LEVERAGING A MULTIPLE-STRAIN MODEL WITH MUTATIONS IN ANALYZING THE SPREAD OF COVID-19

The spread of COVID-19 has been among the most devastating events affecting the health and well-being of humans worldwide since World War II. A key scientific goal concerning COVID-19 is to develop mathematical models that help us to understand and predict its spreading behavior, as well as to provide guidelines on what can be done to limit its spread. In this paper, we discuss how our recent work on a multiple-strain spreading model with mutations can help address some key questions concerning the spread of COVID-19. We highlight the recent reports on a mutation of SARS-CoV-2 that is thought to be more transmissible than the original strain and discuss the importance of incorporating mutation and evolutionary adaptations (together with the network structure) in epidemic models. We also demonstrate how the multiplestrain transmission model can be used to assess the effectiveness of mask-wearing in limiting the spread of COVID19. Finally, we present simulation results to demonstrate our ideas and the utility of the multiple-strain model in the context of COVID-19.

Active Control and Sustained Oscillations in actSIS Epidemic Dynamics

An actively controlled Susceptible-Infected-Susceptible (actSIS) contagion model is presented for studying epidemic dynamics with continuous-time feedback control of infection rates. Our work is inspired by the observation that epidemics can be controlled through decentralized disease-control strategies such as quarantining, sheltering in place, social distancing, etc., where individuals can actively modify their contact rates in response to observations of the infection levels in the population. Accounting for a time lag in observations and categorizing individuals into distinct sub-populations based on their risk profiles, we show that the actSIS model manifests qualitatively different features as compared with the SIS model. In a homogeneous population of risk-averters, the endemic equilibrium is always reduced, although the transient infection level can overshoot or undershoot. In a homogeneous population of risk-tolerating individuals, the system exhibits bistability, which can also lead to reduced infection. For a heterogeneous population comprised of risk-tolerators and risk-averters, we prove conditions on model parameters for the existence of a Hopf bifurcation and sustained oscillations in the infected population. Copyright (C) 2020 The Authors.

Optimal, near-optimal, and robust epidemic control

In the absence of drugs and vaccines, policymakers use non-pharmaceutical interventions such as social distancing to decrease rates of disease-causing contact, with the aim of reducing or delaying the epidemic peak. These measures carry social and economic costs, so societies may be unable to maintain them for more than a short period of time. Intervention policy design often relies on numerical simulations of epidemic models, but comparing policies and assessing their robustness demands clear principles that apply across strategies. Here we derive the theoretically optimal strategy for using a time-limited intervention to reduce the peak prevalence of a novel disease in the classic Susceptible-Infectious-Recovered epidemic model. We show that broad classes of easier-to-implement strategies can perform nearly as well as the theoretically optimal strategy. But neither the optimal strategy nor any of these near-optimal strategies is robust to implementation error: small errors in timing the intervention produce large increases in peak prevalence. Our results reveal fundamental principles of non-pharmaceutical disease control and expose their potential fragility. For robust control, an intervention must be strong, early, and ideally sustained. The COVID-19 pandemic has demonstrated the need for non-pharmaceutical epidemic mitigation strategies that can be effective even if they are limited in duration. Here, the authors derive analytically optimal and near-optimal time-limited strategies for limiting the epidemic peak in the Susceptible-Infectious-Recovered model and show that, due to the sensitivity of such strategies to implementation errors, timely action is fundamental to non-pharmaceutical disease control.

Boat to bowl: Resilience through network rewiring of a community-supported fishery amid the COVID-19 pandemic

Fisheries are coupled human-natural systems locally, regionally, and globally. However, human-nature interactions within and between adjacent and distant systems (metacouplings) are rarely studied in fisheries despite their prevalence and policy relevance. We filled this knowledge gap by using network models to identify how the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has rewired couplings and reshaped resilience of Fishadelphia, a community-supported fishery program (CSF) in New Jersey and Pennsylvania, USA. As abstractions illustrating interactions among supply-chain actors, networks are helpful for characterizing flows and assessing resilience to disturbances such as those induced by the SARS-CoV-2 pandemic. Since Fall 2018, 18 seafood (finfish and shellfish) species totaling 6273 lbs have flowed from harvesters (n = 4), to processors (n = 2), to a distributor, to retailers (n = 2), and finally to customers (n = 183). The pandemic reduced the number of seafood harvesters and processors (−50%), seafood flow quantity (−25%), species diversity in the marketplace (−67%), and species per supplier (−50%) before stopping flows in mid-March 2020, when Fishadelphia closed for 3 months. Models of network optimality indicated that the pandemic fragmented metacouplings that previously allowed multiple seafood suppliers to provide diverse products to customers. However, demand-side resilience increased through dispersed, socially distanced, efficient seafood delivery that expanded the customer base and generally increased customer satisfaction. This resilience dichotomy-wherein the post-closure network was less resilient than the pre-closure network in supply-side species diversity, but more resilient in demand-side social distancing, delivery efficiency, and customer satisfaction-has implications for rewiring networks to sustain CSFs and other local food systems amid ecological and social disturbances. © 2021 The Author(s). Published by IOP Publishing Ltd