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The epidemic disease model is often used to predict an epidemic's characteristics and help plan an effective control strategy. Motivated by recent COVID-19 outbreaks, we develop and mathematically analyze a simple stochastic model that considers random perturbations in transmission rates and shows its validity using data from the Colombian city of Bogota. The stochastic epidemic model is stratified in the Susceptible-Exposed-Infectious-Recovered, \textit{SEIR}, type compartmental model with the randomness depicting the impact of stochastic variations due to external factors such as changes in environmental conditions and human behaviors that may impact the dynamics of an infectious disease. The analysis resulted in derivation of approximate distribution of \textit{eventual extinction of infection state}, \textit{persistence of infection in the mean}, and \textit{the quasi-stationary infectious state}). Finally, we illustrate inferences and model parameters using reported COVID-19 epidemic data from the Colombian city of Bogotá. The outbreak in Bogota is divided into six distinct periods, with transmission rates high during the initiation of the epidemic and even much higher during the last period, potentially due to the rapid spread of the Omicron variant in Colombia still has a low vaccination rate. Mathematics Subject Classification: 92D30

College-aged students are one of the most vulnerable populations to high-risk alcohol drinking behaviors that could cause them consequences such as injury or sexual assault. An important factor that may influence college students’ decision on alcohol drinking behavior is socializing in certain contexts across the university environment. In the present study, we aim to identify and better understand the ecological conditions driving the dynamics of distribution of alcohol use among college-aged students. To this end, a pilot survey study is conducted to evaluate students’ movement patterns in different contexts across the Arizona State University (ASU) campus, and the results are used to develop an agent-based simulation model designed for examining the role of environmental factors on development and maintenance of alcohol drinking behavior by a representative sample of ASU students. The proposed model, which resembles an approximate reaction–diffusion model, accounts for movement of agents (i.e., diffusion) in various contexts and alcohol drinking influences within those contexts (i.e., reaction) via a Susceptible-Infected-Recovered type individual-based model. Of the four most visited contexts at ASU Tempe campus—Library, Memorial Union, Fitness Center, and Dorm—the context with the highest visiting probability, Memorial Union, is the most influential and most sensitive context (around sixteen times higher an impact of alcohol-related influences than the other contexts) in terms of spreading alcohol drinking behavior. Our findings highlight the crucial role of socialization in local environments on the dynamics of students’ alcohol use as well as on the long-term prediction of the college drinking prevalence.

Schistosomiasis is one of the major neglected tropical diseases (NTDs) with more than 240 million people currently infected, primarily ( 90%) in sub-Saharan Africa. It is treated and primarily controlled through mass drug administration (MDA) in endemic regions. Determining the extent to which an intervention to control Schistosomiasis has been suc- cessful, is essential, but its evaluation become complicated by the potential for an outbreak to evolve over time via change in behaviors of individuals in the affected community. We study the role of changes in population behaviors on the patterns of Schistosomiasis via a novel transmission dynamics model which explicitly involves two free living larval stages, the Cercariae and Miracidia , and two host populations, the snail and the human. The average number of Miracidia reduces due to the change in human behaviors (like avoid wading, swimming and other contacts to contaminated water), consequently, reduction in the average number of Miracidia affect the rate of infection transmission. Thus, the model assumes that change in population behaviors indirectly affect the rate of infection transmission. Rigorous mathematical analysis on the model was carried and it suggests threshold behavior in long term dynamics governed by reproductive number R 0 . The dynamics shows existence of at least two equilibria (disease-free and endemic state) and regions of their global stability in the parameter space. Our results also suggest that removal of snails from the water bodies is most effective control strategy for elimination of Schistosomiasis infection as compared to either increasing of human treatment rates or cleaning of free-living Cercariae from local water bodies. The indirect human behav- ioral changes do not affect the likelihood of the existence/eradication of epidemic (i.e., R 0 does not depend on parameters related to behavioral change), however, their impact are critical on the endemic prevalence. Our results provide some new understanding about elimination of Schistosomiasis and could be useful for optimal distribution of resources for control programs.

The resurgence of the May 2021 COVID-19 wave in India not only pointed to the explosive speed with which SARS-CoV-2 can spread in vulnerable populations if unchecked, but also to the gross misreading of the status of the pandemic when decisions to reopen the economy were made in March 2021. In this combined modelling and scenario-based analysis, we isolated the population and policy-related factors underlying the May 2021 viral resurgence by projecting the growth and magnitude of the health impact and demand for hospital care that would have arisen if the spread was not impeded, and by evaluating the intervention options best able to curb the observed rapidly developing contagion. We show that only by immediately re-introducing a moderately high level of social mitigation over a medium-term period alongside a swift ramping up of vaccinations could the country be able to contain and ultimately end the pandemic safely. We also show that delaying the delivery of the 2nd dose of the Astra Zeneca vaccine, as proposed by the Government of India, would have had only slightly more deleterious impacts, supporting the government’s decision to vaccinate a greater fraction of the population with at least a single dose as rapidly as possible. Our projections of the scale of the virus resurgence based on the observed May 2021 growth in cases and impacts of intervention scenarios to control the wave, along with the diverse range of variable control actions taken by state authorities, also exemplify the importance of shifting from the use of science and knowledge in an ad hoc reactive fashion to a more effective proactive strategy for assessing and managing the risk of fast-changing hazards, like a pandemic. We show that epidemic models parameterized with data can be used in combination with plausible intervention scenarios to enable such policy-making.

Obtaining reasonable estimates for transmission rates from observed data is a challenge when using mathematical models to study the dynamics of “infectious” diseases, like Ebola. Most models assume the transmission rate of a contagion does not vary over time. However, these rates do vary during an epidemic due to environmental conditions, social behaviors, and public-health interventions deployed to control the disease. Therefore, obtaining time-dependent rates can aid in understanding the progression of disease through a population. We derive an analytical expression using a standard SIR-type mathematical model to compute time-dependent transmission rate estimates for an epidemic in terms of either incidence or prevalence type available data. We illustrate applicability of our method by applying data on various public health problems, including infectious diseases (Ebola, SARS, and Leishmaniasis) and social issues (obesity and alcohol drinking) to compute transmission rates over time. We show that transmission rate estimates can have a large variation over time, depending on the type of available data and other epidemiological parameters. Time-dependent estimation of transmission rates captures the dynamics of the problem and can be utilized to understand disease progression through population accurately. Alternatively, constant estimations may provide unacceptable results that could have major public health consequences.

Stochastic epidemic models, generally more realistic than deterministic counterparts, have often been seen too complex for rigorous mathematical analysis because of level of details it requires to comprehensively capture the dynamics of diseases. This problem further becomes intense when complexity of diseases increases as in the case of vector-borne diseases (VBD). The VBDs are human illnesses caused by pathogens transmitted among humans by intermediate species, which are primarily arthropods. In this study, a stochastic VBD model is developed and novel mathematical methods are described and evaluated to systematically analyze the model and understand its complex dynamics. The VBD model incorporates some relevant features of the VBD transmission process including demographical, ecological and social mechanisms, and different host and vector dynamic scales. The analysis is based on dimensional reductions and model simplifications via scaling limit theorems. The results suggest that the dynamics of the stochastic VBD depends on a threshold quantity R 0 , the initial size of infectives, and the type of scaling in terms of host population size. The quantity R 0 for deterministic counterpart of the model is interpreted as a threshold condition for infection persistence as is mentioned in the literature for many infectious disease models. Different scalings yield different approximations of the model, and in particular, if vectors have much faster dynamics, the effect of the vector dynamics on the host population averages out, which largely reduces the dimension of the model. Specific scenarios are also studied using simulations for some fixed sets of parameters to draw conclusions on dynamics.

The advent of vaccinations has heightened global optimism that the end of the SARS-CoV-2 pandemic could be in sight. However, concerns, including the impact of variations in the rates of vaccination between countries, raise questions about the use of mass vaccination for accomplishing a quick recovery from the contagion. Here, we used a SEIR-based model calibrated to data on the pandemic and vaccinations reported for the United States (US) and India to gain strategic insights into using mass vaccinations for ending COVID-19. We estimate that while up to 65% of the US population is already immune to the virus due to the recent rapid mass vaccinations carried out, only 13% of the Indian population may be immune currently owing to a slow rate of vaccination and the effect of a stricter lockdown imposed to curb the first wave of the pandemic. We project that due to the higher immune to susceptible ratio already achieved in the US, the pandemic will only decline if the present rates of vaccinations and social mitigations are continued and remain effective. By contrast, the recent loosening of social measures coupled with a slow rate of vaccination is the chief reason for the virus resurgence in India, with only immediate lockdowns coupled with ramping up of vaccinations providing the means to control the present wave. These results highlight that using mass vaccination to achieve a speedy recovery from the SARS-CoV-2 pandemic will depend crucially on the ability to carry out national vaccinations as rapidly as possible.

Acetaminophen (APAP) overdose is one of the predominant causes of drug induced acute liver injury in the U.S and U.K. Clinical studies show that ingestion of alcohol may increase the risk of APAP induced liver injury. Chronic alcoholism may potentiate APAP hepatotoxicity and this increased risk of APAP toxicity is observed when APAP is ingested even shortly after alcohol is cleared from the body. However, clinical reports also suggest that acute alcohol consumption may have a protective effect against hepatotoxicity by inhibiting microsomal acetaminophen oxidation and thereby reducing N-acetyl-p-benzoquinone imine (NAPQI) production. The aim of this study is to model this dual role of alcohol to determine how the timing of alcohol ingestion affects APAP metabolism and resulting liver injury and identify mechanisms of APAP induced liver injury. The mathematical model is developed to capture condition of a patient of single time APAP overdose who may be an acute or chronic alcohol user. The analysis suggests that the risk of APAP-induced hepatotoxicity is increased if APAP is ingested shortly after alcohol is cleared from the body in chronic alcohol users. A protective effect of acute consumption of alcohol is also observed in patients with APAP overdose. For example, simultaneous ingestion of alcohol and APAP overdose or alcohol intake after or before few hours of APAP overdose may result in less APAP-induced hepatotoxicity when compared to a single time APAP overdose. The rate of hepatocyte damage in APAP overdose patients depends on trade-off between induction and inhibition of CYP enzyme.

We study a general multi-host model of visceral leishmaniasis including both humans and animals, and where host and vector characteristics are captured via host competence along with vector biting preference. Additionally, the model accounts for spatial heterogeneity in human population and heterogeneity in biting behaviour of sandflies. We then use parameters for visceral leishmaniasis in the Indian subcontinent as an example and demonstrate that the model exhibits backward bifurcation, i.e. it has a human infection and a sandfly population threshold, characterized by a bi-stable region. These thresholds shift as a function of host competence, host population size, vector feeding preference, spatial heterogeneity, biting heterogeneity and control efforts. In particular, if control is applied through human treatment a new and lower human infection threshold is created, making elimination difficult to achieve, before eventually the human infection threshold no longer exists, making it impossible to control the disease by only reducing the infection levels below a certain threshold. A better strategy would be to reduce the human infection below a certain threshold potentially by early diagnosis, control animal population levels and keep the vector population under check. Spatial heterogeneity in human populations lowers the overall thresholds as a result of weak migration between patches.

IntroductionAs SARS-COV-2 and the disease COVID-19 is sweeping through countries after countries around the globe, it is critical to understand potential burden of a future outbreak in cities of Colombia. This pandemic has affected most of the countries in the world because the high global movement of individuals and excessive cost in interventions.ObjectiveUsing demographic data from city of Cali, disease epidemiological information from affected countries and mathematical models, we estimated the rate of initial exponential growth of new cases and the basic reproductive rate for a potential outbreak in city of Cali in Colombia.Materials and methodsWe used dynamical models with different modeling assumptions such as use of various types of interventions and/or epidemiological characteristics to compare and contrast the differences between Colombian cities and between Latin American countries.ResultsUnder the assumption of homogeneously mixing population and limited resources, we predicted expected number of infected, hospitalized, in Intensive Care Units (ICU) and deaths during this potential COVID-19 outbreak. Our results suggest that on a given day in Cali there may be up to around 73000 cases who might need hospitalization under no intervention. However, this number drastically reduces if we carry out only-isolation intervention (with 16 days of symptomatic infection; ~13,000 cases) versus both quarantining for 6 days and isolation within 16 days (~3500 cases). The peak in Cali will reach in 2-3 months.ConclusionsThe estimates from these studies provides different scenarios of outbreaks and can help Cali to be better prepared during the ongoing COVID-19 outbreak.

Airlines have introduced a back-to-front boarding process in response to the COVID-19 pandemic. It is motivated by the desire to reduce passengers' likelihood of passing close to seated passengers when they take their seats. However, our prior work on the risk of Ebola spread in airplanes suggested that the driving force for increased exposure to infection transmission risk is the clustering of passengers while waiting for others to stow their luggage and take their seats. In this work, we examine whether the new boarding processes lead to increased or decreased risk of infection spread. We also study the reasons behind the risk differences associated with different boarding processes. We accomplish this by simulating the new boarding processes using pedestrian dynamics and compare them against alternatives. Our results show that back-to-front boarding roughly doubles the infection exposure compared with random boarding. It also increases exposure by around 50% compared to a typical boarding process prior to the outbreak of COVID-19. While keeping middle seats empty yields a substantial reduction in exposure, our results show that the different boarding processes have similar relative strengths in this case as with middle seats occupied. We show that the increased exposure arises from the proximity between passengers moving in the aisle and while seated. Our results suggest that airlines either revert to their earlier boarding process or adopt the better random process., Comment: 9 pages, 5 figures

Obtaining reasonable estimates for transmission rates from observed data is a challenge when using mathematical models to study the dynamics of ?infectious? diseases, like Ebola. Most models assume the transmission rate of a contagion either does not vary over time or change in a fixed pre-determined adhoc ways. However, these rates do vary during an outbreak due to multitude of factors such as environmental conditions, social behaviors, and public-health interventions deployed to control the disease, which are in-part guided by changing size of an outbreak. We derive analytical estimates of time-dependent transmission rate for an epidemic in terms of either incidence or prevalence using a standard mathematical SIR-type epidemic model. We illustrate applicability of our method by applying data on various public health problems, including infectious diseases (Ebola, SARS, and Leishmaniasis) and social issues (obesity and alcohol drinking) to compute transmission rates over time. We show that time-dependent transmission rate estimates can have a large variation, depending on the type of available data and other epidemiological parameters. Time-dependent estimation of transmission rates captures the dynamics of the problem better and can be utilized to understand disease progression more accurately.

Prevention of crops from pest has always been a serious cause of concern throughout the world, especially in agricultural countries as it contributes majorly to the economy of these countries. Keeping in mind this issue, we have formulated a mathematical model of Eggplant fruit and shoot borer which has been controlled through biological control. The dynamical analysis of the system has been discussed which includes the boundedness and positivity of the system. Local stability analysis of the system is discussed followed by local bifurcation analysis of each steady state solutions.The global stability analysis of non-trivial is done by graph theoretic approach by constructing a Lyapunov function. The optimization of the biological control strategy has also been discussed using linear feedback control. Further, we have also proposed a Spatio-temporal model and investigated it to observe the evolution of diffusive pattern in a biological pest control model. The reason of discussing the Spatio-temporal model is to focus on how diffusion can also be a method to control the pest population. The numerical simulation has been discussed to validate our analytical results obtained. Finally, we have also obtained the global sensitivity analysis using LHS method for U , x 2 and x 3 .

Objective To stratify and understand the potential transmission processes of Zika virus in Colombia, in order to effectively address the efforts on surveillance and disease control. Methods We compare R0, of Zika for municipalities based on data from the regional surveillance system of Antioquia, Colombia. The basic reproduction number (R0) and its 95% confidence intervals were estimated from an SIR model with implicit vector dynamics, in terms of recovered individuals in each time unit, using an approximate solution. These parameters were estimated fitting the solution of the model to the daily cumulative frequency of each Zika case according to symptoms onset date relative to the index case reported to the local surveillance system. Results R0 was estimated for 20 municipalities with a median of 30,000 inhabitants, all located less than 2,200 m above sea level. The reported cases ranged from 17 to 347 between these municipalities within four months (January to April of 2016). The results suggest that 15 municipalities had a high transmission potential (R0>1), whereas in 5 municipalities transmission were potentially not sustaining (R01) and provide a technique to optimize surveillance and control of Zika. Health authorities should promote the collection, analysis, modeling and sharing of anonymous data onto individual cases to estimate R0. This article is protected by copyright. All rights reserved.

Background: Underreporting of Visceral Leishmaniasis (VL) in India remains a problem to public health controls. Effective and reliable surveillance systems are critical for monitoring disease outbreaks and public health control programs. However, in India, government surveillance systems are affected by levels of scarcity in resources and therefore, uncertainty surrounds the true incidence of asymptomatic and clinical cases, affecting morbidity and mortality rates. The State of Bihar alone contributes up to the 40\% of the worldwide VL cases. The inefficiency of surveillance systems occurs because of multiple reasons including delay in seeking health care, accessing non-authentic health care clinics, and existence of significant asymptomatic self healing infectious cases. This results in a failure of the system to adequately report true transmission rates and number of symptomatic cases that have sought medical advice (thus, high underreporting of cases). Objectives and Methods: There are several methods to estimate the extent of underreporting in the surveillance system. In this research, we use a mathematical dynamic model and two different types of data sets, namely, monthly incidence for 2003-2005 and yearly incidence from 2006-2012 from the Bihar's 21 most VL affected districts out of its 38 districts. The goals of the study are to estimate critical metrics to measure level of transmission and to evaluate the estimation process between the two data sets and 21 districts. In particularly, our focus is on (i) estimating infection transmission potential, underreporting level in incidence and proportion of self-healing cases, (ii) quantifying reproduction number of the$R_0$, and (iii) comparing underreporting incidence levels and proportion of self-healing cases between the two periods 2003-2005 and 2006-2012 and between 21 districts. Results: Our research suggests that the number of asymptomatic individuals in the population who eventually self-heal may have a significant effect on the dynamics of VL spread. The estimated mean self-healing proportion (out of all infected) is found to be $\sim 0.6$ with only 7 out of 21 affected districts having self-healing proportion less than $0.5$ for both data sets. The estimated mean underreporting level is at least $64$\% for the state of Bihar. The estimates of the basic reproduction numbers obtained are similar in magnitude for most of the districts, being in the range of (0.88, 2.79) and (0.98, 1.01) for 2003-2005 and 2006-2012, respectively. Conclusions: The estimates for the two types (monthly and yearly) of temporal data suggest that monthly data are better for estimation if less number of data points are available, however, in general, using such data set results in larger variances in parameters as compared to estimates obtained through aggregated yearly data. Estimated values of transmission related metrics are lower than those obtained from earlier analyses in the literature, and the implications of this for VL control are discussed. The spatial heterogeneity in these control metrics increases the risk of epidemics and makes the control strategies more complex.

In several academic activities students experience a considerable stress in their daily life. Stressors may affect the mental and physical health of college students, leading some of them to the burnout syndrome. To evaluate this syndrome in students at Arizona State University (ASU), USA, a survey was conducted using a questionnaire previously developed, with ordinal variables. A theoretical Structural Equation Modeling (SEM) was proposed and the estimated model was obtained applying the Partial Least Squares (PLS) approach. Based on the PLS-SEM path coefficients, the latent construct behavioural stress has a direct effect on distress and insecurity, and an indirect effect on both quantitative demands and academic burnout. Both mediator constructs quantitative demands and distress have a direct effect on the academic burnout in students. A multi-group analysis was also conducted to compare the path coefficients of the estimated model by gender, but the differences are not statistically significant. These results allow a better understanding of burnout syndrome in ASU college students, which can severely limit their psychosocial well-being and performance.

Cutaneous Leishmaniasis (CL) is a neglected tropical disease transmitted by species of Phlebotominae sand flies. CL is responsible for more than 1000 reported cases per year in Ecuador. Vector collection studies in Ecuador suggest that there is a strong association between the ecological diversity of an ecosystem, the presence of potential alternative or reservoir hosts and the abundance of sandfly species. Data collected from a coastal community in Ecuador showed that Leishmania parasites may be circulating in diverse hosts, including mammalian and potentially avian species, and these hosts may serve as potential hosts for the parasite. There has been limited reporting of CL cases in Ecuador because the disease is non-fatal and its surveillance system is passive. Hence, the actual incidence of the CL is unknown. In this study, an epidemic model was developed and analysed to understand the complexity of CL transmission dynamics with potential non-humans hosts in the coastal ecosystem and to estimate critical epidemiological quantities for Ecuador. The model is fitted to the 2010 CL outbreak in the town of Valle Hermoso in the Santo Domingo de los Tsachilas province of Ecuador and parameters such as CL transmission rates in different types of hosts (primary and alternative), and levels of case reporting in the town are estimated. The results suggest that the current surveillance in this region fails to capture 38% (with 95% CI (29%, 47%)) of the actual number of cases under the assumption that alternative hosts are dead-end hosts and that the mean CL reproduction number in the town is 3.9. This means that on the average 3.9 new human CL cases were generated by a single infectious human in the town during the initial period of the 2010 outbreak. Moreover, major outbreaks of CL in Ecuador in the coastal settings are unavoidable until reporting through the surveillance system is improved and alternative hosts are managed properly. The estimated infection transmission probabilities from alternative hosts to sand flies, and sand flies to alternative hosts are 27%, and 32%, respectively. The analysis highlights that vector control and alternative host management are two effective programs for Ecuador but need to be implemented concurrently to avoid future major outbreaks.

Stochasticity provides a way to evaluate risk in complex population dynamics. This chapter describes how basic stochastic processes and algorithms are used to build models of ecological populations which can then derive probability distributions for population persistence and extinction, explain sustained oscillations generated by stochastic resonance, and measure emergent properties of a biological system, all as functions of demographic or environmental stochastic effects.

Infection of Herpes Simplex Virus type 2 (HSV-2) is a lifelong sexually transmitted disease. According to the Center for Disease Control and Prevention (CDC), 11.9% of the United States (U.S.) population was infected with HSV-2 in 2015-2016. The HSV-2 pathogen establishes latent infections in neural cells and can reactivate causing lesions later in life, a strategy that increases pathogenicity and allows the virus to evade the immune system. HSV-2 infections are currently treated by Acyclovir only in the non-constitutional stage, marked by genital skin lesions and ulcers. However, patients in the constitutional stage expressing mild and common (with other diseases) symptoms, such as fever, itching and painful urination, remain difficult to detect and are untreated. In this study, we develop and analyze a mathematical model to study the transmission and control of HSV-2 among the U.S. population between the ages of 15-49 when there are options to treat individuals in different stages of their pathogenicity. In particular, the goals of this work are to study the effect on HSV-2 transmission dynamics and to evaluate and compare the cost-effectiveness of treating HSV-2 infections in both constitutional and non-constitutional stages (new strategy) against the current conventional treatment protocol for treating patients in the non-constitutional stage (current strategy). Our results distinguish model parameter regimes where each of the two treatment strategies can optimize the available resources and consequently gives the long-term reduced cost associated with each treatment and incidence. Moreover, we estimated that the public health cost of HSV-2 with the proposed most cost-effective treatment strategy would increase by approximately 1.63% in 4 years of implementation. However, in the same duration, early treatment via the new strategy will reduce HSV-2 incidence by 42.76% yearly and the reproduction number will decrease to 0.84 from its current estimate of 2.5. Thus, the proposed new strategy will be significantly cost-effective in controlling the transmission of HSV-2 if the strategy is properly implemented.

Lyme disease is one of the most prevalent and fastest growing vector-borne bacterial illnesses in the United States, with over 25,000 new confirmed cases every year. Humans contract the bacterium Borrelia burgdorferi through the bite of the tick Ixodes scapularis. The tick can receive the bacterium from a variety of small mammal and bird species, but the white-footed mouse Peromyscus leucopus is the primary reservoir in the northeastern United States, especially near human settlement. The tick's life cycle and behavior depend greatly on the season, with different stages of tick biting at different times. Reducing the infection in the tick-mouse cycle may greatly lower human Lyme incidence in some areas. However, research on the effects of various mouse-targeted interventions is limited. One particularly promising method involves administering vaccine pellets to white-footed mice through special bait boxes. In this study, we develop and analyze a mathematical model consisting of a system of nonlinear difference equations to understand the complex transmission dynamics and vector demographics in both tick and mice populations. We evaluate to what extent vaccination of white-footed mice can affect Lyme incidence in I. scapularis, and under which conditions this method saves money in preventing Lyme disease. We find that, in areas with high human risk, vaccination can eliminate mouse-tick transmission of B. burgdorferi while saving money.

Soil-transmitted helminthiasis (STH), a neglected tropical disease (NTD) remains a major health problem all over the world including Ghana, which has STH prevalence of 25.4%. To control the disease, the government of Ghana currently concentrates on implementing mass drug administration (MDA) efforts focusing only among school-aged children. However, various studies have suggested that focusing on only a specific group for MDA may not be cost-effective. Moreover, some adults such as teachers and school-workers spend large fraction of their time with children, who shed more parasite in environment due to unhygienic behavior, and thus have a higher risk of getting infected as compared to other adults. In this study we use a mathematical model to evaluate age-structured and risk-based policies for implementing MDA while capturing transmission dynamics of STH in Ghana. A cost model was developed that included various costs related to MDA to study cost-effectiveness of current policies of MDA in Ghana against novel policies to control STH in Ghana. We carry out analysis for five different scenarios— I: no MDA (baseline), II: current MDA policy (focusing children) in Ghana, III: MDA for different age groups (adults and children groups) for unlimited budget, IV: MDA for different age groups with limitations of number of individuals treated, and, V: MDA for different groups based on their risk of getting infected (adults school workers (high-risk group), adults non-school workers and children groups). Our results suggest that it might be more cost-effective to allocate treatment through MDA to at least some proportion of adults along with children. In case of unlimited budget, the best strategy in Scenario IV would be to treat approximately 22% of adults and approximately 45% of children. The most cost-effective among the 5 scenarios is suggested through scenario V, where high-risk adults group and children are provided MDA at higher level than low-risk adults. In conclusion, age-structured and risk-based allocation of treatment and resources is crucial to reducing STH load in developing countries.

Visceral leishmaniasis (VL) is the most deadly form of the leishmaniasis family of diseases, which affects numerous developing countries. The Indian state of Bihar has the highest prevalence and mortality rate of VL in the world. Insecticide spraying is believed to be an effective vector control program for controlling the spread of VL in Bihar; however, it is expensive and less effective if not implemented systematically. This study develops and analyzes a novel optimization model for VL control in Bihar that identifies an optimal (best possible) allocation of chosen insecticide (dichlorodiphenyltrichloroethane [DDT] or deltamethrin) based on the sizes of human and cattle populations in the region. The model maximizes the insecticide-induced sandfly death rate in human and cattle dwellings while staying within the current state budget for VL vector control efforts. The model results suggest that deltamethrin might not be a good replacement for DDT because the insecticide-induced sandfly deaths are 3.72 times more in case of DDT even after 90 days post spray. Different insecticide allocation strategies between the two types of sites (houses and cattle sheds) are suggested based on the state VL-control budget and have a direct implication on VL elimination efforts in a resource-limited region.

In this paper we develop a multiscale model combining social-force-based pedestrian movement with a population level stochastic infection transmission dynamics framework. The model is then applied to study the infection transmission within airplanes and the transmission of the Ebola virus through casual contacts. Drastic limitations on air-travel during epidemics, such as during the 2014 Ebola outbreak in West Africa, carry considerable economic and human costs. We use the computational model to evaluate the effects of passenger movement within airplanes and air-travel policies on the geospatial spread of infectious diseases. We find that boarding policy by an airline is more critical for infection propagation compared to deplaning policy. Enplaning in two sections resulted in fewer infections than the currently followed strategy with multiple zones. In addition, we found that small commercial airplanes are better than larger ones at reducing the number of new infections in a flight. Aggregated results indicate that passenger movement strategies and airplane size predicted through these network models can have significant impact on an event like the 2014 Ebola epidemic. The methodology developed here is generic and can be readily modified to incorporate the impact from the outbreak of other directly transmitted infectious diseases.

Developing health promotion programs that support healthy lifestyle behaviors require comprehensive understanding of mechanisms that drive such complex social systems. Policy makers can use models and theories to guide this process at the individuals, groups, and communities levels. Individuals can have multiple risky health behaviors including physical inactivity, unhealthy diets, smoking, and alcohol drinking that are often shaped by social and ecological factors. Collective understanding of these factors can provide ability to design and evaluate intervention programs that can change unhealthy or risky behaviors over long period of time. However, it is overwhelming task to optimize intervention based on only empirical and/or cross-sectional studies. Effective long lasting intervention needs a thorough understanding of the role of social and environmental mechanisms at multiple scales on the dynamics of health behaviors. Recent mathematical and computational methods developed in other fields, such as epidemiology and finance, can provide systematic and in-depth understanding of mechanisms. However, the use of such methods in social and behaviors sciences have been limited. In this chapter, some real life working examples of social health behaviors problems are provided which uses some cutting edge methods from dynamical systems and data mining to uncertainty quantification.

In 2012, more than three million students dropped out from high school. At this pace, we will have more than 30 million Americans without a high school degree by 2022 and relatively high dropout rates among Hispanic and African American students. We have developed and analysed a data-driven mathematical model that includes multiple interacting mechanisms and estimates of parameters using data from a specifically designed survey applied to a certain group of students of a high school in Chicago to understand dynamics of dropouts. Our analysis suggests students' academic achievement is directly related to the level of parental involvement more than any other factors in our study. However, if the negative peer influence (leading to lower academic grades) increases beyond a critical value, the effect of parental involvement on the dynamics of dropouts becomes negligible.

Neglected tropical diseases (NTDs) pose a devastating obstacle to health and remain a serious concern to poverty and socio-economic upliftment. Recently, WHO with the local public health department has scaled up efforts in control and even eliminating some of the neglected diseases. In spite of this there seems to be long road ahead to achieve complete elimination of NTDs. Because of scarcity in resources and lack of data, implications from field studies have been limited. Mathematical modeling techniques have been proven to be extremely helpful for other directly transmitted nonneglected infectious diseases such as HIV and tuberculosis. This chapter collects some of the recent mathematical techniques and its implementation procedures used for other diseases to study dynamics of NTDs in the face of scarce data in a resource limited settings.

Cutaneous leishmaniasis (CL) is a neglected tropical disease transmitted by species of Phlebotominae sand flies. CL is responsible for more than 1000 reported cases per year in Ecuador. Vector collection studies in Ecuador suggest that there is a strong association between the ecological diversity of an ecosystem, the presence of potential alternative or reservoir hosts and the abundance of sand fly species. Data collected from a coastal community in Ecuador showed that Leishmania parasites may be circulating in diverse hosts, including mammalian and potentially avian species, and these hosts may serve as potential hosts for the parasite. There has been limited reporting of CL cases in Ecuador because the disease is non-fatal and its surveillance system is passive. Hence, the actual incidence of CL is unknown. In this study, an epidemic model was developed and analysed to understand the complexity of CL transmission dynamics with potential non-human hosts in the coastal ecosystem and to estimate critical epidemiological quantities for Ecuador. The model is fitted to the 2010 CL outbreak in the town of Valle Hermoso in the Santo Domingo de los Tsachilas province of Ecuador and parameters such as CL transmission rates in different types of hosts (primary and alternative), and levels of case reporting in the town are estimated. The results suggest that the current surveillance in this region fails to capture 38% (with 95% CI (29%, 47%)) of the actual number of cases under the assumption that alternative hosts are dead-end hosts and that the mean CL reproduction number in the town is 3.9. This means that on the average 3.9 new human CL cases were generated by a single infectious human in the town during the initial period of the 2010 outbreak. Moreover, major outbreaks of CL in Ecuador in coastal settings are unavoidable until reporting through the surveillance system is improved and alternative hosts are managed properly. The estimated infection transmission probabilities from alternative hosts to sand flies, and sand flies to alternative hosts are 27% and 32%, respectively. The analysis highlights that vector control and alternative host management are two effective programmes for Ecuador but need to be implemented concurrently to avoid future major outbreaks.

Various simple mathematical models have been used to investigate dengue transmission. Some of these models explicitly model the mosquito population, while others model the mosquitoes implicitly in the transmission term. We study the impact of modeling assumptions on the dynamics of dengue in Thailand by fitting dengue hemorrhagic fever (DHF) data to simple vector–host and SIR models using Bayesian Markov chain Monte Carlo estimation. The parameter estimates obtained for both models were consistent with previous studies. Most importantly, model selection found that the SIR model was substantially better than the vector–host model for the DHF data from Thailand. Therefore, explicitly incorporating the mosquito population may not be necessary in modeling dengue transmission for some populations.

(See the editorial commentary by Celum et al on pages 189–91) Oral antiretroviral (ARV) pre-exposure prophylaxis (PrEP) is a new biomedical intervention against human immunodeficiency virus (HIV) transmission with proven efficacy [1–3]. There is concern, however, about the potential emergence and spread of HIV drug resistance arising from the rollout of PrEP, particularly in resource-constrained settings, where antiretroviral therapy (ART) options are limited [4]. This concern is amplified by the possibility that the same ARVs will be used for both ART and PrEP. The combination of 2 nucleoside reverse-transcriptase inhibitors, tenofovir (TDF) and lamivudine or emtricitabine (3TC or FTC, respectively), with 1 nonnucleoside reverse-transcriptase inhibitor (NNRTI), efavirenz or nevirapine, is the World Health Organization–recommended first-line ART regimen in several countries worldwide, including South Africa [4], and TDF or TDF + FTC have shown efficacy in HIV prevention trials [1–3]. Thus far, only 9 drug-resistant cases have been observed among clinical trial participants on PrEP, most of whom had unrecognized acute infection at enrollment. However, clinical trials of PrEP are not designed to address the population-level and/or long-term epidemiological impact of PrEP, including consequences of drug resistance. We therefore used a mathematical model [5] to examine the potential impact of orally administered overlapping and nonoverlapping PrEP and ART on HIV transmission and drug resistance in South Africa.

The dynamics of an alcohol drinking population are subject to environment-specific control programs. The stochastic model is developed that includes populations of light, moderate, and heavy drinkers, interacting in two contrasting risk-level drinking environments. For colleges with serious drinking problems, the times to disappearance of serious drinkers show that environment-dependent control programs have lasting efficacy when they are implemented according to the risk level of the environments and not by simply focusing on heavy drinking.

Background The transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. The impact that effective population size and the distribution of individuals’ residence times in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates. Methods This study aims at enhancing the understanding of TB dynamics, within simplified, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates via a mathematical model. The modeling framework captures the role of individuals’ ‘daily’ dynamics within and between places of residency, work or business via the average proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the effective population size of Patch i (home of i-residents) at time t must account for visitors and residents of Patch i, at time t. Results The study identifies critical social behaviors mechanisms that can facilitate or eliminate TB infection in vulnerable populations. The results suggest that short-term mobility between heterogeneous patches contributes to significant overall increases in TB prevalence when risk is considered only in terms of direct new infection transmission, compared to the effect of exogenous reinfection. Although, the role of exogenous reinfection increases the risk that come from large movement of individuals, due to catastrophes or conflict, to TB-free areas. Conclusions The study highlights that allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall population. The higher the population size heterogeneity between distinct risk patches, the larger the benefit (low overall prevalence) under the same “traveling” patterns. Policies need to account for population specific factors (such as risks that are inherent with high levels of migration, local and regional mobility patterns, and first time infection rates) in order to be long lasting, effective and results in low number of drug resistant cases.

Vector-transmitted diseases such as dengue fever and chikungunya have been spreading rapidly in many parts of the world. The Zika virus has been known since 1947 and invaded South America in 2013. It can be transmitted not only by (mosquito) vectors but also directly through sexual contact. Zika has developed into a serious global health problem because, while most cases are asymptomatic or very light, babies born to Zika - infected mothers may develop microcephaly and other very serious birth defects. We formulate and analyze two epidemic models for vector-transmitted diseases, one appropriate for dengue and chikungunya fever outbreaks and one that includes direct transmission appropriate for Zika virus outbreaks. This is especially important because the Zika virus is the first example of a disease that can be spread both indirectly through a vector and directly (through sexual contact). In both cases, we obtain expressions for the basic reproduction number and show how to use the initial exponential growth rate to estimate the basic reproduction number. However, for the model that includes direct transmission some additional data would be needed to identify the fraction of cases transmitted directly. Data for the 2015 Zika virus outbreak in Barranquilla, Colombia has been used to fit parameters to the model developed here and to estimate the basic reproduction number., Comment: 13 pages, Infectious Disease Modeling, 2016

This study presents a continuous-time model for the sylvatic transmission dynamics of two strains of Trypanosoma cruzi enzootic in North America, in order to study the role that adaptations of each strain to distinct modes of transmission (classical stercorarian transmission on the one hand, and vertical and oral transmission on the other) may play in the competition between the two strains. A deterministic model incorporating contact process saturation predicts competitive exclusion, and reproductive numbers for the infection provide a framework for evaluating the competition in terms of adaptive trade-off between distinct transmission modes. Results highlight the importance of oral transmission in mediating the competition between horizontal (stercorarian) and vertical transmission; its presence as a competing contact process advantages vertical transmission even without adaptation to oral transmission, but such adaptation appears necessary to explain the persistence of (vertically-adapted) T. cruzi IV in raccoons and woodrats in the southeastern United States.

Aims US college drinking data and a simple population model of alcohol consumption are used to explore the impact of social and contextual parameters on the distribution of light, moderate and heavy drinkers. Light drinkers become moderate drinkers under social influence, moderate drinkers may change environments and become heavy drinkers. We estimate the drinking reproduction number, R(d) , the average number of individual transitions from light to moderate drinking that result from the introduction of a moderate drinker in a population of light drinkers. Design and Settings Ways of assessing and ranking progression of drinking risks and data-driven definitions of high- and low-risk drinking environments are introduced. Uncertainty and sensitivity analyses, via a novel statistical approach, are conducted to assess R(d) variability and to analyze the role of context on drinking dynamics. Findings Our estimates show R(d) well above the critical value of 1. R(d) estimates correlate positively with the proportion of time spent by moderate drinkers in high-risk drinking environments. R(d) is most sensitive to variations in local social mixing contact rates within low-risk environments. The parameterized model with college data suggests that high residence times of moderate drinkers in low-risk environments maintain heavy drinking. Conclusions With regard to alcohol consumption in US college students, drinking places, the connectivity (traffic) between drinking venues and the strength of socialization in local environments are important determinants in transitions between light, moderate and heavy drinking as well as in long-term prediction of the drinking dynamics. Language: en

Visceral leishmaniasis (VL) is one of the world's worst parasitic killers, second only to Malaria, claiming thousands of lives every year. More than three fifths of the world's VL cases occur in the Indian state of Bihar alone. While some research has been conducted with emphasis on the effects of climatic variables on the VL incidence rate, rigorous analysis of the effects of socio-economic variables is still lacking. In this paper a regression model is developed that describes the relationship between VL incidence rate and a variety of socio-economic factors. It uses data from 2005 and explains 92% of the observed variance. In addition, a stepwise regression model is also used to identify the most important factors that facilitate the prevalence of the VL disease. A discussion on how to most effectively distribute Bihar's limited resources on various control measures to decrease the incidence of VL is also presented.

Background Public transportation in general and air travel in particular have been identified as leading factors in the spread of many different viruses. The high economic and public perception costs on the transportation sector due to pandemic events necessitates transportation policy that addresses mitigation. We formulate a multiphysics computational model that incorporates particle dynamics based pedestrian movement of travelers in transit hubs (e.g. airports), contact analysis and stochastic infectious disease spread, to frame and analyze transportation policies that can mitigate infectious disease spread. Methods In the multi-scale model, we model the pedestrian movement using a particle dynamics social force model. A location dependence of desired velocity is introduced to reduce reliance on repulsive forces that are difficult to estimate. The pedestrian trajectory information from the above model is integrated with a discrete-time stochastic Susceptible-Exposed-Infectious (SEI) model for infection transmission. The probability-distribution of infection transmission varies depending on the incubation periods and transmission rates for specific diseases. The amount of RNA virus copies in blood serum is used to estimate the infection probability profile for diseases including Ebola and SARS. The number of infected travelers for a given transportation policy (e.g. boarding strategy, gate size etc) can then be estimated using this multi-scale model. Further, the transportation policies that minimize the infection spread are evaluated and algorithms to quantify policy robustness using massively parallel supercomputers are discussed. Results Several Boarding and deplaning strategies, airplane and gate size are evaluated as policies for three directly transmitted diseases Ebola, SARS and H1N1 influenza. We found that deplaning had a smaller impact on infection dynamics because of the lower number of new contacts and lower time of exposure during the comparatively faster process. Among boarding strategies, we found that two-zone randomized boarding process results in fewer number of infections. Smaller airplanes such as 50 seaters are more effective in reducing infection. Comparing the infectivity of first day and peak day for the three diseases (day 3 for Ebola, day 5 for H1N1 and day 4 or 5 for SARS), the mean of the Poisson distribution increases marginally by one unit for Ebola and SARS but expands significantly for H1N1. The information is presented in terms of policy analysis maps. Conclusions A multi-scale pedestrian dynamics–infection dynamics model is presented as an approach for exploring air-travel policies that minimize infectious disease spread. Several transportation policy options that impact the disease spread are presented.

Alcohol consumption is a function of social dynamics, environmental contexts, individuals' preferences and family history. Empirical surveys have focused primarily on identification of risk factors for high-level drinking but have done little to clarify the underlying mechanisms at work. Also, there have been few attempts to apply nonlinear dynamics to the study of these mechanisms and processes at the population level. A simple framework where drinking is modeled as a socially contagious process in low- and high-risk connected environments is introduced. Individuals are classified as light, moderate (assumed mobile), and heavy drinkers. Moderate drinkers provide the link between both environments, that is, they are assumed to be the only individuals drinking in both settings. The focus here is on the effect of moderate drinkers, measured by the proportion of their time spent in "low-" versus "high-" risk drinking environments, on the distribution of drinkers.A simple model within our contact framework predicts that if the relative residence times of moderate drinkers is distributed randomly between low- and high-risk environments then the proportion of heavy drinkers is likely to be higher than expected. However, the full story even in a highly simplified setting is not so simple because "strong" local social mixing tends to increase high-risk drinking on its own. High levels of social interaction between light and moderate drinkers in low-risk environments can diminish the importance of the distribution of relative drinking times on the prevalence of heavy drinking.

A classical epidemiological framework is used to provide a preliminary cost analysis of the effects of quarantine and isolation on the dynamics of infectious diseases for which no treatment or immediate diagnosis tools are available. Within this framework we consider the cost incurred from the implementation of three types of dynamic control strategies. Taking the context of the 2003 SARS outbreak in Hong Kong as an example, we use a simple cost function to compare the total cost of each mixed (quarantine and isolation) control strategy from a public health resource allocation perspective. The goal is to extend existing epi-economics methodology by developing a theoretical framework of dynamic quarantine strategies aimed at emerging diseases, by drawing upon the large body of literature on the dynamics of infectious diseases. We find that the total cost decreases with increases in the quarantine rates past a critical value, regardless of the resource allocation strategy. In the case of a manageable outbreak resources must be used early to achieve the best results whereas in case of an unmanageable outbreak, a constant-effort strategy seems the best among our limited plausible sets.

The parasite Trypanosoma cruzi, spread by triatomine vectors, affects over 100 mammalian species throughout the Americas, including humans, in whom it causes Chagas' disease. In the U.S., only a few autochthonous cases have been documented in humans, but prevalence is high in sylvatic hosts (primarily raccoons in the southeast and woodrats in Texas). The sylvatic transmission of T. cruzi is spread by the vector species Triatoma sanguisuga and Triatoma gerstaeckeri biting their preferred hosts and thus creating multiple interacting vector-host cycles. The goal of this study is to quantify the rate of contacts between different host and vector species native to Texas using an agent-based model framework. The contact rates, which represent bites, are required to estimate transmission coefficients, which can be applied to models of infection dynamics. In addition to quantitative estimates, results confirm host irritability (in conjunction with host density) and vector starvation thresholds and dispersal as determining factors for vector density as well as host-vector contact rates.

Cancer risk behaviors, including tobacco use, alcohol binge drinking, and physical inactivity, co-occur at high rates among racially and ethnically diverse college students. The overall goal of this paper is to develop a robust framework, founded on mathematical and computational models, to enhance our understanding of the social and environmental mechanisms that determine changes in health risk behavior, including alcohol binge drinking, tobacco use, and physical activity, among students enrolled in a commuter, federally designated Minority-Serving Institution in Chicago (Northeastern Illinois University, NEIU). Our underlying conceptual premise is that mutually affecting health risk behaviors spread interpersonally through social interactions and influences but can also occur spontaneously, through non-social contextual influences and increased time spent in high-risk settings. Our work acknowledges the existence of an adaptive dynamic culture of college students and incorporates a new metric, the distribution of time spent in distinct venues of students, to study its impact on population's health behaviors patterns. Our work characterizes, for the first time, social contexts of the NEIU students' diverse population and examines the effect of residence times in environments and contextual social networks on dynamics of multiple health behaviors. Our fall 2015 survey parses out social and environmental contextual pathways that are particularly relevant for college students and includes information on close friendships, peer health behavior, student allocations of time, engagement in activities on, near and off-campus and duration. Preliminary findings regarding environments in which students spend their time show that student smokers most often frequent on-campus fine arts events. All varieties of on-campus and near campus events reveal high participation rates of physically inactive students. Binge drinkers (defined as at least one episode of drinking 5 or more drinks in a row over the last 2 weeks) favored attending intercollegiate sports events over all other activity on or near campus. Intercollegiate sport events were most often frequented by students declaring multiple unhealthy behaviors. Our findings suggest to what extent (and where) interventions aimed at altering observed patterns of socialization (e.g. through tailored residence assignments or fraternity and sorority recruiting policies) and/or distribution of the times spent in students' potential environments (e.g., through sports or religious events) hold promise in controlling unhealthy behaviors among college students, especially, students of color. Citation Format: Christina Ciecierski, Brian Hitsman, Daniel Romero, Anuj Mubayi. Social activities and events and their role in determining health behavior adoption among minority college students. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr A04.

In 2012, more than three million students dropped out from high school. At this pace, we will have more than 30 million Americans without a high school degree by 2022 and relatively high dropout rates among Hispanic and African American students. We have developed and analysed a data-driven mathematical model that includes multiple interacting mechanisms and estimates of parameters using data from a specifically designed survey applied to a certain group of students of a high school in Chicago to understand the dynamics of dropouts. Our analysis suggests students' academic achievement is directly related to the level of parental involvement more than any other factors in our study. However, if the negative peer influence (leading to lower academic grades) increases beyond a critical value, the effect of parental involvement on the dynamics of dropouts becomes negligible.

Alcohol abuse is a major problem, especially among students on and around college campuses. We use the mathematical framework of [16] and study the role of environmental factors on the long term dynamics of an alcohol drinking population. Sensitivity and uncertainty analyses are carried out on the relevant functions (for example, on the drinking reproduction number and the extinction time of moderate and heavy drinking because of interventions) to understand the impact of environmental interventions on the distributions of drinkers. The reproduction number helps determine whether or not the high-risk alcohol drinking behavior will spread and become persistent in the population, whereas extinction time of high-risk drinking measures the effectiveness of control programs. We found that the reproduction number is most sensitive to social interactions, while the time to extinction of high-risk drinkers is significantly sensitive to the intervention programs that reduce initiation, and the college drop-out rate. The results also suggest that in a population, higher rates of intervention programs in low-risk environments (more than intervention rates in high-risk environments) are needed to reduce heavy drinking in the population.

The 2012-13 influenza season had an unusually early and severe start in the US, succeeding the record mild 2011-12 influenza season, which occurred during the fourth warmest winter on record. Our analysis of climate and past US influenza epidemic seasons between 1997-98 to present indicates that warm winters tend to be followed by severe epidemics with early onset, and that these patterns are seen for both influenza A and B. We posit that fewer people are infected with influenza during warm winters, thereby leaving an unnaturally large fraction of susceptible individuals in the population going into the next season, which can lead to early and severe epidemics. In the event of continued global warming, warm winters such as that of 2011-12 are expected to occur more frequently. Our results thus suggest that expedited manufacture and distribution of influenza vaccines after mild winters has the potential to mitigate the severity of future influenza epidemics.

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.