An evaluation of the impact of COVID-19 lockdowns on electricity demand

The COVID-19 pandemic has given rise to significant changes in electricity demand around the world. Although these changes differ from region to region, countries that have implemented stringent lockdown measures to curtail the spread of the virus have experienced the greatest alterations in demand. Within Australia, the state of Victoria has been subject to the largest number of days in hard lockdown during the COVID-19 pandemic. We conduct an exploratory data analysis to identify predictors of demand, and have built a time series forecasting model to predict the half-hourly electricity demand in Victoria. Our model distinguishes between lockdown periods and non-restrictive periods, and aims to identify a variety of patterns that we show to be influential on electricity demand. The model thereby provides a nuanced prediction of electricity demand that captures the shifting demand profile of intermittent lockdowns.

Access to healthcare as an important moderating variable for understanding the geography of COVID-19 outcomes - preliminary insights from Poland

Introduction: Biases in the measurement of COVID-19 burden and the uncertainty in estimation of the corresponding epidemiologic indexes are known and common phenomena in infectious diseases. We investigated to what extent healthcare access (HCA)-related supply/demand interfered with the registered data on COVID-19 in Poland. Material and Methods: We ran a multiple linear regression model with interactions to explain the geographic variation in seroprevalence, hospitalizations (on the voivodeship - NUTS-2 level) and current (beginning of the 4th wave of COVID cases - 15.09-21.11.2021) case notifications/crude mortality (on poviat - old NUTS-4 level). We took vaccination coverage and cumulative case notifications up to the so called 3rd wave as predictor variables and supply/demand (HCA) as moderating variables. Results: HCA with interacting terms (mainly demand) explained to the great extent the variance of current incidence and most of the variance in the current mortality rates. HCA (mainly supply) was significantly moderating cumulative case notifications until the 3rd wave of cases, thus explaining the variance in seroprevalence and hospitalization. Conclusion: Seeking causal relations between the vaccination- or infection-gained immunity level and the current infection dynamics could be misleading without understanding the socio-epidemiologic context such as the moderating role of HCA (sensu lato). After quantification, HCA could be incorporated into epidemiologic models for improved prediction of the actual disease burden. Copyright © Medical University of Gdańsk.

Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia.

BACKGROUND: A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients. METHODS: This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers. FINDINGS: Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses. INTERPRETATION: SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated. FUNDING: This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).

Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia.

In response to the COVID-19 pandemic, mobile-phone data on population movement became publicly available, including Google Community Mobility Reports (CMR). This study explored the utilization of mobility data to predict COVID-19 dynamics in Jakarta, Indonesia. We acquired aggregated and anonymized mobility data sets from 15 February to 31 December 2020. Three statistical models were explored: Poisson Regression Generalized Linear Model (GLM), Negative Binomial Regression GLM, and Multiple Linear Regression (MLR). Due to multicollinearity, three categories were reduced into one single index using Principal Component Analysis (PCA). Multiple Linear Regression with variable adjustments using PCA was the best-fit model, explaining 52% of COVID-19 cases in Jakarta (R-Square: 0.52; p < 0.05). This study found that different types of mobility were significant predictors for COVID-19 cases and have different levels of impact on COVID-19 dynamics in Jakarta, with the highest observed in "grocery and pharmacy" (4.12%). This study demonstrates the practicality of using CMR data to help policymakers in decision making and policy formulation, especially when there are limited data available, and can be used to improve health system readiness by anticipating case surge, such as in the places with a high potential for transmission risk and during seasonal events.

Forecasting Diurnal Covid-19 Cases for Top-5 Countries Using Various Time-series Forecasting Algorithms

On January 30, 2020, the World Health Organisation classified the Covid-19 outbreak a Public Health Emergency of International Concern, and a pandemic was proclaimed on March 11, 2020. Two years after the Covid-19 outbreak, the virus has new transmutations plus is turning out to be more difficult for forecasting in terms of both its behaviour and severity. Various techniques for time series analysis of coronavirus (Covid-19) cases were examined in this study. The Deep Learning model chosen, Long Short-Term Memory (LSTM) is compared against Statistical approaches, such as Linear Regression, Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), based on a variety of performance metrics. Following the estimates of the superior algorithm, medical care professionals can act at the appropriate moment to supply Equipment to health care institutions and further help the public. According to our data, as the number of projected days grows, so does the model's error rate. Forecasted trends also suggest that statistical approaches are relatively better overall for predictions of fewer days, but Deep Learning methods are relatively better for forecasts of more days. © 2022 IEEE.

MIGRATION EFFECT ON THE COMPETITIVE GROWTH IN GERMANY AND THE UK

The article considers the impact of migration processes on the GDP per capita. Numerous studies examine the reasons for changes in the vector of migration policy and indicate various factors of migration influx rate. However, the most recent observations indicate an almost stagnant migration flow being affected by the COVID-19. As a result of the ongoing impacts, the requirements for the quality of human capital will be changed completely, and the volume of GDP will be decreased. As examples of two countries, Germany and Great Britain, having different migration policy approaches, authors analyze the importance of migration flows. Based on this fact, the authors suggest that migration processes can be one of the statistically significant factors affecting GDP per capita. It also can be used in the building of mathematical forecasting models. Analyses of variance (ANOVA) method is used to test the hypothesis. The linear regression is used to build models and show how factor influences the linear approximation. As a result, the hypothesis of the impact of the migration flows on GDP per capita was confirmed with the econometric models' support and their indicated explanatory abilities. Inflation and unemployment rates were considered as complement factors. The results of the models allow predicting migration policy based on migration quotas. (C) 2021 Published by European Publisher.

Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020.

BackgroundThe COVID-19 pandemic has led to an unprecedented daily use of RT-PCR tests. These tests are interpreted qualitatively for diagnosis, and the relevance of the test result intensity, i.e. the number of quantification cycles (Cq), is debated because of strong potential biases.AimWe explored the possibility to use Cq values from SARS-CoV-2 screening tests to better understand the spread of an epidemic and to better understand the biology of the infection.MethodsWe used linear regression models to analyse a large database of 793,479 Cq values from tests performed on more than 2 million samples between 21 January and 30 November 2020, i.e. the first two pandemic waves. We performed time series analysis using autoregressive integrated moving average (ARIMA) models to estimate whether Cq data information improves short-term predictions of epidemiological dynamics.ResultsAlthough we found that the Cq values varied depending on the testing laboratory or the assay used, we detected strong significant trends associated with patient age, number of days after symptoms onset or the state of the epidemic (the temporal reproduction number) at the time of the test. Furthermore, knowing the quartiles of the Cq distribution greatly reduced the error in predicting the temporal reproduction number of the COVID-19 epidemic.ConclusionOur results suggest that Cq values of screening tests performed in the general population generate testable hypotheses and help improve short-term predictions for epidemic surveillance.

Influenza-associated mortality in Australia, 2010 through 2019: High modelled estimates in 2017.

INTRODUCTION: In Australia, the 2017 and 2019 influenza seasons were severe. High-dose or adjuvanted vaccines were introduced for ≥65 year-olds in 2018. AIM: To compare influenza-associated mortality in 2017 and 2019 with the average for 2010-2019. METHODS: We used time series modelling to obtain estimates of influenza-associated death rates for influenza A(H1N1)pdm09, A(H3N2) and B in Australia, in persons of all ages and <65, 65-74 and ≥75 years. Estimates were made for pneumonia and influenza (P&I, 2010-2018), respiratory (2010-2018), and all-cause outcomes (2010-2019). RESULTS: During 2010 through 2018 (and 2019 for all-cause), influenza was estimated to be associated with an annual average of 2.1 (95% confidence interval (CI) 1.9, 2.4), 4.0 (95% CI 3.4, 4.6), and 11.6 (95% CI 8.4, 15.0) P&I, respiratory and all-cause deaths per 100,000 population, respectively. Influenza A(H1N1)pdm09 was estimated to be associated with less than one quarter of influenza-associated P&I and respiratory deaths, while A(H3N2) and B were each estimated to contribute approximately equally to the remaining influenza-associated deaths. In 2017, the respective rates were 7.8 (95% CI 7.1, 8.4), 12.3 (95% CI 10.9, 13.6) and 26.0 (95% CI 20.8, 32.0) per 100,000. In 2019, the all-cause estimate was 20.8 (95% CI 14.9, 26.7) per 100,000. CONCLUSIONS: Seasonal influenza continues to be associated with substantial mortality in Australia, with at least double the average occurring in 2017. Age-specific monitoring of vaccine effectiveness is needed in Australia to understand higher mortality seasons.

Real-time quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARS-CoV-2.

BACKGROUND: The COVID-19 pandemic poses serious threats to global health, and the emerging mutation in SARS-CoV-2 genomes, e.g., the D614G substitution, is one of the major challenges of disease control. Characterizing the role of the mutation activities is of importance to understand how the evolution of pathogen shapes the epidemiological outcomes at population scale. METHODS: We developed a statistical framework to reconstruct variant-specific reproduction numbers and estimate transmission advantage associated with the mutation activities marked by single substitution empirically. Using likelihood-based approach, the model is exemplified with the COVID-19 surveillance data from January 1 to June 30, 2020 in California, USA. We explore the potential of this framework to generate early warning signals for detecting transmission advantage on a real-time basis. RESULTS: The modelling framework in this study links together the mutation activity at molecular scale and COVID-19 transmissibility at population scale. We find a significant transmission advantage of COVID-19 associated with the D614G substitution, which increases the infectivity by 54% (95%CI: 36, 72). For the early alarming potentials, the analytical framework is demonstrated to detect this transmission advantage, before the mutation reaches dominance, on a real-time basis. CONCLUSIONS: We reported an evidence of transmission advantage associated with D614G substitution, and highlighted the real-time estimating potentials of modelling framework.

Shrinkage in serial intervals across transmission generations of COVID-19.

One of the key epidemiological characteristics that shape the transmission of coronavirus disease 2019 (COVID-19) is the serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, recent studies reported a slight shrinkage (or contraction) of the mean of effective SI across transmission generations or over time. Here, we develop a likelihood-based statistical inference framework with truncation to explore the change in SI across transmission generations after adjusting the impacts of case isolation. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that for COVID-19, the mean of individual SI is likely to shrink with a factor at 0.72 per generation (95%CI: 0.54, 0.96) as the transmission generation increases, where a threshold may exist as the lower boundary of this shrinking process. We speculate that one of the probable explanations for the shrinkage in SI might be an outcome due to the competition among multiple candidate infectors within the same case cluster. Thus, the nonpharmaceutical interventive strategies are crucially important to block the transmission chains, and mitigate the COVID-19 epidemic.

Descriptive understanding and prediction in COVID-19 modelling.

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical question about how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding.

Rapid spread of the SARS-CoV-2 Delta variant in some French regions, June 2021.

We analysed 9,030 variant-specific RT-PCR tests performed on SARS-CoV-2-positive samples collected in France between 31 May and 21 June 2021. This analysis revealed rapid growth of the Delta variant in three of the 13 metropolitan French regions and estimated a +79% (95% confidence interval: 52-110%) transmission advantage compared with the Alpha variant. The next weeks will prove decisive and the magnitude of the estimated transmission advantages of the Delta variant could represent a major challenge for public health authorities.

The SARS-CoV-2 B.1.351 lineage (VOC ß) is outgrowing the B.1.1.7 lineage (VOC α) in some French regions in April 2021.

To assess SARS-CoV-2 variants spread, we analysed 36,590 variant-specific reverse-transcription-PCR tests performed on samples from 12 April-7 May 2021 in France. In this period, contrarily to January-March 2021, variants of concern (VOC) ß (B.1.351 lineage) and/or γ (P.1 lineage) had a significant transmission advantage over VOC α (B.1.1.7 lineage) in Île-de-France (15.8%; 95% confidence interval (CI): 15.5-16.2) and Hauts-de-France (17.3%; 95% CI: 15.9-18.7) regions. This is consistent with VOC ß's immune evasion abilities and high proportions of prior-SARS-CoV-2-infected persons in these regions.

Caprine Arthritis Encephalitis Virus Disease Modelling Review.

Mathematical modelling is used in disease studies to assess the economical impacts of diseases, as well as to better understand the epidemiological dynamics of the biological and environmental factors that are associated with disease spreading. For an incurable disease such as Caprine Arthritis Encephalitis (CAE), this knowledge is extremely valuable. However, the application of modelling techniques to CAE disease studies has not been significantly explored in the literature. The purpose of the present work was to review the published studies, highlighting their scope, strengths and limitations, as well to provide ideas for future modelling approaches for studying CAE disease. The reviewed studies were divided into the following two major themes: Mathematical epidemiological modelling and statistical modelling. Regarding the epidemiological modelling studies, two groups of models have been addressed in the literature: With and without the sexual transmission component. Regarding the statistical modelling studies, the reviewed articles varied on modelling assumptions and goals. These studies modelled the dairy production, the CAE risk factors and the hypothesis of CAE being a risk factor for other diseases. Finally, the present work concludes with further suggestions for modelling studies on CAE.

Detecting Rapid Spread of SARS-CoV-2 Variants, France, January 26-February 16, 2021.

Variants of severe acute respiratory syndrome coronavirus 2 raise concerns regarding the control of coronavirus disease epidemics. We analyzed 40,000 specific reverse transcription PCR tests performed on positive samples during January 26-February 16, 2021, in France. We found high transmission advantage of variants and more advanced spread than anticipated.

Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data.

This study estimates cumulative infection rates from Covid-19 in Great Britain by local authority districts (LADs) and council areas (CAs) and investigates spatial patterns in infection rates. We propose a model-based approach to calculate cumulative infection rates from data on observed and expected deaths from Covid-19. Our analysis of mortality data shows that 7% of people in Great Britain were infected by Covid-19 by the last third of June 2020. It is unlikely that the infection rate was lower than 4% or higher than 15%. Secondly, England had higher infection rates than Scotland and especially Wales, although the differences between countries were not large. Thirdly, we observed a substantial variation in virus infection rates in Great Britain by geographical units. Estimated infection rates were highest in the capital city of London where between 11 and 12% of the population might have been infected and also in other major urban regions, while the lowest were in small towns and rural areas. Finally, spatial regression analysis showed that the virus infection rates increased with the increasing population density of the area and the level of deprivation. The results suggest that people from lower socioeconomic groups in urban areas (including those with minority backgrounds) were most affected by the spread of coronavirus from March to June.

The hidden Markov chain modelling of the COVID-19 spreading using Moroccan dataset.

The World Health Organization (WHO) declared in March 12, 2020 the COVID-19 disease as pandemic. In Morocco, the first local transmission case was detected in March 13. The number of confirmed cases has gradually increased to reach 15,194 on July 10, 2020. To predict the COVID-19 evolution, statistical and mathematical models such as generalized logistic growth model [1], exponential model [2], segmented Poisson model [3], Susceptible-Infected-Recovered derivative models [4] and ARIMA [5] have been proposed and used. Herein, we proposed the use of the Hidden Markov Chain, which is a statistical system modelling transitions from one state (confirmed cases, recovered, active or death) to another according to a transition probability matrix to forecast the evolution of COVID-19 in Morocco from March 14, to October 5, 2020. In our knowledge the Hidden Markov Chain was not yet applied to the COVID-19 spreading. Forecasts for the cumulative number of confirmed, recovered, active and death cases can help the Moroccan authorities to set up adequate protocols for managing the post-confinement due to COVID-19. We provided both the recorded and forecasted data matrices of the cumulative number of the confirmed, recovered and active cases through the range of the studied dates.