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Modeling the Prescription Opioid Epidemic

Abstract

Opioid addiction has become a global epidemic and a national health crisis in recent years, with the number of opioid overdose fatalities steadily increasing since the 1990s. In contrast to the dynamics of a typical illicit drug or disease epidemic, opioid addiction has its roots in legal, prescription medication—a fact which greatly increases the exposed population and provides additional drug accessibility for addicts. In this paper, we present a mathematical model for prescription drug addiction and treatment with parameters and validation based on data from the opioid epidemic. Key dynamics considered include addiction through prescription, addiction from illicit sources, and treatment. Through mathematical analysis, we show that no addiction-free equilibrium can exist without stringent control over how opioids are administered and prescribed, in which case we estimate that the epidemic would cease to be self-sustaining. Numerical sensitivity analysis suggests that relatively low states of endemic addiction can be obtained by primarily focusing on medical prevention followed by aggressive treatment of remaining cases—even when the probability of relapse from treatment remains high. Further empirical study focused on understanding the rate of illicit drug dependence versus overdose risk, along with the current and changing rates of opioid prescription and treatment, would shed significant light on optimal control efforts and feasible outcomes for this epidemic and drug epidemics in general.

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Acknowledgements

The authors would like to thank Christina Battista, Robert Booth, Namdi Brandon, Kathleen Carroll, Jana Gevertz, Anne Ho, Shanda Kamien, Grace McLaughlin, Gianni Migliaccio, Matthew Mizuhara, and Laura Miller for comments, suggestions, and informative conversations. NAB would like to thank Patricia Clark of RIT, whose mathematical biology course gave the original motivation for this project in 2009. We would also like to thank the anonymous reviewers and the associate editor of Bulletin of Mathematical Biology for their helpful comments.

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Correspondence to W. Christopher Strickland.

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Appendix A

Appendix A

Here, we present supplemental material to support our findings including additional model analysis and validation, numerical stability analysis, and simulation data. We also provide details for the calculation determining a condition for backward bifurcation, the explicit Jacobian used in our stability analysis, and simulation results illustrating system sensitivity to the prescription addiction rate (\(\gamma \)), treatment success rate (\(\delta \)), and prescription rate (\(\alpha \)). Finally, we explore the relationship between prescription rate (\(\alpha \)) and prescription addiction rate (\(\gamma \)).

A.1 Initial Conditions for Validation

We estimated the initial prescribed population, \(P_0\), based off of the percentage of US population to whom were prescribed opioids at any given week in 2009 (\(2\%\)) (Boudreau et al. 2009). Since there were more prescriptions given in 2009 than 1999 (Shah et al. 2017), we estimated that roughly \(0.40\times 2\%\) of the population were prescribed opioids at any time in 1999; hence, \(P_0=0.008\). Note we estimated the coefficient of 0.40 by using the ratio of total opioids MME sold in 1999 to 2009 (U.S. Food and Drug Administration 2018).

We backed out the initial addicted population from the number of prescription opioid deaths in 1999 (2749) (Hedegaard et al. 2017), and normalized it by the fraction of deaths attributed to addicted persons (\(54.6\%\)) (Gwira Baumblatt et al. 2014) and the predicted number of deaths from our model with the age-adjusted US population in 1999 (\(259\times 10^6\)) (U.S. Census Bureau: International Database 2018), e.g., \(A_0 = \frac{(0.546)(2749)}{(259\times 10^6)(\mu ^*-\mu )}=0.00136\). We then assumed \(R_0=0.1A_0\) (Office of the Surgeon General 2016) (fraction of population in treatment), making \(S_0=0.990504\).

A.2 Analysis of the Addiction-Free Equilibrium

Here, we derive conditions on the existence of an addiction-free equilibrium (AFE) within the system defined by Eqs. 14. To begin, we set each equation to zero and require that \(A=0\). Equation 3 becomes \(0=-(\delta +\sigma +\mu )R\), and since \(\mu >0\) as a natural death rate, this implies that \(R=0\) at any AFE (conversely, \(R=0\) requires that either \(A=0\) or \(\zeta =0\), which may apply at the beginning of an epidemic). We are left with the system

$$\begin{aligned} 0&= -\alpha S^*- \beta _P S^*P^*+ \epsilon P^*+ \mu P^*\\ 0&= \alpha S^*- (\epsilon + \gamma + \mu )P^*\\ 0&= P^*(\gamma + \beta _P S^*). \end{aligned}$$

\(P^*\ne 0\) since otherwise the only solution is \(S^*=P^*=A^*=R^*=0\) and we require that \(S+P+A+R=1\). Then, \(0 = \gamma +\beta _P S\). Since all our parameters and dependent variables are nonnegative by definition, \(\gamma =\beta _P=0\). In this case, opioids are available only through the presence of current addicts (e.g., on the black market due to illicit demand) and not through currently prescribed users. We can now use our assumption that \(1=S+P+A+R\) to find that

$$\begin{aligned} S^*&= \frac{\epsilon + \mu }{\alpha + \epsilon + \mu }\qquad A^*= 0\\ P^*&= \frac{\alpha }{\alpha + \epsilon + \mu }\qquad R^*= 0. \end{aligned}$$

A.3 Calculating the Basic Reproduction Number, \(R_0\)

Assuming that \(\gamma =\beta _P=0\), the necessary and sufficient conditions for the AFE to exist, Eqs. 3 and 4 reduce to

$$\begin{aligned} \dot{A}&= \sigma R + \beta _A SA - (\zeta +\mu ^{*})A\\ \dot{R}&= \zeta A - (\delta +\sigma +\mu ) R. \end{aligned}$$

Using the next generation method (Diekmann et al. 1990; van den Driessche and Watmough 2002; Heffernan et al. 2005; Diekmann et al. 2010) with both A and R treated as “infected,” we compute the matrices F and V as

$$\begin{aligned} F = \left[ \begin{array}{cc} \frac{\beta _A(\epsilon +\mu )}{\alpha +\epsilon +\mu } &{} 0\\ 0 &{} 0\\ \end{array}\right] \ \ \ \ \text {and}\ \ \ \ V = \left[ \begin{array}{cc} \zeta +\mu ^* &{} -\sigma \\ -\zeta &{} \delta + \sigma + \mu \\ \end{array}\right] . \end{aligned}$$

Then, \(R_0\) is given by the spectral radius of \(FV^{-1}\),

$$\begin{aligned} \begin{gathered} R_0 = \frac{\beta _A(\epsilon + \mu )}{(\alpha + \epsilon +\mu )(\mu ^*+\zeta \varLambda )}=\frac{\beta _A S^*}{\mu ^*+\zeta \varLambda }\\ \text {where}\ \ \ \varLambda =\frac{\delta +\mu }{\delta +\mu +\sigma },\ S^*= \frac{\epsilon + \mu }{\alpha + \epsilon + \mu }. \end{gathered} \end{aligned}$$

Prevalence of opioid addicts will rise when \(R_0>1\) and fall when \(R_0<1\).

A.4 Jacobian Analysis and Alternative Relapse Models

Consider an alternative form of the model with the addition of two relapse rates \(\nu _1SP\) and \(\nu _2SA\),

$$\begin{aligned} \dot{S}&= - \alpha S - \beta _A SA - \beta _P SP + \epsilon P + \delta R + \mu (P+R)+\mu ^{*}A \\ \dot{P}&= \alpha S - (\epsilon +\gamma +\mu ) P\\ \dot{A}&= \gamma P + \sigma R + \beta _A SA + \beta _P SP + \nu _1RP + \nu _2RA - (\zeta +\mu ^{*})A\\ \dot{R}&= \zeta A - (\delta +\sigma +\mu ) R - \nu _1RP - \nu _2RA. \end{aligned}$$

The AFE for this system remains the same (with the same conditions for existence) as in Eq. 5. Calculating the basic reproduction number \(\mathcal {R}_0\) using the next generation method, we arrive at

$$\begin{aligned} \begin{gathered} R_0 = \frac{\beta _A(\epsilon + \mu )}{(\alpha + \epsilon +\mu )(\mu ^*+\zeta \widetilde{\varLambda })}=\frac{\beta _A S^*}{\mu ^*+\zeta \widetilde{\varLambda }}\\ \text {where}\ \ \ \widetilde{\varLambda }=\frac{\delta +\mu }{\delta +\mu +\sigma +\nu _1P^*},\ S^*= \frac{\epsilon + \mu }{\alpha + \epsilon + \mu }, \end{gathered} \end{aligned}$$

so the addition of \(\nu _1RP\) contributes to \(\mathcal {R}_0\) in a way similar to \(\sigma \) (but scaled by \(P^*\)), while the addition of \(\nu _2RA\) does not contribute to \(\mathcal {R}_0\). We will now conduct further analysis on this model which, as a direct extension of our model given in Eqs. (1)–(4), will include it as a subcase.

Reducing the system to three equations for SAR using \(P=1-S-A-R\) gives us

$$\begin{aligned} \dot{S}&= - \alpha S - \beta _A SA - \beta _P S(1-S-A-R) \nonumber \\&\quad + (\epsilon +\mu ) (1-S-A-R) + (\delta +\mu ) R+\mu ^{*}A \nonumber \\ \dot{A}&= \gamma (1-S-A-R) + \sigma R +\beta _A SA \nonumber \\&\quad + \beta _P S(1-S-A-R) + \nu _1 R(1-S-A-R) + \nu RA - (\zeta +\mu ^*)A\nonumber \\ \dot{R}&= \zeta A - \nu _1 R(1-S-A-R) - \nu _2 RA- (\delta +\sigma +\mu ) R, \end{aligned}$$
(15)

The Jacobian, J, of this system is

$$\begin{aligned} \left[ \begin{array}{ccc} -\alpha {-}\beta _A A {+} \beta _P (S-P) {-} (\epsilon +\mu ) &{} (\beta _P-\beta _A)S {-} (\epsilon +\mu ) {+} \mu ^* &{} \beta _P S {+}\delta {-} \epsilon \\ \ &{} \ &{} \ \\ -\gamma {+} \beta _A A {+} \beta _P(P-S) {-} \nu _1R\ \ &{} -\gamma {+} (\beta _A-\beta _P)S {-} \nu _1R {+} \nu _2 R -(\zeta +\mu ^*)\ \ \ &{} -\gamma +\sigma -\beta _P S {+} \nu _1(P-R) {+} \nu _2 A\\ \ &{} \ &{} \ \\ \nu _1R &{} \zeta {+} \nu _1R {-} \nu _2 R &{}-\nu _1(P-R) -\nu _2 A {-} (\delta +\sigma +\mu ) \end{array}\right] \end{aligned}$$

Evaluated at the AFE given by Eq. 5 with \(\gamma =\beta _P=0\), the Jacobian \(J(x_0)\) is

$$\begin{aligned} \left[ \begin{array}{ccc} -(\alpha +\epsilon +\mu ) &{}\ \ \ -\beta _AS^*-(\epsilon +\mu )+\mu ^* &{} \delta - \epsilon \\ 0 &{} \beta _AS^* - (\zeta +\mu ^*) &{}\nu _1P^* + \sigma \\ 0 &{} \zeta &{} -\nu _1P^*-(\delta +\sigma +\mu ) \end{array}\right] . \end{aligned}$$

Following Castillo-Chavez and Song (2004), we now take \(\beta _A\) to be the bifurcation parameter (given the form of \(\mathcal {R}_0\)) and conduct analysis around

$$\begin{aligned} \beta _A^* = \frac{\mu ^*+\zeta \widetilde{\varLambda }}{S^*}. \end{aligned}$$

to analyze the bifurcation of this system when \(R_0=1\) and determine the bifurcation’s direction (Castillo-Chavez and Song 2004). First, we define the matrix \(\mathcal {A}\) as in Castillo-Chavez and Song (2004) but, via a change in coordinates, taking \(x_0\) to be the AFE and the bifurcation parameter to be \(\beta _A\). Writing our system of differential equations (including nonlinear relapse terms) as \(dx/dt=f(x,\beta _A)\), we have

$$\begin{aligned} \begin{aligned} \mathcal {A}&= \frac{\partial f_i}{\partial x_j}(x_0,\beta _A=\beta _A^*) = J(x_0,\beta _A=\beta _A^*) \\&= \left[ \begin{array}{ccc} -(\alpha +\epsilon +\mu ) &{}\ \ \ -\zeta \widetilde{\varLambda }-(\epsilon +\mu ) &{} \delta - \epsilon \\ 0 &{} \zeta (\widetilde{\varLambda } - 1) &{} \sigma + \nu _1P^*\\ 0 &{} \zeta &{} -(\delta +\sigma +\nu _1P^*+\mu ) \end{array}\right] . \end{aligned} \end{aligned}$$
(16)

It is easy to check that zero is a simple eigenvalue of \(\mathcal {A}\) and that all other eigenvalues of \(\mathcal {A}\) have negative real parts. \(\mathcal {A}\) has right eigenvector \(\mathbf {x}={(-S^*(1+\widetilde{\varGamma }),1,\widetilde{\varGamma })^T}\) and left eigenvector \(\mathbf {y} = {(0,1,1-\widetilde{\varLambda })}\) where \(\widetilde{\varGamma }\) is given by

$$\begin{aligned} \widetilde{\varGamma }=\frac{\zeta }{\delta +\mu +\sigma +\nu _1P^*} \end{aligned}$$

and once again

$$\begin{aligned} \widetilde{\varLambda }=\frac{\delta +\mu }{\delta +\mu +\sigma +\nu _1P^*}. \end{aligned}$$

The first component of \(\mathbf {x}\) is negative, but since \(S^*>0\) the analysis still applies (Castillo-Chavez and Song 2004). We now let \(f_k\) be the kth component of f and set

$$\begin{aligned} a&= \sum _{k,i,j=1}y_kx_ix_j\frac{\partial ^2f_k}{\partial x_i\partial x_j}(x_0,\beta _A=\beta _A^*)\\ b&= \sum _{k,i=1}y_kx_i\frac{\partial ^2f_k}{\partial x_i\partial \beta _A}. \end{aligned}$$
Fig. 7
figure7

Model sensitivity to \(\gamma \) and \(\beta _P\). Effect of moving \(\gamma \) and/or \(\beta _P\) away from zero when \(R_0\approx 0.085\) with likely parameter values, \(\epsilon =3\) and \(\zeta =0.25\) (Color figure online)

Fig. 8
figure8

Sobol sensitivity analysis for equilibrium values when \(\gamma =\beta _P=0\) (see Fig. 1 or Table 1 for parameter definitions). The first-order indices do not take into account interactions with other parameters, while total-order indices measure sensitivity through all higher-order interactions. The parameter ranges tested here are the same as in Fig. 4 (Color figure online)

Fig. 9
figure9

Prescription addiction rate color maps illustrating the long-term equilibrium solutions (\(S^*,P^*,R^*\), and \(A^*\)) for prescription-end rates (\(\epsilon \)) and rehabilitation-start rates (\(\zeta \)) between [0.8, 8] and [0.2, 2.0], respectively, and for various prescription addiction rates (\(\gamma \)) (Color figure online)

Fig. 10
figure10

Treatment success rate color maps illustrating the long-term equilibrium solutions (\(S^*,P^*,R^*\), and \(A^*\)) for prescription-end rates (\(\epsilon \)) and rehabilitation-start rates (\(\zeta \)) between [0.8, 8] and [0.2, 2.0], respectively, and for various treatment success rates (\(\delta \)) (Color figure online)

The nonzero derivatives are

$$\begin{aligned} \frac{\partial ^2f_1}{\partial S\partial A}&= \frac{\partial ^2f_1}{\partial A\partial S} = -\beta _A^*\\ \frac{\partial ^2f_2}{\partial S\partial A}&= \frac{\partial ^2f_2}{\partial A\partial S} = \beta _A^*\\ \frac{\partial ^2f_2}{\partial S\partial R}&= \frac{\partial ^2f_2}{\partial R\partial S} = \frac{\partial ^2f_2}{\partial A\partial R} = \frac{\partial ^2f_2}{\partial R\partial A} = -\nu _1\\ \frac{\partial ^2f_2}{\partial R^2}&= -2\nu _1\\ \frac{\partial ^2f_3}{\partial S\partial R}&= \frac{\partial ^2f_3}{\partial R\partial S} = \frac{\partial ^2f_3}{\partial A\partial R} = \frac{\partial ^2f_3}{\partial R\partial A} = \nu _1\\ \frac{\partial ^2f_3}{\partial R^2}&= 2\nu _1\\ \frac{\partial ^2f_2}{\partial A\partial R}&= \frac{\partial ^2f_2}{\partial R\partial A} = \nu _2\\ \frac{\partial ^2f_3}{\partial A\partial R}&= \frac{\partial ^2f_3}{\partial R\partial A} = -\nu _2\\ \frac{\partial ^2f_1}{\partial A\partial \beta _A}&= -S^*\\ \frac{\partial ^2f_2}{\partial A\partial \beta _A}&= S^*. \end{aligned}$$

Now,

$$\begin{aligned} a&= (1)(-S^*(1+\widetilde{\varGamma }))(1)\beta _A^*+(1)(1)(-S^*(1+\widetilde{\varGamma }))\beta _A^*\\&\quad +(1)(-S^*(1+\widetilde{\varGamma }))(\widetilde{\varGamma })(-\nu _1)+(1)(\widetilde{\varGamma })(-S^*(1+\widetilde{\varGamma }))(-\nu _1)\\&\quad +(1)(1)(\widetilde{\varGamma })(-\nu _1)+(1)(\widetilde{\varGamma })(1)(-\nu _1)+(1)(\widetilde{\varGamma })^2(-2\nu _1)\\&\quad +(1-\widetilde{\varLambda })(-S^*(1+\widetilde{\varGamma }))(\widetilde{\varGamma })(\nu _1)+(1-\widetilde{\varLambda })(\widetilde{\varGamma })(-S^*(1+\widetilde{\varGamma }))(\nu _1)\\&\quad +(1-\widetilde{\varLambda })(1)(\widetilde{\varGamma })(\nu _1)+(1-\widetilde{\varLambda })(\widetilde{\varGamma })(1)(\nu _1)+(1-\widetilde{\varLambda })(\widetilde{\varGamma })^2(2\nu _1)\\&\quad +(1)(1)\widetilde{\varGamma }\nu _2 +(1)\widetilde{\varGamma }(1)\nu _2+(1-\widetilde{\varLambda })(1)\widetilde{\varGamma }(-\nu _2)+(1-\widetilde{\varLambda })\widetilde{\varGamma }(1)(-\nu _2)\\&=-2S^*(1+\widetilde{\varGamma })\beta _A^*- 2\widetilde{\varLambda }\widetilde{\varGamma }(1+\widetilde{\varGamma })P^*\nu _1 +2\widetilde{\varLambda }\widetilde{\varGamma }\nu _2\\&= -2(1+\widetilde{\varGamma })(\mu ^*+\zeta \widetilde{\varLambda }+\widetilde{\varLambda }\widetilde{\varGamma }P^*\nu _1)+2\widetilde{\varLambda }\widetilde{\varGamma }\nu _2\\ b&= (1)(1)S^*>0. \end{aligned}$$

To make \(a>0\), we therefore need

$$\begin{aligned} \widetilde{\varLambda }\widetilde{\varGamma }\nu _2 > (1+\widetilde{\varGamma })(\mu ^*+\zeta \widetilde{\varLambda }+\widetilde{\varLambda }\widetilde{\varGamma }P^*\nu _1). \end{aligned}$$
(17)

If this condition is satisfied, there will be a backward bifurcation at \(R_0=1\). Of course, for the model given in Eqs. (1)–(4) where \(\nu _2\) functionally equals zero, it is not possible for a backward bifurcation to occur.

Fig. 11
figure11

Prescription rate color maps illustrating the long-term equilibrium solutions (\(S^*,P^*,R^*\), and \(A^*\)) for prescription-end rates (\(\epsilon \)) and rehabilitation-start rates (\(\zeta \)) between [0.8, 8] and [0.2, 2.0], respectively, and for various prescription rates (\(\alpha \)) (Color figure online)

A.5 Addiction-Free Equilibrium Numerical Analysis

To examine the sensitivity of the model’s addiction-free equilibrium (AFE) to its parameters, we first ran simulations to see how the AFE changes when either \(\gamma \) or \(\beta _P\) shifts away from zero. Parameter values were chosen as in Table 1 with \(\epsilon =3\) and \(\zeta =0.25\). Our results show that for our estimated parameters resulting in \(R_0\approx 0.022\), shifting \(\beta _P\) away from zero has little noticeable effect, while shifting \(\gamma \) away from zero strongly moves the equilibrium away from the addiction-free state (see Fig. 7). This suggests that in a nearly addiction-free population, prescription-induced addiction remains far more important than securing prescriptions away from non-prescribed users. Note that in the exact case of an AFE, it is always stable when \(\gamma =\beta _P=0\) for a parameter space centered around the other parameters listed in Table 1.

Further analysis of the model parameter space when \(\gamma =\beta _P=0\) was conducted using the Sobol method (Sobol 2001). We chose \(N=800{,}000\) and generated \(N(2D+2)\) parameter sets (where \(D=9\) is the dimension of the parameter space) via Saltelli’s extension of the Sobol sequence (Saltelli 2002; Saltelli et al. 2010) for a total of 16 million samples. We then ran the model to 10,000 years for each set of parameters to arrive at an equilibrium. We subsequently conducted Sobol analysis (Sobol 2001) on the values for SPA,  and R after the final year. Initial conditions for each simulation were \(S(0)=0.9435\), \(P(0)=0.05\), \(A(0)=0.0062\), and \(R(0)=0.0003\) (Fig. 8).

A.6: Further Numerical Exploration of Parameter Space

In this section, we expand our parameter space exploration for \(\{\epsilon ,\zeta \}\in [0.8,8.0]\times [0.2,2.0]\) by examining parameter sensitivity for each of SPA,  and R instead of only the addicted class. More specifically, we examine the associated effects of \(\epsilon \) and \(\zeta \) on the predicted populations for 10 years into the future for each of the following cases:

  1. 1.

    Prescription Addiction Rate (\(\gamma \)),

  2. 2.

    Treatment Success Rate (\(\delta \)),

  3. 3.

    Prescription Rate (\(\alpha \)),

  4. 4.

    Prescription Rate vs. Prescription-Induced Addiction (\(\alpha \) vs. \(\gamma \)).

Figures 5 and 9 show that as \(\gamma \) increases the addicted population grows. In particular, if \(\gamma \) doubles from its estimated value, there exists \((\epsilon ,\zeta )\) for which \(2\%\) of the population becomes addicted to opioids, which is approximately three times the number of addicts in 2016. Moreover, as \(\gamma \) increases, so does the rehabilitation class. Interestingly, for values of \((\epsilon ,\zeta )\) that make the addicted class roughly \(2\%\) of the population, the rehabilitation class makes up approximately \(1\%\). On the other hand, when the rehabilitation class composes roughly \(1.5\%\) of the population, the addicted class makes up roughly the same percentage. When \(\delta \) increases the rehabilitation class, population decreases near zero. The population of the addicted class decreases toward zero as well, while the populations of the susceptible class and prescribed class appear unaffected (Fig. 10).

Fig. 12
figure12

Prescription-induced addiction vs. prescription completion. color maps illustrating the long-term equilibrium solutions (\(S^*,P^*,R^*\), and \(A^*\)) for prescription rates (\(\alpha \)) and rehabilitation rates (\(\zeta \)) between 0 and 1 and for various rates of prescription-induced addiction (\(\gamma \)) and rates of finishing prescriptions (\(\epsilon \)) (Color figure online)

Figure 11 shows that if the prescription rate \(\alpha \) is small enough, the entire population almost remains in the susceptible class. However, for certain values of \((\epsilon ,\zeta )\) roughly \(0.5\%\) of the population can still remain in the addicted population. Moreover, for all cases of \(\alpha \) and small \(\zeta \), the rehabilitation class’ population remains near zero for almost all values of \(\epsilon \).

Finally, we explore the relationship between prescription-induced addiction (\(\gamma \)) and completing the prescription and heading back into the susceptible class (\(\epsilon \)). Situations in which these two parameters do not add to one could be used to model long- or short-term opioid prescription use. The data are presented in Fig. 12. It is clear that a decrease in \(\epsilon \) corresponds to an increase in the number of addicts as might be expected for more chronic opioid prescription use. For large \(\gamma \), those differences are more subtle, as increasing \(\gamma \) leads to a profound escalation in the addicted population regardless of \(\epsilon \).

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Battista, N.A., Pearcy, L.B. & Strickland, W.C. Modeling the Prescription Opioid Epidemic. Bull Math Biol 81, 2258–2289 (2019). https://doi.org/10.1007/s11538-019-00605-0

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Keywords

  • Population biology
  • Dynamical systems
  • Epidemiology
  • Compartmental model
  • Mathematical biology
  • Prescription drug addiction