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covid19 [2020/05/21 17:24]
nigam |
covid19 [2020/12/10 11:25] (current)
nigam |
| There are three efforts we are working on. | There are three efforts we are working on. |
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| The **first** effort is to continuously profile the patients screened for SARS-CoV-2 in our health system. Using anonymized data, we watch trends in presenting symptoms of patients, test positivity rates, the age distribution of positive cases and hospitalization rates as well as monitor length of stay. For example, we are analyzing medical notes describing symptoms of patients screened and tested at Stanford Medicine to see if combinations of symptoms, duration of disease, travel history etc can anticipate who will require admission or eventual ICU care. This work as yielded a few insights already such as: | The **first** effort is to continuously profile the patients screened for SARS-CoV-2 in our health system. Using anonymized data, we watch trends in presenting symptoms of patients, test positivity rates, the age distribution of positive cases and hospitalization rates as well as monitor length of stay. This work as yielded a few insights already such as: |
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| * [[https://jamanetwork.com/journals/jama/fullarticle/2764787|High co-infection rates in COVID-19]], //in JAMA// | - [[https://medium.com/@nigam/an-ehr-derived-summary-of-the-presenting-symptoms-of-patients-screened-for-sars-cov-2-910ceb1b22b9|An EHR derived summary of presenting symptoms of patients screened for SARS-CoV-2]], data at [[http://tinyurl.com/symptom-profile|http://tinyurl.com/symptom-profile]] |
| * [[https://www.medrxiv.org/content/10.1101/2020.04.19.20072017v2|Counts of hospitalized patients are a better metric for health system capacity planning for a reopening]], //accepted in JAMIA// | - [[https://jamanetwork.com/journals/jama/fullarticle/2764787|High co-infection rates in COVID-19]], //in JAMA// |
| * [[https://docs.google.com/document/d/1b4QCweo6EmLYqsVSdnaTW0AJMmoPM-of3cO5yTpIOxg/edit#heading=h.gjdgxs|Estimating the feasibility of symptom based screening of COVID19]], //under review at npj Digital Medicine // | - [[https://academic.oup.com/jamia/article/27/7/1026/5858301|Counts of hospitalized patients are a better metric for health system capacity planning for a reopening]], //in JAMIA// |
| * [[https://www.medrxiv.org/content/10.1101/2020.05.03.20089151v1 | frequency of discordant SARS-CoV-2 test results among initially negative patients]] // under review at Clinical Infectious Diseases // | - [[https://www.sciencedirect.com/science/article/pii/S1386653220302195|Persistent detection of SARS-CoV-2 RNA in patients and healthcare workers with COVID-19]], // in the Journal of Clinical Virology// |
| * A predictive tool for identification of SARS-CoV-2 PCR-negative patients using routine test results, //under review at Nature Medicine// | - [[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274223/|Occurrence and timing of subsequent SARS-CoV-2 RT-PCR positivity among initially negative patients]]// in Clinical Infectious Diseases // |
| * [[https://medium.com/@nigam/an-ehr-derived-summary-of-the-presenting-symptoms-of-patients-screened-for-sars-cov-2-910ceb1b22b9|An EHR derived summary of presenting symptoms of patients screened for SARS-CoV-2]], data at [[http://tinyurl.com/symptom-profile|http://tinyurl.com/symptom-profile]] | - [[https://www.nature.com/articles/s41746-020-0300-0|Estimating the feasibility of symptom based screening of COVID19]],// in npj Digital Medicine // |
| | - [[https://doi.org/10.1016/j.jcv.2020.104502|A predictive tool for identification of SARS-CoV-2 PCR-negative patients using routine test results]], // in the Journal of Clinical Virology// |
| | - [[https://immunology.sciencemag.org/content/5/54/eabe0240 | Defining the Features and Duration of Antibody Responses to SARS-CoV- 2 Infection ...]] // Science Immunology // |
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| The **second** effort is to help other faculty in creating better models of the COVID-19 pandemic. Existing predictions of the pandemic are highly uncertain due to lack of accurate input data. We are trying to help, and to some degree coordinate, the efforts of multiple scientists to obtain more accurate, higher-resolution estimates of the parameters that feed into computer models of the COVID-19 pandemic. | The **second** effort is to help others in creating better models of the COVID-19 pandemic. Existing predictions of the pandemic are highly uncertain due to lack of accurate input data. We are trying to help in the efforts of multiple scientists to obtain more accurate estimates of the parameters that feed into computer models of the COVID-19 pandemic. |
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| * [[https://medium.com/@nigam/there-are-enough-models-we-need-accurate-inputs-5a20aef22f01|There are enough models, we need accurate inputs]] | - [[https://medium.com/@nigam/there-are-enough-models-we-need-accurate-inputs-5a20aef22f01|There are enough models, we need accurate inputs]] |
| * [[https://www.medrxiv.org/content/10.1101/2020.03.24.20043067v1|Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County]] | - [[https://www.medrxiv.org/content/10.1101/2020.03.24.20043067v1|Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County]] |
| * [[https://www.medrxiv.org/content/10.1101/2020.03.26.20044842v3|A model to forecast regional demand for COVID-19 related hospital beds]] | - [[https://www.medrxiv.org/content/10.1101/2020.03.26.20044842v3|A model to forecast regional demand for COVID-19 related hospital beds]] |
| * [[https://thehill.com/opinion/white-house/492025-poor-state-reporting-hampers-pandemic-fight|Poor state reporting hampers pandemic fight]], //in The Hill// | - [[https://thehill.com/opinion/white-house/492025-poor-state-reporting-hampers-pandemic-fight|Poor state reporting hampers pandemic fight]], //in The Hill// |
| * [[https://www.brookings.edu/techstream/how-data-science-can-ease-the-covid-19-pandemic|How data science can ease the COVID-19 pandemic]], //in the Brookings Institute's TechStream// | - [[https://www.brookings.edu/techstream/how-data-science-can-ease-the-covid-19-pandemic|How data science can ease the COVID-19 pandemic]], //in the Brookings Institute's TechStream// |
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| The **third** effort is about answering specific questions by participating in network studies on COVID-19 in the OHDSI collaborative. | The **third** effort is about answering specific questions by participating in network studies on COVID-19 in the OHDSI collaborative. |
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| * An international characterisation of patients hospitalised with COVID-19 and a comparison with those hospitalised with influenza. [[https://www.medrxiv.org/content/10.1101/2020.04.22.20074336v1|Pre-print]], [[https://github.com/ohdsi-studies/Covid19HospitalizationCharacterization| Protocol]] and, [[https://data.ohdsi.org/Covid19CharacterizationHospitalization/| Shiny App]] | - [[https://www.nature.com/articles/s41467-020-18849-z|Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study]], [[https://github.com/ohdsi-studies/Covid19HospitalizationCharacterization| Protocol]] and, [[https://data.ohdsi.org/Covid19CharacterizationHospitalization/| Shiny App]] |
| * A networked study investigating the association of ACEis and ARBSs on COVID-19 incidence and complications. [[https://github.com/ohdsi-studies/Covid19EstimationRasInhibitors|Protcol]] | - Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS). [[https://github.com/ohdsi-studies/Covid19CharacterizationCharybdis|Study Package]] and, [[https://data.ohdsi.org/Covid19CharacterizationCharybdis/| Shiny App]] |
| | - A networked study investigating the association of ACEis and ARBSs on COVID-19 incidence and complications. [[https://github.com/ohdsi-studies/Covid19EstimationRasInhibitors|Protcol]] |
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| | {{youtube>4WRYTYfixKs?small&start=73 | Modeling for COVID-19}} |
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| | 5 min clip on how we need to improve the quality of the inputs to our COVID-19 models. |
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| | {{youtube>lxFBknzm88s?small | Data Science Response to a Pandemic}} |
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| | A talk about Stanford's data science efforts at COVID-19 and AI: A Virtual Conference by Stanford HAI. |
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