Human Trafficking
|
Human trafficking supply chains are closed-loop, where human is “reused” as a “commodity”. In collaboration with local, national and international efforts, I lead various multi-disciplinary projects to combat human trafficking through analytical and empirical methods.
|
-
Predicting Recidivism and Resource Allocation in Prostituted/Trafficked Womens
K. Yagci Sokat, N. Altay, D. Salina & M. Hatcher
Working Paper (Invited to NSF Workshop on Decision Analytics on Dynamic Policing, May 2019)
Abstract  Tech Report
-
Do Prostitution Advertisements Reduce Violence Against Women? A Methodological Examination of Cunningham, DeAngelo and Tripp Findings
K. Feifer, J. Raphael & K. Yagci Sokat
Dignity: A Journal on Sexual Exploitation on Violence
Abstract Link  Tech Report
A recent study by Cunningham, DeAngelo, and Tripp (unpublished 2017, 2019) found that advertising prostitution online led to a lower rate of homicide of women in the United States. These findings have circulated widely in the mainstream media as proof that advertising prostitution online increases the safety of prostituted women. The study’s findings were used to argue against the 2018 passage of a federal anti-trafficking bill: Allow States and Victims to Fight Online Sex Trafficking Act (FOSTA) and Stop Enabling Sex Traffickers Act (SESTA), known collectively as FOSTA-SESTA. This new law holds websites that knowingly facilitate sex trafficking accountable for the harms they cause. Passage of the legislation led to the shutdown of sites that profited from prostitution advertising. Backpage.com, a major site for prostitution advertising, was shut down by the U.S. Department of Justice just days following Congress’ passage of the legislation, but prior to FOSTA-SESTA being signed into law. Within days of the passage of the legislation, operators of other prostitution advertising sites shuttered their sites. Our critique of the article is based on the assumptions and methodology employed by Cunningham et al. We find the study is methodologically flawed. First, the study fails to demonstrate a link between the decline in the female homicide rate and online prostitution advertising. Second, the study does not measure the murder rate within the population of women in prostitution to show that online prostitution advertising keeps prostituted women safe. Third, the authors attempt to explain the reasons for a decline in the murder rate of women via speculation. Fourth, the study defines “safety” as not being murdered, ignoring other forms of violence inherent in the sex trade. Fifth, Cunningham et al. wrongly extrapolate findings from 2002 to the present by speculating about the impact of FOSTA-SESTA on prostituted women’s safety, without accounting for shifts in Internet culture and usage. The findings and conclusions from this study could lead people to believe falsely that using and expanding online prostitution advertising sites will reduce violence against all women, as well as prostituted women. The safety of people in prostitution is a serious concern. Consequently, other measures should be examined to protect them.
Humanitarian Logistics
|
In the humanitarian logistics, it is generally very hard to obtain data, especially infrastructural data. My research focus on how utilizing the limited data available in humanitarian logistics and integrating them to optimization models.
|
The volume, accuracy, accessibility and level of detail of near real-time data emerging from disaster-affected regions continue to significantly improve. Integration of dynamically evolving in-field data is an important, yet often overlooked, component of the humanitarian logistics models. In this paper, we present a framework for real-time humanitarian logistics data focused on use in mathematical modeling along with modeling implications of this framework. We also discuss how one might measure the attributes of the framework and describe the application of the presented framework to a case study of near real-time data collection in the days following the landfall of Typhoon Haiyan. We detail our first-hand experience of capturing data as the post-disaster response unfolds starting on November 10, 2013 until March 31, 2014 and assess the characteristics and evolution of data pertaining to humanitarian logistics modeling using the proposed framework. The presented logistical content analysis examines the availability of data and informs modelers about the current state of near real-time data. This analysis illustrates what data is available, how early it is available, and how data changes after the disaster. The study describes how our humanitarian logistics team approached the emergence of dynamic online data after the disaster and the challenges faced during the collection process, as well as recommendations to address these challenges in the future (when possible) from an academic humanitarian logistics perspective.
State of the art humanitarian logistics models have been developed over the past decades. Most of these models assume availability of data and use synthesis data. After a disaster, there is often limited information about infrastructure damage. Given limited information about road damage
immediately after disaster, a field operations manager needs to deploy help. New data sources such as OpenStreetMap are emerging. Utilizing these new data sources, we develop a framework to estimate incomplete information in limited data environments. We present an application of this framework to
a recent disaster. We also develop publicly available test cases for the broader community. The study explores the impact of available level of information, dispersion of available data and imputation techniques used in approximating the incomplete information. Surprisingly, our results suggest that higher granularity yields better estimates of the unknown information.
Disaster responders currently do not have a standardized way to determine the impact of a catastrophic event to prepare and respond in advance. Additionally, there is limited publicly available data after disasters. We develop an algorithm that estimates the expected property damage, transportation, health and healthcare impact, and other outcomes. In addition to the publicly available damage reports and PDF maps, we utilize insurance data (insurance claims and potential insurance fraud data). We develop a Disaster Resiliency Index which ranks disasters and impacts of it on various outcomes.
Uncertainty remains a challenging task in supply chain management. Limited historical data and limited on hand inventory bring up additional difficulty to supply chain management. Overcoming these challenges become more significant when saving lives and improving healthcare is involved at the end of the supply chain. This project is in collaboration with Last Mile Health, a non-governmental organization that brings health care to remote communities in Liberia. In order to service remote communities, LMH trains community healthcare workers (CHWs) to prevent, diagnose, and treat the most common diseases in Liberia. LMH is increasing coverage in other regions of Liberia and needs to determine estimates on the medical supply requirements and corresponding costs. However, LMH currently does not possess historical data on supplies.
We are currently developing a predictive costing model to estimate the medicinal needs for LMH’s CHW scale up targets. This research addresses challenges in resource constrained settings and requires estimation of incomplete data and ensuring accuracy of the limited data available. The study depends upon the analysis and integration of demographics and geographic characteristics as well as corresponding disease progression and health outcome metrics of the regions.
Due to space, funding and time constraints, CHWs cannot access all the supplies at once. Instead, CHWs carry a backpack where they carry a combination of supplies for their visits. Finding the right combination of supplies and their order quantities is crucial to improve the quality of the supply chain and therefore the health outcomes. We are working on an inventory optimization model for the supply backpack and the entire supply chain where we link health outcomes to inventory optimization under limited data.
Public Health
|
In public health, I am interested in modeling infectious diseases and testing the impact of various public health interventions.
|
Background
Rapid scale up of HIV treatment programs in sub-Saharan Africa has refueled the long-standing health policy debate regarding the merits and drawbacks of vertical and integrated system. Recent pilots of integrating outpatient and HIV services have shown an improvement in some patient outcomes but deterioration in waiting times, which can lead to worse health outcomes in the long run.
Methods
A pilot intervention involving integration of outpatient and HIV services in an urban primary care facility in Lusaka, Zambia was studied. Data on waiting time of patients during two seven-day periods before and six months after the integration were collected using a time and motion study. Statistical tests were conducted to investigate whether the two observation periods differed in operational details such as staffing, patient arrival rates, mix of patients etc. A discrete event simulation model was constructed to facilitate a fair comparison of waiting times before and after integration. The simulation model was also used to develop alternative configurations of integration and to estimate the resulting waiting times.
Results
Comparison of raw data showed that waiting times increased by 32% and 36% after integration for OPD and ART patients respectively (p<0.01). Using simulation modeling, we found that a large portion of this increase could be explained by changes in operational conditions before and after integration such as reduced staff availability (p<0.01) and longer breaks between consecutive patients (p<0.05). Controlling for these differences, integration of services, per se, would have resulted in a significant decrease in waiting times for OPD and a moderate decrease for HIV services.
Conclusions
Integrating health services has the potential of reducing waiting times due to more efficient use of resources. However, one needs to ensure that other operational factors such as staff availability are not adversely affected due to integration.
A recent study showed that progestogen-only injectable hormonal contraception (POIHC) doubles the risk of HIV transmission. This may affect contraceptive use and HIV-related outcomes, if women switch away from POIHC. A deterministic compartmental model of individuals aged 15–49 distinguishing gender and HIV status was used to simulate various contraceptive use scenarios. We specifically tracked HIV prevalence, new infections, HIV-related deaths, vertical transmission, and births over a 15-year period for five African countries. Stopping POIHC use will result in a large increase in births and vertical transmission. Switching from POIHC to other contraceptives limits these increases while still improving HIV outcomes.
-
Modeling and Controlling Epidemic Outbreaks: The Role of Population Size, Model Heterogeneity, and Fast Response in The Case of Measles
K. Yagci Sokat & B. Armbruster
Submitted Paper
Abstract Tech Report
Despite the continued efforts on awareness and vaccination campaigns, vaccine hesitancy remains a challenge in certain populations. Vaccination coverage gaps have resulted in spikes in epidemic outbreaks of preventable infectious diseases, among which measles has become the most alarming worldwide. Analysis of outbreak notification and response data is a key part of outbreak response, and modeling can help decision makers analyze different response strategies. Modelers typically use detailed simulation models and vary the fraction vaccinated to study outbreak control. However, there is currently no guidance for modelers on how much detail (i.e., heterogeneity) is necessary and how large a population to simulate: while an outbreak may infect 10 individuals, should the simulation be of 100, 1000, or 100,000 individuals? We provide theoretical and numerical guidance for those decisions and also analyze the benefit of a faster public health response through stochastic simulation model. Theoretically, we prove that the outbreak size converges as the simulation population increases and that the outbreaks are slightly larger with a heterogeneous community structure. We find that the simulated outbreak size is not sensitive to the size of the simulated population beyond a certain size. We also observe that in case of an outbreak, a faster public health response provide benefits similar to increased vaccination.
-
Comparing Direct and Indirect Transmission in a Model of Veterinary Disease
K. Yagci Sokat, S. Edlund, K. Clarkson, & J. Kaufman
Submitted Paper
Abstract Tech Report
Foodborne diseases have been a worldwide public health concern. Modeling the transmission pathways of foodborne pathogens accurately and effectively can aid in understanding the spread of pathogens and facilitate decision making for intervention. A new compartmental model is reported that integrates the effects of both direct and indirect transmission. Depending on the choice of epidemiological parameters, the model can be tuned to be purely direct, purely indirect, or used to explore the dynamics in an intermediate regime. Steady state analysis of the model and limiting cases are studied. A numerical simulation is employed to study the impact of different epidemiological parameters and dose response. Direct transmission can surpass the effect of indirect transmission for the same range of parameter values and result in earlier epidemic. The rate at which the pathogens are removed from the environment can lead to faster epidemic. The environmental contamination can decrease the time to reach the steady state depending on the dose response. These results can inform policy makers for control strategies to reduce foodborne pathogen transmission.
-
Modeling and Forecasting Measles in a London Borough
S. Edlund, D. Lovett, J. Kaufman, K. Yagci Sokat, J. van Wijgerden, & A. Poots
Working Paper
Abstract Link Tech Report
To investigate the feasibility of using freeware to model and forecast disease on a local scale, we report the results of modeling measles using a spatial patch model centered around 73 clinics in the North West London Borough of Ealing. MMR1 and MM2 immunization data was extracted for three cohorts, age 1-3, 4-6 and 7-19 and patient population was estimated using general practice profile records.
We designed the measles model using the open source Spatiotemporal Epidemiological Modeler (STEM), extending a compartmental disease model to include both maternal immunity and delays in antibody response after immunization. Individuals above age 19 are not included in the modeling.
Next, we generate an approximate 20-year model of vaccination coverage for Ealing. In England, children are immunized between age 1 and 2, then again at around age 5; hence immunization events are modeled for the age 1-3 and age 4-6 cohorts. Parameter values were based on measles research literature; transmission coefficients were estimated using the Polymod contact data and also fitted to 2011-2012 case reporting data for Ealing.
To examine possible effects of policy change, we create two scenarios A and B. In A, we increase vaccination coverage by 10% for all clinics; in B, we focus only on the bottom 10% of the poorest performing clinics (8 clinics total) and equivalently improve their coverage. Scenario A reduces measles from an initial level of 60 cases per year (2011) to 26 cases per year in 2017 (a 58% reduction), compared to the status quo which declines to 45 cases per year. Scenario B reduces measles by 44%, or to 34 cases per year in 2017.
We conclude that local scale modeling is possible, and that the transparency of analysis provided by an open source application lends credence to the output of the models.
|
