scholarly journals Basic estimation-prediction techniques for Covid-19, and a prediction for Stockholm

2020 ◽  
Author(s):  
Tom Britton
Keyword(s):  
Doubling Time ◽  
Preventive Measures ◽  
Prediction Method ◽  
Calendar Time ◽  
Fatality Risk ◽  
Time Calibration ◽  
Highly Sensitive ◽  
Specific Time Point ◽  
Simple Estimation ◽  
Prediction Techniques

AbstractAn important task during the current Covid-19 pandemic is to predict the remainder of the epidemic, both without preventive measures and with. In the current paper we address this question using a simple estimation-prediction method. The input is the observed initial doubling time and a known value of R0. The simple General epidemic model is then fitted, and time calibration to calendar time is done using the observed number of case fatalities, together with estimates of the time between infection to death and the infection fatality risk. Finally, predictions are made assuming no change of behaviour, as well as for the situation where preventive measures are put in place at one specific time-point. The overall effect of the preventive measures is assumed to be known, or else estimated from the observed increased doubling time after preventive measures are put in place. The predictions are highly sensitive to the doubling times without and with preventive measures, sensitive to R0, but less sensitive to the estimates used for time-calibration: observed number of case fatalities, typical time between infection and death, and the infection fatality risk. The method is applied to the urban area of Stockholm, and predictions show that the peak of infections appear in mid-April and infections start settling in May.

scholarly journals Basic prediction methodology for covid-19: estimation and sensitivity considerations

2020 ◽  
Author(s):  
Tom Britton
Keyword(s):  
Preventive Measures ◽  
Early Stage ◽  
Prediction Method ◽  
Important Conclusion ◽  
Fatality Risk ◽  
Estimation And Prediction ◽  
Simple Estimation ◽  
Carry Over ◽  
Basic Prediction ◽  
High Uncertainty

SummaryThe purpose of the present paper is to present simple estimation and prediction methods for basic quantities in an emerging epidemic like the ongoing covid-10 pandemic. The simple methods have the advantage that relations between basic quantities become more transparent, thus shedding light to which quantities have biggest impact on predictions, with the additional conclusion that uncertainties in these quantities carry over to high uncertainty also in predictions.A simple non-parametric prediction method for future cumulative case fatalities, as well as future cumulative incidence of infections (assuming a given infection fatality risk f), is presented. The method uses cumulative reported case fatalities up to present time as input data. It is also described how the introduction of preventive measures of a given magnitude ρ will affect the two incidence predictions, using basic theory of epidemic models. This methodology is then reversed, thus enabling estimation of the preventive magnitude ρ, and of the resulting effective reproduction number RE. However, the effects of preventive measures only start affecting case fatalities some 3-4 weeks later, so estimates are only available after this time has elapsed. The methodology is applicable in the early stage of an outbreak, before, say, 10% of the community have been infected.Beside giving simple estimation and prediction tools for an ongoing epidemic, another important conclusion lies in the observation that the two quantities f (infection fatality risk) and ρ (the magnitude of preventive measures) have very big impact on predictions. Further, both of these quantities currently have very high uncertainty: current estimates of f lie in the range 0.2% up to 2% ([9], [7]), and the overall effect of several combined preventive measures is clearly very uncertain.The two main findings from the paper are hence that, a) any prediction containing f, and/or some preventive measures, contain a large amount of uncertainty (which is usually not acknowledged well enough), and b) obtaining more accurate estimates of in particular f, should be highly prioritized. Seroprevalence testing of random samples in a community where the epidemic has ended are urgently needed.

scholarly journals Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Grosso Francesca Maria ◽  
Presanis Anne Margaret ◽  
Kunzmann Kevin ◽  
Jackson Chris ◽  
Corbella Alice ◽  
...  
Keyword(s):  
Cohort Study ◽  
Length Of Stay ◽  
Median Length ◽  
Calendar Time ◽  
Fatality Risk ◽  
Lengths Of Stay ◽  
Hospital Beds ◽  
Hospital Patients ◽  
Poor Outcomes ◽  
Emergency Measures

Abstract Background The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. Methods This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February–June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February–June 2020, with non-missing hospital of admission and non-missing admission date. Results The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56–80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1–28.0%); and steadily decreased from 34.6% (32.5–36.6%) in February to 7.6% (6.3–10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6–12.3) days, compared to 8.1 (7.8–8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5–22.8) days in February to 5.2 (4.7–5.8) days in June. Conclusions The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2 i.

scholarly journals Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shilpa P. Khedkar ◽  
R. Aroul Canessane ◽  
Moslem Lari Najafi
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Prediction Models ◽  
Forecast Model ◽  
Congestion Management ◽  
Traffic Prediction ◽  
Traffic Forecasting ◽  
Highly Sensitive ◽  
Iot Devices ◽  
Prediction Techniques

An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition. Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and R -squared values. The experimental results indicate that LSTM- and FNN-based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.

scholarly journals A Similarity-Inclusive Link Prediction Based Recommender System Approach

2019 ◽  
Vol 25 (6) ◽  
pp. 62-69 ◽  
Author(s):  
Zuhal Kurt ◽  
Kemal Ozkan ◽  
Alper Bilge ◽  
Omer Nezih Gerek
Keyword(s):  
Recommender Systems ◽  
Bipartite Graph ◽  
Recommender System ◽  
Link Prediction ◽  
Prediction Method ◽  
Research Field ◽  
Complex Domain ◽  
Proposed Model ◽  
Prediction Techniques ◽  
Item Item

Despite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users. In this study, a graph-based recommender system using link prediction techniques incorporating similarity metrics is proposed. A graph-based recommender system that has ratings of users on items can be represented as a bipartite graph, where vertices correspond to users and items and edges to ratings. Recommendation generation in a bipartite graph is a link prediction problem. In current literature, modified link prediction approaches are used to distinguish between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationship between users/items is mostly disregarded in the complex domain. The proposed model utilizes user-user and item-item cosine similarity value with the relational dualities in order to improve coverage and hits rate of the system by carefully incorporating similarities. On the standard MovieLens Hetrec and MovieLens datasets, the proposed similarity-inclusive link prediction method performed empirically well compared to other methods operating in the complex domain. The experimental results show that the proposed recommender system can be a plausible alternative to overcome the deficiencies in recommender systems.

scholarly journals The incidence and spectrum of transfusion transmitted infections among the blood donors

2018 ◽  
Vol 6 (3) ◽  
pp. 904
Author(s):  
Smita Mahapatra ◽  
Gopal Krushna Ray ◽  
Rashmita Panigrahi ◽  
Pankaj Parida
Keyword(s):  
Blood Donors ◽  
Preventive Measures ◽  
Test Methods ◽  
Blood Components ◽  
Major Health ◽  
Highly Sensitive ◽  
B Virus ◽  
Immunodeficiency Virus ◽  
Safe Blood ◽  
Donated Blood

Background: Transfusion Transmitted Infections (TTIs), particularly through the viruses lead to the major health problems and challenges faced by the transfusion services for the supply of safe blood and blood components. The objective of the study is to assess the incidence and spectrum of five TTIs like Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Human Immunodeficiency Virus (HIV), Malaria Parasites (MP) and Syphilis among both voluntary and replacement blood donors in a span of seven years.Methods: Total 1, 38,994 voluntary and replacement blood donors were screened and 1,012 (0.73%) cases were seropositive of which 631 cases (0.64%) were found in voluntary donors and 381 cases (0.94%) were seen in replacement donors.Results: Majority of the donors were reactive for HBV infection (0.5%) followed by HCV (0.17%), HIV (0.052%), Syphilis (0.001%) and malaria (0.0005%). There was increase in the trend of seroprevalence of TTIs from 0.35% (2010) to 1.43% (2016) over the period which is a matter of consideration.Conclusions: It is therefore very important to continue preventive measures to combat this problem along with screening of donated blood units with highly sensitive test methods and post donation counselling of the seropositive donors for the prevention of further transmission risks.

scholarly journals Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context

2021 ◽  
Author(s):  
Francesca Maria Grosso ◽  
Anne Margaret Presanis ◽  
Kevin Kunzmann ◽  
Chris Jackson ◽  
Alice Corbella ◽  
...  
Keyword(s):  
Cohort Study ◽  
Length Of Stay ◽  
Median Length ◽  
Calendar Time ◽  
Fatality Risk ◽  
Lengths Of Stay ◽  
Hospital Beds ◽  
Hospital Patients ◽  
Poor Outcomes ◽  
Emergency Measures

Abstract Background: The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. Methods: This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February-June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February-June 2020, with non-missing hospital of admission and non-missing admission date.Results: The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56-80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1-28.0%); and steadily decreased from 34.6% (32.5-36.6%) in February to 7.6% (6.3-10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6-12.3) days, compared to 8.1 (7.8-8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5-22.8) days in February to 5.2 (4.7-5.8) days in June.Conclusions: The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2.

scholarly journals Seasonality and within-subject clustering of rotavirus infections in an eight-site birth cohort study

2018 ◽  
Vol 146 (6) ◽  
pp. 688-697 ◽  
Author(s):  
J. M. Colston ◽  
A. M. S. Ahmed ◽  
S. B. Soofi ◽  
E. Svensen ◽  
R. Haque ◽  
...  
Keyword(s):  
Cohort Study ◽  
Preventive Measures ◽  
Diarrhoeal Disease ◽  
Birth Cohort Study ◽  
Calendar Time ◽  
Programme Design ◽  
Enteric Infections ◽  
Practical Implications ◽  
Rotavirus Infections ◽  
Model Approach

AbstractImproving understanding of the pathogen-specific seasonality of enteric infections is critical to informing policy on the timing of preventive measures and to forecast trends in the burden of diarrhoeal disease. Data obtained from active surveillance of cohorts can capture the underlying infection status as transmission occurs in the community. The purpose of this study was to characterise rotavirus seasonality in eight different locations while adjusting for age, calendar time and within-subject clustering of episodes by applying an adapted Serfling model approach to data from a multi-site cohort study. In the Bangladesh and Peru sites, within-subject clustering was high, with more than half of infants who experienced one rotavirus infection going on to experience a second and more than 20% experiencing a third. In the five sites that are in countries that had not introduced the rotavirus vaccine, the model predicted a primary peak in prevalence during the dry season and, in three of these, a secondary peak during the rainy season. The patterns predicted by this approach are broadly congruent with several emerging hypotheses about rotavirus transmission and are consistent for both symptomatic and asymptomatic rotavirus episodes. These findings have practical implications for programme design, but caution should be exercised in deriving inferences about the underlying pathways driving these trends, particularly when extending the approach to other pathogens.

scholarly journals AN ALARMING PANDEMIC COVID-19 IN ASIA -AN OBSERVATIONAL STUDY

2020 ◽  
pp. 52-54
Author(s):  
Raj Kumar ◽  
Gowrishankar Potturi ◽  
Suraj kumar ◽  
Neha Dubey ◽  
Arushi Kumar
Keyword(s):  
Observational Study ◽  
Doubling Time ◽  
Preventive Measures ◽  
Developed Countries ◽  
Daily Basis ◽  
World Health ◽  
Asian Countries ◽  
Timor Leste ◽  
Back Ground ◽  
Health Organization

Back ground & Purpose: Many countries in Asia as well as developed countries like England, Australia and USA were attacked by the rampant COVID-19.This observational study was conducted to analyze the rate of transmission, death to case ratio, doubling time, role of temperature and other preventive measures on these variables. Methodology: Data of the variables were collected from World Health Organization online daily progress reports on COVID-19 and other reliable Internet sources on a daily basis from 30-03-2020 till 20-04-2020. Results & Conclusion: The transmission was maximum in Turkey, Iran, China, Russia, India, Israel, Japan, South Korea, Saudi Arabia, and Pakistan whereas minimum in Yemen, Bhutan, Laos, Timor-Leste, Nepal, Mongolia, Syria, Maldives, Myanmar, and Cambodia. There were no confirmed cases reported in Turkmenistan, Tajikistan and North Korea. Temperature didn’t appear to be influencing the transmission and death rate due to COVID-19 in Asian countries. There is a positive correlation between the environmental temperatures, preventive measures, lock down and the doubling time of COVID-19 in Asian countries. KEY WORDS: - COVID -19, ASIA, Doubling time, Death – to - case ratio, Temperature

scholarly journals Analyzing the Bills-Voting Dynamics and Predicting Corruption-Convictions Among Brazilian Congressmen Through Temporal Networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tiago Colliri ◽  
Liang Zhao
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Prediction Method ◽  
High Accuracy ◽  
Financial Information ◽  
Prediction Methods ◽  
Temporal Networks ◽  
Public Data ◽  
Financial Crimes ◽  
Prediction Techniques

AbstractIn this paper, we propose a network-based technique to analyze bills-voting data comprising the votes of Brazilian congressmen for a period of 28 years. The voting sessions are initially mapped into static networks, where each node represents a congressman and each edge stands for the similarity of votes between a pair of congressmen. Afterwards, the constructed static networks are converted to temporal networks. Our analyses on the temporal networks capture some of the main political changes happened in Brazil during the period of time under consideration. Moreover, we find out that the bills-voting networks can be used to identify convicted politicians, who commit corruption or other financial crimes. Therefore, we propose two conviction prediction methods, one is based on the highest weighted convicted neighbor and the other is based on link prediction techniques. It is a surprise to us that the high accuracy (up to 90% by the link prediction method) on predicting convictions is achieved only through bills-voting data, without taking into account any financial information beforehand. Such a feature makes possible to monitor congressmen just by considering their legal public activities. In this way, our work contributes to the large scale public data study using complex networks.

scholarly journals A Study on the Improvement of Flood Forecasting Techniques in Urban Areas by Considering Rainfall Intensity and Duration

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1883 ◽  
Author(s):  
Choo ◽  
Jo ◽  
Yun ◽  
Lee
Keyword(s):  
Water Level ◽  
Urban Areas ◽  
Prediction Method ◽  
Flood Forecasting ◽  
Flood Damage ◽  
Rainfall Data ◽  
Runoff Simulation ◽  
Flood Prediction ◽  
Prediction Techniques ◽  
Impervious Areas

Frequent localized torrential rains, excessive population density in urban areas, and increased impervious areas have led to massive flood damage that has been causing overloading of drainage systems (watersheds, reservoirs, drainage pump sites, etc.). Flood concerns are raised around the world in the events of rain. Flood forecasting, a typical nonstructural measure, was developed to help prevent repetitive flood damage. However, it is difficult to apply flood prediction techniques using training processes because training needs to be applied at every usage. Other techniques that use predicted rainfall data are also not appropriate for small watershed, such as single drainage area. Thus, in this paper, a flood prediction method is proposed by improving four criteria (50% water level, 70% water level, 100% water level, and first flooding of water pipes) in an attempt to reduce flooding in urban areas. The four criteria nodes are generated using a rainfall runoff simulation with synthetic rainfall at various durations. When applying real-time rainfall data, these nodes have the advantage of simple application. The improved flood nomograph made in this way is expected to help predict and prepare for rainstorms that can potentially cause flood damage.

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