Understanding how Victoria, Australia gained control of its second COVID-19 wave

AbstractDuring 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria’s second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2−56.4%) and of face coverings of 45.9% (95%CrI 32.9−55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important.

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Understanding how Victoria, Australia gained control of its second COVID-19 wave

10.1101/2021.04.03.21254866 ◽

2021 ◽

Author(s):

James M Trauer ◽

Michael J Lydeamore ◽

Gregory W Dalton ◽

David V Pilcher ◽

Michael T Meehan ◽

...

Keyword(s):

State Level ◽

Credible Interval ◽

Adverse Outcomes ◽

Transmission Risk ◽

Service Needs ◽

Time Varying ◽

Population Mobility ◽

Detection Rates ◽

Individual Level ◽

Policy Interventions

Victoria has been Australia's hardest hit state by the COVID-19 pandemic, but was successful in reversing its second wave of infections through aggressive policy interventions. The clear reversal in the epidemic trajectory combined with information on the timing and geographical scope of policy interventions offers the opportunity to estimate the relative contribution of each change. We developed a compartmental model of the COVID-19 epidemic in Victoria that incorporated age and geographical structure, and calibrated it to data on case notifications, deaths and health service needs according to the administrative divisions of Victoria's healthcare, termed clusters. We achieved a good fit to epidemiological indicators, at both the state level and for individual clusters, through a combination of time-varying processes that included changes to case detection rates, population mobility, school closures, seasonal forcing, physical distancing and use of face coverings. Estimates of the risk of hospitalisation and death among persons with disease that were needed to achieve this close fit were markedly higher than international estimates, likely reflecting the concentration of the epidemic in groups at particular risk of adverse outcomes, such as residential facilities. Otherwise, most fitted parameters were consistent with the existing literature on COVID-19 epidemiology and outcomes. We estimated a significant effect for each of the calibrated time-varying processes on reducing the risk of transmission per contact, with broad estimates of the reduction in transmission risk attributable to seasonal forcing (27.8%, 95% credible interval [95%CI] 9.26-44.7% for mid-summer compared to mid-winter), but narrower estimates for the individual-level effect of physical distancing of 12.5% (95%CI 5.69-27.9%) and of face coverings of 39.1% (95%CI 31.3-45.8%). That the multi-factorial public health interventions and mobility restrictions led to the dramatic reversal in the epidemic trajectory is supported by our model results, with the mandatory face coverings likely to have been particularly important.

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Sex differences in sickness absence and the morbidity-mortality paradox: a longitudinal study using Swedish administrative registers

BMJ Open ◽

10.1136/bmjopen-2018-024098 ◽

2019 ◽

Vol 9 (8) ◽

pp. e024098

Author(s):

Daniel Avdic ◽

Pathric Hägglund ◽

Bertil Lindahl ◽

Per Johansson

Keyword(s):

Hospital Admission ◽

Sickness Absence ◽

Outcome Measures ◽

Cox Regression ◽

Individual Level ◽

Risk Of Death ◽

Preventive Behaviour ◽

The Individual ◽

Coherent Picture ◽

Specific Health

ObjectiveTo analyse whether gender-specific health behaviour can be an explanation for why women outlive men, while having worse morbidity outcomes, known as the morbidity-mortality or gender paradox.SettingThe working population in Sweden.ParticipantsThirty per cent random sample of Swedish women and men aged 40–59 with a hospital admission in the 1993–2004 period were included. The sample for analysis consists of 233 274 individuals (115 430 men and 117 844 women) and in total 1 867 013 observations on sickness absence.InterventionHospital admission across 18 disease categories.Main outcome measuresThe main outcome measures were sickness absence (morbidity) and mortality. Longitudinal data at the individual level allow us to study how sickness absence changed after a hospital admission in men and women using a difference-in-differences regression analysis. Cox regression models are used to study differences in mortality after the admission.ResultsWomen increased their sickness absence after a hospital admission by around five more days per year than men (95% CI 5.25 to 6.22). At the same time, men had higher mortality in the 18 diagnosis categories analysed. The pattern of more sickness absence in women was the same across 17 different diagnosis categories. For neoplasm, with a 57% higher risk of death for men (54.18%–59.89%), the results depended on the imputation method of sickness for those deceased. By using the premortality means of sickness absence, men had an additional 14.47 (-16.30– -12.64) days of absence, but with zero imputation women had an additional 1.6 days of absence (0.05–3.20). Analyses with or without covariates revealed a coherent picture.ConclusionsThe pattern of increased sickness absence (morbidity) and lower mortality in women provides evidence on the more proactive and preventive behaviour of women than of men, which could thus explain the morbidity-mortality paradox.

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The second wave of SARS-CoV-2 infections and COVID-19 deaths in Germany – driven by values, social status and migration background? A county-scale explainable machine learning approach

10.1101/2021.04.14.21255474 ◽

2021 ◽

Author(s):

Gabriele Doblhammer ◽

Constantin Reinke ◽

Daniel Kreft

Keyword(s):

Gradient Boosting ◽

Low Ses ◽

County Level ◽

Nursing Home Population ◽

Death Rates ◽

Individual Level ◽

Shapley Values ◽

And Migration ◽

The Individual ◽

Second Wave

ABSTRACTThere is a general consensus that SARS-CoV-2 infections and COVID-19 deaths have hit lower social groups the hardest, however, for Germany individual level information on socioeconomic characteristics of infections and deaths does not exist. The aim of this study was to identify the key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany.We considered information on COVID-19 diagnoses and deaths from 1. October to 15. December 2020 on the county-level, differentiating five two-week time periods. We used 155 indicators to characterize counties in nine geographic, social, demographic, and health domains. For each period, we calculated directly age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates with the 155 characteristics of the counties for each period. To explore the importance and the direction of the correlation of the regional indicators we used the SHAP procedure. We categorized the top 20 associations identified by the Shapley values into twelve categories depicting the correlation between the feature and the outcome.We found that counties with low SES were important drivers in the second wave, as were those with high international migration and a high proportion of foreigners and a large nursing home population. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates.We concluded that risky working conditions with reduced opportunities for social distancing and a high chronic disease burden put populations in low-SES counties at higher risk of SARS-CoV-2 infections and COVID-19 deaths. In addition, noncompliance with Corona measures and spill-over effects from neighbouring counties increased the spread of the virus. To further substantiate this finding, we urgently need more data at the individual level.

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Multi-scale immunoepidemiological modeling of within-host and between-host HIV dynamics: systematic review of mathematical models

PeerJ ◽

10.7717/peerj.3877 ◽

2017 ◽

Vol 5 ◽

pp. e3877 ◽

Cited By ~ 5

Author(s):

Nargesalsadat Dorratoltaj ◽

Ryan Nikin-Beers ◽

Stanca M. Ciupe ◽

Stephen G. Eubank ◽

Kaja M. Abbas

Keyword(s):

Systematic Review ◽

Population Level ◽

Transmission Dynamics ◽

Time Varying ◽

Individual Level ◽

Multi Scale ◽

Hiv Dynamics ◽

Single Scale ◽

Scale Models ◽

The Individual

Objective The objective of this study is to conduct a systematic review of multi-scale HIV immunoepidemiological models to improve our understanding of the synergistic impact between the HIV viral-immune dynamics at the individual level and HIV transmission dynamics at the population level. Background While within-host and between-host models of HIV dynamics have been well studied at a single scale, connecting the immunological and epidemiological scales through multi-scale models is an emerging method to infer the synergistic dynamics of HIV at the individual and population levels. Methods We reviewed nine articles using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework that focused on the synergistic dynamics of HIV immunoepidemiological models at the individual and population levels. Results HIV immunoepidemiological models simulate viral immune dynamics at the within-host scale and the epidemiological transmission dynamics at the between-host scale. They account for longitudinal changes in the immune viral dynamics of HIV+ individuals, and their corresponding impact on the transmission dynamics in the population. They are useful to analyze the dynamics of HIV super-infection, co-infection, drug resistance, evolution, and treatment in HIV+ individuals, and their impact on the epidemic pathways in the population. We illustrate the coupling mechanisms of the within-host and between-host scales, their mathematical implementation, and the clinical and public health problems that are appropriate for analysis using HIV immunoepidemiological models. Conclusion HIV immunoepidemiological models connect the within-host immune dynamics at the individual level and the epidemiological transmission dynamics at the population level. While multi-scale models add complexity over a single-scale model, they account for the time varying immune viral response of HIV+ individuals, and the corresponding impact on the time-varying risk of transmission of HIV+ individuals to other susceptibles in the population.

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A multilevel analysis of change in hatred of academic work during high school: focusing on the sociocultural background of Korea

Journal of Psychologists and Counsellors in Schools ◽

10.1017/jgc.2019.6 ◽

2019 ◽

Vol 29 (2) ◽

pp. 166-176 ◽

Cited By ~ 1

Author(s):

Minyoung Lee ◽

Soohyun Cho ◽

Sang Min Lee

Keyword(s):

High School ◽

High School Students ◽

Test Anxiety ◽

Autonomy Support ◽

Economic Status ◽

School Students ◽

Individual Level ◽

Policy Interventions ◽

The Individual ◽

Multilevel Growth Model

AbstractDevelopment of academic hatred was examined at four time points across 7 months among 1,015 South Korean high school students. A multilevel growth model showed that the baseline of, and change in, academic hatred varied across individuals and classrooms. At the individual level, gender, parents’ academic pressure, depression, and test anxiety were related to the initial level of academic hatred; gender and test anxiety were associated with a decrease in academic hatred over time. At the class level, lower socio-economic status and higher teachers’ autonomy support were associated with a lower baseline of academic hatred, and higher teachers’ autonomy support decreased academic hatred. Influence mechanisms of protective and risk factors on students’ academic hatred can be considered for strategic and policy interventions.

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Influences of cycle facilities on cycling to work

International Journal for Population Data Science ◽

10.23889/ijpds.v3i2.547 ◽

2018 ◽

Vol 3 (2) ◽

Author(s):

Zhiqiang Feng ◽

Chris Dibben

Keyword(s):

Regression Models ◽

Census Data ◽

Control Group ◽

Ecological Fallacy ◽

Exact Matching ◽

Individual Level ◽

Policy Interventions ◽

Research Findings ◽

Policy Agendas ◽

The Individual

BackgroundIn recent years, promoting cycling as a means of transport has been put on multiple policy agendas, including health, transport and climate change. Provision of cycling facilities is important in promotion of bicycle use. However, there are no conclusive findings in terms of what cycling facilities are associated with higher levels of bicycle use. ObjectivesIn this paper we aim to assess the influence of off-road cycle paths and on-road lanes, by examining whether proximity to two types of cycle route is associated to the level of cycling to work. The research findings has potential to provide further evidence on policy interventions in promoting cycling to work through development of new cycling infrastructures. MethodsThe cycling to work data are from the 2001 and 2011 censuses at the output area level. Proximity to cycling facilities is defined for output areas if they are within 400 metres from their centroid to a cycle route. We fit regression models to examine the association of cycle paths and cycle lanes with levels of cycling to work adjusting for a number of socioeconomic factors. FindingsThe modelling results show that proximity to off-road paths is associated with increased levels of cycling to work but proximity to on-road lanes shows no effect. ConclusionsAlthough this is by no means indicative of a causal relationship, this research provides further evidence on the effects of cycle facilities on cycling behaviour, lending support on policy interventions that cycle facilities can be built to promote cycling to work. We will extend this research using the individual level census data which will give us opportunity to overcome the ecological fallacy. In addition we will use coarsened exact matching to identify a control group in order to make causal inference on the effect of cycle paths.

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Structural Nested Mean Models to Estimate the Effects of Time-Varying Treatments on Clustered Outcomes

The International Journal of Biostatistics ◽

10.1515/ijb-2014-0055 ◽

2015 ◽

Vol 11 (2) ◽

Cited By ~ 1

Author(s):

Jiwei He ◽

Alisa Stephens-Shields ◽

Marshall Joffe

Keyword(s):

Repeated Measures ◽

Regression Method ◽

Time Varying ◽

Treatment Allocation ◽

Individual Level ◽

Nested Models ◽

Unit Level ◽

Effect Of Treatment ◽

Structural Nested Models ◽

The Individual

AbstractIn assessing the efficacy of a time-varying treatment structural nested models (SNMs) are useful in dealing with confounding by variables affected by earlier treatments. These models often consider treatment allocation and repeated measures at the individual level. We extend SNMMs to clustered observations with time-varying confounding and treatments. We demonstrate how to formulate models with both cluster- and unit-level treatments and show how to derive semiparametric estimators of parameters in such models. For unit-level treatments, we consider interference, namely the effect of treatment on outcomes in other units of the same cluster. The properties of estimators are evaluated through simulations and compared with the conventional GEE regression method for clustered outcomes. To illustrate our method, we use data from the treatment arm of a glaucoma clinical trial to compare the effectiveness of two commonly used ocular hypertension medications.

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Adapting neuroeconomics for environmental and energy policy

Behavioural Public Policy ◽

10.1017/bpp.2018.2 ◽

2018 ◽

Vol 3 (1) ◽

pp. 17-36 ◽

Cited By ~ 2

Author(s):

NIK SAWE

Keyword(s):

Decision Process ◽

Residential Energy ◽

Environmental Behaviors ◽

Policy Makers ◽

Individual Level ◽

Environmental Decision Making ◽

Policy Interventions ◽

Economics Experiments ◽

Change Beliefs ◽

The Individual

AbstractNeuroimaging methods provide insight into the neural mechanisms underlying the decision process, characterizing choice at the individual level and, in a growing number of contexts, predicting national- and market-level behavior. This dual capacity to examine heterogeneity while forecasting aggregate choice is particularly beneficial to those studying environmental decision-making. To effectively reduce residential energy usage and foster other pro-environmental behaviors, policy-makers must understand the effects of information frames and behavioral nudges across individuals who hold a diverse array of attitudes toward the environment and face a broad range of barriers to action. This paper articulates the potential of neuroeconomic methods to aid environmental policy-makers interested in behavior change, especially those interested in closing the energy efficiency gap. Investigation into the roles of affect, eco-labeling and social norms will be discussed, as well as personal identity and climate change beliefs. Combining neuroimaging with behavioral economics experiments can inform the development of effective messaging, characterize the influence of individual differences on the decision process and aid in forecasting the efficacy of policy interventions at scale.

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Psychometric evaluation of dynamic managerial capability scale in the context of early internationalizing firms from an emerging economy

Asia-Pacific Journal of Business Administration ◽

10.1108/apjba-06-2019-0140 ◽

2019 ◽

Vol 11 (4) ◽

pp. 371-386 ◽

Cited By ~ 4

Author(s):

Md Imtiaz Mostafiz ◽

Murali Sambasivan ◽

See Kwong Goh

Keyword(s):

Factor Analysis ◽

Psychometric Evaluation ◽

Emerging Economy ◽

Three Dimension ◽

Measurement Scales ◽

Content Type ◽

Individual Level ◽

Order Construct ◽

The Individual ◽

Second Wave

Purpose The purpose of this paper is to perform a psychometric evaluation of dynamic managerial capability (DMC) scale in the context of early internationalizing firms from an emerging economy. Drawing on DMC theory, this study validates the measurement scales to operationalize DMC of entrepreneurs as managerial human capital (MHC), managerial social capital (MSC) and managerial cognition (MC). Design/methodology/approach Sample firms were drawn from the apparel industry in Bangladesh, an emerging economy. Data were collected from entrepreneurs in two waves through a questionnaire-based survey. In total, 185 firms responded during the first wave and 223 firms responded during the second wave. The first wave of data was used to conduct exploratory factor analysis (EFA) to uncover the underlying dimensions of DMC and the data from the second wave were used to test the validity of the DMC scale through confirmatory factor analysis (CFA). Findings EFA suggested a three-dimension scale which was supported by CFA. The findings of the study demonstrate that DMC is a valid and reliable scale to capture the individual-level capability of entrepreneurs. Originality/value DMC is rooted in three underlying attributes as MHC, MSC and MC. It is advisable to the practitioner and researcher to operationalize DMC as a second-order construct in future studies.

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Differential Effects of Competence and Morality on Self-Esteem at the Individual and the Collective Level

Social Psychology ◽

10.1027/1864-9335/a000410 ◽

2020 ◽

Vol 51 (3) ◽

pp. 183-198

Author(s):

Wiktor Soral ◽

Mirosław Kofta

Keyword(s):

Self Esteem ◽

Differential Effects ◽

Individual Level ◽

The Subject ◽

The Individual

Abstract. The importance of various trait dimensions explaining positive global self-esteem has been the subject of numerous studies. While some have provided support for the importance of agency, others have highlighted the importance of communion. This discrepancy can be explained, if one takes into account that people define and value their self both in individual and in collective terms. Two studies ( N = 367 and N = 263) examined the extent to which competence (an aspect of agency), morality, and sociability (the aspects of communion) promote high self-esteem at the individual and the collective level. In both studies, competence was the strongest predictor of self-esteem at the individual level, whereas morality was the strongest predictor of self-esteem at the collective level.

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