Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus.

We consider a proposed system that would place sensors in a number of wastewater manholes in a community in order to detect genetic remnants of SARS-Cov-2 found in the excreted stool of infected persons. These sensors would continually monitor the manhole's wastewater, and whenever virus remnants are detected, transmit an alert signal. In a recent paper, we described two new algorithms, each sequentially opening and testing successive manholes for genetic remnants, each algorithm homing in on a neighborhood where the infected person or persons are located. This paper extends that work in six important ways: (1) we introduce the concept of in-manhole sensors, as these sensors will reduce the number of manholes requiring on-site testing; (2) we present a realistic tree network depicting the topology of the sewer pipeline network; (3) for simulations, we present a method to create random tree networks exhibiting key attributes of a given community; (4) using the simulations, we empirically demonstrate that the mean and median number of manholes to be opened in a search follows a well-known logarithmic function; (5) we develop procedures for determining the number of sensors to deploy; (6) we formulate the sensor location problem as an integer nonlinear optimization and develop heuristics to solve it. Our sensor-manhole system, to be implemented, would require at least three additional steps in R&D: (a) an accurate, inexpensive and fast SARS-Cov-2 genetic-remnants test that can be done at the manhole; (b) design, test and manufacture of the sensors; (c) in-the-field testing and fine tuning of an implemented system.

Social network theory-an underutilized opportunity to align innovative methods with the demands of the opioid epidemic.

It has been almost 3 years since the opioid epidemic was declared a national public health emergency under federal law. Solutions have focused on supply-reduction strategies. These approaches, however, have failed to significantly curtail opioid overdose and related death. Demand for opioid use arising from social networks and environment is an important contributing factor to the current opioid epidemic. Adoption of existing underused methods is needed to drive further progress. This Perspective proposes the social contagion model as a promising framework through which to operationalize evaluation of the influence of social networks and environment in the opioid epidemic and argues for its greater application. Comparing the current epidemic with previous opioid epidemics reiterates the utility of the social contagion model. This model acknowledges social network influence on individual behavior. It leverages tools from epidemiology, permits evaluation of interpersonal influence, facilitates consideration of disproportionate and collateral effects, and overcomes limitations of traditional models and geographic assumptions inherent to many approaches surrounding the current opioid epidemic. Analyzing the opioid epidemic within a social contagion framework will enhance evaluation methods and enable the design of interventions to reflect the actual demands of the current crisis. If the influence of social networks and environment is not considered, the devastating toll of the opioid epidemic could grow.

Sampling manholes to home in on SARS-CoV-2 infections.

About 50% of individuals infected with the novel Coronavirus (SARS-CoV-2) suffer from intestinal infection as well as respiratory infection. They shed virus in their stool. Municipal sewage systems carry the virus and its genetic remnants. These viral traces can be detected in the sewage entering a wastewater treatment plant (WTP). Such virus signals indicate community infections but not locations of the infection within the community. In this paper, we frame and formulate the problem in a way that leads to algorithmic procedures homing in on locations and/or neighborhoods within the community that are most likely to have infections. Our data source is wastewater sampled and real-time tested from selected manholes. Our algorithms dynamically and adaptively develop a sequence of manholes to sample and test. The algorithms are often finished after 5 to 10 manhole samples, meaning that-in the field-the procedure can be carried out within one day. The goal is to provide timely information that will support faster more productive human testing for viral infection and thus reduce community disease spread. Leveraging the tree graph structure of the sewage system, we develop two algorithms, the first designed for a community that is certified at a given time to have zero infections and the second for a community known to have many infections. For the first, we assume that wastewater at the WTP has just revealed traces of SARS-CoV-2, indicating existence of a "Patient Zero" in the community. This first algorithm identifies the city block in which the infected person resides. For the second, we home in on a most infected neighborhood of the community, where a neighborhood is usually several city blocks. We present extensive computational results, some applied to a small New England city.

SD Meets OR: A New Synergy to Address Policy Problems.

We reflect on our past seven years of collaboration to develop systems models of U.S. higher education and scientific workforce development. Based on three recent modeling examples, we offer a methodological proposition that many traditional Operations Research (OR) models can be improved by including feedback processes as is commonly done in system dynamics (SD) modeling. Such models, even if simple and approximate, can be powerful, insightful, easy to communicate, and effective. While these modeling examples may not follow conventional SD or OR modeling, they benefit from and contribute to both schools of modeling. We argue that to build such synergy, modeling teams should be willing to create models building on the strengths of each school of modeling.

Nosocomial amplification of MERS-coronavirus in South Korea, 2015.

Background: Nosocomial amplification resulted in nearly 200 cases of Middle East respiratory syndrome (MERS) during the 2015 South Korean MERS-coronavirus outbreak. It remains unclear whether certain types of cases were more likely to cause secondary infections than others, and if so, why. Methods: Publicly available demographic and transmission network data for all cases were collected from the Ministry of Health and Welfare. Statistical analyses were conducted to determine the relationship between demographic characteristics and the likelihood of human-to-human transmission. Findings from the statistical analyses were used to inform a hypothesis-directed literature review, through which mechanistic explanations for nosocomial amplification were developed. Results: Cases that failed to recover from MERS were more likely to cause secondary infections than those that did. Increased probability of direct, human-to-human transmission due to clinical manifestations associated with death, as well as indirect transmission via environmental contamination (e.g., fomites and indoor ventilation systems), may serve as mechanistic explanations for nosocomial amplification of MERS-coronavirus in South Korea. Conclusions: In addition to closely monitoring contacts of MERS cases that fail to recover during future nosocomial outbreaks, potential fomites with which they may have had contact should be sanitized. Furthermore, indoor ventilation systems that minimize recirculation of pathogen-bearing droplets should be implemented whenever possible.

Work-Education Mismatch: An Endogenous Theory of Professionalization.

We model the education-workforce pipeline and offer an endogenous theory of professionalization and ever-higher degree attainment. We introduce two mechanisms that act on the education enterprise, causing the number of educated people to increase dramatically with relatively short-term changes in the job market. Using our illustrative dynamic model, we argue that the system is susceptible to small changes and the introduced self-driving growth engines are adequate to over-incentivize degree attainment. We also show that the mechanisms magnify effects of short-term recessions or technological changes, and create long-term waves of mismatch between workforce and jobs. The implication of the theory is degree inflation, magnified pressures on those with lower degrees, underemployment, and job market mismatch and inefficiency.

Cross-Sectional Surveys: Inferring Total Eventual Time in Current State Using Only Elapsed Time-to-Date.

We focus on snapshot surveying of sub-populations whose members are in a temporary state and where one of the questions asked is the elapsed time already spent in that state. From these answers we develop probabilistic and statistical procedures to estimate the distribution of total time that will eventually be spent in that state by any random individual who enters the state. The method relies on a selection bias often found in temporal sampling, sometimes called "random incidence" or "longevity bias." We develop results for several types of sampling, including random and fixed times of surveying, random and fixed times of entering the state, and sampling only those who have already spent some minimal specified time in the targeted state. An example with post-doc data is included to demonstrate the steps.

Posttraumatic Stress Disorder: Five Vicious Cycles that Inhibit Effective Treatment.

Despite a wide range of studies and medical progress, it seems that we are far from significantly mitigating the problem of posttraumatic stress disorder (PTSD). The problem has major social and behavioral components. Developing innovative and effective policies requires a broad scope of analysis and consideration of the highly interconnected social, behavioral, and medical variables. In this article, we take a systems approach and offer an illustrative causal loop diagram which includes individual and social dynamics. Based on the map, we discuss 5 major barriers for effective interventions in PTSD. These barriers work as vicious cycles in the system, reduce effectiveness and therefore value of PTSD treatment. We also discuss policy implications of this perspective.

STEM crisis or STEM surplus? Yes and yes.

The last decade has seen considerable concern regarding a shortage of science, technology, engineering, and mathematics (STEM) workers to meet the demands of the labor market. At the same time, many experts have presented evidence of a STEM worker surplus. A comprehensive literature review, in conjunction with employment statistics, newspaper articles, and our own interviews with company recruiters, reveals a significant heterogeneity in the STEM labor market: the academic sector is generally oversupplied, while the government sector and private industry have shortages in specific areas.

Too Many PhD Graduates or Too Few Academic Job Openings: The Basic Reproductive Number R0 in Academia.

The academic job market has become increasingly competitive for PhD graduates. In this note, we ask the basic question of 'Are we producing more PhDs than needed?' We take a systems approach and offer a 'birth rate' perspective: professors graduate PhDs who later become professors themselves, an analogue to how a population grows. We show that the reproduction rate in academia is very high. For example, in engineering, a professor in the US graduates 7.8 new PhDs during his/her whole career on average, and only one of these graduates can replace the professor's position. This implies that in a steady state, only 12.8% of PhD graduates can attain academic positions in the USA. The key insight is that the system in many places is saturated, far beyond capacity to absorb new PhDs in academia at the rates that they are being produced. Based on the analysis, we discuss policy implications.

Modeling the effects of H1N1 influenza vaccine distribution in the United States.

OBJECTIVE: We analyzed the effects of the timing of vaccine distribution in 11 U.S. states during the 2009 H1N1 influenza pandemic. METHODS: By using reported data on the fraction of patients presenting with flu-related symptoms, we developed a transformation that allowed estimation of the state-specific temporal flu wave curve, representing the number of new infections during each week. We also utilized data describing the weekly numbers of vaccine doses delivered and administered. By using a simple difference equations model of flu progression, we developed two influenza wave curves: first, an "observable" curve that included the beneficial effects of vaccinations, and second, an unobservable curve that depicted how the flu would have progressed with no vaccine administered. We fit the observable curve to match the estimated epidemic curve and early exponential growth associated with R0, the reproductive number. By comparing the number of infections in each scenario, we estimated the infections averted by the administration of vaccine. RESULTS: Southern states experienced peak infection several weeks before northern states, and most of the vaccine was delivered well after the peak of the southern flu wave. Our models suggest that the vaccine had minimal ameliorative impact in the southern states and measurable positive impact in the northern states. Vaccine delivery after peak also results in a smaller fraction of the population's seeking the vaccine. CONCLUSIONS: Our analysis suggests that current Centers for Disease Control and Prevention policy of allocating flu vaccine over time in direct proportion to states' populations may not be best in terms of averting nationally the maximum possible number of infections.

Magnified Effects of Changes in NIH Research Funding Levels.

What happens within the university-based research enterprise when a federal funding agency abruptly changes research grant funding levels, up or down? We use simple difference equation models to show that an apparently modest increase or decrease in funding levels can have dramatic effects on researchers, graduate students, postdocs, and the overall research enterprise. The amplified effect is due to grants lasting for an extended period, thereby requiring the majority of funds available in one year to pay for grants awarded in previous years. We demonstrate the effect in various ways, using National Institutes of Health data for two situations: the historical doubling of research funding from 1998 to 2003 and the possible effects of "sequestration" in January 2013. We posit human responses to such sharp movements in funding levels and offer suggestions for amelioration.

Nonfixed Retirement Age for University Professors: Modeling Its Effects on New Faculty Hires.

We model the set of tenure-track faculty members at a university as a queue, where "customers" in queue are faculty members in active careers. Arrivals to the queue are usually young, untenured assistant professors, and departures from the queue are primarily those who do not pass a promotion or tenure hurdle and those who retire. There are other less-often-used ways to enter and leave the queue. Our focus is on system effects of the elimination of mandatory retirement age. In particular, we are concerned with estimating the number of assistant professor slots that annually are no longer available because of the elimination of mandatory retirement. We start with steady-state assumptions that require use of Little's Law of Queueing, and we progress to a transient model using system dynamics. We apply these simple models using available data from our home university, the Massachusetts Institute of Technology.

Vaccine availability in the United States during the 2009 H1N1 outbreak.

OBJECTIVE: After initial flu cases are reported, months elapse before vaccine becomes available. The authors report the experience of US states during the fall of 2009 on H1N1 vaccine availability in relation to the occurrence of disease. DESIGN: The authors used data from the Centers for Disease Control and prevention and state health departments to approximate second wave H1N1 epidemic curves. The authors compared these curves to two sources of vaccine distribution data-shipment and administration. RESULTS: Ten states received their first shipments of vaccine after the epidemic peaked, four states during the week of the peak, and 10 states only 1 week prior to the peak. In nearly half of all states, the epidemic had already begun to decline before any individuals could have been protected. CONCLUSIONS: A sensible approach would be to highlight the importance of diligent hygienic behavior and to reduce the rate of human-to-human contacts before vaccine is available.

Engineering responses to pandemics.

Focusing on pandemic influenza, this chapter approaches the planning for and response to such a major worldwide health event as a complex engineering systems problem. Action-oriented analysis of pandemics requires a broad inclusion of academic disciplines since no one domain can cover a significant fraction of the problem. Numerous research papers and action plans have treated pandemics as purely medical happenings, focusing on hospitals, health care professionals, creation and distribution of vaccines and anti-virals, etc. But human behavior with regard to hygiene and social distancing constitutes a first-order partial brake or control of the spread and intensity of infection. Such behavioral options are "non-pharmaceutical interventions." (NPIs) The chapter employs simple mathematical models to study alternative controls of infection, addressing a well-known parameter in epidemiology, R0, the "reproductive number," defined as the mean number of new infections generated by an index case. Values of R0 greater than 1.0 usually indicate that the infection begins with exponential growth, the generation-to-generation growth rate being R0. R0 is broken down into constituent parts related to the frequency and intensity of human contacts, both partially under our control. It is suggested that any numerical value for R0 has little meaning outside the social context to which it pertains. Difference equation models are then employed to study the effects of heterogeneity of population social contact rates, the analysis showing that the disease tends to be driven by high frequency individuals. Related analyses show the futility of trying geographically to isolate the disease. Finally, the models are operated under a variety of assumptions related to social distancing and changes in hygienic behavior. The results are promising in terms of potentially reducing the total impact of the pandemic.