Toolbox to simulate and mitigate COVID-19 propagation

Abstract

In this paper, we present an age-and-migration-dependent Markov-chain-based model for the propagation of the COVID-19, together with a user-friendly MatLab-based toolbox. Furthermore, we present examples to simulate the propagation of the virus internationally and locally (in a particular country).

Code metadata

Current code version	v1
Permanent link to code/repository used for this code version	https://github.com/ElsevierSoftwareX/SOFTX_2020_259
Code Ocean compute capsule
Legal Code License	MIT license
Code versioning system used	none
Software code languages, tools, and services used	MatLab
Compilation requirements, operating environments & dependencies
If available Link to developer documentation/manual
Support email for questions	zahrate.frihi@univ-annaba.org

1. Motivation and significance

COVID-19 is a new disease that was discovered in December 2019 in humans. It is believed that the virus causing the disease jumped from a wild animal to the human body in a local wildlife meat market in Wuhan in China. Since then, the disease started to spread rapidly, from human to human, in China and neighboring countries such as South Korea. The epidemic was declared as pandemic by World Health Organization (WHO) on 11 March 2020, after reaching tens of countries and leaving hundreds of deaths. As of writing the world counts thousands of deaths and hundreds of thousands of infections in 180 countries and territories. The continents with the biggest number of infections are Asia (mainly China, Iran and South Korea), Europe (mainly western Europe) and the American continents (mainly USA and Brazil).

In the absence of a fully vaccinated population, countries try to control and slow down the spread of the pandemic by reducing the international mobility and imposing quarantine for infected people or newly arriving persons. Some countries imposed a total lockdown such as Italy and Spain.

The rapid international spread of the pandemic and the policies applied by countries to combat it, inspired us to suggest, in this paper, a model for the propagation of COVID-19 via Markov chains over a graph (network) representing the map of the world.

Mathematical modeling of infectious diseases has been an important tool in studying the propagation of a number of diseases. Epidemic models aim to answer questions such as: is there a possibility to have a big outbreak of a certain infectious disease? how many infections will we have? how many people should be vaccinated to prevent an outbreak ?

The deterministic SIR model is one of the simplest epidemic models where the propagation dynamics of the disease can be defined by 2 differential equations (see e.g. [1]), one for the number of susceptible people and the other for the infected people. The population is divided into three classes: susceptible, infected and removed (recovered or dead). In this type of model individuals can gain a permanent immunity. There exist other variants of SIR model such as SIS (susceptible–infected–susceptible) and SEIR (susceptible-exposed-infected-removed). All previous mentioned models assume that people mix homogeneously and the community is closed (no births, immigration, emigration and deaths). The closure assumption means that the total size of the population is constant, and it is equal to the sum of the number of susceptible, infected and removed people, which makes the derivation of an equation for the number of removed people trivial, given the equations for susceptible and infected people.

The output of the model relies on the assumption that people mix uniformly with each other. However, that might not be sufficient to obtain good results. For example, if we consider an epidemic outbreak in a school (small community), it seems reasonable to assume some uncertainty in the final number of infected people. Moreover, even if the basic reproduction number is larger than one and the community is large but the outbreak is initiated by few infected people, it can be possible, by chance, that the epidemic never takes off [2]. For extensions to more realistic stochastic models, see e.g. [3].

Many stochastic models of infectious diseases are based on continuous- or discrete-time Markov chains. In these models, the states of the Markov chains are the number of people that are susceptible, infected, $\dots$etc. The major issues with these approaches are the big size of the state space and the large number of the associated Kolmogorov’s differential equations describing the spread of the disease. For continuous-time Markov chain models, see e.g. [4], [5], [6], [7]. For discrete-time models, see e.g. [8], [9], [10].

Another type of epidemic models is the network epidemic model [11]. This model is based on an undirected graph that describes the social structure in the community where the epidemic spreads. The nodes represent individuals and the edges represent the connection between individuals. Most of network-based models consider the propagation as a stochastic process or a mean-field approximation of it. The network epidemic models have other applications such as a malware propagation over computer networks, see e.g. [12], [13].

1.1. A network Markov chain model

We model the spread of COVID-19 in the whole world via Markov chains over an undirected graph $G=(N,E)$, where $N=\{1,2,\dots ,n\}$ is the set of nodes that are connected to each other by a set of edges $E$. The $n\times n$ adjacency matrix of the graph is given, for any $i,j\in N$, by: ${\left[A\right]}_{ij}=1$ if $i,j\in E,0$ otherwise. The graph represents the map of the world and each node represents an individual, the state of each individual is described by a Markov chain. Each Markov chain has a discrete state space $E=\{S,I,R,D\}$ where $S$ is susceptible, $I$ is infected, $R$ is recovered and $D$ is dead. The chain jumps from a state to another following certain transition probabilities. The infection occurs, with a probability $\alpha \in [0,1]$, when a susceptible individual interacts with an infected one from his neighbor $\mathcal{N}$. Any infected individual (or node) can recover with probability $\gamma$. The recovered persons do not gain a permanent immunity as they can be reinfected with probability $\beta$ (see Fig. 1).

Fig. 1.

Diagram representing the states of the Markov Chains.

Our Markov chain model takes in consideration the age distribution in each continent. The transition probability from the state $I$ to the state $D$ (the probability $\delta$) is affected by the age. Old infected people have higher probability of death. Therefore, we set 3 different transition probabilities ${\delta }_1,{\delta }_2$ and ${\delta }_3$ for the different age categories (0,40), [40,70) and 70+, respectively. The sum of the three previous transition probabilities equals $\delta$. The age distributions for each continent is generated (by default) via normal distributions with suitable parameters. It is possible to change to other distributions depending on the continent.

Furthermore, our model incorporates long distance interactions that can happen by traveling inside the same country/ continent or to a different country/continent. We do that by considering a wider set of neighbors ${\mathcal{N}}^{\prime }$. Each person $i\in N$ has her own set of local neighborhood ${\mathcal{N}}_i$ and a set of global neighborhood ${\mathcal{N}}_i^{\prime }$, for simplicity we dropped the subscripts.

Next, we introduce the proposed toolbox in order to simulate the propagation of the virus depending on the age and migration constraints between regions.

2. Toolbox description

The toolbox we propose is based on MatLab. It is mainly composed of six functions, which are explained briefly below. For details, refer to the associated toolbox documentation.

2.1. Construction of the continents data

The function construct_continents(I,resolution_ grid) is in charge of generating the required data for each (populated) continent/region on the map of the world. This function takes as input the image of the world (or a country) I and the chosen resolution resolution_grid of its discretization (the number of nodes associated). As an output, the function returns six clusters (corresponding to each continent) composed of three elements: the number of nodes corresponding to the specific continent, a matrix of x–y coordinates of each node specific to the continent and the corresponding filtered continent from the input image.

2.2. Initial visualization of the sets $\mathcal{S}$, $\mathcal{I}$, $\mathcal{R}$, and $\mathcal{D}$ for the continents

The function show_map(S,I,R,D,C,color) plots the discretized map by using the nodes constructed by the function construct_continents. This functions take as inputs: the set of susceptible people $\mathcal{S}$, the set of infected people $\mathcal{I}$, the set of recovered people $\mathcal{R}$, the set of dead people $\mathcal{D}$, the set of coordinates of the nodes corresponding to the map of the world $\mathcal{C}$ and the color of the nodes corresponding to susceptible people. As an output, this function generates the plot of the map with the characteristics selected in the inputs.

2.3. Computation of a neighborhood set around a node

The function neighbor(agent,radius,xpos,ypos) allows computing a set around a certain node. This function is fundamental in order to compute the set of susceptible nodes around a sick node to propagate the virus. It takes the following inputs: a given node/case called agent, a scalar value called radius which determines the size of the neighborhood of a node (high values for international travels), xpos corresponding to all x coordinates of all the nodes on the map of the world, and ypos corresponding to all y coordinates of all the nodes on the map of the world.

This function returns a unique output which is a set of nodes corresponding to the neighborhood set of agent.

2.4. Generation of the age distribution

The function show_age(C,age1,age2,age3) plots the map of the world and showing the distribution of three age ranges defined by age1, age2, and age3. After inserting the distribution of ages for each continent (there are three ranges of age), this function plots the map of the world and assigning each node an age accordingly.

2.5. Computation of the flight connections

The function flight_connections(A,S_NorthAmerica,...,S_Oceania) computes the set of nodes belonging to the regions to which a node can travel.

This functions takes as input the set of nodes (susceptible people) corresponding to each continent and an adjacency matrix $A\in {\{0,1\}}^{6\times 6}$ describing the possible intercontinental travel. As outputs, the function returns set of nodes to which an agent belonging to a certain continent can move to another. Each output set corresponds to a continent.

2.6. Computation of the virus propagation

The function update(S,I,R,D,xpos,ypos, flight_ connections,p,Age) updates the situation of the propagation of the virus after each iteration. This is done by updating all the input sets for each continents. The input flight_connections is the set of agents that can be infected from other continent because of flights and connectivity of continents, whereas p is the set of transition probabilities p = [p1,p2,p31,p32,p33,p4, p5] of the underlying Markov chain. As outputs, the sets of susceptible, infected, recovered and dead are updated.

3. Illustrative examples

We present two illustrative examples, the first is a simulation of the propagation of the virus on the map of the world, which is divided into six different continents, the simulation is done with and without intercontinental connections, see Fig. 2, Fig. 3. The second example considers a unique country divided into several regions. Let the country be Colombia, which is divided into 32 departments. Therefore, we consider not only the different transition rates for the departments, but also the migration constraints among them. Fig. 4 shows the propagation of the virus during a year without quarantine whereas Fig. 5 shows the propagation of the virus during a year with quarantine and no allowed domestic flights.

Fig. 2.

Simulation of the propagation of the virus with intercontinental connection.

Fig. 3.

Simulation of the propagation of the virus without intercontinental connection.

Fig. 4.

Propagation of the virus in Colombia with migration constraints among the 32 departments (without quarantine).

Fig. 5.

Propagation of the virus in Colombia with migration constraints among the 32 departments (with quarantine).

4. Impact

The MatLab toolbox can help in raising research questions about the efficiency of the lockdown and the closure of aerial spaces. Using the toolbox one can simulate the spatial propagation of the disease and compare different lockdown strategies. Furthermore, our algorithm can be easily extended to take into account finer division of the map of the world. One can divide each continent into countries, each country into regions and each region into zones and then find out best lockdown policies by managing the interconnection between all of them.

This toolbox simplifies the simulation process of the propagation of COVID-19 (or any other viruses) and made the simulation more realistic by taking into account the age distribution in each continent as well as long distance travels. Our research group at Learning & Game Theory Laboratory uses the toolbox to generate simulations and study the propagation of COVID-19. Moreover, the lab is working on extending it to including different divisions and adding more features such as hospital capacity in each region, economic situation,…etc.

5. Conclusions

We have presented a Markov chain-based model for the propagation of COVID-19 with migration constraints given by a connected graph representing the allowed air traffic. Furthermore, the model takes into account an age-dependent mortality since it has been observed that the elderly people are more prone to death by the virus than younger people. The model is simple and can be easily tuned by means of the transition probabilities of the Markov chain. Besides, we have presented a user-friendly toolbox for the simulation of the propagation throughout multiple regions. The regions can be different continents, countries and/or states in a country. We have presented several examples explaining the use of the toolbox and have also provided the open source codes, which are accessible to any researcher. The goal is that the contribution presented in this article can be used, not only to motivate the research of the propagation of the COVID-19, but also as a model that can be used as a reference in the design of optimal controllers.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is the Supplementary material related to this article.

Video S1

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Video S2

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