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1.
Stoch Environ Res Risk Assess ; : 1-11, 2022 Jan 11.
Article in English | MEDLINE | ID: covidwho-1941672

ABSTRACT

We provide a stochastic modeling framework for the incidence of COVID-19 in Castilla-Leon (Spain) for the period March 1, 2020 to February 12, 2021, which encompasses four waves. Each wave is appropriately described by a generalized logistic growth curve. Accordingly, the four waves are modeled through a sum of four generalized logistic growth curves. Pointwise values of the twenty input parameters are fitted by a least-squares optimization procedure. Taking into account the significant variability in the daily reported cases, the input parameters and the errors are regarded as random variables on an abstract probability space. Their probability distributions are inferred from a Bayesian bootstrap procedure. This framework is shown to offer a more accurate estimation of the COVID-19 reported cases than the deterministic formulation.

2.
Spat Stat ; : 100551, 2021 Nov 08.
Article in English | MEDLINE | ID: covidwho-1889892

ABSTRACT

The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models provide geographically detailed and temporally updated overviews of the current state of the pandemic, making public health interventions more effective. These models also allow estimating epidemiological indicators highly demanded for COVID-19 surveillance, such as the instantaneous reproduction number R t , even for small areas. In this paper, we propose a new spatio-temporal spline model particularly suited for COVID-19 surveillance, which allows estimating and monitoring R t for small areas. We illustrate our proposal on the study of the disease pandemic in two Spanish regions. As a result, we show how tourism flows have shaped the spatial distribution of the disease in these regions. In these case studies, we also develop new epidemiological tools to be used by regional public health services for small area surveillance.

3.
Stoch Environ Res Risk Assess ; : 1-19, 2022 Jan 25.
Article in English | MEDLINE | ID: covidwho-1777731

ABSTRACT

Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.

4.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-325921

ABSTRACT

We extend the generalised functional additive mixed model to include (functional) compositional covariates carrying relative information of a whole. Relying on the isometric isomorphism of the Bayes Hilbert space of probability densities with a subspace of the $L

5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-318013

ABSTRACT

Modeling the behavior and spread of infectious diseases on space and time is key in devising public policies for preventive measures. This behavior is so complex that there are lots of uncertainties in both the data and in the process itself. We argue here that these uncertainties should be taken into account in the modeling strategy. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We thus present here a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model.

6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-316549

ABSTRACT

The emergence of Covid-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with highly disaggregated spatial and temporal units of analysis, are a priority in this sense. Spatio-temporal models provide a geographically detailed and temporally updated overview of the current state of the pandemics, making public health interventions to be more effective. Moreover, the use of spatio-temporal disease mapping models in the new Covid-19 epidemic context, facilitates estimating newly demanded epidemiological indicators, such as the instantaneous reproduction number (R_t), even for small areas. This, in turn, allows to adapt traditional disease mapping models to these new circumstancies and make their results more useful in this particular context. In this paper we propose a new spatio-temporal disease mapping model, particularly suited to Covid-19 surveillance. As an additional result, we derive instantaneous reproduction number estimates for small areas, enabling monitoring this parameter with a high spatial disaggregation. We illustrate the use of our proposal with the separate study of the disease pandemics in two Spanish regions. As a result, we illustrate how touristic flows could haved shaped the spatial distribution of the disease. In these real studies, we also propose new surveillance tools that can be used by regional public health services to make a more efficient use of their resources.

7.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-316184

ABSTRACT

The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for effective response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model. This paper implements a contact tracing-based stochastic compartment model as a baseline, to further modify the existing setup to include the spatial risk. This modification of each individual-level contact's intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results highlight that the inclusion of spatial context tends to send more individuals into quarantine which reduces the overall spread of infection. With a simulated example of an induced spatial high-risk, it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective. We conclude that the proposed spatio-SIR tool can be of great help for policymakers to know the consequences of their decision prior to their implementation.

8.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-307762

ABSTRACT

Background: The spread of COVID-19 in the US prompted non-pharmaceutical interventions which caused a sudden reduction in mobility everywhere, although with large local disparities between different counties. Methods: Using a Bayesian spatial modelling framework, we investigated the association of county-level demographic and socioeconomic factors with changes in workplaces mobility at two points in time: during the early stages of the epidemic (lockdown phase) and in the following phase (recovery phase). Findings: While controlling for the epidemiological situation, we found that the county-level socioeconomic and demographic covariates explain about 40% of the variance in changes in workplaces mobility in the lockdown phase, which reduces to about 10% in the recovery phase. During the lockdown phase, larger drops in workplaces mobility were observed in counties with a higher income, an older population, a lower density of Hispanic population, that are less-densely populated but with a larger density of workforce. Additionally, when also accounting for the residual spatial variability, the variance explained by the model in both phases increases up to 80%, suggesting strong proximity effects. Interpretation: This study suggests a strong association in the early stages of the epidemic between county-level changes in workplaces mobility and demographic and socioeconomic inequalities. Similar behaviours in nearby counties are present across the whole period of study, indicating a potential link to state- and county-wise regulations. These results provide community-level insights on the evolution of the US mobility during the COVID-19 epidemic that could directly benefit policy evaluation and interventions. Funding: None.Declaration of Interests: The authors declare no conflict of interests.

9.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-313180

ABSTRACT

The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February to mid September 2020. Using a hierarchical Bayesian framework, we found that the temporal trend of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in August. However decline and increase of the temporal trend seems to be sharper in Spain and smoother in Germany. The spatial heterogeneity of the relative risk of COVID-19 in Spain is also more pronounced than Italy and Germany.

10.
Adv Stat Anal ; : 1-26, 2022 Jan 05.
Article in English | MEDLINE | ID: covidwho-1616168

ABSTRACT

Statistical modelling of a spatial point pattern often begins by testing the hypothesis of spatial randomness. Classical tests are based on quadrat counts and distance-based methods. Alternatively, we propose a new statistical test of spatial randomness based on the fractal dimension, calculated through the box-counting method providing an inferential perspective contrary to the more often descriptive use of this method. We also develop a graphical test based on the log-log plot to calculate the box-counting dimension. We evaluate the performance of our methodology by conducting a simulation study and analysing a COVID-19 dataset. The results reinforce the good performance of the method that arises as an alternative to the more classical distances-based strategies.

11.
Advances in statistical analysis : AStA : a journal of the German Statistical Society : Duplicate, marked for deletion ; : 1-26, 2022.
Article in English | EuropePMC | ID: covidwho-1609826

ABSTRACT

Statistical modelling of a spatial point pattern often begins by testing the hypothesis of spatial randomness. Classical tests are based on quadrat counts and distance-based methods. Alternatively, we propose a new statistical test of spatial randomness based on the fractal dimension, calculated through the box-counting method providing an inferential perspective contrary to the more often descriptive use of this method. We also develop a graphical test based on the log–log plot to calculate the box-counting dimension. We evaluate the performance of our methodology by conducting a simulation study and analysing a COVID-19 dataset. The results reinforce the good performance of the method that arises as an alternative to the more classical distances-based strategies.

12.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-294614

ABSTRACT

Most COVID-19 studies commonly report figures of the overall infection at a state- or county-level, reporting the aggregated number of cases in a particular region at one time. This aggregation tends to miss out on fine details of the propagation patterns of the virus. This paper is motivated by analyzing a high-resolution COVID-19 dataset in Cali, Colombia, that provides every confirmed case's exact location and time information, offering vital insights for the spatio-temporal interaction between individuals concerning the disease spread in a metropolis. We develop a non-stationary spatio-temporal point process, assuming that previously infected cases trigger newly confirmed ones, and introduce a neural network-based kernel to capture the spatially varying triggering effect. The neural network-based kernel is carefully crafted to enhance expressiveness while maintaining results interpretability. We also incorporate some exogenous influences imposed by city landmarks. The numerical results on real data demonstrate good predictive performances of our method compared to the state-of-the-art as well as its interpretable findings.

13.
Spatial statistics ; 2021.
Article in English | EuropePMC | ID: covidwho-1505372

ABSTRACT

The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models provide geographically detailed and temporally updated overviews of the current state of the pandemic, making public health interventions more effective. These models also allow estimating epidemiological indicators highly demanded for COVID-19 surveillance, such as the instantaneous reproduction number

14.
Stoch Environ Res Risk Assess ; 36(3): 893-917, 2022.
Article in English | MEDLINE | ID: covidwho-1491142

ABSTRACT

The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for effective response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model. This paper implements a contact tracing-based stochastic compartment model as a baseline, to further modify the existing setup to include the spatial risk. This modification of each individual-level contact's intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results highlight that the inclusion of spatial context tends to send more individuals into quarantine which reduces the overall spread of infection. With a simulated example of an induced spatial high-risk, it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective. We conclude that the proposed spatio-SIR tool can be of great help for policymakers to know the consequences of their decision prior to their implementation.

15.
Spat Stat ; : 100540, 2021 Sep 28.
Article in English | MEDLINE | ID: covidwho-1440369

ABSTRACT

Spatial dependence is usually introduced into spatial models using measure of physical proximity. When analyzing COVID-19 case counts, this makes sense as regions that are close together are more likely to have more people moving between them, spreading the disease. However, using the actual number of trips between each region may explain COVID-19 case counts better than physical proximity. In this paper, we investigate the efficacy of using telecommunications-derived mobility data to induce spatial dependence in spatial models applied to two Spanish communities' COVID-19 case counts. We do this by extending Besag York Mollié (BYM) models to include both a physical adjacency effect, alongside a mobility effect. The mobility effect is given a Gaussian Markov random field prior, with the number of trips between regions as edge weights. We leverage modern parametrizations of BYM models to conclude that the number of people moving between regions better explains variation in COVID-19 case counts than physical proximity data. We suggest that this data should be used in conjunction with physical proximity data when developing spatial models for COVID-19 case counts.

18.
Stoch Environ Res Risk Assess ; 35(4): 797-812, 2021.
Article in English | MEDLINE | ID: covidwho-1148894

ABSTRACT

The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February 2020 to mid January 2021. Using a hierarchical Bayesian framework, we found that the temporal trends of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in the middle of November. However decline and increase of the temporal trend seems to show different patterns in Spain, Italy and Germany.

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