Estimation of effects of contact tracing and mask adoption on COVID-19 transmission in San Francisco: a modeling study

AbstractThe current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco’s shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, −20.1%–81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.

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COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

10.2196/preprints.23997 ◽

2020 ◽

Author(s):

Eduardo Atem De Carvalho ◽

Rogerio Atem De Carvalho

Keyword(s):

New York ◽

Reproduction Number ◽

Moving Average ◽

Real Data ◽

Time Dependent ◽

Initial Value ◽

Health Authorities ◽

Reproduction Numbers ◽

Effect Estimation ◽

The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

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The war against the homeless: Frank Jordan, broken windows, and anti-homeless politics in San Francisco

Cooking up a revolution ◽

10.7228/manchester/9781526107350.003.0004 ◽

2019 ◽

pp. 63-81

Author(s):

Sean Parson

Keyword(s):

Quality Of Life ◽

Public Space ◽

San Francisco ◽

Broken Windows ◽

The Public ◽

Matrix Quality ◽

Physical Spaces ◽

The Impact ◽

The City

Chapter 4 discusses Mayor Frank Jordan’s (1992–1995) revanchist Matrix Quality of Life Program, which sought to enforce a broken-windows policing system in San Francisco. The impact of the policy was felt largely by the visible homeless in downtown San Francisco, who were regularly harassed and arrested by the police and forced out of the city. Because quality-of-life policing desires to sanitize the public space of disruptive and asocial behaviour, the public meals of Food Not Bombs near City Hall resisted the city’s attempt to criminalize homelessness. This chapter argues that the city attempted to construct the homeless as anti-citizens and exclude them from the political and physical spaces of the city.

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Epidemiological trend of COVID-19 in Nepal and the importance of social distancing to contain the virus

Applied Science and Technology Annals ◽

10.3126/asta.v1i1.30268 ◽

2020 ◽

Vol 1 (1) ◽

pp. 15-25

Author(s):

Amod K. Pokhrel ◽

Yadav P. Joshi ◽

Sopnil Bhattarai

Keyword(s):

Mitigation Measures ◽

Contact Tracing ◽

Limited Information ◽

Social Distancing ◽

Mobility Data ◽

Epidemiological Trend ◽

Reproduction Numbers ◽

The Government ◽

Testing Case ◽

The Impact

There is limited information on the epidemiology and the effects of mitigation measures on the spread of COVID-19 in Nepal. Using publicly available databases, we analyzed the epidemiological trend, the people's movement trends at different intervals across different categories of places and evaluated implications of social mobility on COVID-19. We also estimated the epidemic peak. As of June 9, 2020, Provinces 2 and 5 have most of the cases. People between 15 and 54 years are vulnerable to becoming infected, and more males than females are affected. The cases are growing exponentially. The growth rate of 0.13 and >1 reproduction numbers (R0) over time (median: 1.48; minimum: 0.58, and maximum: 3.71) confirms this trend. The case doubling time is five days. Google's community mobility data suggest that people strictly followed social distancing measures for one month after the lockdown. By around the 4th week of April, the individual's movement started rising, and social contacts increased. The number of cases peaked on May 12, with 83 confirmed cases in one day. The Susceptible-Exposed-Infectious-Removed (SEIR) model suggests that the epidemic will peak approximately on day 41 (July 21, 2020), and start to plateau after day 80. To contain the spread of the virus, people should maintain social distancing. The Government needs to continue active surveillance, more PCR-based testing, case detection, contact tracing, isolation, and quarantine. The Government should also provide financial support and safety-nets to the citizen to limit the impact of COVID-19.

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Bikes or Bust? Analyzing the Impact of Bicycle Infrastructure on Business Performance in San Francisco

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119850465 ◽

2019 ◽

Vol 2673 (12) ◽

pp. 277-289

Author(s):

Raleigh McCoy ◽

Joseph A. Poirier ◽

Karen Chapple

Keyword(s):

San Francisco ◽

Business Performance ◽

The United States ◽

Ordinary Least Squares ◽

Grocery Stores ◽

Least Squares Regression ◽

Mode Shift ◽

Bicycle Infrastructure ◽

The Impact ◽

Over Time

Transportation agencies at the local, state, and federal levels in the United States (U.S.) have shown a growing interest in expanding bicycle infrastructure, given its link to mode shift and safety goals. These projects, however, are far from universally accepted. Business owners have been particularly vocal opponents, claiming that bicycle infrastructure will diminish sales or fundamentally change the character of their neighborhoods. Using the case of San Francisco, this research explores the relationship between bicycle infrastructure and business performance in two ways: change in sales over time, and a comparison of sales for new and existing businesses. An ordinary least squares regression is used to model the change in sales over time, isolating the effect of location on bicycle infrastructure while controlling for characteristics of the business, corridor, and surrounding neighborhood. Through a series of t-tests, average sales for businesses that pre-date bicycle infrastructure and for those that opened after the installation of such projects are compared. Ultimately, the research suggests that location on bicycle infrastructure and changes in on-street parking supply generally did not have a significant effect on the change in sales, with a few exceptions. Businesses that sell goods for the home or auto-related goods and services saw a significant decline in sales when located on corridors with bike lanes. New and existing businesses generally had similar sales, though not across the board. New restaurants and grocery stores had significantly higher sales than their existing counterparts, suggesting bicycle infrastructure may attract more upmarket businesses in those industries.

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San Francisco's Family-Focused Probation: A Conversation with Chief Adult Probation Officer Wendy Still

Federal Sentencing Reporter ◽

10.1525/fsr.2011.24.1.54 ◽

2011 ◽

Vol 24 (1) ◽

pp. 54-56

Author(s):

Margaret Dizerega

Keyword(s):

Criminal Justice ◽

San Francisco ◽

Family Involvement ◽

Justice System ◽

Probation Officer ◽

Family Impact ◽

Decision Point ◽

The Impact ◽

The City ◽

Adult Probation

The strengths of family and the impact of incarceration on family members are often ignored in the sentencing decision. Similarly, despite decades of research demonstrating that families play an important role in the successful reentry of individuals, they are often overlooked as a reentry resource. A family-focused approach to sentencing and supervision would ensure that family involvement is considered at each decision point in the criminal justice system. Believed to be the only U.S. jurisdiction that is using family impact statements at sentencing, the Adult Probation Department in San Francisco is committed to a family-focused approach. To discuss the department's innovative practices, the author interviewed Wendy Still, the chief adult probation officer of the city and county of San Francisco.

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Modelling the evolution of COVID-19 in high-incidence European countries and regions: estimated number of infections and impact of past and future intervention measures

10.1101/2020.05.09.20096735 ◽

2020 ◽

Author(s):

Juan Fernandez-Recio

Keyword(s):

Reproduction Number ◽

Mechanistic Model ◽

Control Measures ◽

European Countries ◽

Intervention Measures ◽

High Incidence ◽

Long Term Impact ◽

The Impact ◽

Over Time

A previously developed mechanistic model of COVID-19 transmission has been adapted and applied here to study the evolution of the disease and the effect of intervention measures in some European countries and territories where the disease had major impact. A clear impact of the major intervention measures on the reproduction number (Rt) has been found in all studied countries and territories, as already suggested by the drop in the number of deaths over time. Interestingly, the impact of such major intervention measures seems to be the same in most of these countries. The model has also provided realistic estimates of the total number of infections, active cases and future outcome. While the predictive capabilities of the model are much more uncertain before the peak of the outbreak, we could still reliably predict the evolution of the disease after a major intervention by assuming the afterwards reproduction number from current study. More challenging is to foresee the long-term impact of softer intervention measures, but this model can estimate the outcome of different scenarios and help planning changes in the implementation of control measures in a given country or region.

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SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

10.1101/2020.07.24.20161752 ◽

2020 ◽

Author(s):

Avaneesh Singh ◽

Manish Kumar Bajpai

Keyword(s):

Basic Reproduction Number ◽

Case Fatality Rate ◽

Reproduction Number ◽

Case Fatality ◽

The United States ◽

Fatality Rate ◽

Hospital Beds ◽

The Impact ◽

Over Time ◽

The Basic Reproduction Number

We have proposed a new mathematical method, SEIHCRD-Model that is an extension of the SEIR-Model adding hospitalized and critical twocompartments. SEIHCRD model has seven compartments: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), recovered (R), and deceased or death (D), collectively termed SEIHCRD. We have studied COVID- 19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. SEIHCRD model is estimating COVID-19 spread and forecasting under uncertainties, constrained by various observed data in the present manuscript. We have first collected the data for a specific period, then fit the model for death cases, got the values of some parameters from it, and then estimate the basic reproduction number over time, which is nearly equal to real data, infection rate, and recovery rate of COVID-19. We also compute the case fatality rate over time of COVID-19 most affected countries. SEIHCRD model computes two types of Case fatality rate one is CFR daily and the second one is total CFR. We analyze the spread and endpoint of COVID-19 based on these estimates. SEIHCRD model is time-dependent hence we estimate the date and magnitude of peaks of corresponding to the number of exposed cases, infected cases, hospitalized cases, critical cases, and the number of deceased cases of COVID-19 over time. SEIHCRD model has incorporated the social distancing parameter, different age groups analysis, number of ICU beds, number of hospital beds, and estimation of how much hospital beds and ICU beds are required in near future.

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Real-time Nowcasting and Forecasting of COVID-19 Dynamics in England: the first wave?

10.1101/2020.08.24.20180737 ◽

2020 ◽

Author(s):

Paul J Birrell ◽

Joshua Blake ◽

Edwin van Leeuwen ◽

Nick Gent ◽

Daniela De Angelis ◽

...

Keyword(s):

Real Time ◽

Reproduction Number ◽

Mitigation Measures ◽

Contact Tracing ◽

The Real ◽

South West ◽

Effective Contact ◽

Ongoing Work ◽

Reproduction Numbers ◽

Second Wave

England has been heavily affected by the SARS-CoV-2 pandemic, with severe 'lock-down' mitigation measures now gradually being lifted. The real-time pandemic monitoring presented here has contributed to the evidence informing this pandemic management. Estimates on the 10th May showed lock-down had reduced transmission by 75%, the reproduction number falling from 2.6 to 0.61. This regionally-varying impact was largest in London of 81% (95% CrI: 77%-84%). Reproduction numbers have since slowly increased, and on 19th June the probability that the epidemic is growing was greater than 50% in two regions, South West and London. An estimated 8% of the population had been infected, with a higher proportion in London (17%). The infection-to-fatality ratio is 1.1% (0.9%-1.4%) overall but 17% (14%-22%) among the over-75s. This ongoing work will be key to quantifying any widespread resurgence should accrued immunity and effective contact tracing be insufficient to preclude a second wave.

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Estimating the reproduction number and forecasting the impact of COVID-19 in Kuwait using a modified compartmental epidemiological model

10.21203/rs.3.rs-49773/v1 ◽

2020 ◽

Author(s):

Mohammad AlHamli

Keyword(s):

Reproduction Number ◽

Intrinsic Growth Rate ◽

General Hospitals ◽

Maximum Value ◽

Reproduction Numbers ◽

Next Generation Matrix ◽

The State Of Kuwait ◽

Using Data ◽

The Impact ◽

The Basic Reproduction Number

Abstract A modified compartmental epidemic model was developed to simulate the state of Kuwait protocol in fighting COVID-19 pandemic. The next generation matrix method was used to drive an expression for the basic reproduction number, R0. Basic and effective reproduction numbers were calculated using data from the intrinsic growth rate of the confirmed COVID-19 cases. R0 was found to be 2.18. Three scenarios that varied by effective reproduction number were used to estimate the future course of the disease: a high value of R = 1.98, a middle value of R = 1.62, and a low value of R = 1.2. The maximum number of beds required in general hospitals in each scenario were estimated at 141 184, 85 341, and 16 412, respectively. For intensive care units, the estimated numbers of beds required were 16 461, 9 645, and 1788. Maximum deaths also varied and were estimated to be 29 202, 23 973, and 11 565. For the maximum value of R, it is estimated to peak on August 27, 2020. For the middle value of R, it is estimated to peak on September 20, 2020. For the minimum value of R, it is estimated to peak on December 21, 2020.

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Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies

10.1101/2021.01.20.21250109 ◽

2021 ◽

Author(s):

Conor G. McAloon ◽

Patrick Wall ◽

Francis Butler ◽

Mary Codd ◽

Eamonn Gormley ◽

...

Keyword(s):

Regression Models ◽

Negative Binomial ◽

Contact Tracing ◽

Government Interventions ◽

Age Cohorts ◽

Epidemiological Modelling ◽

Contact Rates ◽

The Impact ◽

Over Time ◽

Close Contacts

ABSTRACTBackgroundContact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling.MethodsWe analysed data from 140,204 contacts of 39861 cases in Ireland from 1st May to 1st December 2020. Only ‘close’ contacts were included in the analysis. A close contact was defined as any individual who had had > 15 minutes face-to-face (<2 m) contact with a case; any household contact; or any individual sharing a closed space for longer than 2 hours, in any setting.ResultsThe number of contacts per case was overdispersed, the mean varied considerably over time, and was temporally associated with government interventions. Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days.DiscussionThese data were collected for a specific purpose and therefore any inferences must be made with caution. The data are representative of contact rates of cases, and not of the overall population. However, the data may be a more accurate indicator of the likely degree of onward transmission than might be the case if a random sample of the population were taken. Furthermore, since we analysed only the number of close contacts, the total number of contacts per case would have been higher. Nevertheless, this analysis provides useful information for monitoring the impact of government interventions on the number of contacts; for helping pre-empt increases or decreases in case numbers, and for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling.

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