardis C. Sabeti. JUNIPER: Reconstructing transm...

work page doi:10.21203/rs.3.rs-6264999/v1 2025

[21] [21]

Taube, Paige B

Juliana C. Taube, Paige B. Miller, and John M. Drake. An open-access database of infectious dis- ease transmission trees to explore superspreader epidemiology.PLOS Biology, 20(6):e3001685, 2022. doi:10.1371/journal.pbio.3001685. URLhttps://doi.org/10.1371/journal.pbio.3001685

work page doi:10.1371/journal.pbio.3001685 2022

[22] [22]

Vickers and Elena B

Andrew J. Vickers and Elena B. Elkin. Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making, 26(6):565–574, 2006

2006

[23] [23]

Hannah Waddel, Katia Koelle, and Max S. Y . Lau. ScITree: Scalable bayesian inference of transmis- sion tree from epidemiological and genomic data.PLOS Computational Biology, 21(6):e1012657, 2025. doi:10.1371/journal.pcbi.1012657. URLhttps://doi.org/10.1371/journal.pcbi.1012657

work page doi:10.1371/journal.pcbi.1012657 2025

[24] [24]

How generation intervals shape the relationship between growth rates and reproductive numbers.Proceedings of the Royal Society B: Biological Sciences, 274(1609):599–604, 2007

Jacco Wallinga and Marc Lipsitch. How generation intervals shape the relationship between growth rates and reproductive numbers.Proceedings of the Royal Society B: Biological Sciences, 274(1609):599–604, 2007

2007

[25] [25]

Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures.American Journal of Epidemiology, 160(6):509–516, 2004

Jacco Wallinga and Peter Teunis. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures.American Journal of Epidemiology, 160(6):509–516, 2004. doi:10.1093/aje/kwh255

work page doi:10.1093/aje/kwh255 2004

[26] [26]

Michael Walsh, S. K. Srinathan, Daniel F. McAuley, Marko Mrkobrada, Oron Levine, Christine Ribic, et al. The statistical significance of randomized controlled trial results is frequently fragile: A case for a fragility index. Journal of Clinical Epidemiology, 67(6):622–628, 2014

2014

[27] [27]

Wasserstein and Nicole A

Ronald L. Wasserstein and Nicole A. Lazar. The asa’s statement on p-values: Context, process, and purpose.The American Statistician, 70(2):129–133, 2016. 30

2016