COVID-19 Math: You, Me, R0 and Rolling Re-entry

Larson discusses the mathematical equation of the Covid-19 pandemic. He offers a mathematical formula to explain the spread, transmission and infection of the Covid-19. He also explains how measures such as social isolation can help protect the most vulnerable population such as the senior citizens and those with chronic medical conditions and asserts that with the significant number of re-openings, if done with meticulous care, will not increase the current value of infections. The author also emphasizes the importance of masks, social distancing and temperature checks and until vaccine is available, everything will be back to normal.

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.

The 2019-nCoV coronavirus: Are there two routes to infection? The new virus may be expelled from a human via respiratory and intestinal means

It is unclear how lethal the new coronavirus is to those who contract it. There have been more than 1,300 reported deaths among 60,000 infected people, which yields a raw death rate around 2%. Here, Larson raises the possibility that measures that reduce the respiratory spread of the virus might be considerably less effective than anticipated.

STEM education 'BLOSSOMS' in China despite virus: MIT program teams up with Peking University to offer online lessons to quarantined students

Late in 2019, the coronavirus emerged in China. By early 2020, the Chinese government took significant social distancing steps to reduce the spread of infections. As one part of this, schools were closed. Here, Larson and Chunhua discusses the partnership established by the MIT BLOSSOMS program with Peking University to offer 32 BLOSSOMS Mandarin-language interactive video STEM lessons to students in China who are at home and still eager to learn during the coronavirus lockdown.

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.