scholarly journals An exact method for quantifying the reliability of end-of-epidemic declarations in real time

2020 ◽  
Author(s):  
Kris V Parag ◽  
Christl A Donnelly ◽  
Rahul Jha ◽  
Robin N Thompson
Keyword(s):  
Real Time ◽  
Waiting Times ◽  
False Negative ◽  
World Health Organisation ◽  
World Health ◽  
Time Varying ◽  
Economic Sectors ◽  
Past Infection ◽  
Social Distancing ◽  
Intensive Surveillance

AbstractWe derive and validate a novel and analytic method for estimating the probability that an epidemic has been eliminated (i.e. that no future local cases will emerge) in real time. When this probability crosses 0.95 an outbreak can be declared over with 95% confidence. Our method is easy to compute, only requires knowledge of the incidence curve and the serial interval distribution, and evaluates the statistical lifetime of the outbreak of interest. Using this approach, we rigorously show how the time-varying under-reporting of infected cases will artificially inflate the inferred probability of elimination, leading to premature (false-positive) end-of-epidemic declarations. Contrastingly, we prove that incorrectly identifying imported cases as local will deceptively decrease this probability, resulting in delayed (false-negative) declarations. Failing to sustain intensive surveillance during the later phases of an epidemic can therefore substantially mislead policymakers on when it is safe to remove travel bans or relax quarantine and social distancing advisories. World Health Organisation guidelines recommend fixed (though disease-specific) waiting times for end-of-epidemic declarations that cannot accommodate these variations. Consequently, there is an unequivocal need for more active and specialised metrics for reliably identifying the conclusion of an epidemic.Author SummaryDeciding on when to declare an infectious disease epidemic over is an important and non-trivial problem. Early declarations can mean that interventions such as lockdowns, social distancing advisories and travel bans are relaxed prematurely, elevating the risk of additional waves of the disease. Late declarations can unnecessarily delay the re-opening of key economic sectors, for example trade, tourism and agriculture, potentially resulting in significant financial and livelihood losses. Here we develop and test a novel and exact data-driven method for optimising the timing of end-of-epidemic declarations. Our approach converts observations of infected cases up to any given time into a prediction of the likelihood that the epidemic is over at that time. Using this method, we quantify the reliability of end-of-epidemic declarations in real time, under ideal case surveillance, showing that it can depend strongly on past infection numbers. We then prove that failing to compensate for practical issues such as the time-varying under-reporting and importing of cases necessarily results in premature and delayed declarations, respectively. These variations and biases cannot be accommodated by current worldwide declaration guidelines. Sustained and intensive surveillance coupled with more adaptive declaration metrics are vital if informed end-of-epidemic declarations are to be made.

scholarly journals An exact method for quantifying the reliability of end-of-epidemic declarations in real time

2020 ◽  
Vol 16 (11) ◽  
pp. e1008478
Author(s):  
Kris V. Parag ◽  
Christl A. Donnelly ◽  
Rahul Jha ◽  
Robin N. Thompson
Keyword(s):  
Real Time ◽  
Waiting Times ◽  
False Negative ◽  
World Health Organisation ◽  
World Health ◽  
Analytic Method ◽  
Time Varying ◽  
Serial Interval ◽  
Imported Cases ◽  
Interval Distribution

We derive and validate a novel and analytic method for estimating the probability that an epidemic has been eliminated (i.e. that no future local cases will emerge) in real time. When this probability crosses 0.95 an outbreak can be declared over with 95% confidence. Our method is easy to compute, only requires knowledge of the incidence curve and the serial interval distribution, and evaluates the statistical lifetime of the outbreak of interest. Using this approach, we show how the time-varying under-reporting of infected cases will artificially inflate the inferred probability of elimination, leading to premature (false-positive) end-of-epidemic declarations. Contrastingly, we prove that incorrectly identifying imported cases as local will deceptively decrease this probability, resulting in delayed (false-negative) declarations. Failing to sustain intensive surveillance during the later phases of an epidemic can therefore substantially mislead policymakers on when it is safe to remove travel bans or relax quarantine and social distancing advisories. World Health Organisation guidelines recommend fixed (though disease-specific) waiting times for end-of-epidemic declarations that cannot accommodate these variations. Consequently, there is an unequivocal need for more active and specialised metrics for reliably identifying the conclusion of an epidemic.

scholarly journals DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

2020 ◽  
Author(s):  
Mahdi Rezaei ◽  
Mohsen Azarmi
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
World Health Organisation ◽  
Superior Performance ◽  
World Health ◽  
People Detection ◽  
Social Distancing ◽  
Online Risk ◽  
Risk Zones ◽  
Spatio Temporal

Abstract Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a generic Deep Neural Network-Based model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed model includes a YOLOv4-based framework and inverse perspective mapping for accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

scholarly journals DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

2020 ◽  
Author(s):  
Mahdi Rezaei ◽  
Mohsen Azarmi
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
World Health Organisation ◽  
Infection Risk ◽  
Superior Performance ◽  
World Health ◽  
Mapping Technique ◽  
People Detection ◽  
Social Distancing ◽  
Risk Zones

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a Deep Neural Network-based Model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed DNN model along with an inverse perspective mapping technique leads to a very accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online infection risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

scholarly journals O-BN09 The impact of the Covid-19 pandemic on benign upper GI surgery: The first 12 months at a single centre

2021 ◽  
Vol 108 (Supplement_9) ◽  
Author(s):  
Martin Michel ◽  
Helen Fifer ◽  
Emily Moran ◽  
Felix Hammett ◽  
Clare Bonner ◽  
...  
Keyword(s):  
Waiting Times ◽  
World Health Organisation ◽  
Operative Management ◽  
Time Frame ◽  
World Health ◽  
Activity Levels ◽  
Single Centre ◽  
The Road ◽  
Upper Gi ◽  
The Impact

Abstract Background The road to recovery from the Covid-19 pandemic has started but no-one knows when it will end. 18 months on from the World Health Organisation declaring a global pandemic on the 11th March 2020 this has had a dramatic impact on both acute and elective hospital services. Whilst, quite rightly, the focus has been on prioritising cancer resections during the pandemic, many patients awaiting benign operations are facing lengthy waiting times. The aim of this study was to quantify the impact of the COVID-19 pandemic on benign upper GI surgery at a single centre compared to previous operating activity levels. Methods Retrospective analysis of computerised theatre records for the first 12 months of the pandemic (11th March 2020-11th March 2021) were compared to average historical data (HD) over the last five years (2015-2019) over the same time frame. Benign upper Gi operations included were cholecystectomy, anti-reflux/hiatus hernia repairs, cardiomyotomies and bariatric procedures. Results Conclusions The Covid-19 pandemic has dramatically affected benign upper GI surgery at our unit. Overall total operation numbers were down by 31% when compared to HD (440 vs 641). The largest deficit was in bariatrics where no bariatric surgery was performed during the first 12 months of the pandemic, which has restarted as of July 2021. There was also a 30% reduction in the number of cholecystectomies performed likely due to initial guidance recommending non-operative management at the start of the pandemic. Hiatal work numbers remained consistent. This quantitative study can direct future service delivery and help guide the post-pandemic recovery.

scholarly journals Rapid diagnostic test versus microscopy in the diagnosis of acute malaria in a district hospital in Enugu state, Southeast Nigeria

2017 ◽  
Vol 5 (1) ◽  
pp. 54
Author(s):  
Emmanuel I. Umegbolu ◽  
Chinedu N. Madukwe
Keyword(s):  
Predictive Value ◽  
District Hospital ◽  
Systemic Disease ◽  
False Negative ◽  
Malaria Diagnosis ◽  
World Health Organisation ◽  
Malaria Prevalence ◽  
World Health ◽  
Negative Results ◽  
Enugu State

Background: Malaria is a systemic disease caused by various species of Plasmodium, transmitted through the bite of a female Anopheles mosquito. According to the World Health Organisation, there were 214 million cases of malaria worldwide in 2015. Nigeria’s burden of malaria is about 51million cases and 207,000 deaths annually, accounting for 60% of outpatient visits to hospitals, 11% of maternal mortality, and 30% of child mortality. The study aimed to compare RDT and microscopy in malaria diagnosis in a District Hospital in Enugu state, Southeast Nigeria. Methods: Blood samples of 300 suspected cases of acute malaria were tested for malaria parasite using RDT and microscopy simultaneously. Results: In 2017, the study found a malaria prevalence of 25% (46.2% in children, and 18.1% in adults) in Awgu. RDT was positive in 38% and microscopy in 70.3% of cases. Both RDT and microscopy were positive in 36.3%, negative in 28.3%, and discordant in 35.4%. Sensitivity of RDT was 50.7% (89.4% in children, and 25.6% in adults). RDT had a specificity of 100% (both children and adults), positive predictive value of 1 (both children and adults), and negative predictive value of 0.6 (0.5 in children, 0.6 in adults). Conclusions: RDT (SD Malaria Ag P. f) had more sensitivity in children (89.4%) than adults (25.6%), and the occurrence of false negative results was more in adults (46.8%) than children (9.5%). All negative RDT results need to be examined microscopically, to rule out false negative cases.  

scholarly journals Disconnecting for the good: A network-oriented model for social contagion of opinions and social network interventions to increase adherence to social distancing

2020 ◽  
Author(s):  
Eric Araújo ◽  
Mariza Ferro ◽  
Gabrieli Silva
Keyword(s):  
Social Network ◽  
World Health Organisation ◽  
Social Contagion ◽  
Positive Opinion ◽  
World Health ◽  
Current Time ◽  
Social Distancing ◽  
Health Authorities ◽  
Best Response ◽  
The World

The pandemic of the new COVID-19 has raised many questions to a very connected society as to how to best respond to such a challenge at this current time. The best response so far is to call people for following the instructions from the World Health Organisation (WHO) as a way of reducing the spread of the virus and thus relieving the health system, striving to avoid a collapse. This work studies the spread of positive opinion on adhering to social distancing based on network topology and metrics using a network-oriented model for social contagion. It is shown that interventions based on social network measurements can be used to boost the spread of positive opinion about adhering to these measures. It is also shown that our model accounts for the relevance the health authorities have on encouraging people to partake in social distancing voluntarily.

scholarly journals Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds

2019 ◽  
Author(s):  
Carl A. B. Pearson ◽  
Samuel Clifford ◽  
Kaja M. Abbas ◽  
Stefan Flasche ◽  
Thomas J. Hladish
Keyword(s):  
Health Benefit ◽  
Cost Benefit ◽  
World Health Organisation ◽  
World Health ◽  
Dengue Vaccine ◽  
Routine Test ◽  
Past Infection ◽  
Additional Testing ◽  
Bounding Analysis ◽  
Price Thresholds

The World Health Organisation currently recommends pre-screening for past infection prior to administration of the only licensed dengue vaccine, CYD-TDV. Using a bounding analysis, we show that despite additional testing costs, this approach can improve the economic viability of CYD-TDV: effective testing reduces unnecessary vaccination costs while increasing the health benefit for vaccine recipients. When testing is cheap enough, those trends outweigh additional screening costs and make test-then-vaccinate strategies net-beneficial in many settings.We derived these results using a general approach for determining price thresholds for testing and vaccination, as well as indicating optimal start and end ages of routine test-then-vaccinate programs. This approach only requires age-specific seroprevalence and a cost estimate for second infections. We demonstrate this approach across settings commonly used to evaluate CYD-TDV economics, and highlight implications of our simple model for more detailed studies. We found trends showing test-then-vaccinate strategies are generally more beneficial starting at younger ages, and that in some settings multiple years of testing can be more beneficial than only testing once, despite increased investment in testing.

scholarly journals DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

2020 ◽  
Author(s):  
Mahdi Rezaei ◽  
Mohsen Azarmi
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
World Health Organisation ◽  
Superior Performance ◽  
World Health ◽  
People Detection ◽  
Social Distancing ◽  
Online Risk ◽  
Risk Zones ◽  
Spatio Temporal

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a generic Deep Neural Network-Based model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed model includes a YOLOv4-based framework and inverse perspective mapping for accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infections. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

scholarly journals Real-time Estimation of Global CFR Ascribed to COVID-19 Confirmed Cases Applying Machine Learning Technique

2021 ◽  
Author(s):  
MONALISHA PATTNAIK ◽  
ARYAN PATTNAIK
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Regression Tree ◽  
Time Estimation ◽  
World Health Organisation ◽  
World Health ◽  
Global Risk ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Real Time Estimation

The COVID-19 is declared as a public health emergency of global concern by World Health Organisation (WHO) affecting a total of 201 countries across the globe during the period December 2019 to January 2021. As of January 25, 2021, it has caused a pandemic outbreak with more than 99 million confirmed cases and more than 2 million deaths worldwide. The crisp of this paper is to estimate the global risk in terms of CFR of the COVID-19 pandemic for seventy deeply affected countries. An optimal regression tree algorithm under machine learning technique is applied which identified four significant features like diabetes prevalence, total number of deaths in thousands, total number of confirmed cases in thousands, and hospital beds per 1000 out of fifteen input features. This real-time estimation will provide deep insights into the early detection of CFR for the countries under study.

scholarly journals The Effectiveness of Social Distancing in Mitigating COVID-19 Spread: a modelling analysis

2020 ◽  
Author(s):  
George J Milne ◽  
Simon Xie
Keyword(s):  
Virus Transmission ◽  
World Health Organisation ◽  
School Closure ◽  
World Health ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Containment Measures ◽  
Individual Based Simulation ◽  
Novel Coronavirus ◽  
And Control

SummaryBackgroundThe novel coronavirus COVID-19 has been classified by the World Health Organisation as a pandemic due to its worldwide spread. The ability of countries to contain and control transmission is critical in the absence of a vaccine. We evaluated a range of social distancing measures to determine which strategies are most effective in reducing the peak daily infection rate, and consequential pressure on the health care system.MethodsUsing COVID-19 transmission data from the outbreak source in Hubei Province, China, collected prior to activation of containment measures, we adapted an established individual based simulation model of the city of Newcastle, Australia, population 272,409. Simulation of virus transmission in this community model without interventions provided a baseline from which to compare alternative social distancing strategies. The infection history of each individual was determined, as was the time infected. From this model-generated data, the rate of growth in cases, the magnitude of the epidemic peak, and the outbreak duration were obtained.FindingsThe application of all four social distancing interventions: school closure, workplace non-attendance, increased case isolation, and community contact reduction is highly effective in flattening the epidemic curve, reducing the maximum daily case numbers, and lengthening outbreak duration. These were also found to be effective even after 10 weeks delay from index case arrivals. The most effective single intervention was found to be increasing case isolation, to 100% of children and 90% of adults.InterpretationAs strong social distancing intervention strategies had the most effect in reducing the epidemic peak, this strategy may be considered when weaker strategies are first tried and found to be less effective. Questions arise as to the duration of strong social distancing measures, given they are highly disruptive to society. Tradeoffs may need to be made between the effectiveness of social distancing strategies and population willingness to adhere to them.

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