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

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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Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England

Scientific Reports ◽

10.1038/s41598-021-83780-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

B. Sartorius ◽

A. B. Lawson ◽

R. L. Pullan

Keyword(s):

Risk Factors ◽

Small Area ◽

Mortality Rates ◽

Space Time ◽

Spatiotemporal Variability ◽

Population Mobility ◽

Social Distancing ◽

Increased Risk ◽

The Impact ◽

Second Wave

AbstractCOVID-19 caseloads in England have passed through a first peak, and at the time of this analysis appeared to be gradually increasing, potentially signalling the emergence of a 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 at small-area resolution, 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 high spatial resolution in coming weeks. We applied a Bayesian hierarchical 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 of 2020), 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. Reductions in population mobility during 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 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. 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 significantly contributed to 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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Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data

International Journal of Health Geographics ◽

10.1186/s12942-021-00299-5 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Harry E. R. Shepherd ◽

Florence S. Atherden ◽

Ho Man Theophilus Chan ◽

Alexandra Loveridge ◽

Andrew J. Tatem

Keyword(s):

United Kingdom ◽

Spatial Scales ◽

International Travel ◽

Population Mobility ◽

International Mobility ◽

Mobility Patterns ◽

Social Distancing ◽

The United Kingdom ◽

The Uk ◽

The Impact

Abstract Background Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which resulted in changes to mobility across different regions. An understanding of how these policies impacted travel patterns over time and at different spatial scales is important for designing effective strategies, future pandemic planning and in providing broader insights on the population geography of the country. Crowd level data on mobile phone usage can be used as a proxy for population mobility patterns and provide a way of quantifying in near-real time the impact of social distancing measures on changes in mobility. Methods Here we explore patterns of change in densities, domestic and international flows and co-location of Facebook users in the UK from March 2020 to March 2021. Results We find substantial heterogeneities across time and region, with large changes observed compared to pre-pademic patterns. The impacts of periods of lockdown on distances travelled and flow volumes are evident, with each showing variations, but some significant reductions in co-location rates. Clear differences in multiple metrics of mobility are seen in central London compared to the rest of the UK, with each of Scotland, Wales and Northern Ireland showing significant deviations from England at times. Moreover, the impacts of rapid changes in rules on international travel to and from the UK are seen in substantial fluctuations in traveller volumes by destination. Conclusions While questions remain about the representativeness of the Facebook data, previous studies have shown strong correspondence with census-based data and alternative mobility measures, suggesting that findings here are valuable for guiding strategies.

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REDUCING HEALTH DISPARITIES IN THE ERA OF VALUE-BASED CARE

Innovation in Aging ◽

10.1093/geroni/igz038.2080 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S562-S563

Author(s):

Amit Kumar ◽

Elham Mahmoudi ◽

Maricruz Rivera-Hernandez

Keyword(s):

Risk Factors ◽

Health Care ◽

Puerto Rico ◽

Mortality Rates ◽

Minority Populations ◽

Social Risk ◽

Social Risk Factors ◽

Physician Visits ◽

The Us ◽

The Impact

Abstract The US health care system is at a critical moment of transformation. The implementation of value-based models has made significant progress towards improving care quality and coordination, continuity of care and reducing cost. However, concerns have been raised regarding “cherry-picking” healthier people that may negatively impact patients with more complex needs and minority populations. Given that the US is becoming more diverse, there is a need for understanding the impact of social risk factors including ethnicity, immigration status, income and geography on health outcomes and issues of health care disparities. This panel brings together four studies that examine these phenomena in minority populations. These studies will provide novel insight regarding 1) healthcare utilization in Mexican-American Medicare beneficiaries and showing that social determinants of health are associated with a higher risk of hospitalization, emergency room admissions, and outpatient visits. 2) Mortality rates and predialysis care among Hispanics in the US, Hispanics in Puerto Rico, and Whites in the US demonstrating substantial disparities in access to recommended nephrology care for Hispanics in Puerto Rico; 3) Trends in age-adjusted mortality rates and supply of physicians in states with different nurse-practitioners regulation. 4) The impact of social risk factors on disenrollment from Fee-For-Service and enrollment in a Medicare Advantage plan in older Mexican-Americans. 5) Racial disparities in access to physician visits, prescription drugs, and healthcare spending among older adults with cognitive limitation. Studies in this panel will also discuss the effects of changes in care delivery and payment innovations in improving health equity.

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Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA

Scientific Reports ◽

10.1038/s41598-021-03198-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zia U. Ahmed ◽

Kang Sun ◽

Michael Shelly ◽

Lina Mu

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Risk Factors ◽

Mortality Rates ◽

Gradient Boosting ◽

Ensemble Machine Learning ◽

Gradient Boosting Machine ◽

Explainable Artificial Intelligence ◽

The Impact ◽

Bronchus Cancer

AbstractMachine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explore and visualize the spatial distribution of the contributions of known risk factors to lung and bronchus cancer (LBC) mortality rates in the conterminous United States. We used five base-learners—generalized linear model (GLM), random forest (RF), Gradient boosting machine (GBM), extreme Gradient boosting machine (XGBoost), and Deep Neural Network (DNN) for developing stack-ensemble models. Then we applied several model-agnostic approaches to interpret and visualize the stack ensemble model's output in global and local scales (at the county level). The stack ensemble generally performs better than all the base learners and three spatial regression models. A permutation-based feature importance technique ranked smoking prevalence as the most important predictor, followed by poverty and elevation. However, the impact of these risk factors on LBC mortality rates varies spatially. This is the first study to use ensemble machine learning with explainable algorithms to explore and visualize the spatial heterogeneity of the relationships between LBC mortality and risk factors in the contiguous USA.

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The impact of early social distancing at COVID-19 Outbreak in the largest Metropolitan Area of Brazil

10.1101/2020.04.06.20055103 ◽

2020 ◽

Cited By ~ 6

Author(s):

Fabiana Ganem ◽

Fabio Macedo Mendes ◽

Silvano Barbosa de Oliveira ◽

Victor Bertollo Gomes Porto ◽

Wildo Navegantes de Araújo ◽

...

Keyword(s):

Metropolitan Area ◽

Sao Paulo ◽

São Paulo ◽

Seir Model ◽

Social Distancing ◽

The Impact

AbstractWe calculated the impact of early social distancing on the COVID-19 transmission in the São Paulo metropolitan area and forecasted the ICU beds needed to cope the epidemic demand by using an age-stratified SEIR model. Within 60 days, these measures would avoid 89,133 deaths.

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Modeling the COVID-19 transmission in Italy: The roles of asymptomatic cases, social distancing, and lockdowns in the first and the second waves

10.1101/2021.01.08.21249273 ◽

2021 ◽

Author(s):

Muhamad Khairul Bahri

Keyword(s):

Behavioral Measures ◽

Seir Model ◽

Social Distancing ◽

The World ◽

Second Wave ◽

Very High

AbstractThe SEIR model of COVID-19 is developed to investigate the roles of physical distancing, lockdowns and asymptomatic cases in Italy. In doing so, two types of policies including behavioral measures and lockdown measures are embedded in the model. Compared with existing models, the model successfully reproduces similar multiple observed outputs such as infected and recovered patients in Italy by July 2020. This study concludes that the first policy is important once the number of infected cases is relatively low. However, once the number of infected cases is very high so the society cannot identify infected and disinfected people, the second policy must be applied soon. It is thus this study suggests that relaxed lockdowns lead to the second wave of the COVID-19 around the world. It is hoped that the model can enhance our understanding on the roles of behavioral measures, lockdowns, and undocumented cases, so-called asymptomatic cases, on the COVID-19 flow.

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Changes and evolution among SARS-COV-2 hospitalised patients in terms of severity, mortality and virus genome in a Spanish cohort

10.21203/rs.3.rs-199395/v1 ◽

2021 ◽

Author(s):

Rocío Aznar-Gimeno ◽

J. Ramón Paño-Pardo ◽

Luis M. Esteban ◽

Gorka Labata-Lezaun ◽

M. José Esquillor-R ◽

...

Keyword(s):

Risk Factors ◽

Retrospective Cohort ◽

Mortality Rates ◽

Virus Genome ◽

Hospitalized Patients ◽

Icu Admission ◽

Hospitalised Patients ◽

Survival Differences ◽

Second Wave ◽

Spanish Cohort

Abstract A comparison between pandemic waves could help to understand the evolution of this disease. The objective of this work was to study the evolution of COVID-19 hospitalized patients on different pandemic waves in terms of severity and mortality. We performed an observational retrospective cohort study of hospitalized patients (5,220) with SARS-CoV-2 infection from February to September in Aragon, Spain. In a comparative way, we analyzed ICU admission and 30-day mortality, clinical characteristics and risk factors, of first and second waves. SARS-CoV-2 virus genome were analyzed in 236 samples. Patients in the first wave (n=2,547) were older (74 y, IQR: 60-86 vs. 70 y, IQR: 53-85; p<0.001) and showed worse clinical and analytical parameters related to severe COVID-19 than in the second wave (n=2,673). The probability of ICU admission at 30 days was 16% and 10% in the first and second wave, respectively (p<0.001). The cumulative 30-day mortality rates were 38% in the first wave and 32% in the second one (p=0.007). Survival differences were observed among patients aged 60 to 80 years. There was variability among death risk factors and virus genome between waves. Therefore, the two COVID-19 pandemic waves analyzed were different, in terms of disease severity and mortality.

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Modelling of the second (and subsequent) waves of the coronavirus epidemic. Spain and Germany as case studies

10.1101/2020.06.12.20129429 ◽

2020 ◽

Cited By ~ 2

Author(s):

Francisco de Castro

Keyword(s):

Case Studies ◽

Mortality Rates ◽

Time Dependent ◽

Seir Model ◽

Considerable Uncertainty ◽

Second Wave

AbstractThe first wave of the coronavirus pandemic is waning in many countries. Some of them are starting to lift the confinement measures adopted to control it, but there is considerable uncertainty about if it is too soon and it may cause a second wave of the epidemic. To explore this issue, I fitted a SEIR model with time-dependent transmission and mortality rates to data from Spain and Germany as contrasting case studies. The model reached an excellent fit to the data. I then simulated the post-confinement epidemic under several scenarios. The model shows that (in the absence of a vaccine) a second wave is likely inevitable and will arrive soon, and that a strategy of adaptive confinement may be effective to control it. The model also shows that just a few days delay in starting the confinement may have caused and excess of thousands of deaths in Spain.

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Modeling the Impact of Unreported Cases of the COVID-19 in the North African Countries

Biology ◽

10.3390/biology9110373 ◽

2020 ◽

Vol 9 (11) ◽

pp. 373

Author(s):

Salih Djilali ◽

Lahbib Benahmadi ◽

Abdessamad Tridane ◽

Khadija Niri

Keyword(s):

Population Movement ◽

Population Mobility ◽

North African ◽

Active Infection ◽

African Countries ◽

The North ◽

North African Countries ◽

The Impact ◽

Second Wave ◽

Epidemic Wave

In this paper, we study a mathematical model investigating the impact of unreported cases of the COVID-19 in three North African countries: Algeria, Egypt, and Morocco. To understand how the population respects the restriction of population mobility implemented in each country, we use Google and Apple’s mobility reports. These mobility reports help to quantify the effect of the population movement restrictions on the evolution of the active infection cases. We also approximate the number of the population infected unreported, the proportion of those that need hospitalization, and estimate the end of the epidemic wave. Moreover, we use our model to estimate the second wave of the COVID-19 Algeria and Morocco and to project the end of the second wave. Finally, we suggest some additional measures that can be considered to reduce the burden of the COVID-19 and would lead to a second wave of the spread of the virus in these countries.

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La gripe española y crisis de mortalidad en Salta, Argentina. A principios del siglo XX

HiSTOReLo Revista de Historia Regional y Local ◽

10.15446/historelo.v5n10.37777 ◽

2013 ◽

Vol 5 (10) ◽

pp. 269-300

Author(s):

Adrián Carbonetti ◽

Néstor Javier Gómez ◽

Víctor Eduardo Torres

Keyword(s):

Excess Mortality ◽

Social Inequalities ◽

Mortality Rates ◽

Endemic Diseases ◽

Spanish Flu ◽

The Impact ◽

Siglo Xx ◽

Second Wave ◽

Epidemic Diseases ◽

Overall Mortality

La sociedad salteña, a principios del siglo XX, se caracterizaba por importantes desigualdades de tipo social, que a su vez cristalizaban en problemas en el ámbito de la salud y la educación. Con tasas de mortalidad general e infantil muy altas, ocasionadas por el impacto de dolencias endémicas y epidémicas, la población debía lidiar con graves problemas de salud. No obstante, en 1919 esa situación se agravó, a las epidemias y endemias se sumó la segunda oleada de la pandemia de “gripe española” generando una crisis de mortalidad. En este artículo se pretende analizar el papel que habría tenido la segunda oleada de gripe española en la provincia y en los Departamentos de la misma que habría generado esta crisis. Para ello se realiza un análisis de carácter cuantitativo con base a datos provistos por la Dirección de Estadísticas de la Provincia de Salta (Argentina), con los cuales se generarán tasas de mortalidad y sobremortalidad que se relacionarán con datos provistos por el censo de población de 1914 proyectados, este análisis será relacionado con datos cualitativos que provee el único periódico de la época encontrado.Palabras claves: salud, pandemia, gripe española, Salta.Spanish Flu and Mortality Crisis in Salta, Argentina. In Early Twentieth CenturyAbstract In the early twentieth century, Salta’s society was characterized by significant social inequalities that were also expressed in the field of health and education. With high overall mortality and infant mortality rates due to the impact of endemic and epidemic diseases, the population had to deal with serious health problems. In 1919, the situation worsened: in addition to epidemics and endemic diseases, the second wave of the “Spanish flu” appeared, resulting in a mortality crisis. The article aims to analyze the role that the second wave of the Spanish flu could have played in Salta and its departments’ crisis. In order to do this, an analysis based on quantitative data provided by the Bureau of Statistics of the Province of Salta will carry out. The statistical data will be used to generate mortality rates and excess mortality that will be related with projected data based on the 1914 Census. This data will also be related with the qualitative information obtained from the only newspaper found from that historical period.Keywords: health, pandemic, spanish flu, Salta.

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