479. Mobility Restrictions and COVID-19 Pandemic Outbreak Control

• Published in: Open forum infectious diseases Vol. 7; no. Supplement_1; pp. S305 - S306
• Main Authors: Starling, Carlos Ernesto Ferreira, Marinho Couto, Bráulio Roberto Gonçalves, da Cunha Júnior, Joaquim José, Alvim, André Luiz, Oliveira, Cristóvão de Deus Martins, Souza, Gregory Lauar, de Carvalho, Handerson Dias Duarte, Rocha, Rhayssa Fernanda Andrade
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 31.12.2020
• Subjects: Poster Abstracts
• ISSN: 2328-8957; 2328-8957
• DOI: 10.1093/ofid/ofaa439.672

Abstract Background In December 2009, a cluster of patients with pneumonia was reported in the city of Wuhan, capital of Hubei province in China, caused by a novel coronavirus: SARS-CoV-2. The epidemiological compartmental susceptible-exposed-infected-recovered (SEIR) model has been previously used during the initial wave of the H1N1 influenza pandemic in 2009. This study investigates whether the SEIR model, associated to mobility changes parameters, can determine the likelihood of establishing control over an epidemic in a city, state or country. Methods The critical step in the prediction of COVID-19 by a SEIR model are the values of the basic reproduction number (R0) and the infectious period, in days. R0 and the infectious periods were calculated by mathematical constrained optimization, and used to determine the numerically minimum SEIR model errors in a country, based on COVID-19 data until april 11th. The Community Mobility Reports from Google Maps (https://www.google.com/covid19/mobility/) provided mobility changes on april 5th compared to the baseline (Jan 3th to Feb 6th). The data was used to measure the non-pharmacological intervention adherence. The impact of each mobility component was made by logistic regression models. COVID-19 control was defined by R0 of the SEIR model in a country less than 1.0. Algorithm for the SEIR model applied to COVID-19 (initialization) Table 01: Algorithm for the SEIR model applied to COVID-19 (calculation of new COVID-19 cases day-by-day) Results Residential mobility restriction presented the higher logistic coefficient (17.7), meaning higher impact on outbreak control. Workplace mobility restriction was the second most effective measure, considering a restriction minimum of 56% for a 53% chance of outbreak control. Retail and recreation mobility presented 53%, and 86% respectively. Transit stations (96% and 54%) were also assessed. Park mobility restriction demonstrated the lowest effectiveness in outbreak control, considering that absolute (100%) restriction provided the lowest chance of outbreak control (46%). Table 2: The Community Mobility Reports from Google Maps: Mobility changes on April 5 compared to the baseline (5- week period; Jan 3–Feb 6, 2020): T_infectious and R0 obtained by using COVID-19 new cases day-by-day in each country, adjusted to the SEIR model by mathematical constrained optimization Logistic regression models to evaluate the chance of an epidemic control based on the non-pharmacological interventions adherence Simulation of the impact of the mobility component in the chance of outbreak control: analysis by using the logistic regression model summarized in Table 2 Conclusion Residential mobility restriction is the most effective measure. The degree to which mobility restrictions increase or decrease the overall epidemic size depends on the level of risk in each community and the characteristics of the disease. More research is required in order to estimate the optimal balance between mobility restriction, outbreak control, economy and freedom of movement. Disclosures All Authors: No reported disclosures