Modeling Sustained Transmission of Wolbachia among Anopheles Mosquitoes: Implications for Malaria Control in Haiti.

Wolbachia infection in Anopheles albimanus mosquitoes can render mosquitoes less capable of spreading malaria. We developed and analyzed a mechanistic compartmental ordinary differential equation model to evaluate the effectiveness of Wolbachia-based vector control strategies among wild Anopheles mosquitoes in Haiti. The model tracks the mosquito life stages, including egg, larva, and adult (male and female). It also accounts for critical biological effects, such as the maternal transmission of Wolbachia through infected females and cytoplasmic incompatibility, which effectively sterilizes uninfected females when they mate with infected males. We derive and interpret dimensionless numbers, including the basic reproductive number and next-generation numbers. The proposed system presents a backward bifurcation, which indicates a threshold infection that needs to be exceeded to establish a stable Wolbachia infection. The sensitivity analysis ranks the relative importance of the epidemiological parameters at baseline. We simulate different intervention scenarios, including prerelease mitigation using larviciding and thermal fogging before the release, multiple releases of infected populations, and different release times of the year. Our simulations show that the most efficient approach to establishing Wolbachia is to release all the infected mosquitoes immediately after the prerelease mitigation process. Moreover, the model predicts that it is more efficient to release during the dry season than the wet season.

Sensitivity Analysis in an Immuno-Epidemiological Vector-Host Model.

Sensitivity Analysis (SA) is a useful tool to measure the impact of changes in model parameters on the infection dynamics, particularly to quantify the expected efficacy of disease control strategies. SA has only been applied to epidemic models at the population level, ignoring the effect of within-host virus-with-immune-system interactions on the disease spread. Connecting the scales from individual to population can help inform drug and vaccine development. Thus the value of understanding the impact of immunological parameters on epidemiological quantities. Here we consider an age-since-infection structured vector-host model, in which epidemiological parameters are formulated as functions of within-host virus and antibody densities, governed by an ODE system. We then use SA for these immuno-epidemiological models to investigate the impact of immunological parameters on population-level disease dynamics such as basic reproduction number, final size of the epidemic or the infectiousness at different phases of an outbreak. As a case study, we consider Rift Valley Fever Disease utilizing parameter estimations from prior studies. SA indicates that [Formula: see text] increase in within-host pathogen growth rate can lead up to [Formula: see text] increase in [Formula: see text] up to [Formula: see text] increase in steady-state infected host abundance, and up to [Formula: see text] increase in infectiousness of hosts when the reproduction number [Formula: see text] is larger than one. These significant increases in population-scale disease quantities suggest that control strategies that reduce the within-host pathogen growth can be important in reducing disease prevalence.

Cost-effectiveness of Check It: A Novel Community-Based Chlamydia Screening and Expedited Treatment Program for Young Black Men.

BACKGROUND: We assessed the cost-effectiveness of the Check It program, a novel community-based chlamydia screening and expedited partner treatment program for young Black men conducted in New Orleans since 2017. METHODS: We implemented a probabilistic cost-effectiveness model using a synthetic cohort of 16 181 men and 13 419 women intended to simulate the size of the Black, sexually active population in New Orleans ages 15-24 years. RESULTS: The Check It program cost $196 838 (95% confidence interval [CI]: $117 320-$287 555) to implement, saved 10.2 quality-adjusted life-years (QALYs; 95% CI: 7.7-12.7 QALYs), and saved $140 950 (95% CI: -$197 018 to -$105 620) in medical costs per year. The program cost $5468 (95% CI: cost saving, $16 717) per QALY gained. All iterations of the probabilistic model returned cost-effectiveness ratios less than $50 000 per QALY gained. CONCLUSIONS: The Check It program (a bundled seek, test, and treat chlamydia prevention program for young Black men) is cost-effective under base case assumptions. Communities where Chlamydia trachomatis rates have not declined could consider implementing a similar program.

Generating a heterosexual bipartite network embedded in social network.

We describe an approach to generate a heterosexual network with a prescribed joint-degree distribution embedded in a prescribed large-scale social contact network. The structure of a sexual network plays an important role in how all sexually transmitted infections (STIs) spread. Generating an ensemble of networks that mimics the real-world is crucial to evaluating robust mitigation strategies for controlling STIs. Most of the current algorithms to generate sexual networks only use sexual activity data, such as the number of partners per month, to generate the sexual network. Real-world sexual networks also depend on biased mixing based on age, location, and social and work activities. We describe an approach to use a broad range of social activity data to generate possible heterosexual networks. We start with a large-scale simulation of thousands of people in a city as they go through their daily activities, including work, school, shopping, and activities at home. We extract a social network from these activities where the nodes are the people, and the edges indicate a social interaction, such as working in the same location. This social network captures the correlations between people of different ages, living in different locations, their economic status, and other demographic factors. We use the social contact network to define a bipartite heterosexual network that is embedded within an extended social network. The resulting sexual network captures the biased mixing inherent in the social network, and models based on this pairing of networks can be used to investigate novel intervention strategies based on the social contacts among infected people. We illustrate the approach in a model for the spread of chlamydia in the heterosexual network representing the young sexually active community in New Orleans.

Using an agent-based sexual-network model to analyze the impact of mitigation efforts for controlling chlamydia.

Chlamydia trachomatis (Ct) is the most reported sexually transmitted infection in the United States, with a major cause of infertility, pelvic inflammatory disease, and ectopic pregnancy among women. Despite decades of screening women for Ct, rates increase among young African Americans (AA). We create and analyze a heterosexual agent-based network model to help understand the spread of Ct. We calibrate the model parameters to agree with survey data showing Ct prevalence of 12% of the women and 10% of the men in the 15-25 year-old AA in New Orleans, Louisiana. Our model accounts for both long-term and casual partnerships. The network captures the assortative mixing of individuals by preserving the joint-degree distributions observed in the data. We compare the effectiveness of intervention strategies based on randomly screening men, notifying partners of infected people, which includes partner treatment, partner screening, and rescreening for infection. We compare the difference between treating partners of an infected person both with and without testing them. We observe that although increased Ct screening, rescreening, and treating most of the partners of infected people will reduce the prevalence, these mitigations alone are not sufficient to control the epidemic. The current practice is to treat the partners of an infected individual without first testing them for infection. The model predicts that if a sufficient number of the partners of all infected people are tested and treated, then there is a threshold condition where the epidemic can be mitigated. This threshold results from the expanded treatment network created by treating an individual's infected partners' partners. Although these conclusions can help design future Ct mitigation studies, we caution the reader that these conclusions are for the mathematical model, not the real world, and are contingent on the validity of the model assumptions.

Staged progression epidemic models for the transmission of invasive nontyphoidal Salmonella (iNTS) with treatment.

We develop and analyze a stage-progression compartmental model to study the emerging invasive nontyphoidal Salmonella (iNTS) epidemic in sub-Saharan Africa. iNTS bloodstream infections are often fatal, and the diverse and non-specific clinical features of iNTS make it difficult to diagnose. We focus our study on identifying approaches that can reduce the incidence of new infections. In sub-Saharan Africa, transmission and mortality are correlated with the ongoing HIV epidemic and severe malnutrition. We use our model to quantify the impact that increasing antiretroviral therapy (ART) for HIV infected adults and reducing malnutrition in children would have on mortality from iNTS in the population. We consider immunocompromised subpopulations in the region with major risk factors for mortality, such as malaria and malnutrition among children and HIV infection and ART coverage in both children and adults. We parameterize the progression rates between infection stages using the branching probabilities and estimated time spent at each stage. We interpret the basic reproduction number R0 as the total contribution from an infinite infection loop produced by the asymptomatic carriers in the infection chain. The results indicate that the asymptomatic HIV+ adults without ART serve as the driving force of infection for the iNTS epidemic. We conclude that the worst disease outcome is among the pediatric population, which has the highest infection rates and death counts. Our sensitivity analysis indicates that the most effective strategies to reduce iNTS mortality in the studied population are to improve the ART coverage among high-risk HIV+ adults and reduce malnutrition among children.

Effect of screening young men for Chlamydia trachomatis on the rates among women: a network modelling study for high-prevalence communities.

OBJECTIVE: Chlamydia trachomatis (Ct) is the most commonly reported sexually transmitted infection in the USA and causes important reproductive morbidity in women. The Centers for Disease Control and Prevention recommend routine screening of sexually active women under age 25 but not among men. Despite three decades of screening women, chlamydia prevalence in women remains high. Untested and untreated men can serve as a reservoir of infection in women, and male-screening based intervention can be an effective strategy to reduce infection in women. We assessed the impact of screening men on the Ct prevalence in women. DESIGN: We created an individual-based network model to simulate a realistic chlamydia epidemic on sexual contact networks for a synthetic population (n=5000). The model is calibrated to the ongoing routine screening among African American (AA) women in the USA and detailed a male-screening programme, Check It, that bundles best practices for Ct control. We used sensitivity analysis to quantify the relative importance of each intervention component. SETTING: Community-based venues in New Orleans, Louisiana, USA. PARTICIPANTS: Heterosexual AA men, aged 15 to 24, who had sex with women in the past 2 months. INTERVENTION: Venue-based screening, expedited index treatment, expedited partner treatment and rescreening. RESULTS: We estimate that by annually screening 7.5% of the AA male population in the age-range, the chlamydia prevalence would be reduced relatively by 8.1% (95% CI 5.9% to 10.4%) in AA women and 8.8% (95% CI 6.9% to 10.8%) in AA men. Each man screened could prevent 0.062 (95% CI 0.030 to 0.094) cases in men and 0.204 (95% CI 0.143 to 0.267) cases in women. The model suggested the importance of intervention components ranked from high to low as venue-based screening, expedited index treatment, expedited partner treatment and rescreening. CONCLUSION: The findings indicated that male-screening has the potential to substantially reduce the prevalence among women in high-prevalence communities.

An Investigation of Human-Mosquito Contact Using Surveys and Its Application in Assessing Dengue Viral Transmission Risk.

Aedes-borne viral diseases such as dengue fever are surging in incidence in recent years. To investigate viral transmission risks, the availability of local transmission parameters is essential. One of the most important factors directly determining infection risk is human-mosquito contact. Yet the contact rate is not often characterized, compared with other risk metrics such as vector density, because of the limited research tool options. In this study, human-mosquito contact was assessed in two study sites in the Southern United States using self-administered standardized survey instruments. The fraction of mosquito bites attributed to important vector species was estimated by human landing sampling. The survey participants reported a significantly higher outdoor mosquito bite exposure than indoor. The reported bite number was positively correlated with outdoor time during at-risk periods. There was also a significant effect of the study site on outdoor bite exposure, possibly due to the differing vector density. Thus, the levels of human-mosquito contact in this study were influenced both by the mosquito density and human behaviors. A dengue virus transmission model demonstrated that the observed difference in the contact rates results in differential virus transmission risks. Our findings highlight the practicality of using surveys to investigate human-mosquito contact in a setting where bite exposure levels differ substantially, and serve as a basis for further evaluations. This study underscores a new avenue that can be used in combination with other field methods to understand how changes in human behavior may influence mosquito bite exposure which drives mosquito-borne virus transmission.