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ActiveFromDeath4.py
ActiveFromDeath4.py
ActiveFromDeathByState.py
ActiveFromDeathByState.py
CountryAndStatePredictions.java
CountryAndStatePredictions.java
Covid National Data Updated.csv
Covid National Data Updated.csv
CovidByStateIndia.java
CovidByStateIndia.java
Data
Data
IndiaData_CFR.xlsx
IndiaData_CFR.xlsx
Links.rtf
Links.rtf
Modelling India Cases after other Countries.ipynb
Modelling India Cases after other Countries.ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
README.md
README.md
Using Case Progression in Italy to Predict India's Curve.ipynb
Using Case Progression in Italy to Predict India's Curve.ipynb

Repository files navigation

Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions

Numerous models have tried to predict the spread of COVID-19. Manyinvolve myriad assumptions and parameters which cannot be reliably calculated undercurrent conditions. We describe machine-learning and curve-fitting based models usingfewer assumptions and readily available data

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

For running the python files, you will need to install pandas, numpy, SciPy and sklearn and seaborn libraries. You may install them via an Anacondo distribution or simply use the following in the project directory (preferrably in a virtualenv)

pip install <library name>

Files

  • ActiveFromDeath.py, ActiveFromDeathByState.py - Python files, for curve-fitting and predicting the actual number of COVID-19 infections. To run them on updated data and parameters, change the following section in the code:
# SET THESE PARAMETERS ACCORDINGLYifr=0.41offset=23min_death_fact=1.5max_death_fact=4death_fact= (max_death_fact+min_death_fact)/2data=pd.read_csv("owid-covid-data.csv") # Data Course: OWIDindia_combined=data.loc[data["iso_code"]=="IND",("date","total_cases","total_deaths")] # For any other country, replace "IND" with respective country code
  • CovidByStateIndia.java - Java file for cumulative prediction of cases in Indian districts. To update parameters set the parameters on lines 313 and 314
doublex = (cases_and_projections.size() + i - 70) / (4.65 * x_not);
y[i] = Math.round (1180 * mean * sigmoid(x)) - 5100;
  • CountryAndStatePredictions.java - Java file for prediction of cases in US States; European countries like the UK, France, and Germany. To update parameters set the parameters on lines 95 and 96
doublex = (n + 0.5 * i - 70) / (x_not);
y[i] = Math.round (9 * mean * sigmoid(x)) + 94400;

Miscellaneous Links

National Prediction:https://www.overleaf.com/project/5ea933d883635f0001df191a All training data from covid19india.org and verification data from worldometer.

Google Sheets (25 US States) May 25 onwards: https://docs.google.com/spreadsheets/d/19kGvj9H35VDPCbgKrWeobHbZjOqNprcmMWCL_wjtr4E/edit?usp=sharing

Google Sheets (3 European Countries) July 10 onwards: https://docs.google.com/spreadsheets/d/1miyKh4MWrgUZM75j3y1bQpSuXIaRv_SXYi2Fc_ZSQ8Q/edit?usp=sharing

Google Sheets made to track CFR fluctuations (compiled till May 10th) : https://docs.google.com/spreadsheets/d/1OijyFvOldjteY_3OFgU1Rr_azDGOhm0Zk2rjbDoKvWE/edit?usp=sharing

A simple back-calculation example : https://www.telegraphindia.com/india/coronavirus-outbreak-what-the-numbers-reveal/cid/1771525#.XrYWo77Qbsg.facebook

All data from https://ourworldindata.org/coronavirus

An article very similar to our CFR paper : https://science.thewire.in/the-sciences/covid-19-pandemic-case-fatality-rate-calculation/

Deaths using Active Cases is surprisingly related using a quadratic with a Rsq of 0.998 ...

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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ActiveFromDeath4.py
ActiveFromDeath4.py
ActiveFromDeathByState.py
ActiveFromDeathByState.py
CountryAndStatePredictions.java
CountryAndStatePredictions.java
Covid National Data Updated.csv
Covid National Data Updated.csv
CovidByStateIndia.java
CovidByStateIndia.java
Data
Data
IndiaData_CFR.xlsx
IndiaData_CFR.xlsx
Links.rtf
Links.rtf
Modelling India Cases after other Countries.ipynb
Modelling India Cases after other Countries.ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
README.md
README.md
Using Case Progression in Italy to Predict India's Curve.ipynb
Using Case Progression in Italy to Predict India's Curve.ipynb
Repository files navigation
Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions
Numerous models have tried to predict the spread of COVID-19. Manyinvolve myriad assumptions and parameters which cannot be reliably calculated undercurrent conditions. We describe machine-learning and curve-fitting based models usingfewer assumptions and readily available data
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites
For running the python files, you will need to install pandas, numpy, SciPy and sklearn and seaborn libraries. You may install them via an Anacondo distribution or simply use the following in the project directory (preferrably in a virtualenv)
pip install <library name>

Files
  • ActiveFromDeath.py, ActiveFromDeathByState.py - Python files, for curve-fitting and predicting the actual number of COVID-19 infections. To run them on updated data and parameters, change the following section in the code:
# SET THESE PARAMETERS ACCORDINGLYifr=0.41offset=23min_death_fact=1.5max_death_fact=4death_fact= (max_death_fact+min_death_fact)/2data=pd.read_csv("owid-covid-data.csv") # Data Course: OWIDindia_combined=data.loc[data["iso_code"]=="IND",("date","total_cases","total_deaths")] # For any other country, replace "IND" with respective country code
  • CovidByStateIndia.java - Java file for cumulative prediction of cases in Indian districts. To update parameters set the parameters on lines 313 and 314
doublex = (cases_and_projections.size() + i - 70) / (4.65 * x_not);
y[i] = Math.round (1180 * mean * sigmoid(x)) - 5100;
  • CountryAndStatePredictions.java - Java file for prediction of cases in US States; European countries like the UK, France, and Germany. To update parameters set the parameters on lines 95 and 96
doublex = (n + 0.5 * i - 70) / (x_not);
y[i] = Math.round (9 * mean * sigmoid(x)) + 94400;
Miscellaneous Links
National Prediction:https://www.overleaf.com/project/5ea933d883635f0001df191a
All training data from covid19india.org and verification data from worldometer.
Google Sheets (25 US States) May 25 onwards: https://docs.google.com/spreadsheets/d/19kGvj9H35VDPCbgKrWeobHbZjOqNprcmMWCL_wjtr4E/edit?usp=sharing
Google Sheets (3 European Countries) July 10 onwards: https://docs.google.com/spreadsheets/d/1miyKh4MWrgUZM75j3y1bQpSuXIaRv_SXYi2Fc_ZSQ8Q/edit?usp=sharing
Google Sheets made to track CFR fluctuations (compiled till May 10th) : https://docs.google.com/spreadsheets/d/1OijyFvOldjteY_3OFgU1Rr_azDGOhm0Zk2rjbDoKvWE/edit?usp=sharing
A simple back-calculation example : https://www.telegraphindia.com/india/coronavirus-outbreak-what-the-numbers-reveal/cid/1771525#.XrYWo77Qbsg.facebook
All data from https://ourworldindata.org/coronavirus
An article very similar to our CFR paper : https://science.thewire.in/the-sciences/covid-19-pandemic-case-fatality-rate-calculation/
Deaths using Active Cases is surprisingly related using a quadratic with a Rsq of 0.998 ...
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
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var __m = "*";
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ActiveFromDeath4.py
ActiveFromDeath4.py
ActiveFromDeathByState.py
ActiveFromDeathByState.py
CountryAndStatePredictions.java
CountryAndStatePredictions.java
Covid National Data Updated.csv
Covid National Data Updated.csv
CovidByStateIndia.java
CovidByStateIndia.java
Data
Data
IndiaData_CFR.xlsx
IndiaData_CFR.xlsx
Links.rtf
Links.rtf
Modelling India Cases after other Countries.ipynb
Modelling India Cases after other Countries.ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
README.md
README.md
Using Case Progression in Italy to Predict India's Curve.ipynb
Using Case Progression in Italy to Predict India's Curve.ipynb
Repository files navigation
Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions
Numerous models have tried to predict the spread of COVID-19. Manyinvolve myriad assumptions and parameters which cannot be reliably calculated undercurrent conditions. We describe machine-learning and curve-fitting based models usingfewer assumptions and readily available data
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites
For running the python files, you will need to install pandas, numpy, SciPy and sklearn and seaborn libraries. You may install them via an Anacondo distribution or simply use the following in the project directory (preferrably in a virtualenv)
pip install <library name>

Files
  • ActiveFromDeath.py, ActiveFromDeathByState.py - Python files, for curve-fitting and predicting the actual number of COVID-19 infections. To run them on updated data and parameters, change the following section in the code:
# SET THESE PARAMETERS ACCORDINGLYifr=0.41offset=23min_death_fact=1.5max_death_fact=4death_fact= (max_death_fact+min_death_fact)/2data=pd.read_csv("owid-covid-data.csv") # Data Course: OWIDindia_combined=data.loc[data["iso_code"]=="IND",("date","total_cases","total_deaths")] # For any other country, replace "IND" with respective country code
  • CovidByStateIndia.java - Java file for cumulative prediction of cases in Indian districts. To update parameters set the parameters on lines 313 and 314
doublex = (cases_and_projections.size() + i - 70) / (4.65 * x_not);
y[i] = Math.round (1180 * mean * sigmoid(x)) - 5100;
  • CountryAndStatePredictions.java - Java file for prediction of cases in US States; European countries like the UK, France, and Germany. To update parameters set the parameters on lines 95 and 96
doublex = (n + 0.5 * i - 70) / (x_not);
y[i] = Math.round (9 * mean * sigmoid(x)) + 94400;
Miscellaneous Links
National Prediction:https://www.overleaf.com/project/5ea933d883635f0001df191a
All training data from covid19india.org and verification data from worldometer.
Google Sheets (25 US States) May 25 onwards: https://docs.google.com/spreadsheets/d/19kGvj9H35VDPCbgKrWeobHbZjOqNprcmMWCL_wjtr4E/edit?usp=sharing
Google Sheets (3 European Countries) July 10 onwards: https://docs.google.com/spreadsheets/d/1miyKh4MWrgUZM75j3y1bQpSuXIaRv_SXYi2Fc_ZSQ8Q/edit?usp=sharing
Google Sheets made to track CFR fluctuations (compiled till May 10th) : https://docs.google.com/spreadsheets/d/1OijyFvOldjteY_3OFgU1Rr_azDGOhm0Zk2rjbDoKvWE/edit?usp=sharing
A simple back-calculation example : https://www.telegraphindia.com/india/coronavirus-outbreak-what-the-numbers-reveal/cid/1771525#.XrYWo77Qbsg.facebook
All data from https://ourworldindata.org/coronavirus
An article very similar to our CFR paper : https://science.thewire.in/the-sciences/covid-19-pandemic-case-fatality-rate-calculation/
Deaths using Active Cases is surprisingly related using a quadratic with a Rsq of 0.998 ...
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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ActiveFromDeath4.py
ActiveFromDeath4.py
ActiveFromDeathByState.py
ActiveFromDeathByState.py
CountryAndStatePredictions.java
CountryAndStatePredictions.java
Covid National Data Updated.csv
Covid National Data Updated.csv
CovidByStateIndia.java
CovidByStateIndia.java
Data
Data
IndiaData_CFR.xlsx
IndiaData_CFR.xlsx
Links.rtf
Links.rtf
Modelling India Cases after other Countries.ipynb
Modelling India Cases after other Countries.ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
README.md
README.md
Using Case Progression in Italy to Predict India's Curve.ipynb
Using Case Progression in Italy to Predict India's Curve.ipynb
Repository files navigation
Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions
Numerous models have tried to predict the spread of COVID-19. Manyinvolve myriad assumptions and parameters which cannot be reliably calculated undercurrent conditions. We describe machine-learning and curve-fitting based models usingfewer assumptions and readily available data
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites
For running the python files, you will need to install pandas, numpy, SciPy and sklearn and seaborn libraries. You may install them via an Anacondo distribution or simply use the following in the project directory (preferrably in a virtualenv)
pip install <library name>

Files
  • ActiveFromDeath.py, ActiveFromDeathByState.py - Python files, for curve-fitting and predicting the actual number of COVID-19 infections. To run them on updated data and parameters, change the following section in the code:
# SET THESE PARAMETERS ACCORDINGLYifr=0.41offset=23min_death_fact=1.5max_death_fact=4death_fact= (max_death_fact+min_death_fact)/2data=pd.read_csv("owid-covid-data.csv") # Data Course: OWIDindia_combined=data.loc[data["iso_code"]=="IND",("date","total_cases","total_deaths")] # For any other country, replace "IND" with respective country code
  • CovidByStateIndia.java - Java file for cumulative prediction of cases in Indian districts. To update parameters set the parameters on lines 313 and 314
doublex = (cases_and_projections.size() + i - 70) / (4.65 * x_not);
y[i] = Math.round (1180 * mean * sigmoid(x)) - 5100;
  • CountryAndStatePredictions.java - Java file for prediction of cases in US States; European countries like the UK, France, and Germany. To update parameters set the parameters on lines 95 and 96
doublex = (n + 0.5 * i - 70) / (x_not);
y[i] = Math.round (9 * mean * sigmoid(x)) + 94400;
Miscellaneous Links
National Prediction:https://www.overleaf.com/project/5ea933d883635f0001df191a
All training data from covid19india.org and verification data from worldometer.
Google Sheets (25 US States) May 25 onwards: https://docs.google.com/spreadsheets/d/19kGvj9H35VDPCbgKrWeobHbZjOqNprcmMWCL_wjtr4E/edit?usp=sharing
Google Sheets (3 European Countries) July 10 onwards: https://docs.google.com/spreadsheets/d/1miyKh4MWrgUZM75j3y1bQpSuXIaRv_SXYi2Fc_ZSQ8Q/edit?usp=sharing
Google Sheets made to track CFR fluctuations (compiled till May 10th) : https://docs.google.com/spreadsheets/d/1OijyFvOldjteY_3OFgU1Rr_azDGOhm0Zk2rjbDoKvWE/edit?usp=sharing
A simple back-calculation example : https://www.telegraphindia.com/india/coronavirus-outbreak-what-the-numbers-reveal/cid/1771525#.XrYWo77Qbsg.facebook
All data from https://ourworldindata.org/coronavirus
An article very similar to our CFR paper : https://science.thewire.in/the-sciences/covid-19-pandemic-case-fatality-rate-calculation/
Deaths using Active Cases is surprisingly related using a quadratic with a Rsq of 0.998 ...
About
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Readme
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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ActiveFromDeath4.py
ActiveFromDeath4.py
ActiveFromDeathByState.py
ActiveFromDeathByState.py
CountryAndStatePredictions.java
CountryAndStatePredictions.java
Covid National Data Updated.csv
Covid National Data Updated.csv
CovidByStateIndia.java
CovidByStateIndia.java
Data
Data
IndiaData_CFR.xlsx
IndiaData_CFR.xlsx
Links.rtf
Links.rtf
Modelling India Cases after other Countries.ipynb
Modelling India Cases after other Countries.ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
README.md
README.md
Using Case Progression in Italy to Predict India's Curve.ipynb
Using Case Progression in Italy to Predict India's Curve.ipynb
Repository files navigation
Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions
Numerous models have tried to predict the spread of COVID-19. Manyinvolve myriad assumptions and parameters which cannot be reliably calculated undercurrent conditions. We describe machine-learning and curve-fitting based models usingfewer assumptions and readily available data
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites
For running the python files, you will need to install pandas, numpy, SciPy and sklearn and seaborn libraries. You may install them via an Anacondo distribution or simply use the following in the project directory (preferrably in a virtualenv)
pip install <library name>

Files
  • ActiveFromDeath.py, ActiveFromDeathByState.py - Python files, for curve-fitting and predicting the actual number of COVID-19 infections. To run them on updated data and parameters, change the following section in the code:
# SET THESE PARAMETERS ACCORDINGLYifr=0.41offset=23min_death_fact=1.5max_death_fact=4death_fact= (max_death_fact+min_death_fact)/2data=pd.read_csv("owid-covid-data.csv") # Data Course: OWIDindia_combined=data.loc[data["iso_code"]=="IND",("date","total_cases","total_deaths")] # For any other country, replace "IND" with respective country code
  • CovidByStateIndia.java - Java file for cumulative prediction of cases in Indian districts. To update parameters set the parameters on lines 313 and 314
doublex = (cases_and_projections.size() + i - 70) / (4.65 * x_not);
y[i] = Math.round (1180 * mean * sigmoid(x)) - 5100;
  • CountryAndStatePredictions.java - Java file for prediction of cases in US States; European countries like the UK, France, and Germany. To update parameters set the parameters on lines 95 and 96
doublex = (n + 0.5 * i - 70) / (x_not);
y[i] = Math.round (9 * mean * sigmoid(x)) + 94400;
Miscellaneous Links
National Prediction:https://www.overleaf.com/project/5ea933d883635f0001df191a
All training data from covid19india.org and verification data from worldometer.
Google Sheets (25 US States) May 25 onwards: https://docs.google.com/spreadsheets/d/19kGvj9H35VDPCbgKrWeobHbZjOqNprcmMWCL_wjtr4E/edit?usp=sharing
Google Sheets (3 European Countries) July 10 onwards: https://docs.google.com/spreadsheets/d/1miyKh4MWrgUZM75j3y1bQpSuXIaRv_SXYi2Fc_ZSQ8Q/edit?usp=sharing
Google Sheets made to track CFR fluctuations (compiled till May 10th) : https://docs.google.com/spreadsheets/d/1OijyFvOldjteY_3OFgU1Rr_azDGOhm0Zk2rjbDoKvWE/edit?usp=sharing
A simple back-calculation example : https://www.telegraphindia.com/india/coronavirus-outbreak-what-the-numbers-reveal/cid/1771525#.XrYWo77Qbsg.facebook
All data from https://ourworldindata.org/coronavirus
An article very similar to our CFR paper : https://science.thewire.in/the-sciences/covid-19-pandemic-case-fatality-rate-calculation/
Deaths using Active Cases is surprisingly related using a quadratic with a Rsq of 0.998 ...
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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ActiveFromDeath4.py
ActiveFromDeath4.py
ActiveFromDeathByState.py
ActiveFromDeathByState.py
CountryAndStatePredictions.java
CountryAndStatePredictions.java
Covid National Data Updated.csv
Covid National Data Updated.csv
CovidByStateIndia.java
CovidByStateIndia.java
Data
Data
IndiaData_CFR.xlsx
IndiaData_CFR.xlsx
Links.rtf
Links.rtf
Modelling India Cases after other Countries.ipynb
Modelling India Cases after other Countries.ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
README.md
README.md
Using Case Progression in Italy to Predict India's Curve.ipynb
Using Case Progression in Italy to Predict India's Curve.ipynb
Repository files navigation
Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions
Numerous models have tried to predict the spread of COVID-19. Manyinvolve myriad assumptions and parameters which cannot be reliably calculated undercurrent conditions. We describe machine-learning and curve-fitting based models usingfewer assumptions and readily available data
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites
For running the python files, you will need to install pandas, numpy, SciPy and sklearn and seaborn libraries. You may install them via an Anacondo distribution or simply use the following in the project directory (preferrably in a virtualenv)
pip install <library name>

Files
  • ActiveFromDeath.py, ActiveFromDeathByState.py - Python files, for curve-fitting and predicting the actual number of COVID-19 infections. To run them on updated data and parameters, change the following section in the code:
# SET THESE PARAMETERS ACCORDINGLYifr=0.41offset=23min_death_fact=1.5max_death_fact=4death_fact= (max_death_fact+min_death_fact)/2data=pd.read_csv("owid-covid-data.csv") # Data Course: OWIDindia_combined=data.loc[data["iso_code"]=="IND",("date","total_cases","total_deaths")] # For any other country, replace "IND" with respective country code
  • CovidByStateIndia.java - Java file for cumulative prediction of cases in Indian districts. To update parameters set the parameters on lines 313 and 314
doublex = (cases_and_projections.size() + i - 70) / (4.65 * x_not);
y[i] = Math.round (1180 * mean * sigmoid(x)) - 5100;
  • CountryAndStatePredictions.java - Java file for prediction of cases in US States; European countries like the UK, France, and Germany. To update parameters set the parameters on lines 95 and 96
doublex = (n + 0.5 * i - 70) / (x_not);
y[i] = Math.round (9 * mean * sigmoid(x)) + 94400;
Miscellaneous Links
National Prediction:https://www.overleaf.com/project/5ea933d883635f0001df191a
All training data from covid19india.org and verification data from worldometer.
Google Sheets (25 US States) May 25 onwards: https://docs.google.com/spreadsheets/d/19kGvj9H35VDPCbgKrWeobHbZjOqNprcmMWCL_wjtr4E/edit?usp=sharing
Google Sheets (3 European Countries) July 10 onwards: https://docs.google.com/spreadsheets/d/1miyKh4MWrgUZM75j3y1bQpSuXIaRv_SXYi2Fc_ZSQ8Q/edit?usp=sharing
Google Sheets made to track CFR fluctuations (compiled till May 10th) : https://docs.google.com/spreadsheets/d/1OijyFvOldjteY_3OFgU1Rr_azDGOhm0Zk2rjbDoKvWE/edit?usp=sharing
A simple back-calculation example : https://www.telegraphindia.com/india/coronavirus-outbreak-what-the-numbers-reveal/cid/1771525#.XrYWo77Qbsg.facebook
All data from https://ourworldindata.org/coronavirus
An article very similar to our CFR paper : https://science.thewire.in/the-sciences/covid-19-pandemic-case-fatality-rate-calculation/
Deaths using Active Cases is surprisingly related using a quadratic with a Rsq of 0.998 ...
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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ActiveFromDeath4.py
ActiveFromDeath4.py
ActiveFromDeathByState.py
ActiveFromDeathByState.py
CountryAndStatePredictions.java
CountryAndStatePredictions.java
Covid National Data Updated.csv
Covid National Data Updated.csv
CovidByStateIndia.java
CovidByStateIndia.java
Data
Data
IndiaData_CFR.xlsx
IndiaData_CFR.xlsx
Links.rtf
Links.rtf
Modelling India Cases after other Countries.ipynb
Modelling India Cases after other Countries.ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
README.md
README.md
Using Case Progression in Italy to Predict India's Curve.ipynb
Using Case Progression in Italy to Predict India's Curve.ipynb
Repository files navigation
Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions
Numerous models have tried to predict the spread of COVID-19. Manyinvolve myriad assumptions and parameters which cannot be reliably calculated undercurrent conditions. We describe machine-learning and curve-fitting based models usingfewer assumptions and readily available data
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites
For running the python files, you will need to install pandas, numpy, SciPy and sklearn and seaborn libraries. You may install them via an Anacondo distribution or simply use the following in the project directory (preferrably in a virtualenv)
pip install <library name>

Files
  • ActiveFromDeath.py, ActiveFromDeathByState.py - Python files, for curve-fitting and predicting the actual number of COVID-19 infections. To run them on updated data and parameters, change the following section in the code:
# SET THESE PARAMETERS ACCORDINGLYifr=0.41offset=23min_death_fact=1.5max_death_fact=4death_fact= (max_death_fact+min_death_fact)/2data=pd.read_csv("owid-covid-data.csv") # Data Course: OWIDindia_combined=data.loc[data["iso_code"]=="IND",("date","total_cases","total_deaths")] # For any other country, replace "IND" with respective country code
  • CovidByStateIndia.java - Java file for cumulative prediction of cases in Indian districts. To update parameters set the parameters on lines 313 and 314
doublex = (cases_and_projections.size() + i - 70) / (4.65 * x_not);
y[i] = Math.round (1180 * mean * sigmoid(x)) - 5100;
  • CountryAndStatePredictions.java - Java file for prediction of cases in US States; European countries like the UK, France, and Germany. To update parameters set the parameters on lines 95 and 96
doublex = (n + 0.5 * i - 70) / (x_not);
y[i] = Math.round (9 * mean * sigmoid(x)) + 94400;
Miscellaneous Links
National Prediction:https://www.overleaf.com/project/5ea933d883635f0001df191a
All training data from covid19india.org and verification data from worldometer.
Google Sheets (25 US States) May 25 onwards: https://docs.google.com/spreadsheets/d/19kGvj9H35VDPCbgKrWeobHbZjOqNprcmMWCL_wjtr4E/edit?usp=sharing
Google Sheets (3 European Countries) July 10 onwards: https://docs.google.com/spreadsheets/d/1miyKh4MWrgUZM75j3y1bQpSuXIaRv_SXYi2Fc_ZSQ8Q/edit?usp=sharing
Google Sheets made to track CFR fluctuations (compiled till May 10th) : https://docs.google.com/spreadsheets/d/1OijyFvOldjteY_3OFgU1Rr_azDGOhm0Zk2rjbDoKvWE/edit?usp=sharing
A simple back-calculation example : https://www.telegraphindia.com/india/coronavirus-outbreak-what-the-numbers-reveal/cid/1771525#.XrYWo77Qbsg.facebook
All data from https://ourworldindata.org/coronavirus
An article very similar to our CFR paper : https://science.thewire.in/the-sciences/covid-19-pandemic-case-fatality-rate-calculation/
Deaths using Active Cases is surprisingly related using a quadratic with a Rsq of 0.998 ...
About
No description, website, or topics provided.
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Readme
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0 stars
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1 watching
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1 fork
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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63 Commits
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NameName
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ActiveFromDeath4.py
ActiveFromDeath4.py
ActiveFromDeathByState.py
ActiveFromDeathByState.py
CountryAndStatePredictions.java
CountryAndStatePredictions.java
Covid National Data Updated.csv
Covid National Data Updated.csv
CovidByStateIndia.java
CovidByStateIndia.java
Data
Data
IndiaData_CFR.xlsx
IndiaData_CFR.xlsx
Links.rtf
Links.rtf
Modelling India Cases after other Countries.ipynb
Modelling India Cases after other Countries.ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
National & State Predictions of COVID-19 (Hypertuned Model).ipynb
README.md
README.md
Using Case Progression in Italy to Predict India's Curve.ipynb
Using Case Progression in Italy to Predict India's Curve.ipynb
Repository files navigation
Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions
Numerous models have tried to predict the spread of COVID-19. Manyinvolve myriad assumptions and parameters which cannot be reliably calculated undercurrent conditions. We describe machine-learning and curve-fitting based models usingfewer assumptions and readily available data
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites
For running the python files, you will need to install pandas, numpy, SciPy and sklearn and seaborn libraries. You may install them via an Anacondo distribution or simply use the following in the project directory (preferrably in a virtualenv)
pip install <library name>

Files
  • ActiveFromDeath.py, ActiveFromDeathByState.py - Python files, for curve-fitting and predicting the actual number of COVID-19 infections. To run them on updated data and parameters, change the following section in the code:
# SET THESE PARAMETERS ACCORDINGLYifr=0.41offset=23min_death_fact=1.5max_death_fact=4death_fact= (max_death_fact+min_death_fact)/2data=pd.read_csv("owid-covid-data.csv") # Data Course: OWIDindia_combined=data.loc[data["iso_code"]=="IND",("date","total_cases","total_deaths")] # For any other country, replace "IND" with respective country code
  • CovidByStateIndia.java - Java file for cumulative prediction of cases in Indian districts. To update parameters set the parameters on lines 313 and 314
doublex = (cases_and_projections.size() + i - 70) / (4.65 * x_not);
y[i] = Math.round (1180 * mean * sigmoid(x)) - 5100;
  • CountryAndStatePredictions.java - Java file for prediction of cases in US States; European countries like the UK, France, and Germany. To update parameters set the parameters on lines 95 and 96
doublex = (n + 0.5 * i - 70) / (x_not);
y[i] = Math.round (9 * mean * sigmoid(x)) + 94400;
Miscellaneous Links
National Prediction:https://www.overleaf.com/project/5ea933d883635f0001df191a
All training data from covid19india.org and verification data from worldometer.
Google Sheets (25 US States) May 25 onwards: https://docs.google.com/spreadsheets/d/19kGvj9H35VDPCbgKrWeobHbZjOqNprcmMWCL_wjtr4E/edit?usp=sharing
Google Sheets (3 European Countries) July 10 onwards: https://docs.google.com/spreadsheets/d/1miyKh4MWrgUZM75j3y1bQpSuXIaRv_SXYi2Fc_ZSQ8Q/edit?usp=sharing
Google Sheets made to track CFR fluctuations (compiled till May 10th) : https://docs.google.com/spreadsheets/d/1OijyFvOldjteY_3OFgU1Rr_azDGOhm0Zk2rjbDoKvWE/edit?usp=sharing
A simple back-calculation example : https://www.telegraphindia.com/india/coronavirus-outbreak-what-the-numbers-reveal/cid/1771525#.XrYWo77Qbsg.facebook
All data from https://ourworldindata.org/coronavirus
An article very similar to our CFR paper : https://science.thewire.in/the-sciences/covid-19-pandemic-case-fatality-rate-calculation/
Deaths using Active Cases is surprisingly related using a quadratic with a Rsq of 0.998 ...
About
No description, website, or topics provided.
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Readme
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Stars
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Watchers
1 watching
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1 fork
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