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Paper Citation Record · LEDGER

Physics-informed deep learning for infectious disease forecasting

As of 21 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 1 inbound Pith citation observation for arXiv:2501.09298.

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

pith.paper-citation-record.v1
2501.09298 v2

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

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measured 70 of 70 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T15:42:58.769940Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

69 of 69 outbound references displayed

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External citation measurements

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Outbound references

Observation e715d197-0a41-4fe1-898c-edf69e4f6c6e · outbound

This paper cites Provisional mortality data—united states, 2021.

Physics-informed deep learning for infectious disease forecasting Provisional mortality data—united states, 2021

Reference 1

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Observation 0e5f4082-e228-4216-a638-9d6cfd9a3a4f · outbound

This paper cites The challenge of emerging and re-emerging infectious diseases.

Physics-informed deep learning for infectious disease forecasting The challenge of emerging and re-emerging infectious diseases

Reference 2

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Observation 1740ba75-936d-4f53-80fb-d1396c07d65a · outbound

This paper cites Climate change increases cross-species viral transmission risk.

Physics-informed deep learning for infectious disease forecasting Climate change increases cross-species viral transmission risk

Reference 3

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Observation 66c252bc-ac5c-4208-a556-4f60a8005c63 · outbound

This paper cites Improving pandemic response: employing mathematical modeling to confront coronavirus disease 2019.

Physics-informed deep learning for infectious disease forecasting Improving pandemic response: employing mathematical modeling to confront coronavirus disease 2019

Reference 4

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Observation 3c8db71d-f75f-40bb-84d2-361dee9e2c58 · outbound

This paper cites Applying infectious disease forecasting to public health: a path forward using influenza fore- casting examples.

Physics-informed deep learning for infectious disease forecasting Applying infectious disease forecasting to public health: a path forward using influenza fore- casting examples

Reference 5

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Observation 7970ad41-ce9d-453d-bd0c-00698bc999ec · outbound

This paper cites An accurate hierarchical model to forecast diverse seasonal infectious diseases.

Physics-informed deep learning for infectious disease forecasting An accurate hierarchical model to forecast diverse seasonal infectious diseases

Reference 6

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Observation be2423b9-73e4-4e78-8bde-fb9cb7acd000 · outbound

This paper cites Infectious Diseases of Humans.

Physics-informed deep learning for infectious disease forecasting Infectious Diseases of Humans

Reference 7

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Observation f97bea27-7697-4ab8-85b7-a7fdd4ef94d4 · outbound

This paper cites Modelling Infectious Diseases: In Humans and Animals.

Physics-informed deep learning for infectious disease forecasting Modelling Infectious Diseases: In Humans and Animals

Reference 8

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Observation a0d52093-51f9-4577-b9ab-42ad5fca9cdd · outbound

This paper cites Compartmental models in epidemiology.

Physics-informed deep learning for infectious disease forecasting Compartmental models in epidemiology

Reference 9

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Observation d303a178-6f28-4654-a34a-633595f7a6ae · outbound

This paper cites Mathematical models in epidemiology, volume 32.

Physics-informed deep learning for infectious disease forecasting Mathematical models in epidemiology, volume 32

Reference 10

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Observation 262b8a74-023a-4f8e-a521-7b33ad98b350 · outbound

This paper cites An introduction to mathematical epidemiology , volume 61.

Physics-informed deep learning for infectious disease forecasting An introduction to mathematical epidemiology , volume 61

Reference 11

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Observation 3f6a3f78-41ea-4d08-9d8e-6aa67474fec3 · outbound

This paper cites Mathematical modelling and prediction in infectious disease epidemiology.

Physics-informed deep learning for infectious disease forecasting Mathematical modelling and prediction in infectious disease epidemiology

Reference 12

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Observation d41bde73-8fc8-400b-acf2-183bdfb164f1 · outbound

This paper cites Appropriate Models for the Manage- ment of Infectious Diseases.

Physics-informed deep learning for infectious disease forecasting Appropriate Models for the Manage- ment of Infectious Diseases

Reference 13

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Observation db5c4b2d-a7b0-4b34-abf4-01abf1a7146c · outbound

This paper cites Structural identifiability and observability of compartmental models of the covid-19 pandemic.

Physics-informed deep learning for infectious disease forecasting Structural identifiability and observability of compartmental models of the covid-19 pandemic

Reference 14

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Observation bb0c3570-0fdf-4597-9bb3-798310650d87 · outbound

This paper cites A fractional-order compartmental model for the spread of the covid-19 pandemic.

Physics-informed deep learning for infectious disease forecasting A fractional-order compartmental model for the spread of the covid-19 pandemic

Reference 15

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Observation 12a70365-1ac6-4f17-b337-1c09751ae20c · outbound

This paper cites Mathematical modeling of covid-19 transmission dynamics with a case study of wuhan.

Physics-informed deep learning for infectious disease forecasting Mathematical modeling of covid-19 transmission dynamics with a case study of wuhan

Reference 16

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Observation e3b05c99-387e-48dd-9331-0e55c2991b74 · outbound

This paper cites Compartmental models of the covid-19 pandemic for physicians and physician-scientists.

Physics-informed deep learning for infectious disease forecasting Compartmental models of the covid-19 pandemic for physicians and physician-scientists

Reference 17

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Observation e56dbf3f-12c2-4e57-a47f-3288681e301e · outbound

This paper cites Sirsi compartmental model for covid-19 pandemic with immunity loss.

Physics-informed deep learning for infectious disease forecasting Sirsi compartmental model for covid-19 pandemic with immunity loss

Reference 18

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Observation 100bc043-8471-4146-998c-3bc3ac73beb5 · outbound

This paper cites New compartment model for covid-19.

Physics-informed deep learning for infectious disease forecasting New compartment model for covid-19

Reference 19

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This paper cites an unresolved cited work.

Physics-informed deep learning for infectious disease forecasting Unresolved cited work

Reference 20

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Observation 257a7d73-04b8-4daa-92b7-c666b6ff98b6 · outbound

This paper cites Measuring the impact of Ebola control measures in Sierra Leone.

Physics-informed deep learning for infectious disease forecasting Measuring the impact of Ebola control measures in Sierra Leone

Reference 21

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Observation fe6f9f09-5cf1-4ac2-a1cb-efbf93a34ad7 · outbound

This paper cites A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting covid-19 cases, hospitalizations and deaths.

Physics-informed deep learning for infectious disease forecasting A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting covid-19 cases, hospitalizations and deaths

Reference 22

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Observation f3f4950c-676e-485c-920f-776b7b9daf26 · outbound

This paper cites A data-driven semi-parametric model of sars-cov-2 trans- mission in the united states.

Physics-informed deep learning for infectious disease forecasting A data-driven semi-parametric model of sars-cov-2 trans- mission in the united states

Reference 23

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Observation d1c8d9cc-0194-4dc2-ad08-6acfd4985f7a · outbound

This paper cites Real-time mechanistic bayesian forecasts of covid-19 mortality.

Physics-informed deep learning for infectious disease forecasting Real-time mechanistic bayesian forecasts of covid-19 mortality

Reference 24

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Observation 67c4c92c-4215-426b-8acf-b8f32324e337 · outbound

This paper cites Deep learning for epidemio- logical predictions.

Physics-informed deep learning for infectious disease forecasting Deep learning for epidemio- logical predictions

Reference 25

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Observation a68a8481-895c-4537-bfd9-9dbb6755fbed · outbound

This paper cites Deep learning for few-shot white blood cell image classification and feature learning.

Physics-informed deep learning for infectious disease forecasting Deep learning for few-shot white blood cell image classification and feature learning

Reference 26

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Observation 00ad2aed-40e2-44d3-a4d8-3a19e66a6954 · outbound

This paper cites A systematic study of the performance of machine learning models on analyzing the association between semen quality and environmental pollutants.

Physics-informed deep learning for infectious disease forecasting A systematic study of the performance of machine learning models on analyzing the association between semen quality and environmental pollutants

Reference 27

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This paper cites A deep learning model for efficient end-to-end stratification of thrombotic risk in left atrial appendage.

Physics-informed deep learning for infectious disease forecasting A deep learning model for efficient end-to-end stratification of thrombotic risk in left atrial appendage

Reference 28

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Observation af3f3328-3e87-4b06-a6d5-cf7ca2093877 · outbound

This paper cites Mathematical models for predicting covid-19 pandemic: a review.

Physics-informed deep learning for infectious disease forecasting Mathematical models for predicting covid-19 pandemic: a review

Reference 29

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Observation 26a2815d-5d03-499e-a93d-d8587ee17e31 · outbound

This paper cites Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang.

Physics-informed deep learning for infectious disease forecasting Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang

Reference 30

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This paper cites Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.

Physics-informed deep learning for infectious disease forecasting Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Reference 31

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Observation 2aac7962-8c6a-4819-be53-52205ce2bb0f · outbound

This paper cites Extraction of mechanical properties of materials through deep learning from instru- mented indentation.

Physics-informed deep learning for infectious disease forecasting Extraction of mechanical properties of materials through deep learning from instru- mented indentation

Reference 32

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Observation eeb70d79-738b-4c4b-9847-6f7b57110437 · outbound

This paper cites Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification.

Physics-informed deep learning for infectious disease forecasting Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

Reference 33

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Observation f8052219-6754-4400-bb7c-b9287f7cadc6 · outbound

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Physics-informed deep learning for infectious disease forecasting Correcting model misspecification in physics-informed neural networks (PINNs)

Reference 34

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Observation f1c8d090-d0b2-40af-8354-130f8b63dda4 · outbound

This paper cites Discovering a reaction– diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression.

Physics-informed deep learning for infectious disease forecasting Discovering a reaction– diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.931714Z digest=sha256:bea6557d1792b30c0b3e20c42b9d81996d98f8c77af3aa501ce2f6ec36119ef2

Observation e2c1773f-4459-4c9c-b482-350b0b9e093f · outbound

This paper cites Uncertainty quantification for noisy inputs–outputs in physics-informed neural networks and neural operators.

Physics-informed deep learning for infectious disease forecasting Uncertainty quantification for noisy inputs–outputs in physics-informed neural networks and neural operators

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.936791Z digest=sha256:1258eca0dd36403c54f2694d711bab74f86cd1ba8ae76ca762b32df22d583803

Observation 0542f176-ee9d-4849-a7f7-91f423397495 · outbound

This paper cites NeuralUQ: A com- prehensive library for uncertainty quantification in neural differential equations and operators.

Physics-informed deep learning for infectious disease forecasting NeuralUQ: A com- prehensive library for uncertainty quantification in neural differential equations and operators

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.768872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.942043Z digest=sha256:bf31fee6deffad20aab1c4b122e3bd3f05904ff57344bd94956ddac91dab4cce

Observation 08f273ec-1980-416e-999a-cf35d9d6a7da · outbound

This paper cites B-pinns: Bayesian physics-informed neu- ral networks for forward and inverse PDE problems with noisy data.

Physics-informed deep learning for infectious disease forecasting B-pinns: Bayesian physics-informed neu- ral networks for forward and inverse PDE problems with noisy data

Reference 38

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raw_fallback, observed 2026-08-10T20:12:24.747273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.947361Z digest=sha256:16e646ce0fbe95ee212f96f8c06cb4c0e5bbe87907e4c303a5cd97b67fb65c6f

Observation cf8e6768-9d77-43b7-bf88-c33f3db0ee57 · outbound

This paper cites Ppinn: Parareal physics- informed neural network for time-dependent pdes.

Physics-informed deep learning for infectious disease forecasting Ppinn: Parareal physics- informed neural network for time-dependent pdes

Reference 39

Resolution
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raw_fallback, observed 2026-08-10T20:12:24.728104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.953754Z digest=sha256:7007fc7334e457768560ec92bd79f840f9042910107b27542715619af3c2dd9b

Observation 8c374663-68e9-4c75-8e28-0b0b58664191 · outbound

This paper cites Gradient-enhanced physics- informed neural networks for forward and inverse pde problems.

Physics-informed deep learning for infectious disease forecasting Gradient-enhanced physics- informed neural networks for forward and inverse pde problems

Reference 40

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no resolver link, observed 2026-08-10T20:12:23.958850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:12:23.958850Z digest=sha256:e13e9a8eeaa826bc4d754782004b501bc5d583f8360f8a5d6c55ee67996afb34

Observation 7f5a7ab4-ce1a-4eac-9c63-d1e442d7ce62 · outbound

This paper cites Physics-informed neural networks for solving forward and inverse flow problems via the boltzmann-bgk formulation.

Physics-informed deep learning for infectious disease forecasting Physics-informed neural networks for solving forward and inverse flow problems via the boltzmann-bgk formulation

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.695714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.964190Z digest=sha256:e70ae52f7a13c08bf4d1e4839faf5545153b0d1dc054460f5c93253bdc137549

Observation 95fd9431-a0ce-4e41-b34c-7da9670ddf22 · outbound

This paper cites Deepxde: A deep learning library for solving differential equations.

Physics-informed deep learning for infectious disease forecasting Deepxde: A deep learning library for solving differential equations

Reference 42

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no resolver link, observed 2026-08-10T20:12:23.969684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:12:23.969684Z digest=sha256:bcb5b6bdcf6ccebd2c9e121dfe08692bbb6b3a81906444f6bab439f53d7c57fc

Observation b5339ccf-df8d-4cf4-996f-ed08cd7238a6 · outbound

This paper cites From PINNs to PIKANs: Recent advances in physics-informed machine learning.

Physics-informed deep learning for infectious disease forecasting From PINNs to PIKANs: Recent advances in physics-informed machine learning

Reference 43

Resolution
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raw_fallback, observed 2026-08-10T20:12:24.663790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.975049Z digest=sha256:f6ec21d5b27a3e8eb9508aea53ba0ff381f0b7854ff9ac73ebcbeb3ea88de375

Observation cbe585dc-afd9-4b8b-8af1-1b4b0b59e256 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Physics-informed deep learning for infectious disease forecasting Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 44

Resolution
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no resolver link, observed 2026-08-10T20:12:23.980386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:12:23.980386Z digest=sha256:55b01f4e5ed93a566c04ba0a959ff682266904cc418c22bcb605cad1a14b79d1

Observation 24533a04-9032-426e-99b3-dcdded5ea88f · outbound

This paper cites Aoslo-net: a deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscopy images.

Physics-informed deep learning for infectious disease forecasting Aoslo-net: a deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscopy images

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.624700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.985816Z digest=sha256:5002db4526b8c2847193f6726f204bcee4fc1975a30c784d371468b7487607b5

Observation eda326dd-7e59-4f76-a23f-29bf2ec54e30 · outbound

This paper cites Tgm-nets: A deep learning framework for enhanced forecasting of tumor growth by integrating imaging and modeling.

Physics-informed deep learning for infectious disease forecasting Tgm-nets: A deep learning framework for enhanced forecasting of tumor growth by integrating imaging and modeling

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.606378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.991333Z digest=sha256:a83141d0c8e746e1b436790623fe1731cf236aeaacf1c92013937730b7e17e18

Observation 7f65faff-91c1-4536-83e1-34c849e7cf1b · outbound

This paper cites Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta.

Physics-informed deep learning for infectious disease forecasting Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta

Reference 47

Resolution
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raw_fallback, observed 2026-08-10T20:12:24.586067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:23.998912Z digest=sha256:35515e267876723b0b28b4ed3c15f6425c36da7b072d21eeec624ca7f57b4daf

Observation dff41b3c-f7ab-494c-837a-95932ff61d71 · outbound

This paper cites A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth.

Physics-informed deep learning for infectious disease forecasting A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.567861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.005033Z digest=sha256:b3a3cc2812aaf1576ed2dd94e6dcfd3859192167fd523fb91d4d1f3f4a3db85d

Observation 7ffb3e30-48ff-4bdd-9492-f81afd8d9f6a · outbound

This paper cites Coagulo-net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks.

Physics-informed deep learning for infectious disease forecasting Coagulo-net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.549492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.010797Z digest=sha256:799082ce9d421b517e234c17e2d2c15a2d94db6eb6feabef66841122e06bd4c9

Observation b7d2be43-a84f-40db-b8c8-65843667bcb9 · outbound

This paper cites Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease.

Physics-informed deep learning for infectious disease forecasting Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.531454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.017101Z digest=sha256:de189b670a1c4b05ec6c3a5a2a0b39a7608e51dcb03f91b40b04a2ff633382ae

Observation c57cfe4c-d352-4b1c-a7c5-4ff9b2ebf1c6 · outbound

This paper cites Bayesian physics informed neural networks for real-world nonlinear dynamical systems.

Physics-informed deep learning for infectious disease forecasting Bayesian physics informed neural networks for real-world nonlinear dynamical systems

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.512949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.022552Z digest=sha256:e6e66ca00cded4dbaac9a088360e156eff22e1f7bf8c8294059d3362e82ac024

Observation ae81090d-3458-4f6b-b0fa-82f2d2010fa8 · outbound

This paper cites Thermodynamically consistent physics-informed neural networks for hyperbolic systems.

Physics-informed deep learning for infectious disease forecasting Thermodynamically consistent physics-informed neural networks for hyperbolic systems

Reference 52

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no resolver link, observed 2026-08-10T20:12:24.028259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:12:24.028259Z digest=sha256:1199432053ca3dc890eb2dbf32cce224db8edd2dd3f9bd6860766695a485ef32

Observation 0a36c28a-1025-44b3-bdf6-4ca3bbd502de · outbound

This paper cites A physics-informed op- erator regression framework for extracting data-driven continuum models.

Physics-informed deep learning for infectious disease forecasting A physics-informed op- erator regression framework for extracting data-driven continuum models

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.484075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.033978Z digest=sha256:6ed754f544e17db623454ca212cdb93e4bd1dc16a34714392997ed0de32e19dc

Observation 55473a14-c3f2-4707-a535-83f4293bfaab · outbound

This paper cites Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble.

Physics-informed deep learning for infectious disease forecasting Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-10T20:12:24.039618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:12:24.039618Z digest=sha256:39591d89cf2f9880eda2c468cf709f56a5187926bb99d5c7548961491856682a

Observation 26fff2ed-140e-4202-badc-3148f9b55e84 · outbound

This paper cites Evaluating epidemic forecasts in an interval format.

Physics-informed deep learning for infectious disease forecasting Evaluating epidemic forecasts in an interval format

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.466312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.046245Z digest=sha256:1c278774b11625b68548d270708ab3bb020bfb77a1949acd6d3e251636821fa9

Observation 3da30f93-1720-4601-99c4-a0b89d1c3a68 · outbound

This paper cites Ensemble forecasts of coronavirus disease 2019 (covid-19) in the us.

Physics-informed deep learning for infectious disease forecasting Ensemble forecasts of coronavirus disease 2019 (covid-19) in the us

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.444927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.051677Z digest=sha256:d31e9c35349a1804d255519c93a87eaf0bb5da722b4146755173148320e0fb24

Observation 3b25eaa8-8bb6-4a21-ab66-63b80e8e0521 · outbound

This paper cites An interactive web-based dashboard to track covid-19 in real time.

Physics-informed deep learning for infectious disease forecasting An interactive web-based dashboard to track covid-19 in real time

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.426327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.057029Z digest=sha256:ebef9e4b2d3781bb8cef8e9739f24ab8a54f02694eddede2935d8f58af53e397

Observation 72513860-7146-4de5-bb33-eb9cbbf3d915 · outbound

This paper cites An open reposi- tory of real-time covid-19 indicators.

Physics-informed deep learning for infectious disease forecasting An open reposi- tory of real-time covid-19 indicators

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.409223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.062827Z digest=sha256:43284f96958397276640f8f7627fa53a7fcdcdec93c11205209ea403896819ac

Observation 87a05e2b-15dc-4dcc-b4a0-3288bbba1863 · outbound

This paper cites Google covid-19 community mobility reports.

Physics-informed deep learning for infectious disease forecasting Google covid-19 community mobility reports

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.392843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.068808Z digest=sha256:e7ce554c7408c8d2a3961041ad1ac71e671dc1e55ac70b5e0a6ae2714c098b9b

Observation 9825b861-f701-4dd8-96c2-2466299a24c8 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Physics-informed deep learning for infectious disease forecasting Adam: A Method for Stochastic Optimization

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-10T20:12:24.075249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:12:24.075249Z digest=sha256:9032a68258823641f0bd440630c6f2ac698b7edd9590d3285ca9bf7025228708

Observation 84aec14a-8ab5-49bc-aa19-7113e86c76b5 · outbound

This paper cites Another look at measures of forecast accuracy.

Physics-informed deep learning for infectious disease forecasting Another look at measures of forecast accuracy

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.375554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.083046Z digest=sha256:21c3270068bd6a5265bd12ea8fdaf2e555f9f21903a726260ea6fd31bd1e9e26

Observation 4d201c23-6ea3-4ff0-a3fc-e12e95be50cc · outbound

This paper cites Recurrent neural networks.

Physics-informed deep learning for infectious disease forecasting Recurrent neural networks

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.357766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.088654Z digest=sha256:c54022436c29bce515f98715f25b010adad942cadbcbe9299ae2880254986b2b

Observation 9b7dccf8-ae6a-499a-9c5f-378820b764fd · outbound

This paper cites Long short-term memory.

Physics-informed deep learning for infectious disease forecasting Long short-term memory

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.340827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.095010Z digest=sha256:97cd7560b6f1a8f08ab21f5578455c9a3ee07c8623bf6e925df3f34cb330db6d

Observation 56795450-7252-46b8-8489-ff0b9db31ff7 · outbound

This paper cites Gate-variants of gated recurrent unit (gru) neural networks.

Physics-informed deep learning for infectious disease forecasting Gate-variants of gated recurrent unit (gru) neural networks

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.322010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.100002Z digest=sha256:5898f0c1d138fcc83c0dfd0f567247c28e5355c5cfdc81278de2062bc146167a

Observation acdbb1c4-14e1-4284-85fa-b6a8b69a06d1 · outbound

This paper cites Attention mechanism, transformers, bert, and gpt: tuto- rial and survey.

Physics-informed deep learning for infectious disease forecasting Attention mechanism, transformers, bert, and gpt: tuto- rial and survey

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.301964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.105331Z digest=sha256:e47d221a2cb57f316f6723b79019971ccdfd9eb2307a28d5f7d398cca01fd341

Observation e21b55ad-b6eb-4189-9c9c-1e26f0633cd7 · outbound

This paper cites A physics-informed neural network to model covid- 19 infection and hospitalization scenarios.

Physics-informed deep learning for infectious disease forecasting A physics-informed neural network to model covid- 19 infection and hospitalization scenarios

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.283491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.110387Z digest=sha256:84a832b8fb984d9928944edccdcce5e84d125feca4a576a3fbbea180ce05a6de

Observation dcc4abfd-f286-48ba-a291-2b1dcf880109 · outbound

This paper cites Einns: epidemiologically-informed neural networks.

Physics-informed deep learning for infectious disease forecasting Einns: epidemiologically-informed neural networks

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.264268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.116958Z digest=sha256:6469fd7add057fa44225195d5a35efbb4d6c0e6d52e9e1833721449da65caf13

Observation 6bf6d75e-0b45-4139-9e80-8d456135e4f6 · outbound

This paper cites Physics-informed machine learning for the covid-19 pandemic: Adherence to social distancing and short-term predictions for eight coun- tries.

Physics-informed deep learning for infectious disease forecasting Physics-informed machine learning for the covid-19 pandemic: Adherence to social distancing and short-term predictions for eight coun- tries

Reference 68

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verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.245249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.122591Z digest=sha256:76cd8617e186562e3f163b252c20c2c7b73470b7324ddc5623d9eb5cbc6da7e4

Observation 5c506693-a4c4-4e35-8123-40b7fc3d46d0 · outbound

This paper cites Neural networks for endemic measles dynamics: compar- ative analysis and integration with mechanistic models.

Physics-informed deep learning for infectious disease forecasting Neural networks for endemic measles dynamics: compar- ative analysis and integration with mechanistic models

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:12:24.226699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T20:12:24.128648Z digest=sha256:e1b2f080a95d8ad2519dd41a48a51325369236b9c14cb3d5ffa832d32318d78d

Pith citing papers

Observation a149a51e-d0e5-4c37-a507-f48320ceb681 · inbound

Using Machine Learning to Enhance Hyperparameter Optimization in Pandemic Modeling: Case study of COVID-19 Dynamics in Ghana cites this paper.

Using Machine Learning to Enhance Hyperparameter Optimization in Pandemic Modeling: Case study of COVID-19 Dynamics in Ghana Physics-informed deep learning for infectious disease forecasting

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-06-28T15:52:21.846817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-28T15:42:58.769940Z digest=sha256:f0614cd37c238d92a68d6e63891c5b1d9f1f767bbe49e890c2476f3fc473aea8