scholarly journals Updated COVID-19 Outbreak Forecast for San Diego County, April 2020

Outbreak ◽  
2020 ◽  
Author(s):  
Emily Eshraghian ◽  
Nathan Jacobs ◽  
Jeffrey Morgan
Keyword(s):  
San Diego ◽  
Weighted Average ◽  
San Diego County ◽  
Case Scenario ◽  
Worst Case ◽  
Social Distancing ◽  
Hospital Capacity ◽  
First Case ◽  
Scenario Model ◽  
Incident Cases

Here we extend and update our earlier projections of COVID-19 hospitalizations in San Diego County (1), and report a more optimistic outlook through the end of April 2020. San Diego confirmed its first case of COVID-19 on March 7, 2020. Several mitigation efforts were enacted on various dates, including a state-mandated stay-at-home order and enforcement of social distancing in public areas. Though mitigation strategies are helping lower the burden of disease, incident cases continue to increase exponentially. Our updated model includes data up to April 7 and does not forecast beyond April 30. Our approach uses a “wisdom of crowds” strategy (see link to methods for details) where a range of outbreak models from worst case scenario (Model A) to best case scenario (Model C) were presented to experts and non-experts (n=8) who were asked to vote on a most plausible model for expected COVID-19 spread. Final vote tallies were used to create a weighted average (Model M) as the official model projection. Our model predicts that San Diego County will not hit hospital capacity for standard hospital beds (panel a) nor for intensive care unit (ICU) beds (panel b) within April 2020. If current conditions continue, we predict the expected “surge” in hospitalizations to occur without surpassing hospital capacity, and that hospitalizations will decrease thereafter until the outbreak has been contained. However, it is important to note that factors such as changes in social distancing policies, even if occurring when existing or incident cases are low, may still result in new outbreaks and future spikes in hospitalizations. Furthermore, no models have been extensively validated for COVID-19. We encourage all residents of San Diego to continue rigorously following social distancing practices to improve the likelihood of best case scenarios and limit the scope of possible worst case scenarios.

scholarly journals Flattening the curve and the effect of atypical events on mitigation measures in Mexico: a modeling perspective

2020 ◽  
Author(s):  
Mario Santana-Cibrian ◽  
Manuel Adrian Acuna-Zegarra ◽  
Jorge X. Velasco-Hernandez
Keyword(s):  
Mitigation Measures ◽  
Case Scenario ◽  
Worst Case ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Effective Contact ◽  
High Incidence ◽  
The Government ◽  
Mother’S Day ◽  
Short Time

On 23 and 30 March 2020 the Mexican Federal government implemented social distancing measures to mitigate the COVID-19 epidemic. We use a mathematical model to explore atypical transmission events within the confinement period, triggered by the timing and strength of short time perturbations of social distancing. We show that social distancing measures were successful in achieving a significant reduction of the effective contact rate in the early weeks of the intervention. However, "flattening the curve" had an undesirable effect, since the epidemic peak was delayed too far, almost to the government preset day for lifting restrictions (01 June 2020). If the peak indeed occurs in late May or early June, then the events of children's day and mother's day may either generate a later peak (worst case scenario), a long plateau with relatively constant but high incidence (middle case scenario) or the same peak date as in the original baseline epidemic curve, but with a post-peak interval of slower decay.

Dynamic Value Pricing on I-15 in San Diego: Impact on Travel Time and Its Reliability

2003 ◽  
Vol 1839 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Janusz Supernak ◽  
Christine Kaschade ◽  
Duane Steffey
Keyword(s):  
Travel Time ◽  
San Diego ◽  
Time Profile ◽  
Travel Times ◽  
Case Scenario ◽  
Worst Case ◽  
San Diego State University ◽  
Time Changes ◽  
Value Pricing ◽  
Express Lanes

Selected results are presented of the Traffic Study, one of 12 studies conducted by San Diego State University for the I-15 Congestion (Value) Pricing Project in San Diego, a 3-year demonstration. The focus is on the project's impact on travel times and their distribution on both the main lanes and the express lanes of I-15 for both ExpressPass and FasTrak phases of the project. Specifically addressed is the issue of reliability of on-time arrival enjoyed by the FasTrak subscribers and the high variability of travel times for the I-15 travelers who use only main lanes of I-15 for their commute. Examination of the ramp and freeway delays shows that in the worst-case scenario, FasTrak subscribers who use express lanes can save up to 20 min avoiding delay on the I-15 main lanes. This finding agrees with the drivers’ perceptions about their time savings when using FasTrak. Travel-time changes during the duration of the project also are examined. There were substantial year-to-year changes in travel times along the I-15 main lanes and the I-8 lanes used as control. The travel-time profile along the I-15 main lanes differed significantly from the profile along I-8, the control corridor, in both a.m. and p.m. peak periods.

scholarly journals Estimates of SARS-COV-2 Behavior in the COVID-19 Crisis: Addressing Sample-Selection Bias for Public Health Applications

2020 ◽  
Author(s):  
John Straka
Keyword(s):  
Selection Bias ◽  
Sample Selection ◽  
Herd Immunity ◽  
Model Specification ◽  
Sample Selection Bias ◽  
Case Scenario ◽  
Worst Case ◽  
Reproduction Ratio ◽  
Social Distancing ◽  
The U.S

This study surveys and assesses the implications from recent empirical studies and reports to highlight the characteristics of SARS-Cov-2 and the COVID-19 crisis, and then proposes a recursive bivariate probit (RBP) model specification and possible applications. The RBP model addresses sample selection bias to estimate key determinants of virus infection given nonrandom testing. Applicable to anonymized case-level or widely available local-area data in the U.S., multiple data sources are shown. With suitable data the model can control for observed (e.g. population density) and unobserved factors to estimate the marginal effects of varying state-prescribed measures and behavioral social distancing. Case-level scoring models may, in addition, eventually assist in clinical diagnostic assessments. Although not proposed to substitute for more random population testing and other methods, results could also be used in advance of more testing. Uncertain assumptions in epidemiological models reflect unclear effects from gradations of social distancing now occurring. Despite many calls for broader testing and targeted quarantining in the U.S., many practical obstacles remain, leaving unknowns, especially across local areas. Differing local transmission rates respond to stronger or weaker social distancing and quarantining. High risks from latent non-quarantining spread warn of potential overwhelming local outbreaks. The insidious nature of SARS-Cov-2 invites complacency, especially in non-hotspot areas. Complacent behaviors can fail to adequately address the public-goods problem, leading to various forms of continued local and macro COVID-19 waves and crises. To assess a worst case scenario, no model projection is needed, only the herd immunity threshold equation, estimates of the reproduction ratio, and the estimated mortality rate. With no ultimately successful countermeasures in treatment, vaccine, and non-pharmaceutical interventions (NPIs), the analysis here suggests an eventual number of deaths much like the 1918 pandemic in U.S. deaths per capita (1.8-2.7 million U.S. deaths) and in the total number of deaths worldwide (around 50 million). This toll also reflects a hypothetical global “surrender” strategy of business-as-usual and no social distancing, which in practice no nation has followed. Some successes across the three broad social countermeasure efforts – which appears most likely, in a mix of outcomes – can lessen the high social costs.

scholarly journals Trend Analysis and Forecasting of COVID-19 outbreak in India

2020 ◽  
Author(s):  
Rajan Gupta ◽  
Saibal K Pal
Keyword(s):  
Arima Model ◽  
Regional Analysis ◽  
Control Measures ◽  
Government Officials ◽  
Case Scenario ◽  
Indian States ◽  
Worst Case ◽  
Medical Practitioners ◽  
Infected Case ◽  
First Case

AbstractCOVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holt’s linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.

scholarly journals Unfolding trends of COVID-19 transmission in India: Critical review of available Mathematical models

2020 ◽  
Vol 32 (2 (Supp)) ◽  
pp. 206-214
Author(s):  
Komal Shah ◽  
Ashish Awasthi ◽  
Bhavesh Modi ◽  
Rashmi Kundapur ◽  
Deepak Saxena
Keyword(s):  
Prediction Models ◽  
Real Data ◽  
Sudden Onset ◽  
Policy Implications ◽  
Epidemiological Models ◽  
Case Scenario ◽  
Worst Case ◽  
Social Distancing ◽  
Final Destination ◽  
Data Gaps

Background: There is a surge in epidemiological modeling research due to sudden onset of COVID-19 pandemic across the globe. In the absence of any pharmaceutical interventions to control the epidemic, nonpharmaceutical interventions like containment, mitigation and suppression are tried and tested partners in epidemiological theories. But policy and planning needs estimates of disease burden in various scenarios in absence of real data and epidemiological models helps to fill this gap. Aims and Objectives: To review the models of COVID-19 prediction in Indian scenario, critically evaluate the range, concepts, strength and limitations of these prediction models and its potential policy implications. Results: Though we conducted data search for last three months, it was found that the predictive models reporting from Indian context have started publishing very recently. Majority of the Indian models predicted COVID-19 spread, projected best-, worst case scenario and forecasted effect of various preventive measurements such as lockdown and social distancing. Though the models provided some of the critical information regarding spread of the disease and fatality rate associated with COVID-19, it should be used with caution due to severe data gaps, distinct socio-demographic profiling of the population and diverse statistics of co-morbid condition. Conclusion: Although the models were designed to predict COVID spread, and claimed to be accurate, significant data gaps and need for adjust confounding variables such as effect of lockdown, risk factors and adherence to social distancing should be considered before generalizing the findings. Results of epidemiological models should be considered as guiding beacon instead of final destination.

scholarly journals Estimates of SARS-COV-2 Behavior in the COVID-19 Crisis: Addressing Sample-Selection Bias for Public Health Applications

2020 ◽  
Author(s):  
John Straka
Keyword(s):  
Selection Bias ◽  
Sample Selection ◽  
Herd Immunity ◽  
Model Specification ◽  
Sample Selection Bias ◽  
Case Scenario ◽  
Worst Case ◽  
Reproduction Ratio ◽  
Social Distancing ◽  
The U.S

This study surveys and assesses the implications from recent empirical studies and reports to highlight the characteristics of SARS-Cov-2 and the COVID-19 crisis, and then proposes a recursive bivariate probit (RBP) model specification and possible applications. The RBP model addresses sample selection bias to estimate key determinants of virus infection given nonrandom testing. Applicable to anonymized case-level or widely available local-area data in the U.S., multiple data sources are shown. With suitable data the model can control for observed (e.g. population density) and unobserved factors to estimate the marginal effects of varying state-prescribed measures and behavioral social distancing. Case-level scoring models may, in addition, eventually assist in clinical diagnostic assessments. Although not proposed to substitute for more random population testing and other methods, results could also be used in advance of more testing. Uncertain assumptions in epidemiological models reflect unclear effects from gradations of social distancing now occurring. Despite many calls for broader testing and targeted quarantining in the U.S., many practical obstacles remain, leaving unknowns, especially across local areas. Differing local transmission rates respond to stronger or weaker social distancing and quarantining. High risks from latent non-quarantining spread warn of potential overwhelming local outbreaks. The insidious nature of SARS-Cov-2 invites complacency, especially in non-hotspot areas. Complacent behaviors can fail to adequately address the public-goods problem, leading to various forms of continued local and macro COVID-19 waves and crises. To assess a worst-case scenario, no model projection is needed, only the herd immunity threshold equation, estimates of the reproduction ratio, and the estimated mortality rate. With no ultimately successful countermeasures in treatment, vaccine, and non-pharmaceutical interventions (NPIs), the analysis here suggests an eventual number of deaths much like the 1918 pandemic in U.S. deaths per capita (1.8-2.7 million U.S. deaths) and in the total number of deaths worldwide (around 50 million). This toll also reflects a hypothetical global “surrender” strategy of business-as-usual and no social distancing, which in practice no nation has followed. Some successes across the three broad social countermeasure efforts – which appears most likely, in a mix of outcomes – can lessen the high social costs.

scholarly journals Randomized Incremental Construction of Delaunay Triangulations of Nice Point Sets

2020 ◽  
Author(s):  
Jean-Daniel Boissonnat ◽  
Olivier Devillers ◽  
Kunal Dutta ◽  
Marc Glisse
Keyword(s):  
Delaunay Triangulation ◽  
Case Scenario ◽  
Worst Case ◽  
Geometric Data Structures ◽  
Delaunay Triangulations ◽  
Randomized Incremental Construction ◽  
First Case ◽  
Worst Case Complexity ◽  
Time Optimal ◽  
Incremental Construction

Abstract Randomized incremental construction (RIC) is one of the most important paradigms for building geometric data structures. Clarkson and Shor developed a general theory that led to numerous algorithms which are both simple and efficient in theory and in practice. Randomized incremental constructions are usually space-optimal and time-optimal in the worst case, as exemplified by the construction of convex hulls, Delaunay triangulations, and arrangements of line segments. However, the worst-case scenario occurs rarely in practice and we would like to understand how RIC behaves when the input is nice in the sense that the associated output is significantly smaller than in the worst case. For example, it is known that the Delaunay triangulation of nicely distributed points in $${\mathbb {E}}^d$$ E d or on polyhedral surfaces in $${\mathbb {E}}^3$$ E 3 has linear complexity, as opposed to a worst-case complexity of $$\Theta (n^{\lfloor d/2\rfloor })$$ Θ ( n ⌊ d / 2 ⌋ ) in the first case and quadratic in the second. The standard analysis does not provide accurate bounds on the complexity of such cases and we aim at establishing such bounds in this paper. More precisely, we will show that, in the two cases above and variants of them, the complexity of the usual RIC is $$O(n\log n)$$ O ( n log n ) , which is optimal. In other words, without any modification, RIC nicely adapts to good cases of practical value. At the heart of our proof is a bound on the complexity of the Delaunay triangulation of random subsets of $${\varepsilon }$$ ε -nets. Along the way, we prove a probabilistic lemma for sampling without replacement, which may be of independent interest.

scholarly journals Trend Analysis and Forecasting of COVID-19 outbreak in India

2020 ◽  
Author(s):  
Rajan Gupta ◽  
Saibal K Pal
Keyword(s):  
Arima Model ◽  
Regional Analysis ◽  
Control Measures ◽  
Government Officials ◽  
Case Scenario ◽  
Indian States ◽  
Worst Case ◽  
Medical Practitioners ◽  
Infected Case ◽  
First Case

COVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holt’s linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.

scholarly journals Next weeks of SARS-CoV-2: Projection model to predict time evolution scenarios of accumulated cases in Spain

2020 ◽  
Author(s):  
Antonio Monleon-Getino ◽  
Jaume Canela-Soler
Keyword(s):  
Parametric Model ◽  
Short Report ◽  
Case Scenario ◽  
Projection Model ◽  
Worst Case ◽  
First Case ◽  
Projective Curves ◽  
New Type ◽  
General Goal ◽  
Entire Planet

AbstractBackground and objectivesSARS-CoV-2 is a new type of coronavirus that can affect people and causes respiratory disease, COVID-19. It is affecting the entire planet and we focus in Spain, where the first case was detected at the end of January 2020 and in recent weeks it has increased in many cases. We need predictive models in order to be efficient and take actions. The general goal of this work is present a new model of SARS-CoV-2 to predict different scenarios of accumulated cases in Spain.Material and methodsIn this short report is used a model proposed previously, based on a parametric model Weibull and in a the library BDSbiost3 developed in R to infer and predict different scenarios of the evolution of SARS-CoV-2 for the accumulated cases in Spain after the spread that affects Spain detected at the end of January of this year.ResultsIn the analyses presented, projective curves have been generated for the evolution of accumulated cases in which they reach about 4,000 cases or about 15,000 cases, for which the lines of the day in which the value for 90 will be reached can be seen vertically 90, 95 and 99% of the asymptote (maximum number of cases, from that day they will begin to descend or remain the same), that is why the vertical lines would indicate the brake of the disease. For the worst-case scenario, it takes 118, 126 or 142 days to reach the maximum number of cases (n = 15,000) to reach 90, 95 and 99% of the asymptote (maximum number of cases), respectively. This means translated in a time scale that in the worst case the virus will not stop its progress, in Spain, until summer 2020, hopefully before.Comments and conclusionsThis model could be used to plan the resources and see if the policies or means dedicated to the virus are slowing the progress of the virus or it is necessary to implement others that are more effective, and can also validate a method for future outbreaks of diseases such as these.

Reducing Probabilistic Weather Forecasts to the Worst Case Scenario: Anchoring Effects

2008 ◽  
Author(s):  
Sonia Savelli ◽  
Susan Joslyn ◽  
Limor Nadav-Greenberg ◽  
Queena Chen
Keyword(s):  
Case Scenario ◽  
Worst Case ◽  
Weather Forecasts ◽  
Worst Case Scenario ◽  
Anchoring Effects
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