The effects of population structure on the spread of the HIV infection

Pandemics, past and present: The role of biological anthropology in interdisciplinary pandemic studies

Biological anthropologists are ideally suited for the study of pandemics given their strengths in human biology, health, culture, and behavior, yet pandemics have historically not been a major focus of research. The COVID‐19 pandemic has reinforced the need to understand pandemic causes and unequal consequences at multiple levels. Insights from past pandemics can strengthen the knowledge base and inform the study of current and future pandemics through an anthropological lens. In this paper, we discuss the distinctive social and epidemiological features of pandemics, as well as the ways in which biological anthropologists have previously studied infectious diseases, epidemics, and pandemics. We then review interdisciplinary research on three pandemics–1918 influenza, 2009 influenza, and COVID‐19–focusing on persistent social inequalities in morbidity and mortality related to sex and gender; race, ethnicity, and Indigeneity; and pre‐existing health and disability. Following this review of the current state of pandemic research on these topics, we conclude with a discussion of ways biological anthropologists can contribute to this field moving forward. Biological anthropologists can add rich historical and cross‐cultural depth to the study of pandemics, provide insights into the biosocial complexities of pandemics using the theory of syndemics, investigate the social and health impacts of stress and stigma, and address important methodological and ethical issues. As COVID‐19 is unlikely to be the last global pandemic, stronger involvement of biological anthropology in pandemic studies and public health policy and research is vital.

Influence of concurrency, partner choice, and viral suppression on racial disparity in the prevalence of HIV infected women

In 1992, Watts and May introduced a simple dynamical systems model of the spread of HIV based on disease transmission per partnership including the length of partnership duration. This model allowed for the treatment of concurrent partnerships, although it was hampered by the assumption of an important latent phase which generated a non-autonomous system. Subsequent models including concurrency have been based on networks, Monte Carlo, and stochastic simulations which lose a qualitative understanding of the effects of concurrency. We present a new autonomous deterministic model of the effect of concurrent sexual partnerships that allows for an analytical study of disease transmission. We incorporate the effect of concurrency through the newly derived force of infection term in a mathematical model of the transmission of HIV through sexual contact in a population stratified by sexual behavior and race/ethnicity. The model also includes variations in population mixing (partner choice) and non-uniform Highly Active Anti-Retroviral Treatment (HAART) leading to viral suppression. We use this mathematical model to understand the non-uniform spread of HIV in women who were infected through heterosexual contact. In addition, an analytical study shows the importance of continued condom use in virally suppressed MSM. Numerical simulations of the reproduction number as a function of concurrency, viral suppression level, and mixing show a reservoir of disease present in both heterosexual and MSM populations. Statistical analysis of parameter values show that viral suppression level, mixing and progression to AIDS without viral suppression have a strong correlation (either positive or negative) with the number of HIV positive women. Concurrency and assortative mixing are shown to be essential to reproduce infection levels in women, as reported by 2010 data from the Center for Disease Control (CDC).

Mathematical models for the study of HIV spread and control amongst men who have sex with men

For a quarter of century, mathematical models have been used to study the spread and control of HIV amongst men who have sex with men (MSM). We searched MEDLINE and EMBASE databases up to the end of 2010 and reviewed this literature to summarise the methodologies used, key model developments, and the recommended strategies for HIV control amongst MSM. Of 742 studies identified, 127 studies met the inclusion criteria. Most studies employed deterministic modelling methods (80%). Over time we saw an increase in model complexity regarding antiretroviral therapy (ART), and a corresponding decrease in complexity regarding sexual behaviours. Formal estimation of model parameters was carried out in only a small proportion of the studies (22%) while model validation was considered by an even smaller proportion (17%), somewhat reducing confidence in the findings from the studies. Nonetheless, a number of common conclusions emerged, including (1) identification of the importance of assumptions regarding changes in infectivity and sexual contact rates on the impact of ART on HIV incidence, that subsequently led to empirical studies to gather these data, and (2) recommendation that multiple strategies would be required for effective HIV control amongst MSM. The role of mathematical models in studying epidemics is clear, and the lack of formal inference and validation highlights the need for further developments in this area. Improved methodologies for parameter estimation and systematic sensitivity analysis will help generate predictions that more fully express uncertainty, allowing better informed decision making in public health.

Notes from the heterogeneous: a few observations on the implications and necessity of affinity

The problem of who is mixing with whom is of great theoretical importance in the context of heterosexual mixing. In this article, we publish for the first time, data from a study carried out in 1989 that had the goal of estimating who is mixing with whom, in heterosexually active college populations in the presence of co-factors like drinking. The gathering of these data and the challenges involved in modelling the interaction between and among heterosexually active populations of individuals are highlighted in this manuscript. The modelling is based on the assumptions that at least two processes are involved: individual affinities or preferences determine 'what we want' while mixing patterns describe 'what we get'. We revisit past results on the role of affinity/preference on observed mixing patterns in one- and two-sex mixing populations. Some new results for homosexually active populations are presented. The study of mixing is but the means to an end and consequently, we also look at the role of affinity on epidemics as filtered by observed mixing patterns. It would not be surprising to observe that highly distinct preference or mixing structures may actually lead to quite similar epidemic patterns.

Mathematical models for HIV transmission dynamics: tools for social and behavioral science research

HIV researchers have long appreciated the need to understand the social and behavioral determinants of HIV-related risk behavior, but the cumulative impact of individual behaviors on population-level HIV outcomes can be subtle and counterintuitive, and the methods for studying this are rarely part of a traditional social science or epidemiology training program. Mathematical models provide a way to examine the potential effects of the proximate biologic and behavioral determinants of HIV transmission dynamics, alone and in combination. The purpose of this article is to show how mathematical modeling studies have contributed to our understanding of the dynamics and disparities in the global spread of HIV. Our aims are to demonstrate the value that these analytic tools have for social and behavioral sciences in HIV prevention research, to identify gaps in the current literature, and to suggest directions for future research.

Stochastic effects on endemic infection levels of disseminating versus local contacts

The effects of two levels of mixing on endemic infection levels are shown to differ for identically conformed deterministic compartmental (DC) and stochastic compartmental (SC) models. Both DC and SC models give similar endemic levels when populations are large, immunity is short lived, and mixing is universal. But local transmissions and/or transient immunity decrease overall population infection levels in SC but not in DC models. DC models also fail to detect the greater effects of eliminating disseminating transmissions in comparison to eliminating local transmissions shown by SC models. These differences in model behavior arise because localities that encounter few infections from distant sites and that have stochastically low infection levels have decreased infection rates while localities with stochastically high levels of infection do not decrease the rate at which they lose infection. At the extreme this generates local stochastic die out with subsequent build up of susceptibility in SC but not DC models. This phenomenon should act upon all endemic infections that have changing geographic or social foci of infection. Neither standard epidemiological investigations nor sufficient-component cause models can capture these effects because they occur in the absence of differences between individuals.

Infection dynamics on scale-free networks

We discuss properties of infection processes on scale-free networks, relating them to the node-connectivity distribution that characterizes the network. Considering the epidemiologically important case of a disease that confers permanent immunity upon recovery, we derive analytic expressions for the final size of an epidemic in an infinite closed population and for the dependence of infection probability on an individual's degree of connectivity within the population. As in an earlier study [R. Pastor-Satorras and A. Vesipignani, Phys. Rev. Lett. 86, 3200 (2001); Phys. Rev. E. 63, 006117 (2001)] for an infection that did not confer immunity upon recovery, the epidemic process--in contrast with many traditional epidemiological models--does not exhibit threshold behavior, and we demonstrate that this is a consequence of the extreme heterogeneity in the connectivity distribution of a scale-free network. Finally, we discuss effects that arise from finite population sizes, showing that networks of finite size do exhibit threshold effects: infections cannot spread for arbitrarily low transmission probabilities.

Network analytic methods for epidemiological risk assessment

The authors measure the efficacy of three methods for predicting the time to infection for susceptible individuals in a population undergoing an HIV epidemic. The methods differ in whether they require detailed information of the contact network and whether they require knowledge of the initial source of infection. Efficacy is evaluated using simulations for 20 different contact patterns. Only the risk score that uses both kinds of information accounts for more than 15 per cent of individual variability. The efficacy of this score ranges from 10 per cent in very unstructured populations to 60 per cent for spatially localized contact networks. This improved performance may be explained by the larger fraction of the total variability not due to the disease dynamics. When all variables are dichotomized, the two poorer methods produce odds ratios between 1.4 and 2.3. The odds ratio for the risk score with full information ranges from 2.5 to 17. Risk assessment protocols and intervention programmes are encouraged to assess contact patterns and detect sources of infection.

AIDS and promiscuity: muddles in the models of HIV prevention

AIDS has been blamed on promiscuity and the promiscuous, and a major goal of many HIV-prevention programs has been to induce people to reduce the number of their sexual partners. Despite the salience of this concept in the AIDS discourse of scientists, policymakers, the media, religious leaders, and the gay community, critical analysis of the role of promiscuity in this epidemic has been lacking. Following a review of promiscuity in various genres of AIDS discourse, this article discusses promiscuity in American society and in HIV-prevention campaigns. The relative risks associated with monogamy, abstinence and promiscuity are examined, and the author concludes that the partner-reduction strategy, instead of contributing to a reduction in HIV transmission has been an impediment to AIDS prevention efforts, exacerbating the problem by undermining the sex-positive approaches to risk reduction that have proven effective. Responsibility for this misguided strategy is attributed to a moralistic approach to AIDS and to the misapplication of epidemiological concepts and inappropriate social science models to the task of promoting healthy forms of sexuality.