A mathematical modeling study of the HIV epidemics at two rural townships in the Liangshan Prefecture of the Sichuan Province of China

Mathematical modeling of HIV-HCV co-infection model: Impact of parameters on reproduction number

Background: Hepatitis C Virus (HCV) and Human Immunodeficiency Virus (HIV) are both as classified blood-borne viruses since they are transmitted through contact with contaminated blood. Approximately 1.3 million of the 2.75 million global HIV/HCV carriers inject drugs (PWID). HIV co-infection has a harmful effect on the progression of HCV, resulting in greater rates of HCV persistence after acute infection, higher viral levels, and accelerated progression of liver fibrosis and end-stage liver disease. In this study, we developed and investigated a mathematical model for the dynamical behavior of HIV/AIDS and HCV co-infection, which includes therapy for both diseases, vertical transmission in HIV cases, unawareness and awareness of HIV infection, inefficient HIV treatment follow-up, and efficient condom use. Methods: Positivity and boundedness of the model under investigation were established using well-known theorems. The equilibria were demonstrated by bringing all differential equations to zero. The associative reproduction numbers for mono-infected and dual-infected models were calculated using the next-generation matrix approach. The local and global stabilities of the models were validated using the linearization and comparison theorem and the negative criterion techniques of bendixson and dulac, respectively. Results: The growing prevalence of HIV treatment dropout in each compartment of the HIV model led to a reduction in HIV on treatment compartments while other compartments exhibited an increase in populations . In dually infected patients, treating HCV first reduces co-infection reproduction number R ech , which reduces liver cancer risk. Conclusions: From the model's results, we infer various steps that policymakers could take to reduce the number of mono-infected and co-infected individuals.

Mathematical modeling of HIV-HCV co-infection model: Impact of parameters on reproduction number

Background: Hepatitis C Virus (HCV) and Human Immunodeficiency Virus (HIV) are both classified as blood-borne viruses since they are transmitted through contact with contaminated blood. Approximately 1.3 million of the 2.75 million global HIV/HCV carriers are people who inject drugs (PWID). HIV co-infection has a harmful effect on the progression of HCV, resulting in greater rates of HCV persistence after acute infection, higher viral levels, and accelerated progression of liver fibrosis and end-stage liver disease. In this study, we developed and investigated a mathematical model for the dynamical behavior of HIV/AIDS and HCV co-infection, which includes therapy for both diseases, vertical transmission in HIV cases, unawareness and awareness of HIV infection, inefficient HIV treatment follow-up, and efficient condom use. Methods: Positivity and boundedness of the model under investigation were established using well-known theorems. The equilibria were demonstrated by bringing all differential equations to zero. The associative reproduction numbers for mono-infected and dual-infected models were calculated using the next-generation matrix approach. The local and global stabilities of the models were validated using the linearization and comparison theorem and the negative criterion techniques of bendixson and dulac, respectively. Results: The growing prevalence of HIV treatment dropout in each compartment of the HIV model led to a reduction in HIV on treatment compartments while other compartments exhibited an increase in populations . In dually infected patients, treating HCV first reduces co-infection reproduction number R ech , which reduces liver cancer risk. Conclusions: From the model's results, we infer various steps (such as: campaigns to warn individuals about the consequences of having multiple sexual partners; distributing more condoms to individuals; continuing treatment for chronic HCV and AIDS) that policymakers could take to reduce the number of mono-infected and co-infected individuals.

Epidemic models with discrete state structures

The state of an infectious disease can represent the degree of infectivity of infected individuals, or susceptibility of susceptible individuals, or immunity of recovered individuals, or a combination of these measures. When the disease progression is long such as for HIV, individuals often experience switches among different states. We derive an epidemic model in which infected individuals have a discrete set of states of infectivity and can switch among different states. The model also incorporates a general incidence form in which new infections are distributed among different disease states. We discuss the importance of the transmission-transfer network for infectious diseases. Under the assumption that the transmission-transfer network is strongly connected, we establish that the basic reproduction numberR 0 is a sharp threshold parameter: ifR 0 ≤ 1 , the disease-free equilibrium is globally asymptotically stable and the disease always dies out; ifR 0 > 1 , the disease-free equilibrium is unstable, the system is uniformly persistent and initial outbreaks lead to persistent disease infection. For a restricted class of incidence functions, we prove that there is a unique endemic equilibrium and it is globally asymptotically stable whenR 0 > 1 . Furthermore, we discuss the impact of different state structures onR 0 , on the distribution of the disease states at the unique endemic equilibrium, and on disease control and preventions. Implications to the COVID-19 pandemic are also discussed.

Survey of Pretreatment HIV Drug Resistance and Genetic Transmission Network Analysis Among HIV Patients in a High Drug-Use Area of Southwest China

Background: Pretreatment drug resistance (PDR) poses an increasing threat to the success of antiretroviral treatment (ART) programs in China. We aimed to conduct a survey of PDR among HIV patients in an area in Southwest China with extensive drug trafficking.

Methods: Consecutive cross-sectional surveys were conducted in Liangshan Prefecture of Sichuan Province from 2009 to 2018 based on the WHO-recommended method. PDR was identified by testing pol region sequences with the Stanford HIVdb algorithm (version 7.0). PDR prevalence and related factors were assessed by multivariable logistic regression. The transmission of HIV drug resistance was analyzed using a genetic transmission network.

Results: HIV-1 pol genes from 1889 patients were successfully amplified. The distribution of HIV-1 genotypes was as follows: CRF07_BC (94.0%), CRF08_BC (2.3%), CRF01_AE (2.0%) and others (1.4%). Of the participants, 6.9% (95% CI: 4.1-8.1%) had pretreatment resistance to 12 antiretroviral drugs recommended by the WHO, and nucleoside reverse transcriptase inhibitor (NRTI), non-nucleoside reverse transcriptase inhibitor (NNRTI) and protease inhibitors (PI) resistance were identified among 1.4% (95% CI: 0.7-3.4%), 5.8% (95% CI: 1.2-8.7%) and 0.4% (95% CI: 0.1-3.0%) of the patients, respectively. In the multivariate logistic model, the prevalence of PDR was 1.52-fold higher among intravenous drug users (IDUs) than among patients infected by heterosexual transmission (95% CI: 1.07-2.38; P=0.049), and the prevalence of PDR among patients diagnosed from 2017-2018 was 2.03-fold higher than that among patients diagnosed from 2009-2016 (95% CI: 1.18-5.76; P=0.018). A total of 26 clusters containing PDR and a rapidly growing drug resistance-related cluster containing the E138Q and V179D mutations were identified by genetic transmission network analysis.

Conclusion: The results show a moderate overall level of PDR prevalence and rapidly growing drug resistance over time. Preventive intervention should be focused on controlling the HIV epidemic among drug users, and surveillance is urgently needed to monitor the trend of PDR.

Evidence-based modeling of combination control on Kenyan youth HIV/AIDS dynamics

We formulate a sex-structured deterministic model to study the effects of varying HIV testing rates, condom use rates and ART adherence rates among Adolescent Girls and Young Women (AGYW) and, Adolescent Boys and Young Men (ABYM) populations in Kenya. Attitudes influencing the Kenyan youth HIV/AIDS control measures both positively and negatively were considered. Using the 2012 Kenya AIDS Indicator Survey (KAIS) microdata we constructed our model, which we fitted to the UNAIDS-Kenya youth prevalence estimates to understand factors influencing Kenyan youth HIV/AIDS prevalence trends. While highly efficacious combination control approach significantly reduces HIV/AIDS prevalence rates among the youth, the disease remains endemic provided infected unaware sexual interactions persist. Disproportional gender-wise attitudes towards HIV/AIDS control measures play a key role in reducing the Kenyan youth HIV/AIDS prevalence trends. The female youth HIV/AIDS prevalence trend seems to be directly linked to increased male infectivity with decreased female infectivity while the male youth prevalence trend seems to be directly associated with increased female infectivity and reduced male infectivity.