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Modelling HIV/AIDS in the presence of an HIV testing and screening campaign.

  • F Nyabadza
  • Z Mukandavire
J. Theor. Biol. 2011; 280 (1): 167-179
Abstract
Preventing and managing the HIV/AIDS epidemic in South Africa will dominate the next decade and beyond. Reduction of new HIV infections by implementing a comprehensive national HIV prevention programme at a sufficient scale to have real impact remains a priority. In this paper, a deterministic HIV/AIDS model that incorporates condom use, screening through HIV counseling and testing (HCT), regular testing and treatment as control strategies is proposed with the objective of quantifying the effectiveness of HCT in preventing new infections and predicting the long-term dynamics of the epidemic. It is found that a backward bifurcation occurs if the rate of screening is below a certain threshold, suggesting that the classical requirement for the basic reproduction number to be below unity though necessary, is not sufficient for disease control in this case. The global stabilities of the equilibria under certain conditions are determined in terms of the model reproduction number R(0). Numerical simulations are performed and the model is fitted to data on HIV prevalence in South Africa. The effects of changes in some key epidemiological parameters are investigated. Projections are made to predict the long-term dynamics of the disease. The epidemiological implications of such projections on public health planning and management are discussed.

Unit definitions have no effect on the numerical analysis of the model. It remains the responsibility of the modeler to ensure the internal numerical consistency of the model. If units are provided, however, the consistency of the model units will be checked.

Name Definition
Id Name Spatial dimensions Size
default 0.0 1.0
Id Name Initial quantity Compartment Fixed
An Unscreened AIDS 20000.0 default
As Screened AIDS 0.0 default
EXT EXT 1.0 default
Ins Unscreened HIV infected 145000.0 default
Is Screened HIV infected 0.0 default
It Treated HIV infected 0.0 default
Sn Unscreened susceptible 18373000.0 default
Ss Screened susceptible 0.0 default

Initial assignments are expressions that are evaluated at time=0. It is not recommended to create initial assignments for all model entities. Restrict the use of initial assignments to cases where a value is expressed in terms of values or sizes of other model entities. Note that it is not permitted to have both an initial assignment and an assignment rule for a single model entity.

Definition
Id Name Objective coefficient Reaction Equation and Kinetic Law Flux bounds
v_1 EXT = Sn

Pi_n
v_10 v_10 Ins = An

Rho_1 * Ins
v_11 Ss = Is

p * (1 - Psi) * Lambda * Ss
v_12 Is = EXT

Mu * Is
v_13 Is = It

Sigma * Is
v_14 Is = As

Rho_2 * Is
v_15 It = EXT

Mu * It
v_16 It = As

Rho_3 * It
v_17 An = EXT

Mu * An
v_18 An = EXT

Delta_1 * An
v_19 As = EXT

Mu * As
v_2 Sn = Ins

Lambda * Sn
v_20 As = EXT

Delta_2 * As
v_3 Sn = EXT

Mu * Sn
v_4 Sn = Ss

k * Sn
v_5 EXT = Ss

Pi_s
v_6 Ss = Ins

q * (1 - Psi) * Lambda * Ss
v_7 Ss = EXT

Mu * Ss
v_8 Ins = EXT

Mu * Ins
v_9 Ins = Is

k * Ins

Global parameters

Id Value
Beta 0.4584
Delta_1 0.293
Delta_2 0.3379
Eta_1 0.7203
Eta_2 1.3785
Lambda 0.0
Mu 0.032
Phi_1 0.1
Phi_2 0.000073471
Pi_n 920540.0
Pi_s 750000.0
Psi 0.2915
Rho_1 0.2372
Rho_2 0.1828
Rho_3 0.13
Sigma 0.0655
Theta 0.1605
c 1.5892
k 0.0388
m 45.1202
p 0.1
people_with_HIV 0.0
q 0.9

Local parameters

Id Value Reaction

Assignment rules

Definition
Lambda = c*(1.0 - Theta)*Beta*exp ((-m*(Delta_1*An + Delta_2*As))/(Sn + Ss + Ins + Is + It + An + As))*((Ins + Eta_1*(Is + Phi_1*It) + Eta_2*(An + Phi_2*As))/(Sn + Ss + Ins + Is + It + An + As))
people_with_HIV = Ins + Is +It

Rate rules

Definition

Algebraic rules

Definition
Trigger Assignments