Mitigation Policy for The  Covid-19 Pandemic: Intertemporal  Optimisationusing an Seir  Model

2022 ◽  
Author(s):  
Jan Maciejowski ◽  
Robert Rowthorn ◽  
Scott Sheffield ◽  
David Vines ◽  
Anne Williamson
Keyword(s):  
Mitigation Policy ◽  
Seir Model

The Role of Late-Night Infotainment Comedy in Communicating Climate Change Consensus

2019 ◽  
Author(s):  
Edward John Roy Clarke ◽  
Anna Klas ◽  
Joshua Stevenson ◽  
Emily Jane Kothe
Keyword(s):  
Climate Change ◽  
Policy Support ◽  
Mitigation Policy ◽  
Behavioural Intentions ◽  
Late Night ◽  
Mitigation And Adaptation ◽  
Climate Action ◽  
Adaptation Policies ◽  
Diverse Audience

Climate change is a politically-polarised issue, with conservatives less likely than liberals to perceive it as human-caused and consequential. Furthermore, they are less likely to support mitigation and adaptation policies needed to reduce its impacts. This study aimed to examine whether John Oliver’s “A Mathematically Representative Climate Change Debate” clip on his program Last Week Tonight polarised or depolarised a politically-diverse audience on climate policy support and behavioural intentions. One hundred and fifty-nine participants, recruited via Amazon MTurk (94 female, 64 male, one gender unspecified, Mage = 51.07, SDage = 16.35), were presented with either John Oliver’s climate change consensus clip, or a humorous video unrelated to climate change. Although the climate change consensus clip did not reduce polarisation (or increase it) relative to a control on mitigation policy support, it resulted in hyperpolarisation on support for adaptation policies and increased climate action intentions among liberals but not conservatives.

Forecast of COVID-19 Trend Following Countrywide Movement Control Order in Malaysia: SEIR Model Approach (Preprint)

2020 ◽  
Author(s):  
Aidalina Mahmud ◽  
Poh Ying Lim ◽  
Hayati Kadir Shahar
Keyword(s):  
Mathematical Model ◽  
Incidence Rate ◽  
Movement Control ◽  
Seir Model ◽  
Infection Incidence ◽  
Contact Rates ◽  
Exposed Population ◽  
Control Order ◽  
Future Work ◽  
Model Approach

BACKGROUND On March 18, 2020, the Malaysian government implemented Movement Control Order (MCO) to limit the contact rates among the population and infected individuals. OBJECTIVE The objective of this study was to forecast the trend of the COVID-19 epidemic in Malaysia in terms of its magnitude and duration. METHODS Data for this analysis was obtained from publicly available databases, from March 17 until March 27, 2020. By applying the Susceptible, Exposed, Infectious and Removed (SEIR) mathematical model and several predetermined assumptions, two analyses were carried out: without and with MCO implementation. RESULTS Without MCO, it is forecasted that it would take 18 days to reach the peak of infection incidence. The incidence rate would plateau at day 80 and end by day 94, with 43% of the exposed population infected. With the implementation of the MCO, it is forecasted that new cases of infection would peak at day 25, plateau at day 90 and end by day 100. At its peak, the infection could affect up to about 40% of the exposed population. CONCLUSIONS It is forecasted that the COVID-19 epidemic in Malaysia will subside soon after the mid-year of 2020. Although the implementation of MCO can flatten the epidemiological curve, it also prolongs the duration of the epidemic. The MCO can result in several unfavorable consequences in economic and psychosocial aspects. A future work of an exit plan for the MCO should also be devised and implemented gradually. The exit plan raises several timely issues of re-infection resurgence after MCO are lifted.

scholarly journals Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model

2021 ◽  
Author(s):  
Bernd Brüggenjürgen ◽  
Hans-Peter Stricker ◽  
Lilian Krist ◽  
Miriam Ortiz ◽  
Thomas Reinhold ◽  
...  
Keyword(s):  
Public Health ◽  
Transmission Rate ◽  
Latency Period ◽  
Policy Decision ◽  
Sensitivity Analyses ◽  
Delphi Panel ◽  
Time Step ◽  
Seir Model ◽  
Health Policy Decision ◽  
The Impact

Abstract Aim To use a Delphi-panel-based assessment of the effectiveness of different non-pharmaceutical interventions (NPI) in order to retrospectively approximate and to prospectively predict the SARS-CoV-2 pandemic progression via a SEIR model (susceptible, exposed, infectious, removed). Methods We applied an evidence-educated Delphi-panel approach to elicit the impact of NPIs on the SARS-CoV-2 transmission rate R0 in Germany. Effectiveness was defined as the product of efficacy and compliance. A discrete, deterministic SEIR model with time step of 1 day, a latency period of 1.8 days, duration of infectiousness of 5 days, and a share of the total population of 15% assumed to be protected by immunity was developed in order to estimate the impact of selected NPI measures on the course of the pandemic. The model was populated with the Delphi-panel results and varied in sensitivity analyses. Results Efficacy and compliance estimates for the three most effective NPIs were as follows: test and isolate 49% (efficacy)/78% (compliance), keeping distance 42%/74%, personal protection masks (cloth masks or other face masks) 33%/79%. Applying all NPI effectiveness estimates to the SEIR model resulted in a valid replication of reported occurrence of the German SARS-CoV-2 pandemic. A combination of four NPIs at consented compliance rates might curb the CoViD-19 pandemic. Conclusion Employing an evidence-educated Delphi-panel approach can support SARS-CoV-2 modelling. Future curbing scenarios require a combination of NPIs. A Delphi-panel-based NPI assessment and modelling might support public health policy decision making by informing sequence and number of needed public health measures.

scholarly journals Effects of B.1.1.7 and B.1.351 on COVID-19 Dynamics: A Campus Reopening Study

2021 ◽  
Author(s):  
Kevin Linka ◽  
Mathias Peirlinck ◽  
Amelie Schäfer ◽  
Oguz Ziya Tikenogullari ◽  
Alain Goriely ◽  
...  
Keyword(s):  
Online Education ◽  
The United States ◽  
Wild Type ◽  
Seir Model ◽  
Population Mixing ◽  
Santa Clara County ◽  
Increased Risk ◽  
Controversial Topic ◽  
Almost All ◽  
Local Reproduction

AbstractThe timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under baseline reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.

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