Paper Citation Record · LEDGER

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting

As of 12 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2506.05752.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.05752 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:17:16.607239Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy9
unresolved6
parse uncertain0
malformed identifier2
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3750f4aa-066e-4a3f-9324-585c166fac85 · outbound

This paper cites Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T10:17:15.564174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2675356-db6d-4816-a130-976e2ba4fd60 · outbound

This paper cites PLoS computational biology, 20 (5), e1011200.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting PLoS computational biology, 20 (5), e1011200

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:17:18.040009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 40ed1cc9-34f2-4be1-9411-aeccc9d82045 · outbound

This paper cites Migration patterns, friendship networks, and the diaspora: the potential of Facebook Social Connectedness Index to anticipate displacement patterns induced by Russia invasion of Ukraine in the European Union.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Migration patterns, friendship networks, and the diaspora: the potential of Facebook Social Connectedness Index to anticipate displacement patterns induced by Russia invasion of Ukraine in the European Union

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T10:17:16.917566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 399f4fa6-1594-4333-858b-bc582512412d · outbound

This paper cites medRxiv, 2024–01.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting medRxiv, 2024–01

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:17:17.893682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4fae9a18-a3ae-4085-a0ba-2ddda2c1ad44 · outbound

This paper cites Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$\alpha$ Model.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$\alpha$ Model

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T10:17:16.777229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6f50f29-7692-4de0-9b69-0790cb88d5e0 · outbound

This paper cites International Journal of Medical Informatics , 164, 104804.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting International Journal of Medical Informatics , 164, 104804

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:17:17.634421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ed19237e-653e-465f-93be-e2f98b9ccd43 · outbound

This paper cites 31 Appendix C.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting 31 Appendix C

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:17:17.513674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 460d9558-d252-4275-977b-f17db6aa287b · outbound

This paper cites an unresolved cited work.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Unresolved cited work

Reference 17

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T10:17:17.276431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6342945a-b160-417e-8801-eb9a030e4735 · outbound

This paper cites an unresolved cited work.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:17:17.162000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 292f7eea-b08e-42d5-8cc9-57a3dc654b9c · outbound

This paper cites an unresolved cited work.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Unresolved cited work

Reference 19

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T10:17:17.065452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a32f170d-edbb-4b16-ac9e-43eac89ddb7b · outbound

This paper cites Center for spatially integrated social science, 1963,.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Center for spatially integrated social science, 1963,

Reference 2005

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:17:18.620884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d987ea4e-cb8c-4cba-9b5f-5a62da6afdd0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Adam: A Method for Stochastic Optimization

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-07T10:17:15.635456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0830b42-861a-4e9d-845f-c8a6d9f1f9e4 · outbound

This paper cites A Multi-Horizon Quantile Recurrent Forecaster.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting A Multi-Horizon Quantile Recurrent Forecaster

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T10:17:16.070871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a845de6-3c9e-4abe-acd4-1c4829d2a445 · outbound

This paper cites BMC Public Health, 19, 1–9.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting BMC Public Health, 19, 1–9

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:17:17.795019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b77ee24-73c7-42b0-82a4-72ff60152574 · outbound

This paper cites Data in brief , 29, 105340.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Data in brief , 29, 105340

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:17:18.406509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7d9a88f7-48ac-499f-9ad6-9de605005109 · outbound

This paper cites an unresolved cited work.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:17:17.362480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 42224b9f-b47d-423d-99ca-09e6870cb6fc · outbound

This paper cites medRxiv, 2022–08.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting medRxiv, 2022–08

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:17:18.273174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 57eb6dbd-cd36-4b07-b7c1-e0fa19beec34 · outbound

This paper cites A Survey on Oversmoothing in Graph Neural Networks.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting A Survey on Oversmoothing in Graph Neural Networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T10:17:15.838666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaee3be0-ef58-47b2-af2f-7fea75c51099 · outbound

This paper cites influenza surveillance report.

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting influenza surveillance report

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:17:18.162679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.