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Paper Citation Record · LEDGER

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2509.10668.

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pith.paper-citation-record.v1
2509.10668 v1

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measured 31 of 31 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation 9fb667c3-dc92-4008-aa48-cc21e4ef1f5d · outbound

This paper cites Stratified S pace-- T ime I nfectious D isease M odelling, with an A pplication to H and, F oot and M outh D isease in C hina.

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Stratified S pace-- T ime I nfectious D isease M odelling, with an A pplication to H and, F oot and M outh D isease in C hina

Reference 1

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This paper cites Rotavirus O verview.

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Rotavirus O verview

Reference 2

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This paper cites A M arginal M oment M atching A pproach for F itting E ndemic- E pidemic M odels to U nderreported D isease S urveillance C ounts.

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction A M arginal M oment M atching A pproach for F itting E ndemic- E pidemic M odels to U nderreported D isease S urveillance C ounts

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Observation 866be2fc-cc53-418d-a77a-5bf35a4dca80 · outbound

This paper cites Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction.

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction

Reference 4

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This paper cites Statistical A nalysis of T ime S eries: Some R ecent D evelopments.

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Statistical A nalysis of T ime S eries: Some R ecent D evelopments

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This paper cites de Valpine, C.

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction de Valpine, C

Reference 6

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This paper cites Endemic-Epidemic Framework Used in Covid-19 Modelling:[Discussion on the paper by Nunes, Caetano, Antunes and Dias].

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Endemic-Epidemic Framework Used in Covid-19 Modelling:[Discussion on the paper by Nunes, Caetano, Antunes and Dias]

Reference 7

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Poisson A utoregression

Reference 8

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction A statistical framework for the analysis of multivariate infectious disease surveillance counts

Reference 9

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This paper cites Better together? Statistical learning in models made of modules.

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Better together? Statistical learning in models made of modules

Reference 10

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This paper cites Emulating a G ravity M odel to I nfer the S patiotemporal D ynamics of an I nfectious D isease.

Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Emulating a G ravity M odel to I nfer the S patiotemporal D ynamics of an I nfectious D isease

Reference 11

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Berg, Hans Skaug, and Bradley M

Reference 13

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Spatio- T emporal A nalysis of E pidemic P henomena using the R P ackage surveillance

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Slice sampling

Reference 15

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Coronavirus (covid-19) I nfection S urvey, antibody data, UK: 29 March 2023

Reference 16

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Faster asymptotic solutions for n-mixtures on large populations

Reference 17

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Cuts in B ayesian graphical models

Reference 18

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Regression M odels for U nderstanding COVID-19 E pidemic D ynamics with I ncomplete D ata

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction RE al-time A ssessment of C ommunity T ransmission (REACT) of SARS-CoV-2 virus: S tudy protocol

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Modelling zero inflated and under-reported count data

Reference 21

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Estimation of mean using under-reported and overdispersed count data

Reference 22

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Leveraging cellphone-derived mobility networks to assess C ovid-19 travel risk

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Wastewater surveillance using differentiable G aussian processes

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction RStan : the R interface to Stan , 2025

Reference 25

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in G ermany

Reference 26

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction A Hierarchical Framework for Correcting Under-Reporting in Count Data

Reference 27

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction COVID-19 A rchive D ata D ownload , 2025

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in A otearoa N ew Z ealand

Reference 29

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Modelling the epidemiological impact of rotavirus vaccination in G ermany-- A B ayesian approach

Reference 30

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction Some statistical aspects of the C ovid-19 response

Reference 31

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Modelling Under-Reported Data: Pitfalls of Na\"ive Approaches and a New Statistical Framework for Epidemic Curve Reconstruction A negative binomial integer-valued GARCH model

Reference 32

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