EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks

Package EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. EpiModel integrates recent advances in statistical methods for network analysis (temporal exponential random graph models) that allow the epidemic modeling to be grounded in empirical data on contacts that can spread infection. This article provides an overview of both the modeling tools built into EpiModel, designed to facilitate learning for students new to modeling, and the application programming interface for extending package EpiModel, designed to facilitate the exploration of novel research questions for advanced modelers

Sexual and injection-related risks in Puerto Rican-born injection drug users living in New York City: A mixed-methods analysis

Background These data were collected as part of the National HIV Behavioral Surveillance (NHBS) study. NHBS is a cross-sectional study to investigate HIV behavioral risks among core risk groups in 21 U.S. cities with the highest HIV/AIDS prevalence. This analysis examines data from the NHBS data collection cycle with IDU conducted in New York City in 2009. We explored how the recency of migration from Puerto Rico (PR) to New York City (NYC) impacts both syringe sharing and unprotected sex among injection drug users (IDU) currently living in NYC. Methods We used a mixed-methods approach to examine differences in risk between US-born IDU, PR IDU who migrated to NYC more than three years ago (non-recent migrants), and PR IDU who migrated in the last three years (recent migrants). Respondent-driven sampling (RDS) was used to recruit the sample (n = 514). In addition, qualitative individual and group interviews with recent PR migrants (n = 12) and community experts (n = 2) allowed for an in-depth exploration of the IDU migration process and the material and cultural factors behind continued risk behaviors in NYC. Results In multiple logistic regression controlling for confounding factors, recent migrants were significantly more likely to report unprotected sexual intercourse with casual or exchange partners (adjusted odds ratio [AOR]: 2.81; 95% confidence intervals [CI]: 1.37-5.76) and receptive syringe sharing (AOR = 2.44; 95% CI: 1.20-4.97) in the past year, compared to US-born IDU. HIV and HCV seroprevalence were highest among non-recent migrants. Qualitative results showed that risky injection practices are partly based on cultural norms acquired while injecting drugs in Puerto Rico. These same results also illustrate how homelessness influences risky sexual practices. Conclusions Poor material conditions (especially homelessness) may be key in triggering risky sexual practices. Cultural norms (ingrained while using drugs in PR) around injection drug use are perpetuated in their new setting following an almost natural flow. These norms may have a particular stronghold over risky drug injection practices. These results indicate that culturally appropriate HIV and HCV prevention and education services are needed. In addition, homelessness should be addressed to reduce risky sexual practices

Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021

Non-pharmaceutical interventions minimize social contacts, hence the spread of respiratory pathogens such as influenza and SARS-CoV-2. Globally, there is a paucity of social contact data from the workforce. In this study, we quantified two-day contact patterns among USA employees. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic self-kept diaries. Data were collected over 4 rounds from 2020 to 2021 during the COVID-19 pandemic. Mean (standard deviation) contacts reported by 1456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April-June 2020), 2 (November 2020-January 2021), 3 (June-August 2021), and 4 (November-December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2-4. We then modeled SARS-CoV-2 transmission at home, work, and community settings. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained exceptionally low in work settings. To accurately parameterize models of infection transmission and control, we need empirical social contact data that capture human mixing behavior across time

Factors influencing the decision to receive seasonal influenza vaccination among US corporate non-healthcare workers

Influenza causes significant mortality and morbidity in the United States (US). Employees are exposed to influenza at work and can spread it to others. The influenza vaccine is safe, effective, and prevents severe outcomes; however, coverage among US adults (50.2%) is below Healthy People 2030 target of 70%. These highlights need for more effective vaccination promotion interventions. Understanding predictors of vaccination acceptance could inform vaccine promotion messages, improve coverage, and reduce illness-related work absences. We aimed to identify factors influencing influenza vaccination among US non-healthcare workers. Using mixed-methods approach, we evaluated factors influencing influenza vaccination among employees in three US companies during April-June 2020. Survey questions were adapted from the WHO seasonal influenza survey. Most respondents (n = 454) were women (272, 59.9%), 20-39 years old (n = 250, 55.1%); white (n = 254, 56.0%); had a college degree (n = 431, 95.0%); and reported receiving influenza vaccine in preceding influenza season (n = 297, 65.4%). Logistic regression model was statistically significant, X (16, N = 450) = 31.6, p = .01. Education [(OR) = 0.3, 95%CI = 0.1-0.6)] and race (OR = 0.4, 95%CI = 0.2-0.8) were significant predictors of influenza vaccine acceptance among participants. The majority had favorable attitudes toward influenza vaccination and reported that physician recommendation would influence their vaccination decisions. Seven themes were identified in qualitative analysis: "Protecting others" (109, 24.0%), "Protecting self" (105, 23.1%), "Vaccine accessibility" (94, 20.7%), "Education/messaging" (71, 15.6%), "Policies/requirements" (15, 3.3%), "Reminders" (9, 2.0%), and "Incentives" (3, 0.7%). Our findings could facilitate the development of effective influenza vaccination promotion messages and programs for employers, and workplace vaccination programs for other diseases such as COVID-19, by public health authorities

Mineralisation of soft and hard tissues and the stability of biofluids

Evidence is provided from studies on natural and artificial biofluids that the sequestration of amorphous calcium phosphate by peptides or proteins to form nanocluster complexes is of general importance in the control of physiological calcification. A naturally occurring mixture of osteopontin peptides was shown, by light and neutron scattering, to form calcium phosphate nanoclusters with a core–shell structure. In blood serum and stimulated saliva, an invariant calcium phosphate ion activity product was found which corresponds closely in form and magnitude to the ion activity product observed in solutions of these osteopontin nanoclusters. This suggests that types of nanocluster complexes are present in these biofluids as well as in milk. Precipitation of amorphous calcium phosphate from artificial blood serum, urine and saliva was determined as a function of pH and the concentration of osteopontin or casein phosphopeptides. The position of the boundary between stability and precipitation was found to agree quantitatively with the theory of nanocluster formation. Artificial biofluids were prepared that closely matched their natural counterparts in calcium and phosphate concentrations, pH, saturation, ionic strength and osmolality. Such fluids, stabilised by a low concentration of sequestering phosphopeptides, were found to be highly stable and may have a number of beneficial applications in medicine

Modeling missing cases and transmission links in networks of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa

Patterns of transmission of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases worldwide in 2017. Modeling TB transmission networks can provide insight into drivers of transmission, but incomplete sampling of TB cases can pose challenges for inference from individual epidemiologic and molecular data. We assessed the effect of missing cases on a transmission network inferred from Mycobacterium tuberculosis sequencing data on extensively drug-resistant TB cases in KwaZulu-Natal, South Africa, diagnosed in 2011–2014. We tested scenarios in which cases were missing at random, missing differentially by clinical characteristics, or missing differentially by transmission (i.e., cases with many links were under- or oversampled). Under the assumption that cases were missing randomly, the mean number of transmissions per case in the complete network needed to be larger than 20, far higher than expected, to reproduce the observed network. Instead, the most likely scenario involved undersampling of high-transmitting cases, and models provided evidence for super-spreading. To our knowledge, this is the first analysis to have assessed support for different mechanisms of missingness in a TB transmission study, but our results are subject to the distributional assumptions of the network models we used. Transmission studies should consider the potential biases introduced by incomplete sampling and identify host, pathogen, or environmental factors driving super-spreading.This work was presented at the Seventh International Conference on Infectious Disease Dynamics (Epidemics7), Charleston, South Carolina, December 3–6, 2019.The National Institute of Allergy and Infectious Diseases, US National Institutes of Health, the National Institute of Allergy and Infectious Diseases, the Emory Center for AIDS Research, the Einstein Center for AIDS Research and the Einstein/Montefiore Institute for Clinical and Translational Research.https://academic.oup.com/ajehj2021Medical Microbiolog

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

Stochastic network models for HIV-1 transmission dynamics

Thesis (Ph.D.)--University of Washington, 2015The studies in this dissertation investigate how the structure of dynamically evolving sexual networks shape the HIV-1 epidemic among heterosexuals in Sub-Saharan Africa (SSA). The aims of this project were to: 1) develop a comprehensive set of demographic, behavioral, and biological parameters characterizing the target population of heterosexuals in SSA for use in network-based mathematical models of HIV transmission dynamics; 2) mathematically model the synergistic effects of network structure and male circumcision on HIV transmission in SSA; and 3) use mathematical modeling to estimate the total, direct, and indirect effects of pre-exposure prophylaxis (PreP) on HIV incidence within a simulated randomized control trial (RCT) environment with counterfactual scenarios for threshold levels of network structures that can bias the estimation of treatment efficacy. The heterosexual spread of HIV-1 infection in SSA depends on the unique configurations of how sexually active persons form and break sexual partnerships over time. For disease transmission, individuals are linked in dyads through partnerships, dyads are connected to other dyads when persons have multiple ongoing partnerships, and this forms the larger sexual network within the population. Effective HIV prevention tools, like male circumcision and PreP, operate within this network context. The studies here ask how these interventions, targeted at individuals, function given dynamic networks. Stochastic network models were developed to test key hypotheses for this interaction, aimed both at the population level and within RCT settings. Parameters for these models were primarily drawn from an original retrospective panel study we conducted in Accra, Ghana specifically for mathematical modeling. The findings from these studies address important empirical questions on the relationship between biomedical prevention tools and socio-behavioral risk, and also provide insight into the design and targeting of single-element and combination prevention packages for HIV in high-incidence settings. The broader mathematical modeling methods in this project have many potential applications for future HIV prevention research