Algorithms and code, written by me, to analyse outbreak data and so epidemiological simulations - in my favourite computer language Julia.

Data: The folder data contains time series data taken at some time point from John Hopkins CSSE repository https://github.com/CSSEGISandData/COVID-19
Additionally, the folder contains a file with arrival dates of the virus in different countries (calculated by the function arrival_time in the src-folder).

Results: The folder figures contains some figures with analysis results.
Further results of the analysis can be found at my web page or in my publication (see below)

Code: The folder src contains algorithms for simulation of outbreak and analysis of data. This is code that was used in my publication Blasius B. Blasius B. (2020) Power-law distribution in the number of confirmed COVID-19 cases. Chaos, https://doi.org/10.1063/5.0013031

Some small auxillary functions, used in the other files

Functions to read-in outbreak time series for a given country. Data are taken from the John Hopkins CSSE repository. I will try to update the data regularly. The functions include some minor preprocessing, such as interpolation for known irregularities in the John Hopkins data set. The returned number of infected gives the prevalence, that is, the number of active cases, in contrast to the often given cumulative number of cases.
Additionally a function to calculate arrival dates in countries.

Some functions for time series analysis of outbreak data. The focus is on plotting current dynamics and trends of the outbreaks in different countries. In particular, daily incidence, visualization of log-transformed data and analysis of local trends.

Anylsis of power-law distribution in Covid-19 cases in countries worlwide and in US-counties. This is the code, used in Fig.1 in the publication https://doi.org/10.1063/5.0013031

Analyise the arrival times in different countries. This corresponds to Fig.2 of the publication.

A dual-scale metapopulation model to simulate prevalence distributions in a network of countries. This is the model, used in my publication https://arxiv.org/abs/2004.00940 or Fig.3 in https://doi.org/10.1063/5.0013031

A julia implementation of the SEIR-like model describing the Coronavirus COVID-19 outbreak 2019/20, based on the publication: Wang et al. (2020) Evolving epidemiology and impact of non-pharmaceutical interventions on the outbreak of coronavirus disease 2019 in Wuhan, China. MedRxive https://doi.org/10.1101/2020.03.03.20030593
The model is a variant of a standard Susceptible-Exposed-Infectious-Recovered (SEIR) model. Additionally, the model distinguishes between the reported (or acertained) number of infected (I) and the unreported number of infected (A). Individuals in the A-class can cause further infections, in the same way as the reported infected, but they are 'invisible' to mitigation and hospitalization. The model also includes a class of hospitalized (or quarantined) individuals (H). Only acertained infected can enter the H-class and individuals from the H-class cannot infect susceptibles. Additionally the model allows to consider inflows and outflows of people into the region. The motivations for this code is merely to give people a starting ground for running and exploring outbreak simulations.

This code in this repository is not meant to be a quantitive tool for predicting the current outbreak dynamics in different parts of the world: