COVID-19 pandemic-related lockdown: response time is more important than its strictness

AbstractThe rapid spread of SARS-CoV-2 and its threat to health systems worldwide have led governments to take acute actions to enforce social distancing. Previous studies used complex epidemiological models to quantify the effect of lockdown policies on infection rates. However, these rely on prior assumptions or on official regulations. Here, we use country-specific reports of daily mobility from people cellular usage to model social distancing. Our data-driven model enabled the extraction of mobility characteristics which were crossed with observed mortality rates to show that: (1) the time at which social distancing was initiated is of utmost importance and explains 62% of the number of deaths, while the lockdown strictness or its duration are not as informative; (2) a delay of 7.49 days in initiating social distancing would double the number of deaths; and (3) the expected time from infection to fatality is 25.75 days and significantly varies among countries.

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Impact of early detection and vaccination strategy in COVID-19 eradication program in Jakarta, Indonesia

BMC Research Notes ◽

10.1186/s13104-021-05540-9 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Dipo Aldila ◽

Brenda M. Samiadji ◽

Gracia M. Simorangkir ◽

Sarbaz H. A. Khosnaw ◽

Muhammad Shahzad

Keyword(s):

Mathematical Model ◽

Basic Reproduction Number ◽

Reproduction Number ◽

Vaccination Strategy ◽

Vaccination Rate ◽

Rapid Testing ◽

Social Distancing ◽

Rapid Spread ◽

Vaccine Potential ◽

The Basic Reproduction Number

Abstract Objective Several essential factors have played a crucial role in the spreading mechanism of COVID-19 (Coronavirus disease 2019) in the human population. These factors include undetected cases, asymptomatic cases, and several non-pharmaceutical interventions. Because of the rapid spread of COVID-19 worldwide, understanding the significance of these factors is crucial in determining whether COVID-19 will be eradicated or persist in the population. Hence, in this study, we establish a new mathematical model to predict the spread of COVID-19 considering mentioned factors. Results Infection detection and vaccination have the potential to eradicate COVID-19 from Jakarta. From the sensitivity analysis, we find that rapid testing is crucial in reducing the basic reproduction number when COVID-19 is endemic in the population rather than contact trace. Furthermore, our results indicate that a vaccination strategy has the potential to relax social distancing rules, while maintaining the basic reproduction number at the minimum possible, and also eradicate COVID-19 from the population with a higher vaccination rate. In conclusion, our model proposed a mathematical model that can be used by Jakarta’s government to relax social distancing policy by relying on future COVID-19 vaccine potential.

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Research on data-driven identification and prediction of heat response time of urban centralized heating system

Energy ◽

10.1016/j.energy.2020.118742 ◽

2020 ◽

Vol 212 ◽

pp. 118742

Author(s):

Wei Zhong ◽

Wei Huang ◽

Xiaojie Lin ◽

Zhongbo Li ◽

Yi Zhou

Keyword(s):

Response Time ◽

Heating System ◽

Data Driven ◽

Heat Response

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Bayesian small-area modelling of COVID-19 cases and deaths in England and association with key risk factors: a combined space–time Susceptible-Exposed-Infected-Removed (SEIR) model

10.21203/rs.3.rs-102191/v1 ◽

2020 ◽

Author(s):

Benn Sartorius ◽

Andrew Lawson ◽

Rachel L. Pullan

Keyword(s):

Risk Factors ◽

Small Area ◽

Mortality Rates ◽

Space Time ◽

Spatiotemporal Variability ◽

Population Mobility ◽

Seir Model ◽

Social Distancing ◽

The Impact ◽

Second Wave

Abstract Background: COVID-19 caseloads in England appear have passed through a first peak, with evidence of an emerging second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths, identify localised areas in space-time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at small-area resolution in coming weeks.Methods: We applied a Bayesian space–time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England (Middle Layer Super Output Area [MSOA], 6791 units) and by week (using observed data from week 5 to 34), including key determinants, the modelled transmission dynamics and spatial-temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA.Results: Reductions in population mobility due the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent steady increase signalling the start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates.Conclusions: While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have contributed to the current increase signalling the start of the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced.

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Bioinformatic Modelling of SARS-CoV-2 Pandemic With a Focus on Country-Specific Dynamics

10.21203/rs.3.rs-745759/v1 ◽

2021 ◽

Author(s):

Jakub Liu ◽

Tomasz Suchocki ◽

Joanna Szyda

Keyword(s):

Mortality Rates ◽

Health Care Facilities ◽

Public Health Care ◽

Detection Methods ◽

The Public ◽

Johns Hopkins University ◽

Mixture Of Normal Distributions ◽

The Uk ◽

Two Peaks ◽

Country Specific

Abstract One of the seminal events since 2019 has been the outbreak of the SARS-CoV-2 pandemic. Countries have adopted various policies to deal with it, but they also differ in their socio-geographical characteristics and in the public health care facilities. The aim of our study was to investigate differences between epidemiological parameters across countries. The analysed data represents SARS-CoV-2 repository provided by the Johns Hopkins University. Separately for each country we estimated recovery and mortality rates using the SIRD model applied to the first 30, 60, 150 and 300 days of the pandemic. Moreover, a mixture of normal distributions was fitted to the number of confirmed cases and deaths during the first 300 days. The estimates of peaks’ means and variances were used to identify countries with outlying parameters. For the period of 300 days Belgium, Cyprus, France, the Netherlands, Serbia and the UK were classified as outliers by all three outlier detection methods. Yemen was classified as an outlier for each of the four considered timeframes, due to high mortality rates. During the first 300 days of the pandemic the majority of countries underwent three peaks in the number of confirmed cases, except Australia and Kazakhstan with two peaks.

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Languages and Context Issues of ICTs for a New Role of Museums in the COVID-19 Era

Heritage ◽

10.3390/heritage4040171 ◽

2021 ◽

Vol 4 (4) ◽

pp. 3065-3080

Author(s):

Antonella Lerario

Keyword(s):

Cultural Resources ◽

Social Distancing ◽

The Real ◽

Daily Experiences ◽

The Social ◽

Rapid Spread ◽

People’S Daily ◽

People's Daily

The rapid spread of the COVID pandemic is deeply changing people’s lives and upsetting consolidated models and lifestyles. The social distancing measures for the reduction of contagion have been heavily affecting people’s daily experiences, such as for example the public’s relationship with cultural resources. Museums, in particular, are paying the highest price for that, forced to find new forms for heritage fruition, thus representing an emblematic case. Taking its steps from the analysis of the pandemic’s effects on global museum heritage and of museums’ response, the article focuses then on ICTs’ role as communication languages between heritage and its audiences in the solutions adopted, and on their suitability to the changed context. Finally, reflections on structural and contextual aspects of the dialogue between cultural resources and their public, beyond strictly technological matters, are proposed, to highlight the real extent of the challenges facing the museum sector.

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NeMoR: a New Method Based on Data-Driven for Neonatal Mortality Rate Forecasting

10.1101/2021.04.22.21255916 ◽

2021 ◽

Author(s):

Carlos Eduardo Beluzo ◽

Luciana Correia Alves ◽

Natália Martins Arruda ◽

Cátia Sepetauskas ◽

Everton Silva ◽

...

Keyword(s):

Public Health ◽

Machine Learning ◽

Neonatal Mortality ◽

Regression Models ◽

Mortality Rates ◽

Data Driven ◽

Health Policies ◽

Neonatal Mortality Rate ◽

Policy Makers ◽

Public Health Policies

ABSTRACTReduction in child mortality is one of the United Nations Sustainable Development Goals for 2030. In Brazil, despite recent reduction in child mortality in the last decades, the neonatal mortality is a persistent problem and it is associated with the quality of prenatal, childbirth care and social-environmental factors. In a proper health system, the effect of some of these factors could be minimized by the appropriate number of newborn intensive care units, number of health care units, number of neonatal incubators and even by the correct level of instruction of mothers, which can lead to a proper care along the prenatal period. With the intent of providing knowledge resources for planning public health policies focused on neonatal mortality reduction, we propose a new data-driven machine leaning method for Neonatal Mortality Rate forecasting called NeMoR, which predicts neonatal mortality rates for 4 months ahead, using NeoDeathForecast, a monthly base time series dataset composed by these factors and by neonatal mortality rates history (2006-2016), having 57,816 samples, for all 438 Brazilian administrative health regions. In order to build the model, Extra-Tree, XGBoost Regressor, Gradient Boosting Regressor and Lasso machine learning regression models were evaluated and a hyperparameters search was also performed as a fine tune step. The method has been validated using São Paulo city data, mainly because of data quality. On the better configuration the method predicted the neonatal mortality rates with a Mean Square Error lower than 0.18. Besides that, the forecast results may be useful as it provides a way for policy makers to anticipate trends on neonatal mortality rates curves, an important resource for planning public health policies.Graphical AbstractHighlightsProposition of a new data-driven approach for neonatal mortality rate forecast, which provides a way for policy-makers to anticipate trends on neonatal mortality rates curves, making a better planning of health policies focused on NMR reduction possible;a method for NMR forecasting with a MSE lower than 0.18;an extensive evaluation of different Machine Learning (ML) regression models, as well as hyperparameters search, which accounts for the last stage in NeMoR;a new time series database for NMR prediction problems;a new features projection space for NMR forecasting problems, which considerably reduces errors in NRM prediction.

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An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

10.1101/2021.02.22.21252255 ◽

2021 ◽

Author(s):

Sheng Zhang ◽

Joan Ponce ◽

Zhen Zhang ◽

Guang Lin ◽

George Karniadakis

Keyword(s):

New York ◽

New York City ◽

York City ◽

Robustness Analysis ◽

Data Driven ◽

Model Parameters ◽

Calibration Model ◽

Epidemiological Models ◽

Analysis Model ◽

Identifiability Analysis

AbstractEpidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time when the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and forecasting with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible–exposed–infectious–recovered (SEIR) model, including new compartments and model vaccination in order to forecast the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately predict the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC’s government’s website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.

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Epidemiology of hip fracture and the development of a FRAX model for Uzbekistan

Archives of Osteoporosis ◽

10.1007/s11657-020-00792-7 ◽

2020 ◽

Vol 15 (1) ◽

Cited By ~ 1

Author(s):

O Lesnyak ◽

S Ismailov ◽

M Shakirova ◽

N Alikhanova ◽

A Zakroyeva ◽

...

Keyword(s):

Hip Fracture ◽

Hip Fractures ◽

Mortality Rates ◽

Population Based ◽

Fracture Probability ◽

Medical Attention ◽

National Mortality ◽

The Republic ◽

Country Specific ◽

Population Based Survey

Abstract Summary A prospective population-based survey in a region of the Republic of Uzbekistan determined the incidence of fractures at the hip. The hip fracture rates were used to create a FRAX® model to facilitate fracture risk assessment in Uzbekistan. Objective This paper describes the epidemiology of hip fracture in the Republic of Uzbekistan that was used to develop a country-specific FRAX® tool for fracture prediction. Methods During a 1-year (2016/17) prospective population-based survey in the Pap district of the Republic of Uzbekistan, hip fractures were prospectively identified from hospital registers, trauma centres and primary care and community sources. Age- and sex-specific incidence of hip fracture and national mortality rates were incorporated into a FRAX model for Uzbekistan. Fracture probabilities were compared with those from neighbouring Kazakhstan and Kyrgystan. Results Approximately 41% of hip fracture cases did not come to medical attention, and two thirds of patients overall were not admitted to hospital. The incidence of hip fracture applied nationally suggested that the estimated number of hip fractures nationwide in persons over the age of 50 years for 2015 was 16,764 and is predicted to increase more than three-fold to 60,272 in 2050. FRAX-based probabilities were higher in Uzbekistan than Kazakhstan or Kyrgystan. Conclusion The FRAX model should enhance accuracy of determining fracture probability among the Uzbek population and help guide decisions about treatment.

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Effects and Challenges of the Covid-19 Pandemic on the Beach Economy: The Case of the Tents Business in Rio de Janeiro, Brazil

REvista COSTAS ◽

10.26359/costas.2102 ◽

2020 ◽

pp. 263-286

Author(s):

Julia Valentin Laurindo Santos ◽

João Vitor Prudente ◽

Letícia Parente-Ribeiro ◽

Flavia Lins-de-Barros

Keyword(s):

Rio De Janeiro ◽

Public Spaces ◽

Structured Interviews ◽

Economic Activities ◽

Daily Lives ◽

Social Distancing ◽

Period 2 ◽

Rapid Spread ◽

Traditional Sector ◽

The City

In 2020, the rapid spread of Covid-19, a disease caused by a highly contagious virus, led many governments to adopt measures of social distancing, including the suspension of activities considered non-essential and the closure of public spaces. In Brazil, a country that is distinguished by sun, sea and sand tourism (3s), the effects were immediate in the months of March, April, May and June: closed beaches and the suspension of all economic activities linked to it. This article seeks to understand the effects of the Covid-19 pandemic on a traditional sector of the beach economy in Rio de Janeiro, the “tent business”. For that, we analyzed: 1) the organization of this sector in the pre-pandemic period; 2) the legal measures adopted to contain the spread of the new coronavirus and which affected the uses of beaches; 3) the effects of the pandemic on the daily lives of beach workers 4) the challenges for the resumption of activities in the post-pandemic period. The data used in this research are the result of surveys and fieldwork carried out in the period before the pandemic and the application, during quarantine, of semi-structured interviews, via social networks, with owners and employees of tents on the beaches of the city’s waterfront. For this study, the normative measures that affected the beaches of the city of Rio de Janeiro during the pandemic were also analyzed. As main results, we highlight, first, the importance of the “tent business” in the economic circuits associated with Rio beaches, as well as the role that tents play as poles of concentration of bathers in the sand strip. Regarding governmental measures of social distance, we noticed that the beaches were one of the areas affected for the longest time by the suspension of activities and that, until the total reopening occurred in October, the activities associated with the solarium, such as the “tent business”, were those that presented a more uncertain horizon of recovery. The impacts on the daily lives of the owners of the tents and their employees were enormous, with the vertiginous decrease of their incomes and the difficulties of finding alternative occupations. These effects were partially offset by the adoption of assistance measures by governments and the creation of support networks involving beachgoers, both Brazilian and foreigner, as a result of a relationship built over the years with stallholders and other beach workers. Finally, from a comparative exercise with other situations in the world, we highlight the challenges that are already being faced for the adoption of new ways of ordering the uses of beaches in the post-pandemic world. Keywords: Coastal management, social distancing, beach workers, beachfront, solarium.

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Modeling the COVID-19 Dynamics and Explaining the Portuguese CaseA Contribution for Fiting Differential Equation Solutions to Epidemiological Data

10.21203/rs.3.rs-284108/v1 ◽

2021 ◽

Author(s):

Maria Jardim Beira ◽

Pedro José Sebastião

Keyword(s):

Differential Equation ◽

Compartmental Model ◽

Model Fitting ◽

Epidemiological Data ◽

Data Driven ◽

Data Sets ◽

Future Scenarios ◽

Epidemiological Models ◽

Related Data ◽

Time Availability

Abstract Compartmental epidemiological models are, by far, the most popular in the study of dynamics related with infectious diseases. It is, therefore, not surprising that they are frequently used to study the current COVID-19 pandemic. Taking advantage of the real-time availability of COVID-19 related data, we perform a compartmental model fitting analysis of the portuguese case, using an online open-access platform with the integrated capability of solving systems of differential equations. This analysis enabled the data-driven validation of the used model and was the basis for robust projections of different future scenarios, namely, increasing the detected infected population, reopening schools at different moments, allowing Easter celebrations to take place and population vaccination. The method presented in this work can easily be used to perform the non-trivial task of simultaneously fitting differential equation solutions to different epidemiological data sets, regardless of the model or country that might be considered in the analysis.

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