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Arnoud Buzing

[NB] The SIR Model for Spread of Disease

Arnoud Buzing, Wolfram Research

Posted 1 year ago

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MODERATOR NOTE: coronavirus resources & updates: https://wolfr.am/coronavirus Introduction The SIR (Susceptible-Infected-Recovered) model for the spread of infectious diseases is a very simple model of three linear differential equations. It examines how an infected population spreads a disease to a susceptible population which transforms into a recovered population. This model assumes no one dies from the disease and that the population is also isolated and stable (no new people arrive by birth or migration during the model simulation). The independent & dependent variables In this model, the independent variable is t (time, in days): In[]:= Clear[t] The dependent variables are S (number of susceptible individuals, at time t), I (number of infected individuals, at time t), and R (number of recovered individuals, at time t). To simplify the differential equations we will use the scaled versions of the S, I, and R variables. The scaled variables are s, i, and r. The scale runs from 0 (representing a zero population) to 1 (representing the full population). In[]:= Clear[s,i,r] The susceptible population starts near 1, with almost everyone being susceptible. It drops over time. The infected population starts with a positive small value near 0. The recovered population starts at 0, with no one having recovered. It increases over time. At any given time s+i+r=1 , because the sum represents the full scaled population which is 1. Each infected individual is assumed to spread its disease to b new individuals each day. If b is high the disease will spread faster. In[]:= Clear[b] The fraction k represents the fraction of the infected population that recovers each day. For example, if the disease lasts 7 days, then k is 1/7. In[]:= Clear[k] The differential equations The first equation describes how the susceptible population changes over time. Because we start with a susceptible population of 1 which decreases over time, we can assume the rate of change will be negative. Also the rate of change is proportional to the number of contacts b (more contacts decreases the susceptible population faster). Additionally the rate of change is proportionally related to the current susceptible and infected population (but not the recovered population): equationS=s'[t]-bs[t]i[t] Out[]= ′ s [t]-bi[t]s[t] The second equation describes how the infected population changes over time. It has two terms. The first term is the same as the right hand side of the previous equation, except the sign is now positive. It represents the increase in infected population. The second term is negative because it represents the decrease of infected cases (people who recover): equationI=i'[t]bs[t]i[t]-ki[t] Out[]= ′ i [t]-ki[t]+bi[t]s[t] The final equation describes how the recovered population changes over time. The right hand side is the same as the last term of the previous equation. It represents the transition of infected cases to recovered cases: equationR=r'[t]ki[t] Out[]= ′ r [t]ki[t] Before solving the differential equations we need to set the parameters b and k. Let' s assume that, on average, each infected person spreads their disease to one person every two days: b=0.5 Out[]= 0.5 Here we assume that the average recovery time is 14 days, which means that, on average, 1/14th of the infected population recovers: k=1/14.0 Out[]= 0.0714286 We also need initial values at t=0 for the susceptible, infected, and recovered population. We need a small non-zero value for the initial infected population, so will will set it to 0.0001. We will set the initial value for the susceptible population to 0.9999 (everyone else, who is not infected). And we will set the initial recovered population to 0. We pass the differential equations and the initial values to NDSolve and request a solution for t=100 days: In[]:= solution=NDSolve[{equationS,equationI,equationR,s[0]0.9999,i[0]0.0001,r[0]0.0000},{s,r,i},{t,100}]; Next we get the individual solutions for S, I, and R: In[]:= solutionS=First[s/.solution];solutionI=First[i/.solution];solutionR=First[r/.solution]; We can plot the results. Here, the blue line represents the susceptible population over time, red is infected, and green is recovered: Plot[{solutionS[t],solutionI[t],solutionR[t]},{t,0,100},PlotRange{0,1.01},PlotStyle{Blue,Red,Green}] Out[]= Alternatively we can stack these plots to highlight their sum is always 1: Plot[{solutionS[t],solutionS[t]+solutionI[t],solutionS[t]+solutionI[t]+solutionR[t]},{t,0,100},PlotRange{0,1.01},Filling{1Axis,2{1},3{2}},PlotStyle{Blue,Red,Green}] Out[]=

POSTED BY: Arnoud Buzing

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Luc Hens

Luc Hens, Vrije Universiteit Brussel

Posted 1 year ago

Why, if the average recovery time is 14 days, does this imply that on average one third of the infected population recovers?

POSTED BY: Luc Hens

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Arnoud Buzing

Arnoud Buzing, Wolfram Research

Posted 1 year ago

Sorry, that was a typo. I will see if I can fix the notebook text.

POSTED BY: Arnoud Buzing

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Arnoud Buzing

Arnoud Buzing, Wolfram Research

Posted 1 year ago

I think I fixed it.

POSTED BY: Arnoud Buzing

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Robert Nachbar

Robert Nachbar, Wolfram Research Inc.

Posted 1 year ago

The statement is still incorrect. In the model with these parameter values, all the susceptible individuals get infected and all recover. If the recovery rate is fast enough, R0 = b N / k = b / k will be less than 1 and the infection will die out before everyone gets infected. The exact value can be worked out from the equations in Brauer.

POSTED BY: Robert Nachbar

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Arnoud Buzing

Arnoud Buzing, Wolfram Research

Posted 1 year ago

Sure, but why are the values I am using incorrect? I am just using example values (which anyone can vary to experiment with this model?)

POSTED BY: Arnoud Buzing

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Robert Nachbar

Robert Nachbar, Wolfram Research Inc.

Posted 1 year ago

I see the difficulty now. I was interpreting the statement "Here we assume that the average recovery time is 14 days, which means that, on average, 1/14th of the infected population recovers” too broadly, that is, I was assuming you meant the total number of infected and not the current number of infected. What you have is indeed correct. Sorry for the confusion.

POSTED BY: Robert Nachbar

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Luc Hens

Luc Hens, Vrije Universiteit Brussel

Posted 1 year ago

The sentence would be clearer if it would say: "... 1/14th of the infected population recovers every day:"

POSTED BY: Luc Hens

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Robert Nachbar

Robert Nachbar, Wolfram Research Inc.

Posted 1 year ago

No, this is an instantaneous rate, not a daily rate.

POSTED BY: Robert Nachbar

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Daniel Lichtblau

Daniel Lichtblau, Wolfram Research

Posted 1 year ago

It has to have units of recoveries/time, and by design the time units are days.

POSTED BY: Daniel Lichtblau

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Robert Nachbar

Robert Nachbar, Wolfram Research Inc.

Posted 1 year ago

Yes, the units are day^-1, but the fraction of infected that recover in a given day is the integral Integrate[k i[t], {t, t1, t2}] / i[t1] where t2 - t1 = 1 and i[t] is the solution from NDSolve, is not necessarily exactly 1/k.

POSTED BY: Robert Nachbar

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Christos Papahristodoulou

Christos Papahristodoulou, Mälardalen University

Posted 1 year ago

What about if the Recovery equation changes to something like: equationR = r'[t] == (1 - w) k i[t], where w = a "death" parameter, such as w = 0.005? Then, DSolve[equationR, {r[t]}, {t}] gives: r[t] -> C[1] + Inactive[Integrate][(k i[K[1]] - k w i[K[1]]), {K[1], 1, t}]

POSTED BY: Christos Papahristodoulou

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Arnoud Buzing

Arnoud Buzing, Wolfram Research

Posted 1 year ago

Yes, I think this basic set of differential equations can be extended in many ways to make it more useful and precise. It would be interesting to see what you can do with it!

POSTED BY: Arnoud Buzing

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Moderation Team

Moderation Team, WOLFRAM

Posted 1 year ago

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POSTED BY: Moderation Team

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Updating Name

Posted 1 year ago

How do I calculate b from real data? Here are the cases the begin of infecteds in São Paulo Brazilian State data = {1,1,1,2,2,2,2,3,6,10,13,16,19,30,42,56,65,136,152,164,240,286,396,459,631,745,810}; Clear[a, k]; NonlinearModelFit[data, a Exp[k t], {a, k}, t]["BestFitParameters"] {a -> 2.35641, k -> 0.219352} It's somehow related to k?

POSTED BY: Updating Name

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Arnoud Buzing

Arnoud Buzing, Wolfram Research

Posted 1 year ago

I am not sure how to best calculate b and k from actual data. This model is very simple and the real world is quite complex (different spreading rates for different people and places, and time-dependent spreading rates due to changing policies). It may be possible to get a fit for a current situation, but this fit probably has limited predictive value. This model does have value to understand how changing the parameters affects the spreading of infectious diseases.

POSTED BY: Arnoud Buzing

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David G

Posted 1 year ago

I was wondering if you could change the WindowSize option to Automatic for this notebook. I downloaded it from WolframCloud and it displayed in Full Screen mode, making it impossible (AFAIK) to resize it. I was finally about to open options dialog and reset the WindowSize option. Would others have the same issue?

POSTED BY: David G

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Y Getachew

Posted 1 year ago

Can this be replicated with discrete time?

POSTED BY: Y Getachew

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Arnoud Buzing

Arnoud Buzing, Wolfram Research

Posted 1 year ago

I think so. But I have not tried it.

POSTED BY: Arnoud Buzing

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Y Getachew

Posted 1 year ago

II tried to replicate it in discrete time but RSolve/RSikvevakye couldn't solve it. Attachments: SIR_Dicrete_time.nb

POSTED BY: Y Getachew

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Robert Nachbar

Robert Nachbar, Wolfram Research Inc.

Posted 1 year ago

I've just finished making a stochastic solver for compartmental models, and will be making a post to Community later this week.

POSTED BY: Robert Nachbar

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Daniel Lichtblau

Daniel Lichtblau, Wolfram Research

Posted 1 year ago

I would suggest either `RecurrenceTable` or an explicit loop for obtaining a numerical solution in discrete time steps. The system might not be amenable to exact analytic results such as would be provided by `RSolve`.

POSTED BY: Daniel Lichtblau

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[NB] The SIR Model for Spread of Disease

Introduction

The independent & dependent variables

The differential equations