Using data from 'visible' populations to estimate the size and importance of 'hidden' populations in an epidemic: A modelling technique.

We used reported behavioural data from cisgender men who have sex with men and transgender women (MSM/TGW) in Bangalore, mainly collected from 'hot-spot' locations that attract MSM/TGW, to illustrate a technique to deal with potential issues with the representativeness of this sample. A deterministic dynamic model of HIV transmission was developed, incorporating three subgroups of MSM/TGW, grouped according to their reported predominant sexual role (insertive, receptive or versatile). Using mathematical modelling and data triangulation for 'balancing' numbers of partners and role preferences, we compared three different approaches to determine if our technique could be useful for inferring characteristics of a more 'hidden' insertive MSM subpopulation, and explored their potential importance for the HIV epidemic. Projections for 2009 across all three approaches suggest that HIV prevalence among insertive MSM was likely to be less than half that recorded in the surveys (4.5-6.5% versus 13.1%), but that the relative size of this subgroup was over four times larger (61-69% of all MSM/TGW versus 15%). We infer that the insertive MSM accounted for 10-20% of all prevalent HIV infections among urban males aged 15-49. Mathematical modelling can be used with data on 'visible' MSM/TGW to provide insights into the characteristics of 'hidden' MSM. A greater understanding of the sexual behaviour of all MSM/TGW is important for effective HIV programming. More broadly, a hidden subgroup with a lower infectious disease prevalence than more visible subgroups, has the potential to contain more infections, if the hidden subgroup is considerably larger in size

Meeting Report: WHO Workshop on modelling global mortality and aetiology estimates of enteric pathogens in children under five. Cape Town, 28-29th November 2018.

Investment in vaccine product development should be guided by up-to-date and transparent global burden of disease estimates, which are also fundamental to policy recommendation and vaccine introduction decisions. For low- and middle-income countries (LMICs), vaccine prioritization is primarily driven by the number of deaths caused by different pathogens. Enteric diseases are known to be a major cause of death in LMICs. The two main modelling groups providing mortality estimates for enteric diseases are the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, Seattle and the Maternal Child Epidemiology Estimation (MCEE) group, led by Johns Hopkins Bloomberg School of Public Health. Whilst previous global diarrhoea mortality estimates for under five-year-olds from these two groups were closely aligned, more recent estimates for 2016 have diverged, particularly with respect to numbers of deaths attributable to different enteric pathogens. This has impacted prioritization and investment decisions for vaccines in the development pipeline. The mission of the Product Development for Vaccines Advisory Committee (PDVAC) at the World Health Organisation (WHO) is to accelerate product development of vaccines and technologies that are urgently needed and ensure they are appropriately targeted for use in LMICs. At their 2018 meeting, PDVAC recommended the formation of an independent working group of subject matter experts to explore the reasons for the difference between the IHME and MCEE estimates, and to assess the respective strengths and limitations of the estimation approaches adopted, including a review of the data on which the estimates are based. Here, we report on the proceedings and recommendations from a consultation with the working group of experts, the IHME and MCEE modelling groups, and other key stakeholders. We briefly review the methodological approaches of both groups and provide a series of proposals for investigating the drivers for the differences in enteric disease burden estimates

Who mixes with whom among men who have sex with men? Implications for modelling the HIV epidemic in southern India

In India, the identity of men who have sex with men (MSM) is closely related to the role taken in anal sex (insertive, receptive or both), but little is known about sexual mixing between identity groups. Both role segregation (taking only the insertive or receptive role) and the extent of assortative (within-group) mixing are known to affect HIV epidemic size in other settings and populations. This study explores how different possible mixing scenarios, consistent with behavioural data collected in Bangalore, south India, affect both the HIV epidemic, and the impact of a targeted intervention. Deterministic models describing HIV transmission between three MSM identity groups (mostly insertive Panthis/Bisexuals, mostly receptive Kothis/Hijras and versatile Double Deckers), were parameterised with behavioural data from Bangalore. We extended previous models of MSM role segregation to allow each of the identity groups to have both insertive and receptive acts, in differing ratios, in line with field data. The models were used to explore four different mixing scenarios ranging from assortative (maximising within-group mixing) to disassortative (minimising within-group mixing). A simple model was used to obtain insights into the relationship between the degree of within-group mixing, R0 and equilibrium HIV prevalence under different mixing scenarios. A more complex, extended version of the model was used to compare the predicted HIV prevalence trends and impact of an HIV intervention when fitted to data from Bangalore. With the simple model, mixing scenarios with increased amounts of assortative (within-group) mixing tended to give rise to a higher R0 and increased the likelihood that an epidemic would occur. When the complex model was fit to HIV prevalence data, large differences in the level of assortative mixing were seen between the fits identified using different mixing scenarios, but little difference was projected in future HIV prevalence trends. An oral pre-exposure prophylaxis (PrEP) intervention was modelled, targeted at the different identity groups. For intervention strategies targeting the receptive or receptive and versatile MSM together, the overall impact was very similar for different mixing patterns. However, for PrEP scenarios targeting insertive or versatile MSM alone, the overall impact varied considerably for different mixing scenarios; more impact was achieved with greater levels of disassortative mixing

Determinants of Population Variability in HIV across West Africa: Ecological and Mathematical Modelling Analyses.

Introduction: Mathematical models of HIV transmission have played an important role in helping to understand the drivers of the HIV epidemic, and shape the global HIV response. The underlying approaches, assumptions and structures used in HIV modelling have the potential to fundamentally influence the conclusions of any modelling analyses. For this reason, it is important that approaches to modelling HIV transmission in different contexts carefully consider how best to ‘characterise’ a populations distribution of risk and networks of sexual interaction based on data, and the implications of incorporating different levels of epidemiological complexity into their modelling. Across West Africa there are substantial variations in population HIV prevalence - ranging from 0.5-6%. To date, there has been limited exploration of the potential factors influencing this population variation. This PhD aims to inform our understanding of the determinants of population variations in HIV prevalence across West Africa, using a combination of ecological analysis of population data, and both simple and more complex epidemiological modelling. The findings are used both to explore the determinants of HIV transmission across West Africa, and to discuss the implications for future modelling and epidemic appraisal approaches. Methods: A range of modelling and epidemiological analytical approaches were used. Firstly, an existing policy model, The Modes of Transmission (MoT) model, designed to predict patterns of HIV incidence, was revised and re-parameterised using data from Nigeria, to explore the effect on overall conclusions of adding additional heterogeneity into the model, and considering more explicitly how to model HIV risk amongst lower-risk subgroups. Secondly, population data from 13 West African countries were compiled. Linear regression analyses were used to assess potential relationships between HIV prevalence in high-risk groups and population HIV prevalence and the size of high-risk population subgroups and HIV prevalence in the general population. Based on the findings from the MoT and ecological analysis, a dynamic deterministic model was developed to explore the variations in HIV prevalence across West Africa. The population model not only included sex work, client and general population sub-groups, but also included a category of adolescent females (15-24) and a category of males with multiple sexual partners, with a mixing formulation being used to vary the degree the adolescent females form partnerships with clients of female sex workers and the subgroup of males who have multiple partnerships Input parameters were sampled from ranges relevant for West Africa, using Latin Hypercube sampling. The model was fitted to equilibrium prevalence in the general population. Results: A critique and revisions to the MoT, identified high levels of infections in previously unrecognised subgroups. These included 16% of new infections occurring in young females engaging in transactional sex. Findings from the ecological analysis, showed that across West Africa HIV prevalence in FSWs and their clients is not associated with higher HIV prevalence in the general population. Instead, the size of groups of males and females with multiple partners is correlated with higher HIV prevalence levels. The deterministic model generated 11000 fits. Grouping fits, based on epidemic size (with 1% incremental increases from 0-6%), the findings revealed that population sizes of key subgroups is the predominant driver of the epidemic. For epidemics where prevalence is less than 3%, FSW population size is the most important determinant of HIV prevalence. For epidemics above 3%, it is the size of the group of adolescent females with multiple partners and their level of interaction with clients of FSWs that is the most significant variable related to higher HIV prevalence. When the limiting effects on HIV transmission of male circumcision are removed from the model, the findings are less clear, with both sex work and the role of adolescent females with multiple partners being important determinants of the epidemic. Circumcision is however shown to significantly limit the magnitude of an epidemic and epidemic categorisation should account for these variations accordingly. Conclusions: Behavioural heterogeneity has long been recognised as an important component of model development. The results from this thesis show the importance of carefully considering how to compartmentalise population HIV models. Even for simple static models, the inclusion of additional subgroups change model conclusions and suggests different intervention priorities. The use of results and findings from ecological analyses, whilst unable to provide strong evidence of causality, can provide useful insights into the relationship between population level factors or behavioural variables and HIV prevalence in the general population. These findings may then be used to inform model development. Deterministic dynamic modelling used in this thesis demonstrates that the size and sexual networks of vulnerable subgroups in the population may be of key importance in determining levels of HIV epidemics in West Africa. In-particular, adolescent females engaging in noncommercial multiple partnerships, often associated with transactional exchange are an important determinant of the HIV epidemic in West Africa. An improved understanding of this group, their size and motivations for engaging in multiple partnerships, through the use of epidemic mapping techniques and social research, will be important to future HIV intervention activities