Estimating the Local Burden of Disease During the First Wave of the COVID-19 Epidemic in England, Using Different Data Sources from Changing Surveillance Practices

Abstract Background The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need (“pillar 1”) before expanding to community-wide symptomatics (“pillar 2”). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. MethodsWe fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January - 30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA.ResultsA model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. ConclusionsLimitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.

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A Spatio-Temporal Investigation of the Growth Rate of COVID-19 Incidents in Ohio, USA, Early in the Pandemic

The Ohio Journal of Science ◽

10.18061/ojs.v121i2.8059 ◽

2021 ◽

Vol 121 (2) ◽

pp. 33-47

Author(s):

Alessandro M. Selvitella ◽

Liam Carolan ◽

Justin Smethers ◽

Christopher Hernandez ◽

Kathleen L. Foster

Keyword(s):

Growth Rate ◽

Transmission Rate ◽

Time Lag ◽

Early Period ◽

Mitigation Strategies ◽

Initial Growth ◽

School Closures ◽

Policy Makers ◽

Susceptible Population ◽

Spatio Temporal

Understanding the initial growth rate of an epidemic is important for epidemiologists and policy makers as it can impact their mitigation strategies such as school closures, quarantines, or social distancing. Because the transmission rate depends on the contact rate of the susceptible population with infected individuals, similar growth rates might be experienced in nearby geographical areas. This research determined the growth rate of cases and deaths associated with COVID-19 in the early period of the 2020 pandemic in Ohio, United States. The evolution of cases and deaths was modeled through a Besag-York-Molliè model with linear- and power-type deterministic time dependence. The analysis showed that the growth rate of the time component of the model was subexponential in both cases and deaths once the time-lag across counties of the appearance of the first COVID-19 case was considered. Moreover, deaths in the northeast counties in Ohio were strongly related to the deaths in nearby counties.

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Impact of Allocation Decisions and Market Factors on Foreign News Coverage

Newspaper Research Journal ◽

10.1177/073953298901000403 ◽

1989 ◽

Vol 10 (4) ◽

pp. 23-32 ◽

Cited By ~ 5

Author(s):

Stephen Lacy ◽

Tsan-Kuo Chang ◽

Tuen-Yu Lau

Keyword(s):

Local Population ◽

News Coverage ◽

Population Characteristics ◽

Foreign News ◽

Market Factors ◽

Market Variables ◽

Allocation Decisions

The study finds that the business nature of newspaper organizations influence foreign news coverage and content. The role of market variables is unclear, but it appears that local population characteristics that might affect demand have no influence on this kind of content.

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Parameterization of pharmaceutical emissions and removal rates for use in UK predictive exposure models: steroid estrogens as a case study

Environmental Science Processes & Impacts ◽

10.1039/c4em00374h ◽

2014 ◽

Vol 16 (11) ◽

pp. 2571-2579

Author(s):

J. D. Heffley ◽

S. D. W. Comber ◽

B. W. Wheeler ◽

C. H. Redshaw

Keyword(s):

Local Population ◽

Population Characteristics ◽

Prescription Data ◽

Removal Rates ◽

Exposure Models ◽

Steroid Estrogens

Using local population characteristics and prescription data to predict pharmaceutical concentrations in sewage influent and effluent.

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Size and spatio-temporal variations of the Andean condor Vultur gryphus population in north-west Patagonia, Argentina: communal roosts and conservation

Oryx ◽

10.1017/s0030605310000451 ◽

2010 ◽

Vol 44 (3) ◽

pp. 441-447 ◽

Cited By ~ 30

Author(s):

Sergio A. Lambertucci

Keyword(s):

Local Population ◽

Temporal Variations ◽

Current Estimate ◽

Population Number ◽

North West ◽

Asymptotic Models ◽

Population Sizes ◽

Spatio Temporal ◽

Andean Condor ◽

Vultur Gryphus

AbstractEstimations of the population sizes of threatened species are fundamental for conservation. The current estimate of the population of the Andean condor Vultur gryphus is based on limited local counts. Simultaneous censuses of 10 condor communal roosts were therefore conducted during 2006–2008 in north-west Patagonia, Argentina, to obtain a minimum population number, to estimate the size of the local population, and to describe use of the roosts by season and age classes. I fitted the data to two asymptotic models to calculate the population of condors as a function of the number of communal roosts surveyed. In an area of c. 6,300 km2 I obtained a minimum population size of 246 individuals by direct observation, and a population estimate of 296 condors (range 260–332) by applying the models. This population, the largest known of this species, comprises 68.5% adults and 31.5% immatures. Condors had large aggregations in some communal roosts and used the area seasonally, increasing in numbers from autumn to spring and decreasing in summer. Long-term monitoring of communal roosts across the Andean condor’s range is essential for the monitoring of this rare and vulnerable species.

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Modeling the Spatio-Temporal Dynamics of a Population with Age Structure and Long-Range Interactions: Synchronization and Clustering

Математическая биология и биоинформатика ◽

10.17537/2019.14.1 ◽

2019 ◽

Vol 14 (1) ◽

pp. 1-18 ◽

Cited By ~ 1

Author(s):

Matvey Kulakov ◽

E.Ya. Frisman

Keyword(s):

Mathematical Model ◽

Model Equation ◽

Temporal Dynamics ◽

Local Population ◽

Two Dimensional ◽

Attraction Basin ◽

Local Populations ◽

Long Range Interactions ◽

Spatio Temporal ◽

Formation Of Groups

The paper proposed a mathematical model for spatio-temporal dynamics of two-age populations coupled by migration living on a two-dimensional areal. The model equation is a system of nonlocal coupled two-dimensional maps. We considered cases when populations are coupled in a certain neighborhood of different form: circle, square or rhombus. Special attention is paid to the situation when the intensity of the migrants flow between the territories decreases with increasing distance between them. For this model we study the conditions for the formation of groups of synchronous populations or clusters that form, in space, typical structures like spots or stripes mixed with solitary states. It is shown that the dynamics, in time, of different clusters may differ significantly and may not be coherent and correspond to several simultaneous multistable regimes or potential states of the local population. Such spatio-temporal regimes are forced and are caused by impacts or perturbations on a single or several populations when their number falls into the attraction basin of another regime. With strong coupling, such clusters are rare and are represented by single outbursts or solitary states. However, the decrease in the coupling strength leads to the fact that these outbursts cause oscillations of their neighbors, and in their neighborhood a cluster of solitary states is formed which is surrounded by subpopulations with a different type of dynamics. It was found that the interaction of different type of clusters leads to the formation of a large number of groups with transitional dynamics that were not described for local populations.

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Different Models, Different Outcomes? A Comparison of Approaches to Land Use Modeling in the Dutch Limes

Heritage ◽

10.3390/heritage4030118 ◽

2021 ◽

Vol 4 (3) ◽

pp. 2081-2104

Author(s):

Philip Verhagen ◽

Maurice de Kleijn ◽

Jamie Joyce

Keyword(s):

Land Use ◽

Roman Empire ◽

Local Population ◽

Population Increase ◽

Temporal Modeling ◽

Land Use Modeling ◽

Agent Based ◽

Spatio Temporal ◽

The Impact ◽

Agricultural Demand

Current advances in spatial simulation bring unprecedented possibilities for spatio-temporal modeling. In this paper, we focus on modeling the impact of settlement on land use in the Roman period in the Dutch river area, on the northern frontier of the Roman Empire. During this period, the area witnessed a strong population increase that put more demands on the available land to produce food, not only for the local population, but also for the soldiers stationed on the frontier and the citizens of the newly founded towns. We compare an agent-based model (ABM) of agricultural production in the region (ROMFARMS), and a model using the Past Land Use Scanner (PLUS. Both were used to estimate the effects of increased agricultural demand through simulations of food production, taking into account the available workforce and the productivity and availability of suitable land. However, how should we evaluate the model outcomes? What are the advantages and limitations of each? We discuss issues of scale, temporal resolution and model inputs, together with questions of technical implementation and validation. In this way, we aim to point the way to future researchers to implement these approaches effectively in other contexts.

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A fine-scale multi-step approach to understand fish recruitment variability

Scientific Reports ◽

10.1038/s41598-020-73025-z ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Pablo Brosset ◽

Andrew Douglas Smith ◽

Stéphane Plourde ◽

Martin Castonguay ◽

Caroline Lehoux ◽

...

Keyword(s):

Egg Production ◽

Adult Population ◽

Synergetic Effect ◽

Fish Population ◽

Larval Survival ◽

Population Characteristics ◽

Environmental Drivers ◽

Fine Scale ◽

Atlantic Mackerel ◽

Spatio Temporal

Abstract Recruitment is one of the dominant processes regulating fish population productivity. It is, however, notoriously difficult to predict, as it is the result of a complex multi-step process. Various fine-scale drivers might act on the pathway from adult population characteristics to spawning behaviour and egg production, and then to recruitment. Here, we provide a holistic analysis of the Northwest Atlantic mackerel recruitment process from 1982 to 2017 and exemplify why broad-scale recruitment–environment relationships could become unstable over time. Various demographic and environmental drivers had a synergetic effect on recruitment, but larval survival through a spatio-temporal match with prey was shown to be the key process. Recruitment was also mediated by maternal effects and a parent–offspring fitness trade-off due to the different feeding regimes of adults and larvae. A mismatch curtails the effects of high larval prey densities, so that despite the abundance of food in recent years, recruitment was relatively low and the pre-existing relationship with overall prey abundance broke down. Our results reaffirm major recruitment hypotheses and demonstrate the importance of fine-scale processes along the recruitment pathway, helping to improve recruitment predictions and potentially fisheries management.

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Revealing spatio-temporal patterns of rabies spread among various categories of animals in the Republic of Kazakhstan, 2010-2013

Geospatial health ◽

10.4081/gh.2016.455 ◽

2016 ◽

Vol 11 (2) ◽

Cited By ~ 1

Author(s):

Sarsenbay K. Abdrakhmanov ◽

Kanatzhan K. Beisembayev ◽

Fedor I. Кorennoy ◽

Gulzhan N. Yessembekova ◽

Dosym B. Кushubaev ◽

...

Keyword(s):

Population Level ◽

Epidemiological Data ◽

Domestic Animals ◽

Scan Statistics ◽

Farm Animals ◽

Initial Growth ◽

Wild Animals ◽

Basic Reproductive Ratio ◽

Spatio Temporal ◽

The Republic

This study estimated the basic reproductive ratio of rabies at the population level in wild animals (foxes), farm animals (cattle, camels, horses, sheep) and what we classified as domestic animals (cats, dogs) in the Republic of Kazakhstan (RK). It also aimed at forecasting the possible number of new outbreaks in case of emergence of the disease in new territories. We considered cases of rabies in animals in RK from 2010 to 2013, recorded by regional veterinary services. Statistically significant space-time clusters of outbreaks in three subpopulations were detected by means of Kulldorff Scan statistics. Theoretical curves were then fitted to epidemiological data within each cluster assuming exponential initial growth, which was followed up by calculation of the basic reproductive ratio R<sub>0</sub>. For farm animals, the value of R<sub>0</sub> was 1.62 (1.11-2.26) and for wild animals 1.84 (1.08- 3.13), while it was close to 1 for domestic animals. Using the values obtained, an initial phase of possible epidemic was simulated in order to predict the expected number of secondary cases if the disease were introduced into a new area. The possible number of new cases for 20 weeks was estimated at 5 (1-16) for farm animals, 17 (1-113) for wild animals and about 1 in the category of domestic animals. These results have been used to produce set of recommendations for organising of preventive and contra-epizootic measures against rabies expected to be applied by state veterinarian services.

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Vultures attacking livestock: a problem of vulture behavioural change or farmers’ perception?

Bird Conservation International ◽

10.1017/s0959270918000345 ◽

2019 ◽

Vol 29 (03) ◽

pp. 437-453 ◽

Cited By ~ 3

Author(s):

OLIVIER DURIEZ ◽

SANDRINE DESCAVES ◽

REGIS GALLAIS ◽

RAPHAËL NEOUZE ◽

JULIE FLUHR ◽

...

Keyword(s):

Behavioural Change ◽

Temporal Distribution ◽

Recent Change ◽

Post Mortem ◽

Spatially Correlated ◽

Gyps Fulvus ◽

Perception Bias ◽

Human Perceptions ◽

Spatio Temporal ◽

Biased Perception

SummaryHuman-wildlife conflicts are often partly due to biased human perceptions about the real damage caused by wildlife. While Griffon Vultures Gyps fulvus are obligate scavengers, 156 complaint reports about vultures attacking livestock were officially recorded over eight years (2007–2014) in France. We investigated whether this conflict could be explained by a change in vulture behaviour, or by a biased perception by farmers. If vultures became predators, as a consequence of density-dependent processes, we predicted that reports would concern mostly ante-mortem consumption of healthy livestock and would be temporally and spatially correlated to vulture population size and space use. Under the hypothesis of perception bias of farmers, we predicted that reports would concern mostly post-mortem consumption, and would be more numerous in areas where farmers are less familiar with vultures and where herds are less attended by shepherds. The spatio-temporal distribution of reports was not correlated with the vulture’s population trend and was not centred on the core area of vulture home range. In 67% of reports, vultures consumed post-mortem an animal that had died for other reasons. In 18% of reports, vultures consumed ante-mortem an animal that was immobile and close to death before vulture arrival. The fact that 90% of complaining farmers did not own vulture supplementary feeding stations and that 40% of these farms were located outside protected areas (where most education programmes take place) suggests that most farmers had little familiarity or personal knowledge of vultures. There was no shepherd witness present in 95% of the reports. Therefore, the hypothesis of a perception bias due to lack of knowledge was most likely to explain this vulture-livestock conflict rather than the hypothesis of a recent change in vulture feeding behaviour. Environmental education should be better included in conservation programmes and enhanced in areas where vultures are expanding to recolonise their former distribution range.

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COVID-19 Mortality in English Neighborhoods: The Relative Role of Socioeconomic and Environmental Factors

J ◽

10.3390/j4020011 ◽

2021 ◽

Vol 4 (2) ◽

pp. 131-146

Author(s):

Peter Congdon

Keyword(s):

Risk Factors ◽

Air Quality ◽

Mortality Risk ◽

Spatial Clustering ◽

Ethnic Composition ◽

Relative Role ◽

Ethnic Segregation ◽

Spatially Correlated ◽

The Uk

Factors underlying neighborhood variation in COVID-19 mortality are important to assess in order to prioritize resourcing and policy intervention. As well as characteristics of area populations, such as health status and ethnic mix, it is important to assess the role of more specifically environmental variables (e.g., air quality, green space access). The analysis of this study focuses on neighborhood mortality variations during the first wave of the COVID-19 epidemic in England against a range of postulated area risk factors, both socio-demographic and environmental. We assess mortality gradients across levels of each risk factor and use regression methods to control for multicollinearity and spatially correlated unobserved risks. An analysis of spatial clustering is based on relative mortality risks estimated from the regression. We find mortality gradients in most risk factors showing appreciable differences in COVID mortality risk between English neighborhoods. A regression analysis shows that after allowing for health deprivation, ethnic mix, and ethnic segregation, environment (especially air quality) is an important influence on COVID mortality. Hence, environmental influences on COVID mortality risk in the UK first wave are substantial, after allowing for socio-demographic factors. Spatial clustering of high mortality shows a pronounced metropolitan-rural contrast, reflecting especially ethnic composition and air quality.

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