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

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.

scholarly journals Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave

2021 ◽  
Vol 376 (1829) ◽  
pp. 20200279
Author(s):  
Paul Birrell ◽  
Joshua Blake ◽  
Edwin van Leeuwen ◽  
Nick Gent ◽  
Daniela De Angelis
Keyword(s):  
Real Time ◽  
Credible Interval ◽  
Mitigation Measures ◽  
Contact Tracing ◽  
Effective Contact ◽  
Ongoing Work ◽  
Reproduction Numbers ◽  
Pandemic Response ◽  
The Uk ◽  
Second Wave

England has been heavily affected by the SARS-CoV-2 pandemic, with severe ‘lockdown’ mitigation measures now gradually being lifted. The real-time pandemic monitoring presented here has contributed to the evidence informing this pandemic management throughout the first wave. Estimates on the 10 May showed lockdown had reduced transmission by 75%, the reproduction number falling from 2.6 to 0.61. This regionally varying impact was largest in London with a reduction of 81% (95% credible interval: 77–84%). Reproduction numbers have since then slowly increased, and on 19 June the probability of the epidemic growing was greater than 5% in two regions, South West and London. By this date, 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 continues to be key to quantifying any widespread resurgence, should accrued immunity and effective contact tracing be insufficient to preclude a second wave. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

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

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.

scholarly journals TW-SIR: Time-window based SIR for COVID-19 forecasts

2020 ◽  
Author(s):  
Zhifang Liao ◽  
Peng Lan ◽  
Zhingning Liao ◽  
Yan Zhang ◽  
Shengzong Liu
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Time Window ◽  
Data Sets ◽  
Machine Learning Method ◽  
The Real ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
Time Changes ◽  
Epidemic Diseases

Abstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In order to reflect the real-time trend of the epidemic in the process of infection for different areas, different policies and different epidemic diseases, a general adapted time- window based SIR model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the Basic reproduction number R0 and the exponential growth rate of the epidemic. Multiple data sets of epidemic diseases are analyzed, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%

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

2020 ◽  
Author(s):  
Lee Worden ◽  
Rae Wannier ◽  
Seth Blumberg ◽  
Alex Y. Ge ◽  
George W. Rutherford ◽  
...  
Keyword(s):  
San Francisco ◽  
Statistical Estimation ◽  
Detection Rate ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Reproduction Numbers ◽  
The Impact ◽  
The City ◽  
Testing Coverage ◽  
Over Time

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.

scholarly journals Predicted effects of summer holidays and seasonality on the SARS-Cov-2 epidemic in France

2020 ◽  
Author(s):  
Louis Duchemin ◽  
Philippe Veber ◽  
Mathilde Paris ◽  
Bastien Boussau
Keyword(s):  
Death Rate ◽  
Reproduction Number ◽  
Seir Model ◽  
Low Level ◽  
Death Toll ◽  
Reproduction Numbers ◽  
Second Wave ◽  
Epidemic Wave ◽  
Do So

1AbstractThe SARS-CoV-2 epidemic in France has had a large death toll. It has not affected all regions similarly, since the death rate can vary several folds between regions where the epidemic has remained at a low level and regions where it got an early burst. The epidemic has been slowed down by a lockdown that lasted for almost eight weeks, and individuals can now move between metropolitan French regions without restriction. In this report we investigate the effect on the epidemic of summer holidays, during which millions of individuals will move between French regions. Additionally, we evaluate the effect of strong or weak seasonality and of several values for the reproduction number on the epidemic, in particular on the timing, the height and the spread of a second wave. To do so, we extend a SEIR model to simulate the effect of summer migrations between regions on the number and distribution of new infections. We find that the model predicts little effect of summer migrations on the epidemic. However, all the reproduction numbers above 1.0 and the seasonality parameters we tried result in a second epidemic wave, with a peak date that can vary between October 2020 and April 2021. If the sanitary measures currently in place manage to keep the reproduction number below 1.0, the second wave will be avoided. If they keep the reproduction number at a low value, for instance at 1.1 as in one of our simulations, the second wave is flattened and could be similar to the first wave.

scholarly journals Short-Term Projection of COVID 19 Cases in Kenya using an Exponential Model

2020 ◽  
Author(s):  
Samuel Kiruri Kirichu
Keyword(s):  
Growth Rate ◽  
Real Time ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Daily Growth Rate ◽  
Daily Growth ◽  
The Real ◽  
Time Reproduction ◽  
First Case

Abstract Introduction: The COVID-19 disease has spread to over 200 countries and territories since the first case was recorded in Wuhan, China in December 2019. In Kenya, the first case of COVID-19 was recorded on 13th March 2020 and since then over five thousand cases have been confirmed as of 26th June 2020. In the same period, one hundred and forty four mortality cases had been recorded in the country. With the rapid changing situation, timely and reliable data is required for monitoring, planning and rapid decision making with an aim of reversing the already deteriorating situation (economic, health, learning among others) in the country. Methods: The study used the exponential growth model to estimate the daily growth rate and the real-time-effective reproduction number. The study also estimated the naïve and the adjusted Case Fatality Rates. Results: The naïve-Case Fatality Rate of 26th June 2020 which was the 106 day after the first case was confirmed in Kenya was estimated as 2.5% while the adjusted Case Fatality Rate with a lag of 2 days was estimated as 2.6%. The daily exponential growth rate was estimated as 0.22 while the real-time reproduction number as of 26th June 2020 was estimated as 1.28 [95% CI: 1.27 – 1.29]. Conclusion: The daily growth rate and the real-time reproduction number indicated that the outbreak was still growing as of the time of analysis.

scholarly journals Stepping out of lockdown should start with school re-openings while maintaining distancing measures. Insights from mixing matrices and mathematical models.

2020 ◽  
Author(s):  
Emma Sue McBryde ◽  
James M Trauer ◽  
Adeshina Adekunle ◽  
Romain Ragonnet ◽  
Michael T Meehan
Keyword(s):  
Spectral Radius ◽  
Reproduction Number ◽  
Positive Outcome ◽  
Attendance Rates ◽  
Considerable Uncertainty ◽  
Effective Contact ◽  
Reproduction Numbers ◽  
The World ◽  
The Impact ◽  
Sensitivity And Uncertainty Analysis

Australia is one of a few countries which has managed to control COVID-19 epidemic before a major epidemic took place. Currently with just under 7000 cases and 100 deaths, Australia is seeing less than 20 new cases per day. This is a positive outcome, but makes estimation of current effective reproduction numbers difficult to estimate. Australia, like much of the world is poised to step out of lockdown and looking at which measures to relax first. We use age-based contact matrices, calibrated to Chinese data on reproduction numbers and difference in infectiousness and susceptibility of children to generate next generation matrices (NGMs) for Australia. These matrices have a spectral radius of 2.49, which is hence our estimated basic reproduction number for Australia. The effective reproduction number (Reff) for Australia during the April/May lockdown period is estimated by other means to be around 0.8. We simulate the impact of lockdown on the NGM by first applying observations through Google Mobility Report for Australia at 3 locations: home (increased contacts by 18%), work (reduced contacts by 34%) and other (reduced contacts by 40%), and we reduce schools to 3% reflecting attendance rates during lockdown. Applying macro-distancing to the NGM leads to a spectral radius of 1.76. We estimate that the further reduction of the reproduction number to current levels of Reff = 0.8 is achieved by a micro-distancing factor of 0.26. That is, in a given location, people are 26% as likely as usual to have an effective contact with another person. We apply both macro and micro-distancing to the NGMs to examine the impact of different exit strategies. We find that reopening schools is estimated to reduce Reff from 0.8 to 0.78. This is because increase in school contact is offset by decrease in home contact. The NGMs all estimate that adults aged 30-50 are responsible for the majority of transmission. We also find that micro-distancing is critically important to maintain Reff <1. There is considerable uncertainty in these estimates and a sensitivity and uncertainty analysis is presented.

scholarly journals Evaluate the risk of resumption of business for the states of New York, New Jersey and Connecticut via a pre-symptomatic and asymptomatic transmission model of COVID-19

2020 ◽  
Author(s):  
Ting Tian ◽  
Jianbin Tan ◽  
Yukang Jiang ◽  
Xueqin Wang ◽  
Heping Zhang
Keyword(s):  
New York ◽  
New Jersey ◽  
Real Time ◽  
Hot Spot ◽  
The United States ◽  
Transmission Model ◽  
The Real ◽  
Work From Home ◽  
Opposite Situation ◽  
Second Wave

AbstractBackgroundThe United States has the highest numbers of confirmed cases of COVID-19, where they took up nearly half in the hot spot states of New York, New Jersey, Connecticut, and California. The workforce in these states was required to work from home except for essential services. It is necessary to evaluate an appropriate date for resumption of business since premature reopening of economy will lead to broader spread of COVID-19, while the opposite situation would cause greater loss of economy.MethodsTo consider pre-symptomatic and asymptomatic transmission of COIVD-19, it is crucial to evaluate the unobserved numbers of unidentified infectious individuals but not the observed number of confirmed cases, which reflect the real-time risks of different stage of infectious disease. We proposed an epidemic model in considering the pre-symptomatic transmission and asymptomatic transmission of COVID-19 to evaluate the real-time risk of epidemic for the states of New York, New Jersey and Connecticut, and compared with California state (where it effectively phased reopened on May 8) for assessments of the appropriate Monday for resumption of business.ResultsThe predicted numbers of unidentified infectious individuals per 100,000 for states of New York, New Jersey and Connecticut which are close to those in California state are 12.23 with 95% CI (10.68, 13.57) on June 1, 25.65 with 95% CI (20.04, 30.43) on June 15, 28.49 with 95% CI (19.10, 38.65) on June 22, respectively, which may cause 11.23%, 15.64% and 17.32% higher than the estimated number of cumulative confirmed cases on July 11, if the second wave of the infection would happen after people return to work.ConclusionsIt may be feasible for states of New York, New Jersey and Connecticut to reopen business on June 1 (or even May 18), June 15 and June 22. For the period after resumption of work, if the number of unidentified infectious individuals is still non-zero, the risks for the second wave of the infection would never vanish.

scholarly journals Forward-looking serial intervals correctly link epidemic growth to reproduction numbers

2020 ◽  
Vol 118 (2) ◽  
pp. e2011548118
Author(s):  
Sang Woo Park ◽  
Kaiyuan Sun ◽  
David Champredon ◽  
Michael Li ◽  
Benjamin M. Bolker ◽  
...  
Keyword(s):  
Symptom Onset ◽  
Reproduction Number ◽  
Heuristic Method ◽  
Population Level ◽  
Contact Tracing ◽  
Epidemic Dynamics ◽  
Transmission Chain ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Forward Looking

The reproduction number R and the growth rate r are critical epidemiological quantities. They are linked by generation intervals, the time between infection and onward transmission. Because generation intervals are difficult to observe, epidemiologists often substitute serial intervals, the time between symptom onset in successive links in a transmission chain. Recent studies suggest that such substitution biases estimates of R based on r. Here we explore how these intervals vary over the course of an epidemic, and the implications for R estimation. Forward-looking serial intervals, measuring time forward from symptom onset of an infector, correctly describe the renewal process of symptomatic cases and therefore reliably link R with r. In contrast, backward-looking intervals, which measure time backward, and intrinsic intervals, which neglect population-level dynamics, give incorrect R estimates. Forward-looking intervals are affected both by epidemic dynamics and by censoring, changing in complex ways over the course of an epidemic. We present a heuristic method for addressing biases that arise from neglecting changes in serial intervals. We apply the method to early (21 January to February 8, 2020) serial interval-based estimates of R for the COVID-19 outbreak in China outside Hubei province; using improperly defined serial intervals in this context biases estimates of initial R by up to a factor of 2.6. This study demonstrates the importance of early contact tracing efforts and provides a framework for reassessing generation intervals, serial intervals, and R estimates for COVID-19.

Sign in / Sign up

Export Citation Format

Share Document