# COVID-19 BioHackathon: Biostatistics topic

---
title: 'COVID-19 BioHackathon: Biostatistics'
tags:
- replace with your own keywords
- at least is recommended
authors:
- name: First Last
orcid: 0000-0000-0000-0000
affiliation: 1
- name: Second Last
orcid: 0000-0000-0000-0000
affiliation: 2
affiliations:
- name: Institution 1, address, city, country
index: 1
- name: Institution 1, address, city, country
index: 2
date: 01 January 2020
bibliography: paper.bib
---

# Introduction or Background

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# Acknowledgements
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# References

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paper.bib

## 1st Call (Sunday, April 5th)

### Quick round of introductions

Add name / affiliation / location / three "keywords" relevant to your expertise

- Fotis Psomopoulos / Institute of Applied Biosciences (INAB|CERTH) / Thessaloniki, Greece / Bioinformatics, Machine Learning, Training
- Lukas Heumos / Quantitative Biology Center Tübingen (QBiC), nf-core / Tübingen, Germany / Bioinformatics, Machine Learning, Nextflow
- Kostis Zagganas / "ATHENA" Research Center / Athens, Greece / Cloud computing (Docker & Kubernetes), Web development, Bioinformatics

The current model focuses on the entire population

1. H-compartamentla SEIR model
2. Account for asymptomatic cases
3. Build-upon it, not replicate it

Felizitas:
- Discussion about a SEIR or a SEIRS model. So far, using a simpler model (valid prediction). Would rather work on a SEIR model.
- Compare countries. We should decide on countries to focus on. Eg. Germany divided in counties - different policies in different cases.

Janne:
- modelling on the country level might be difficult. EG Finland: capital is on lockdonw, rest of the country is case-free. For example, there is no lockdown in Turkey but restrictions for people below 20 and above 65 to go outside

- building a model and fitting it for the entire country (identify an impact of the weahter condition)

Guney:
- could we make a model that we can somewhat as an ensemble
- Instead of getting one-by one cities and counties, using ML as a process of combining the

In EU/US:
- most countries are on lock-down. So little transfer between
- easier to model within the country
- Going into separate section within country, would be difficult (too complx to model)

## Project Idea #1

### Short description

Implement a SEIR model (similar to [here]( https://gabgoh.github.io/COVID/)) as a function, and tested against the various data points we have (e.g. vs country or other geographical split). This can be consequently tested against the different policies in place (of which we know at least), trying to identify "interesting" cases. An interesting case could be characterized as the case of two geographical locations that implemented similar measures/policies across time, but have significantly different outcome (i.e. the SEIR model parameters do not align). For these cases, the corresponding entries of the COVID-19 phylogeny can be tested for molecular markers, as they could be of potential value. Also try to correlate the SEIR parameters to environmental data (to confirm the "evidence that a 1C increase in local temperature reduces transmission by 13%" - article [here](http://www.kylemeng.com/research) )

### Available Resources

_Retrieved from [Data and Resources wiki page](https://github.com/virtual-biohackathons/covid-19-bh20/blob/master/datasets_and_tools.md)_

#### Data
- [Johns Hopkins repo](https://github.com/CSSEGISandData/COVID-19/tree/master/who_covid_19_situation_reports)
- [European Centre for Disease Prevention and Control](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide)
- [Automated Data Collection: COVID-19/SARS-COV-2 Cases in EU by Country, State/Province/Local Authorities, and Date](https://github.com/covid19-eu-zh/covid19-eu-data)
- - [COVID Epidemiology](https://covid19.fyi/#/)
- [NY Times data](https://github.com/nytimes/covid-19-data)
- [NHS Covid19 symptom tracker](https://covid.joinzoe.com/)

#### Tools
- [Coronavirus Tracker API](https://github.com/ExpDev07/coronavirus-tracker-api)
- [R package for the data colated by Johns Hopkins](https://github.com/rOpenStats/COVID19)
- [Penn Medicine - COVID19 Hospital Impact Model for Epidemics](https://penn-chime.phl.io/)
- [Epidemic Calculator by Gabriel Goh](https://gabgoh.github.io/COVID/)

#### Models
- [Models repo by Pedro Mendes of University of Connecticut](https://github.com/pmendes/COVID19)
- [Potential Long-Term Intervention Strategies for COVID-19](https://covid-measures.github.io/)
- (**new**) [Contagiousness of COVID-19 Part I: Improvements of Mathematical Fitting](https://www.r-bloggers.com/contagiousness-of-covid-19-part-i-improvements-of-mathematical-fitting-guest-post/)

#### R packages
- [covid19us (R wrapper around the COVID Tracking Project API)](https://github.com/aedobbyn/covid19us)

#### Literature

- (**new**)[Assessment for the seasonality of Covid-19 should focus on ultraviolet radiation and not 'warmer days'](http://scholar.google.com/scholar_url?url=https://osf.io/397yg/download&hl=en&sa=X&d=6273272101295147084&scisig=AAGBfm1aK6izUDG5EY2P82N5l7wPLxDSZw&nossl=1&oi=scholaralrt&hist=Fp0LAqsAAAAJ:12061300051190403369:AAGBfm36WLmyKZY6fOzMgJREpMudTJB7uA)

### Participants interested for this

- Fotis Psomopoulos
-

### Skills needed

- Knowledge of SEIR models
- Knowledge of statistics
- Programming experience
- Data wrangling, cleaning, formatting

## Project Idea #2

### Short description

Since public life can only stand still for a certain time, one could model different scenarios such as suggested by [Uri Allon](https://medium.com/@urialonw/containing-sars-cov-2-with-a-two-day-workweek-fbdea4030d30). Scenarios could include various combinations of work-week vs. lock-down (2-3, 3-2 etc.), which is either homogenous (all follow the same rhythm) or heterogeneous (rhythm is shifted) in a society. Another potential scenario is a regular switch such as a one-week-work - one-week-lock-down scenario.

### Resources and Skills needed

In alignment with Project 1

## Project Idea #3

### Short description

Having online SEIR models at hand is one thing, but "feeding" them with the right assumptions is another. One could establish a repository with an overview of the parameters needed combined with all identified literature sources (if possible stratified for countries). This could serve as a solid starting point e.g. for epidemiologists currently building models on regional level.

### Skills needed

- Structured literature search
- Experience in SEIR modelling (previously or for Covid-19) / Knowledge of SEIR models