scholarly journals SIR-simulation of Corona pandemic dynamics in Europe

2020 ◽  
Author(s):  
Igor Nesteruk
Keyword(s):  
Parameter Identification ◽  
Statistical Approach ◽  
Cumulative Number ◽  
Daily Data

ABSTRACTThe SIR (susceptible-infected-removed) model, statistical approach to the parameter identification and the official WHO daily data about the confirmed cumulative number of cases were used to estimate the characteristics of COVID-19 pandemic in Italy, Spain, Germany, France, Austria and Moldova. The final sizes and durations of epidemic outbreaks in these countries are calculated.

scholarly journals Long-term predictions for COVID-19 pandemic dynamics in Ukraine, Austria and Italy

2020 ◽  
Author(s):  
Igor Nesteruk
Keyword(s):  
Parameter Identification ◽  
Statistical Approach ◽  
Data Sets ◽  
Cumulative Number ◽  
Daily Data ◽  
Long Time

ABSTRACTThe SIR (susceptible-infected-removed) model, statistical approach to the parameter identification and the official WHO daily data about the confirmed cumulative number of cases were used to make some estimations for the dynamics of the coronavirus pandemic dynamics in Ukraine, Italy and Austria. The volume of the data sets and the influence of the information about the initial stages of the epidemics were discussed in order to have reliable long-time predictions. The final sizes and durations for the pandemic in these countries are estimated.

scholarly journals Hidden periods, duration and final size of COVID-19 pandemic

2020 ◽  
Author(s):  
Igor Nesteruk
Keyword(s):  
South Korea ◽  
Parameter Identification ◽  
Statistical Approach ◽  
Cumulative Number ◽  
Final Size ◽  
Current Trends ◽  
The World ◽  
If Current

The SIR (susceptible-infected-removed) model, statistical approach for the parameter identification and the official WHO data about the confirmed cumulative number of cases were used to estimate the characteristics of COVID-19 pandemic in USA, Germany, UK, South Korea and in the world. Epidemic in every country has rather long hidden period before fist cases were confirmed. In particular, the pandemic began in China no later than October, 2019. If current trends continue, the end of the pandemic should be expected no earlier than March 2021, the global number of cases will exceed 5 million.

scholarly journals Stabilization of the coronavirus pandemic in Italy and global prospects

2020 ◽  
Author(s):  
Igor Nesteruk
Keyword(s):  
Statistical Approach ◽  
Statistical Data ◽  
Sir Model ◽  
Data Sets ◽  
Cumulative Number ◽  
Disease Dynamics ◽  
Global Pandemic ◽  
Daily Data ◽  
Scientific Analysis ◽  
To Come

ABSTRACTThe pandemic caused by coronavirus COVID-19 are of great concern. A detailed scientific analysis of this phenomenon is still to come, but now it is urgently needed to evaluate the parameters of the disease dynamics in order to make some preliminary estimations of the number of cases and possible duration of the pandemic. The corresponding mathematical models must be simple enough, since their parameters are unknown and have to be estimated using limited statistical data sets. The SIR model, statistical approach to the parameter identification and the official WHO daily data about the confirmed cumulative number of cases were used to calculate the SIR curves and make some estimations and predictions. New cases in Italy could stop to appear after May 12, 2020, and the final number of such accumulated cases could be around 112 thousand. Some prospects for the global pandemic dynamics are discussed.

scholarly journals Impact estimation on COVID-19 infections following school reopening in September 2020 in Italy

2021 ◽  
Author(s):  
Livio Fenga ◽  
Massimo Galli
Keyword(s):  
Moving Average ◽  
External Information ◽  
Cumulative Number ◽  
Public Authorities ◽  
School Officials ◽  
School Activities ◽  
Beneficial Effects ◽  
Daily Data ◽  
Italian Regions ◽  
Short Run

Background Since its outbreak, CoViD-19 (formally known as 2019-nCoV) has been triggering many questions among public authorities, social organisms and school officials, as to when students should be allowed to return to school. Such a decision is critical and must take into account, other than its beneficial effects, also those associated with an increased exposition of the students to the virus, which, as a result, might spread at a faster rate. To date, in Italy, a few studies have rigorously investigated the correlation between school reopening and number of people tested positive to CoViD-19. Therefore, this paper aims to provide an assessment of such an impact as well as to illustrate the methodology followed. Methods Official daily data on the cumulative number of people tested positive to CoViD-19, in conjunction with external information accounting for the different points in time schools reopened in the various Italian regions, have been employed to build a stochastic model of the type Seasonal Autoregressive Moving Average embodying external information. Results There was a statistically significant increase in the number of positive cases in all the Italian regions related to schools reopening. Such an increase occurred, in average, about 18.9 days after the schools have been reopened. Schools reopening have been significantly contributed to the diffusion of the pandemic, with an overall estimated impact of about 228,724 positive cases. Conclusions The results suggest the need for strict control of all in-school activities. This could be done by using, to a variable extent, all the non-pharmaceutical interventions available, such as limited access to school spaces, no overlapping practices between different sports in the same space, universal masking, bubble-size classroom. However, in many cases, such measures might not be a viable option, at least in the short run, nor be reasonably applicable. Therefore, whenever the established safety criteria could not be met, school buildings should remain closed.

Development of an Inverse Method to Estimate Overall Building and Ventilation Parameters of Large Commercial Buildings

1999 ◽  
Vol 121 (1) ◽  
pp. 40-46 ◽  
Author(s):  
T. A. Reddy ◽  
S. Deng ◽  
D. E. Claridge
Keyword(s):  
Linear Regression ◽  
Parameter Identification ◽  
Inverse Method ◽  
Commercial Buildings ◽  
Physical Parameters ◽  
Monthly Data ◽  
Heating And Cooling ◽  
Daily Data ◽  
Regression Approach ◽  
Data Points

We propose an inverse method to estimate building and ventilation parameters from non-intrusive monitoring of heating and cooling thermal energy use of large commercial buildings. The procedure involves first deducing the loads of an ideal one-zone building from the monitored data, and then in the framework of a mechanistic macro-model, using a multistep linear regression approach to determine the regression coefficients (along with their standard errors) which can be finally translated into estimates of the physical parameters (along with the associated errors). Several different identification schemes have been evaluated using heating and cooling data generated from a detailed building simulation program for two different building geometries and building mass at two different climatic locations. A multistep identification scheme has been found to yield very accurate results, and an explanation as to why it should be so is also given. This approach has been shown to remove much of the bias introduced in multiple linear regression approach with correlated regressor variables. We have found that the parameter identification process is very accurate when daily data over an entire year are used. Parameter identification accuracy using twelve monthly data points and daily data over three months of the year was also investigated. Identification with twelve monthly data points seems to be fairly accurate while that using daily data over a season does not yield very good results. This latter issue needs to be investigated further because of its practical relevance.

scholarly journals The association between COVID-19 data and meteorological factors in Indonesia

2020 ◽  
Author(s):  
Supari ◽  
Danang Eko Nuryanto ◽  
Amsari Muzakir Setiawan ◽  
Ardhasena Sopaheluwakan ◽  
Furqon AlFahmi ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Weather Data ◽  
Data Series ◽  
Distribution Analysis ◽  
Cumulative Number ◽  
Daily Data

Abstract On March 2, 2020, the first Coronavirus Disease (COVID-19) case was reported in Jakarta, Indonesia. One and half month later (15/05/2020), the cumulative number of infection cases was 16496 with a total of 1076 mortalities. This study is aimed to investigate the possible role of weather in the early cases of COVID-19 incidence in six selected cities in Indonesia. Daily data of temperature and relative humidity from weather stations nearby each city were collected during the period 3 March - 30 April 2020, together with data of COVID-19 cases. Correlation tests and regression analysis were performed to examine the association of those two data series. In addition, we analysed the distribution of COVID-19 with respect to weather data to estimate the effective range of weather data supporting COVID-19 incidence. Our results reveal that weather data is generally associated with COVID-19 incidence. The daily average temperature (T-ave) and relative humidity (RH) presents significant positive and negative correlation with COVID-19 data, respectively. However, the correlation coefficients are weak with the strongest correlations found at 5 day lag time i.e. 0.37 (-0.41) for T-ave (RH). The regression analysis consistently confirmed this relation. The distribution analysis reveals that the majority of COVID-19 cases in Indonesia occurred in the daily temperature range of 25-31oC and relative humidity of 74-92%. Our findings suggest that COVID-19 incidence in Indonesia has a weak association with weather conditions. Therefore, non-meteorological factors seem to play a larger role and should be given greater consideration in preventing the spread of COVID-19.

scholarly journals Impact of relaxing Covid-19 social distancing measures on rural North Wales: a simulation analysis

2020 ◽  
Author(s):  
Rhodri P Hughes ◽  
Dyfrig A Hughes
Keyword(s):  
Simulation Analysis ◽  
Model Averaging ◽  
Incidence Rates ◽  
Cumulative Number ◽  
Social Distancing ◽  
Daily Data ◽  
North Wales ◽  
The Uk ◽  
Incident Cases ◽  
The Impact

Background: Social distancing policies aimed to limit Covid-19 are gradually being relaxed as nationally reported peaks in incident cases are passed. Population density is an important driver of national incidence rates; however peak incidences in rural regions may lag national figures by several weeks. We aimed to forecast the impact of relaxed social distancing rules on rural North Wales. Methods: Daily data on the deaths of people with a positive test for Covid-19 were obtained from Public Health Wales and the UK Government. Sigmoidal growth functions were fitted by non-linear least squares and model averaging used to extrapolate mortality over time. The dates of peak mortality incidences for North Wales, Wales and the UK; and the percentage predicted maximum mortality (as of 7th May 2020) were estimated. Results: The peak daily death rates in Wales and the UK were estimated to have occurred on the 14/04/2020 and 15/04/2020, respectively. For North Wales, this occurred on the 07/05/2020, corresponding to the date of analysis. The number of deaths reported in North Wales represents 31% of the predicted total cumulative number, compared with 71% and 60% for Wales and the UK, respectively. Conclusion: Policies governing the movement of people in the gradual release from lockdown are likely to impact significantly on areas −principally rural in nature− where cases of Covid-19, deaths and immunity are likely to be much lower than in populated areas. This is particularly difficult to manage across jurisdictions, such as between England and Wales, and in popular holiday destinations.

scholarly journals Spatio Temporal Modelling for Government Policy the COVID-19 Pandemic in East Java

CAUCHY ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 218-226
Author(s):  
Atiek Iriany ◽  
Novi Nur Aini ◽  
Agus Dwi Sulistyono
Keyword(s):  
Government Policy ◽  
Large Scale ◽  
Cumulative Number ◽  
Data Modelling ◽  
Model Method ◽  
Daily Data ◽  
Temporal Modelling ◽  
Temporal Model ◽  
Spatio Temporal ◽  
The Government

COVID-19 has cursorily spread globally. Just in four months, its status altered into a pandemic. In Indonesia, the virus epicenter is identified in Java. The first positive case was identified in West Java and later spread in all Java. The Large-scale Social Restrictions are seemingly inefficient as the SARS-CoV-2 transmission remains. As such, the government is struggling to find anticipatory policies and steps best to mitigate the transmission. In this particular article, we used a Spatio-temporal model method for the total COVID-19 cases in Java and forecasted the total cases for the next 14 days, allowing the stakeholders to make more effective policies. The data we were using were the daily data of the cumulative number of COVID-19 cases taken from www.covid19.go.id. Data modelling was conducted using a generalized spatio-temporal autoregressive model. The model acquired to model the COVID-19 cases in Java was the GSTAR(1)(1,0,0) model.

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