Inferring HIV Transmission Network Determinants Using Agent-Based Models Calibrated to Multi-Data Sources
Abstract
:1. Background
2. Methods
2.1. Simulation Tool: Simpact Cyan
2.2. Simulation of HIV Epidemic
2.3. Selected Parameters for Calibration
2.4. Selected Summary Features
2.5. Calibration Scheme
3. Results
3.1. Age Mixing Patterns in Sexual Partnerships
3.2. Onward Hiv Transmission
3.3. Temporal Trend of Hiv Incidence
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Simpact Cyan Simulation Model
Appendix A.1. Configuration
Appendix A.1.1. Create the Initial Population
Appendix A.1.2. Schedule the Initial Events
Appendix A.1.3. Hazard Functions and Parameters
Appendix A.2. Model Parameters: Events’ Hazard Functions and Associated Settings
Appendix A.2.1. Sexual Partnership Event
- , the value of in the expression for the hazard, allowing one to establish a baseline value.
- controls , which allows you to vary the preferred age gap with the age of the man in the relationship; and
- controls , which allows you to vary the preferred age gap with the age of the woman in the relationship.
- , the value of in the hazard formula, corresponding to a weight for the number of relationships the man in the relationship has; and
- , the value of in the hazard formula, corresponding to a weight for the number of relationships the woman in the relationship has.
- , the value of in the hazard expression, by which the influence of the difference in number of partners can be specified.
- , the value of weight of in the expression for the hazard, a weight for the average age of the partners.
Appendix A.2.2. Sexual Partnership Dissolution Event
- , the value of in the expression for the hazard, allowing one to establish a baseline value.
- , the value of in the expression for the hazard, corresponding to a weight for the number of relationships the man in the relationship has.
Appendix A.2.3. Relationship-Related Settings
Eagerness
- , represents parameter for a man’s eagerness following a gamma distribution
- , represents parameter for a woman’s eagerness following a gamma distribution
- , represents parameter for a man’s eagerness following a gamma distribution
- , represents parameter for a woman’s eagerness following a gamma distribution
Age Gap Preference
- , for a man’s mean of normal distribution of age preference between men and women
- , for a woman’s mean of normal distribution of age preference between women and men
- , for a man’s standard deviation of normal distribution of age preference between men and women
- , for a woman’s standard deviation of normal distribution of age preference between women and men
HIV Transmission Event
- refers to the value of a in the expression for the hazard, providing a baseline value.
- refers to the value of b in the expression for the hazard. Together with the value of c this specifies the influence of the current viral load of the infected person.
- refers to the value of c in the expression for the hazard. Together with the value of b, this specifies the influence of the current viral load of the infected person.
- refers to the value of in the expression of the hazard.
- refers to the value of in the expression of the hazard. Furthermore, by configuring the weights and , it becomes possible to change the susceptibility of a woman depending on her age.
Appendix A.2.4. Hiv Infection Monitoring Event
- parameter for the threshold set for the infected person to be offered antiretroviral treatment.
- parameter to lower the person’s set-point viral load value if treatment is started.
- ART acceptance.
Appendix A.2.5. Diagnosis Event
- controls the corresponding baseline value in the expression for the hazard.
Appendix A.2.6. Hiv Infection Stage and Viral Load
- : when the viral load during the acute stage is needed, it is determined in such a way that the transmission hazard increases by this factor, possibly clipped to a maximum value ().
- : when the viral load during the initial AIDS stage is needed, it is determined in such a way that the transmission hazard increases by this factor, possibly clipped to a maximum value ().
- : when the viral load during the final AIDS stage is needed, it is determined in such a way that the transmission hazard increases by this factor, possibly clipped to a maximum value ().
Appendix A.2.7. Art Treatment Dropout Event
Appendix A.2.8. Aids Mortality Event
- C is set in the model by
- k is set in the model by
- one dimensional distribution can be used to add some randomness to the survival time until the person dies of AIDS-related causes after becoming infected with HIV.
Appendix A.2.9. Conception Event
- set by baseline for a conception hazard function
Parameter | Explanation | Value |
---|---|---|
Initial configuration | ||
Simulation time | 40 | |
Same sex sexual partnership | no | |
Initial male population | 5000 | |
Initial female population | 5000 | |
Time to introduce HIV into the population | 10 | |
Consider the amount not proportion of seeded individuals among the population | ||
Amount of HIV seeded individuals | 10 | |
Minimum age for seeded individuals | 20 | |
Maximum age for seeded individuals | 50 | |
Age of being sexually active | 15 | |
Maximum events to be simulated (beyond this number of events, the simulation will stop, it can also stop with population.simtime) | 1.2 million 1 | |
Specify how many persons of the opposite sex (who are sexually active), specified as a fraction, someone can possibly have relationships | 0.2 | |
Demographic | ||
boy/girl ratio | ||
Baseline for conception event | ||
Sexual partnership | ||
parameter for male eagerness following a gamma distribution | 0.23 | |
parameter for female eagerness following a gamma distribution | 0.23 | |
parameter for male eagerness following a gamma distribution | 45 | |
parameter for female eagerness following a gamma distribution | 45 | |
Distribution type for age gap preference for men | Normal | |
Distribution type for age gap preference for women | Normal | |
Baseline for sexual partnerships | 2 | |
Mean for the normal distribution of the age gap preference for men | 10 | |
Mean for the normal distribution of the age gap preference for women | 10 | |
Standard deviation for the normal distribution of the age gap preference for men | 5 | |
Standard deviation for the normal distribution of the age gap preference for women | 5 | |
Allows varying of the preferred age gap with the age of the man in the relationship | ||
Allows varying of the preferred age gap with the age of the woman in the relationship | ||
Corresponding to a weight for the number of relationships the man in the relationship has | −0.3 | |
Corresponding to a weight for the number of relationships the man in the relationship has | −0.3 | |
Influence of the difference in number of partners can be specified | −0.1 | |
Baseline for relationship dissolution | ||
Weight for the average age of the partners in relationship dissolution | ||
HIV transmission | ||
Baseline value for HIV transmission. | ||
Influence of the current viral load of the infected person. | ||
Influence of the current viral load of the infected person. | ||
Weight for susceptibility of a woman depending on her age. | ||
Weight for susceptibility of a woman depending on her age. | ||
HIV infection stages | ||
Viral load during the acute stage. | 5 | |
Viral load during the initial AIDS stage. | 7 | |
Viral load during the final AIDS stage. | 12 | |
HIV infection monitoring | ||
Lower the person’s set-point viral load value when someone started ART. | 0 | |
ART interventions | ||
For ART intervention we have: | ||
Time for ART intervention | ||
Diagnosis baseline | ||
CD4 count eligibility cutoff | ||
ART introduction 1: | ||
23 (2000 **) | ||
−2 | ||
100 | ||
ART introduction 2: | ||
25 (2002 **) | ||
−1.8 | ||
150 | ||
ART introduction 3: | ||
28 (2005 **) | ||
−1.5 | ||
200 | ||
ART introduction 4: | ||
33 (2010 **) | ||
−1 | ||
350 | ||
ART introduction 5: | ||
36 (2013 **) | ||
−1 | ||
500 | ||
ART introduction 6: | ||
39 (2016 **) | ||
−1 | ||
700 | ||
ART acceptance | ||
Specification of ART dropout distribution | Uniform | |
Minimum value of the uniform dropout distribution | ||
Maximum value of the uniform dropout distribution | ||
AIDS mortality and survival | ||
Relationship between set-point viral load and survival. | 65 | |
Relationship between set-point viral load and survival. | −0.2 | |
Type of distribution type for survival time randomness. | Normal | |
Mean of the normal distribution for survival time randomness. | ||
Standard deviation of the normal distribution for survival time randomness. |
Sub-Component | Assumptions |
---|---|
Demographic | Birth: when there is a sexual partnership formation, a conception event will be scheduled; after a conception event is triggered, a new birth event will be scheduled, so that the woman in the relationship will give birth to a new person at a specific time, and the gender will be determined by the boy/girl ratio. Mortality: normal mortality model follows Weibull distribution, and the time for AIDS mortality was determined as the time of infection plus the survival time. |
Sexual partnership | We considered sexual partnerships such that the preferred age gap differed from one person to the next, but there was also an age dependent component in this preferred age gap, and we allowed for the weight of the age gap terms to be age-dependent. We assumed the age gap to be normally distributed. The hazard function for partnership depended on the number of partners the man and woman in the relationship had. The debut age was set to be 15 years for men and women. Once a sexual partnership was established, it was subject to dissolution as well. |
HIV transmission | Transmission is likely to occur when one individual in a sexual partnership is infected with HIV. Transmission depends on the viral load level of an infected individual. A woman’s susceptibility to HIV infection was considered to depend on her age. Set-point viral load is the viral load that the person has during the chronic stage. In the acute stage or in the Acquired immunodeficiency syndrome (AIDS) stages, the configuration values , and cause the real viral load to differ from the set-point viral load in such a way that the transmission probability is altered: the hazard for transmission will increase by the factor x that is defined in this way. We also assumed that, once an infected individual is on ART, s/he cannot transmit the infection; thus, we set to 0. |
ART interventions | If this CD4 count is below the threshold set in , the person will be offered antiretroviral treatment (ART). Depending on the person’s willingness to accept treatment, treatment will then be started. We considered gradually increasing ART eligibility based on CD4 count thresholds as implemented in real life. |
Disease progression | Follow up on the progress of the disease is performed by inspecting the person’s CD4 count. When a person receives treatment, the viral load is lowered and if the person drops out of treatment the viral load will increase. |
Appendix A.3. Hiv Evolutionary Dynamic
Name | Value |
---|---|
Relative Frequencies | |
Adenine (A) | 0.3857 |
Cytosine (C) | 0.1609 |
Guanine (G) | 0.2234 |
Thymine (T) | 0.2300 |
Relative substitution rates | |
r(A → G) = r(G → A) | 2.9114 |
r(A → C) = r(C → A) | 12.5112 |
r(A → T) = r(T → A) | 1.2569 |
r(G → C) = r(C → G) | 0.8559 |
r(G → T) = r(T → G) | 12.9379 |
r(C → T) = r(T → C) | 1.0000 |
Rate heterogeneity | |
Shape parameter | 0.9 |
Number of gamma rate categories | 4 |
Fraction of invariant sites | |
Proportion of invariant sites (I) | 0.5230 |
Evolutionary rate | |
Substitutions/site/year 2 |
Appendix B. Calibration of Simpact Cyan: Parameters and Summary Features
Appendix B.1. Selected Parameters for Calibration
cfg.list["dissolution.alpha_0"] <- inputvector[1] cfg.list["dissolution.alpha_4"] <- inputvector [2] cfg.list["formation.hazard.agegapry.baseline"] <- inputvector[3] cfg.list["person.agegap.man.dist.normal.mu"] <- inputvector[4] cfg.list["person.agegap.woman.dist.normal.mu"] <- inputvector[4] cfg.list["person.agegap.man.dist.normal.sigma"] <- inputvector[5] cfg.list["person.agegap.woman.dist.normal.sigma"] <- inputvector[5] cfg.list["formation.hazard.agegapry.gap_agescale_man"] <- inputvector[6] cfg.list["formation.hazard.agegapry.gap_agescale_woman"] <- inputvector[6] cfg.list["formation.hazard.agegapry.numrel_man"] <- inputvector[7] cfg.list["formation.hazard.agegapry.numrel_woman"] <- inputvector[7] cfg.list["formation.hazard.agegapry.numrel_diff"] <- inputvector[8] cfg.list["hivtransmission.param.a"] <- inputvector[9] cfg.list["hivtransmission.param.b"] <- inputvector[10] cfg.list["hivtransmission.param.c"] <- inputvector[11] cfg.list["hivtransmission.param.f1"] <- inputvector[12] cfg.list["hivtransmission.param.f2"] <- inputvector[13]
Parameter | Calibration 1 | Calibration 2 | Calibration 3 | Input | Space |
---|---|---|---|---|---|
−0.514 [−0.654, −0.397] | −0.52 [−0.652, −0.39] | −0.514 [−0.653, −0.391] | −0.52 | [−0.78, −0.26] | |
−0.053 [−0.064, −0.041] | −0.052 [−0.063, −0.04] | −0.053 [−0.064, −0.041] | −0.05 | [−0.075, −0.025] | |
1.957 [1.513, 2.401] | 1.976 [1.546, 2.409] | 1.965 [1.54, 2.398] | 2 | [1, 3] | |
10.277 [8.287, 12.324] | 9.666 [7.433, 11.913] | 10.11 [7.949, 12.195] | 10 | [5, 15] | |
4.898 [3.84, 6.128] | 4.989 [3.728, 6.248] | 4.96 [3.839, 6.191] | 5 | [2.5, 7.5] | |
0.255 [0.2, 0.308] | 0.244 [0.186, 0.299] | 0.252 [0.198, 0.306] | 0.25 | [0.125, 0.375] | |
−0.299 [−0.371, −0.23] | −0.302 [−0.37, −0.237] | −0.302 [−0.371, −0.239] | −0.3 | [−0.45, −0.15] | |
−0.101 [−0.126, −0.076] | −0.099 [−0.122, −0.074] | −0.1 [−0.124, −0.075] | −0.1 | [−0.15, −0.05] | |
−1.016 [−1.215, −0.739] | −0.977 [−1.195, −0.726] | −0.997 [−1.216, −0.739] | −1 | [−1.5, −0.5] | |
−85.888 [−109.426, −65.1] | −84.888 [−107.133, −64.703] | −85.927 [−109.915, −65.038] | −90 | [−135, −45] | |
0.589 [0.501, 0.662] | 0.599 [0.512, 0.676] | 0.59 [0.507, 0.662] | 0.5 | [0.25, 0.75] | |
0.048 [0.036, 0.061] | 0.048 [0.036, 0.06] | 0.048 [0.036, 0.06] | 0.048 | [0.024, 0.072] | |
−0.142 [−0.177, −0.106] | −0.142 [−0.18, −0.106] | −0.141 [−0.177, −0.105] | −0.14 | [−0.21, −0.07] |
Appendix B.2. Parameter Space
Appendix B.3. Summary Features
Appendix B.3.1. Summary Features from Epidemiological and Sexual Behaviour Data
Parameter | Value |
---|---|
Demographic | |
Population growth rate | 0.97933 |
HIV epidemiology & ART intervention | |
Prevalence for men aged between 15 and 24 years | 0.0113 |
Prevalence for men aged between 25 and 29 years | 0.04081 |
Prevalence for men aged between 30 and 34 years | 0.07115 |
Prevalence for men aged between 35 and 39 years | 0.09903 |
Prevalence for men aged between 40 and 44 years | 0.114 |
Prevalence for men aged between 45 and 49 years | 0.10446 |
Prevalence for women aged between 15 and 24 years | 0.06467 |
Prevalence for women aged between 25 and 29 years | 0.12797 |
Prevalence for women aged between 30 and 34 years | 0.14411 |
Prevalence for women aged between 35 and 39 years | 0.1243 |
Prevalence for women aged between 40 and 44 years | 0.07557 |
Prevalence for women aged between 45 and 49 years | 0.05917 |
Incidence for men aged between 15 and 24 years | 0.00198 |
Incidence for men aged between 25 and 29 years | 0.00438 |
Incidence for men aged between 30 and 34 years | 0.0053 |
Incidence for men aged between 35 and 39 years | 0.00505 |
Incidence for men aged between 40 and 44 years | 0.0026 |
Incidence for men aged between 45 and 49 years | 0.00107 |
Incidence for women aged between 15 and 24 years | 0.00619 |
Incidence for women aged between 25 and 29 years | 0.00128 |
Incidence for women aged between 30 and 34 years | 0.00045 |
Incidence for women aged between 35 and 39 years | 0.00027 |
Incidence for women aged between 40 and 44 years | 0.00011 |
Incidence for women aged between 45 and 49 years | 0.00008 |
Antiretrovial treatment coverage at 33 years of simulation time | 0.30333 |
Antiretrovial treatment coverage at 34 years of simulation time | 0.42936 |
Antiretrovial treatment coverage at 35 years of simulation time | 0.46119 |
Antiretrovial treatment coverage at 36 years of simulation time | 0.48869 |
Antiretrovial treatment coverage at 37 years of simulation time | 0.61132 |
Antiretrovial treatment coverage at 38 years of simulation time | 0.64089 |
Antiretrovial treatment coverage at 39 years of simulation time | 0.66607 |
Antiretrovial treatment coverage at 40 years of simulation time | 0.79465 |
Viral suppression | 0.84 |
Sexual behaviour | |
Point prevalence of concurrent partnerships for men | 0.01935 |
Average number of relationships per person per year | 0.4 |
Mean of age gap | 14.91719 |
Median of age gap | 15.28017 |
Standard deviation of age gap | 6.76228 |
Appendix B.3.2. Summary Features from Phylogenetic Tree Data
Parameter | Value |
---|---|
Phylogenetic tree topology | |
Mean of node heights | 14.26082 |
Median of node heights | 15.38992 |
Standard deviation of node heights | 5.96359 |
Colless index | 0.08942 |
Sackin index | 0.13846 |
Mean for tips’ depths | 13.55503 |
Median for tips’ depths | 13.26482 |
Standard deviation for tips’ depths | 5.26124 |
Mean for nodes’ depths | 11.62515 |
Median for nodes’ depths | 11.34367 |
Standard deviation for nodes’ depths | 5.38234 |
Phylogenetic tree branch length | |
Minimum value of branch length | 0.00105 |
First quantile of branch length | 0.69956 |
Median value of branch length | 2.33402 |
Mean value of branch length | 3.87808 |
Third quantile of branch length | 5.50728 |
Maximum value of branch length | 20.1533 |
Maximum Height | 0.13391 |
Appendix B.3.3. Combined Summary Features
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Estimate | Benchmark | Calibration 1 | Calibration 1 * | Calibration 2 | Calibration 2 * | Calibration 3 | Calibration 3 * |
---|---|---|---|---|---|---|---|
AAD | 13.782 | 13.861 | 14.167 | 13.151 | 13.111 | 13.633 | 13.83 |
SDAD | 6.15 | 6.114 | 6.17 | 6.008 | 6.113 | 6.073 | 6.164 |
BSD | 2.138 | 2.013 | 2.077 | 2.026 | 2.137 | 2.004 | 2.098 |
WSD | 1.812 | 1.781 | 1.774 | 1.837 | 1.807 | 1.811 | 1.779 |
Slope | 0.315 | 0.297 | 0.302 | 0.316 | 0.319 | 0.305 | 0.305 |
Intercept | −2.378 | −2.111 | −2.336 | −2.099 | −2.2 | −2.164 | −2.264 |
Estimate | MRE 1 | MRE 1 * | MRE 2 | MRE 2 * | MRE 3 | MRE 3 * |
---|---|---|---|---|---|---|
AAD | 0.16399 | 0.03948 | 0.19238 | 0.05177 | 0.16766 | 0.0325 |
SDAD | 0.10248 | 0.04145 | 0.10447 | 0.04005 | 0.10367 | 0.04208 |
BSD | 0.17373 | 0.06907 | 0.16063 | 0.06254 | 0.16258 | 0.06986 |
WSD | 0.09965 | 0.057 | 0.10925 | 0.05368 | 0.10193 | 0.05984 |
Slope | 0.20558 | 0.08395 | 0.21915 | 0.07315 | 0.20482 | 0.07606 |
Intercept | 0.37034 | 0.16361 | 0.39138 | 0.16511 | 0.39999 | 0.16004 |
Estimate | Benchmark | Calibration 1 | Calibration 1 * | Calibration 2 | Calibration 2 * | Calibration 3 | Calibration 3 * |
---|---|---|---|---|---|---|---|
Mean | 2.242 | 2.384 | 2.245 | 2.323 | 2.24 | 2.356 | 2.246 |
Median | 1.391 | 1.351 | 1.31 | 1.391 | 1.303 | 1.354 | 1.337 |
Standard deviation | 2.12 | 2.622 | 2.27 | 2.428 | 2.296 | 2.579 | 2.273 |
Estimate | MRE 1 | MRE 1 * | MRE 2 | MRE 2 * | MRE 3 | MRE 3 * |
---|---|---|---|---|---|---|
Mean | 0.17513 | 0.1349 | 0.15716 | 0.12686 | 0.16807 | 0.13272 |
Median | 0.3271 | 0.31999 | 0.34116 | 0.31962 | 0.32561 | 0.32279 |
Standard deviation | 0.46932 | 0.32561 | 0.39235 | 0.32676 | 0.45539 | 0.33333 |
Year | Age Group | MRE 1 | MRE 1 * | MRE 2 | MRE 2 * | MRE 3 | MRE 3 * |
---|---|---|---|---|---|---|---|
2013 | 15–24 | 1.05475 | 0.88048 | 1.34137 | 1.0745 | 1.06295 | 0.93416 |
25–29 | 0.87811 | 0.76712 | 0.95202 | 0.88461 | 0.89938 | 0.78916 | |
30–34 | 0.91606 | 0.8875 | 0.96849 | 0.91955 | 0.96005 | 0.88494 | |
35–39 | 0.78408 | 0.66974 | 0.7441 | 0.67547 | 0.75629 | 0.67397 | |
40–44 | 0.89173 | 0.78456 | 0.79473 | 0.73747 | 0.91572 | 0.74142 | |
45–49 | 1.23131 | 1.04045 | 1.13016 | 0.99237 | 1.22711 | 1.00805 | |
2014 | 15–24 | 1.02293 | 0.89883 | 1.26296 | 1.0851 | 1.08533 | 0.97876 |
25–29 | 0.96249 | 0.88694 | 1.10231 | 1.02698 | 0.98951 | 0.90741 | |
30–34 | 0.86408 | 0.87018 | 0.90373 | 0.89681 | 0.92165 | 0.84121 | |
35–39 | 0.97827 | 0.87733 | 0.93042 | 0.88425 | 0.99837 | 0.8737 | |
40–44 | 1.06991 | 0.91003 | 0.92059 | 0.91656 | 0.97718 | 0.88609 | |
45–49 | 1.45055 | 1.29848 | 1.27103 | 1.16657 | 1.4448 | 1.15002 | |
2015 | 15–24 | 1.15855 | 0.89525 | 1.33417 | 1.25347 | 1.27041 | 1.03238 |
25–29 | 1.00525 | 0.93552 | 1.09395 | 1.16055 | 1.12176 | 0.9917 | |
30–34 | 0.92342 | 0.89038 | 0.91207 | 0.96075 | 0.94996 | 0.93358 | |
35–39 | 1.07437 | 0.93358 | 0.95423 | 0.90022 | 0.90746 | 0.87915 | |
40–44 | 1.07316 | 0.88081 | 0.96276 | 0.80236 | 1.00017 | 0.85782 | |
45–49 | 1.75576 | 1.50713 | 1.53888 | 1.44127 | 1.55175 | 1.37258 | |
2016 | 15–24 | 1.13425 | 0.98986 | 1.33453 | 1.22738 | 1.25674 | 1.0544 |
25–29 | 1.02155 | 0.96523 | 1.23536 | 1.05684 | 1.05984 | 0.96505 | |
30–34 | 0.91918 | 0.94391 | 0.97993 | 1.03122 | 0.97103 | 0.97724 | |
35–39 | 1.18662 | 1.2673 | 1.25378 | 1.12695 | 1.16183 | 1.20835 | |
40–44 | 1.03665 | 0.96006 | 0.96548 | 0.83396 | 0.90925 | 0.92091 | |
45–49 | 1.47005 | 1.41002 | 1.40545 | 1.29973 | 1.38828 | 1.31173 | |
2017 | 15–24 | 1.35979 | 1.1209 | 1.35127 | 1.29633 | 1.39386 | 1.21792 |
25–29 | 0.95806 | 1.01259 | 1.08813 | 1.01433 | 0.99 | 0.97441 | |
30–34 | 1.00497 | 0.97803 | 1.08916 | 1.01595 | 1.02405 | 1.00899 | |
35–39 | 1.34227 | 1.46893 | 1.3267 | 1.30232 | 1.35038 | 1.37524 | |
40–44 | 1.13972 | 1.07222 | 1.20909 | 0.99736 | 1.05262 | 1.03022 | |
45–49 | 1.58792 | 1.51293 | 1.74517 | 1.38875 | 1.47887 | 1.41031 |
Year | Age Group | MRE 1 | MRE 1 * | MRE 2 | MRE 2 * | MRE 3 | MRE 3 * |
---|---|---|---|---|---|---|---|
2013 | 15–24 | 0.53162 | 0.47872 | 0.52974 | 0.49637 | 0.49674 | 0.46769 |
25–29 | 1.1711 | 1.10191 | 1.02598 | 1.08174 | 1.06856 | 1.07796 | |
30-34 | 1.4554 | 1.59738 | 1.48093 | 1.4674 | 1.57672 | 1.38211 | |
35–39 | 1.27389 | 1.56912 | 1.43407 | 1.45138 | 1.4723 | 1.39072 | |
40–44 | 1.5237 | 1.77582 | 1.58568 | 1.65677 | 1.69882 | 1.47681 | |
45–49 | 1.41214 | 1.95796 | 1.45311 | 1.63971 | 1.51047 | 1.46651 | |
2014 | 15–24 | 0.54265 | 0.50475 | 0.58463 | 0.5347 | 0.54235 | 0.48523 |
25–29 | 1.35538 | 1.30714 | 1.25242 | 1.19749 | 1.26711 | 1.21816 | |
30–34 | 1.58765 | 1.74194 | 1.51293 | 1.54455 | 1.59564 | 1.3997 | |
35–39 | 1.2958 | 1.44317 | 1.48171 | 1.56704 | 1.47045 | 1.27727 | |
40–44 | 1.47626 | 1.65227 | 1.57011 | 1.61447 | 1.45799 | 1.44798 | |
45–49 | 1.51765 | 1.7599 | 1.51771 | 1.5789 | 1.49514 | 1.36147 | |
2015 | 15–24 | 0.57955 | 0.52997 | 0.60027 | 0.57768 | 0.58239 | 0.53849 |
25–29 | 1.51821 | 1.57128 | 1.53055 | 1.49222 | 1.59013 | 1.42023 | |
30–34 | 1.46685 | 1.64833 | 1.52767 | 1.55935 | 1.57546 | 1.48934 | |
35–39 | 1.87122 | 1.85779 | 1.71533 | 1.56098 | 1.75373 | 1.34148 | |
40–44 | 1.52558 | 1.72044 | 1.53849 | 1.80755 | 1.46566 | 1.42572 | |
45–49 | 1.72515 | 1.60911 | 1.48315 | 1.52458 | 1.62834 | 1.40109 | |
2016 | 15–24 | 0.58743 | 0.60129 | 0.61815 | 0.62563 | 0.59805 | 0.60696 |
25–29 | 1.46132 | 1.5059 | 1.41493 | 1.4317 | 1.48655 | 1.40186 | |
30–34 | 1.52139 | 1.58994 | 1.67728 | 1.57346 | 1.53513 | 1.44627 | |
35–39 | 1.67303 | 1.88848 | 1.78009 | 1.90138 | 1.91832 | 1.4736 | |
40–44 | 1.40186 | 1.73526 | 1.42221 | 1.50055 | 1.78263 | 1.34671 | |
45–49 | 1.52093 | 2.17204 | 2.05912 | 1.89981 | 1.45132 | 1.52321 | |
2017 | 15–24 | 0.70051 | 0.6883 | 0.79729 | 0.74652 | 0.75446 | 0.71317 |
25–29 | 1.58232 | 1.67152 | 1.58225 | 1.48288 | 1.50227 | 1.45087 | |
30–34 | 1.87383 | 2.07842 | 1.77046 | 1.9108 | 1.87073 | 1.54384 | |
35–39 | 1.73521 | 1.61932 | 1.6716 | 2.07031 | 1.49161 | 1.6285 | |
40–44 | 1.94166 | 2.25749 | 1.89354 | 1.8269 | 1.85843 | 1.5933 | |
45–49 | 1.26963 | 1.61916 | 1.24791 | 2.0685 | 1.32776 | 1.45465 |
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Niyukuri, D.; Chibawara, T.; Nyasulu, P.S.; Delva, W. Inferring HIV Transmission Network Determinants Using Agent-Based Models Calibrated to Multi-Data Sources. Mathematics 2021, 9, 2645. https://doi.org/10.3390/math9212645
Niyukuri D, Chibawara T, Nyasulu PS, Delva W. Inferring HIV Transmission Network Determinants Using Agent-Based Models Calibrated to Multi-Data Sources. Mathematics. 2021; 9(21):2645. https://doi.org/10.3390/math9212645
Chicago/Turabian StyleNiyukuri, David, Trust Chibawara, Peter Suwirakwenda Nyasulu, and Wim Delva. 2021. "Inferring HIV Transmission Network Determinants Using Agent-Based Models Calibrated to Multi-Data Sources" Mathematics 9, no. 21: 2645. https://doi.org/10.3390/math9212645
APA StyleNiyukuri, D., Chibawara, T., Nyasulu, P. S., & Delva, W. (2021). Inferring HIV Transmission Network Determinants Using Agent-Based Models Calibrated to Multi-Data Sources. Mathematics, 9(21), 2645. https://doi.org/10.3390/math9212645