1
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Zhang L, Li MY, Zhi C, Zhu M, Ma H. Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention. Curr Med Sci 2024; 44:273-280. [PMID: 38632143 DOI: 10.1007/s11596-024-2850-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/19/2024] [Indexed: 04/19/2024]
Abstract
The global incidence of infectious diseases has increased in recent years, posing a significant threat to human health. Hospitals typically serve as frontline institutions for detecting infectious diseases. However, accurately identifying warning signals of infectious diseases in a timely manner, especially emerging infectious diseases, can be challenging. Consequently, there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals. This paper examines the role of medical data in the early identification of infectious diseases, explores early warning technologies for infectious disease recognition, and assesses monitoring and early warning mechanisms for infectious diseases. We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems, in compliance with national strategies to integrate clinical treatment and disease prevention. Furthermore, hospitals should establish institution-specific, clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.
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Affiliation(s)
- Lei Zhang
- Sixth Medical Center, Chinese PLA General Hospital, Beijing, 100048, China
| | - Min-Ye Li
- The Nursing Department, Chinese PLA General Hospital, Beijing, 100853, China
| | - Chen Zhi
- The Nursing Department, Chinese PLA General Hospital, Beijing, 100853, China
| | - Min Zhu
- Sixth Medical Center, Chinese PLA General Hospital, Beijing, 100048, China
| | - Hui Ma
- The Nursing Department, Chinese PLA General Hospital, Beijing, 100853, China.
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Vandelli V, Palandri L, Coratza P, Rizzi C, Ghinoi A, Righi E, Soldati M. Conditioning factors in the spreading of Covid-19 - Does geography matter? Heliyon 2024; 10:e25810. [PMID: 38356610 PMCID: PMC10865316 DOI: 10.1016/j.heliyon.2024.e25810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 01/23/2024] [Accepted: 02/02/2024] [Indexed: 02/16/2024] Open
Abstract
There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.
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Affiliation(s)
- Vittoria Vandelli
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Lucia Palandri
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Paola Coratza
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Cristiana Rizzi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Alessandro Ghinoi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Elena Righi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Mauro Soldati
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy
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Robert A, Chapman LAC, Grah R, Niehus R, Sandmann F, Prasse B, Funk S, Kucharski AJ. Predicting subnational incidence of COVID-19 cases and deaths in EU countries. BMC Infect Dis 2024; 24:204. [PMID: 38355414 DOI: 10.1186/s12879-024-08986-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Recurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models. METHODS We used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios. RESULTS At a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics. DISCUSSION Our model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.
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Affiliation(s)
- Alexis Robert
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Lloyd A C Chapman
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
| | - Rok Grah
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Rene Niehus
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Frank Sandmann
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
- Current address: Robert Koch Institute, Berlin, Germany
| | - Bastian Prasse
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Mikolai J, Dorey P, Keenan K, Kulu H. Spatial patterns of COVID-19 and non-COVID-19 mortality across waves of infection in England, Wales, and Scotland. Soc Sci Med 2023; 338:116330. [PMID: 37907058 DOI: 10.1016/j.socscimed.2023.116330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 09/12/2023] [Accepted: 10/10/2023] [Indexed: 11/02/2023]
Abstract
Recent studies have established the key individual-level risk factors of COVID-19 mortality such as age, gender, ethnicity, and socio-economic status. However, the spread of infectious diseases is a spatial and temporal process implying that COVID-19 mortality and its determinants may vary sub-nationally and over time. We investigate the spatial patterns of age-standardised death rates due to COVID-19 and their correlates across local authority districts in England, Wales, and Scotland across three waves of infection. Using a Spatial Durbin model, we explore within- and between-country variation and account for spatial dependency. Areas with a higher share of ethnic minorities and higher levels of deprivation had higher rates of COVID-19 mortality. However, the share of ethnic minorities and population density in an area were more important predictors of COVID-19 mortality in earlier waves of the pandemic than in later waves, whereas area-level deprivation has become a more important predictor over time. Second, during the first wave of the pandemic, population density had a significant spillover effect on COVID-19 mortality, indicating that the pandemic spread from big cities to neighbouring areas. Third, after accounting for differences in ethnic composition, deprivation, and population density, initial cross-country differences in COVID-19 mortality almost disappeared. COVID-19 mortality remained higher in Scotland than in England and Wales in the third wave when COVID-19 mortality was relatively low in all three countries. Interpreting these results in the context of higher overall (long-term) non-COVID-19 mortality in Scotland suggests that Scotland may have performed better than expected during the first two waves. Our study highlights that accounting for both spatial and temporal factors is essential for understanding social and demographic risk factors of mortality during pandemics.
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Affiliation(s)
- Júlia Mikolai
- ESRC Centre for Population Change, United Kingdom; University of St Andrews, United Kingdom.
| | | | - Katherine Keenan
- ESRC Centre for Population Change, United Kingdom; University of St Andrews, United Kingdom
| | - Hill Kulu
- ESRC Centre for Population Change, United Kingdom; University of St Andrews, United Kingdom
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Natalia YA, Faes C, Neyens T, Hammami N, Molenberghs G. Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium. Front Public Health 2023; 11:1249141. [PMID: 38026374 PMCID: PMC10654974 DOI: 10.3389/fpubh.2023.1249141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension. Methods We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January - 31 December 2021 using canonical correlation analysis. Results Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition. Conclusion It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.
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Affiliation(s)
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Thomas Neyens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- I-BioStat, Leuven Biostatistics and Statistical Bioinformatics Centre, KU Leuven, Leuven, Belgium
| | - Naïma Hammami
- Department of Care, Team Infection Prevention and Vaccination, Brussels, Belgium
| | - Geert Molenberghs
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- I-BioStat, Leuven Biostatistics and Statistical Bioinformatics Centre, KU Leuven, Leuven, Belgium
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6
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Wood AJ, Sanchez AR, Bessell PR, Wightman R, Kao RR. Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data. PLoS Comput Biol 2023; 19:e1011611. [PMID: 38011282 PMCID: PMC10703279 DOI: 10.1371/journal.pcbi.1011611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 12/07/2023] [Accepted: 10/17/2023] [Indexed: 11/29/2023] Open
Abstract
For the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data from earlier periods are invaluable for studying risk factors for infection in detail. Observed changes in time during these periods may then inform how stable the pattern will be in the long term. To this end we analyse the distribution of cases of COVID-19 across Scotland in 2021, where the location (census areas of order 500-1,000 residents) and reporting date of cases are known. We consider over 450,000 individually recorded cases, in two infection waves triggered by different lineages: B.1.1.529 ("Omicron") and B.1.617.2 ("Delta"). We use random forests, informed by measures of geography, demography, testing and vaccination. We show that the distributions are only adequately explained when considering multiple explanatory variables, implying that case heterogeneity arose from a combination of individual behaviour, immunity, and testing frequency. Despite differences in virus lineage, time of year, and interventions in place, we find the risk factors remained broadly consistent between the two waves. Many of the observed smaller differences could be reasonably explained by changes in control measures.
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Affiliation(s)
- Anthony J. Wood
- Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Aeron R. Sanchez
- Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Paul R. Bessell
- Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Rebecca Wightman
- Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Rowland R. Kao
- Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
- Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, United Kingdom
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7
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Lawson AB. Evaluation of predictive capability of Bayesian spatio-temporal models for Covid-19 spread. BMC Med Res Methodol 2023; 23:182. [PMID: 37568119 PMCID: PMC10422743 DOI: 10.1186/s12874-023-01997-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 07/20/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Bayesian models have been applied throughout the Covid-19 pandemic especially to model time series of case counts or deaths. Fewer examples exist of spatio-temporal modeling, even though the spatial spread of disease is a crucial factor in public health monitoring. The predictive capabilities of infectious disease models is also important. METHODS In this study, the ability of Bayesian hierarchical models to recover different parts of the variation in disease counts is the focus. It is clear that different measures provide different views of behavior when models are fitted prospectively. Over a series of time horizons one step predictions have been generated and compared for different models (for case counts and death counts). These Bayesian SIR models were fitted using MCMC at 28 time horizons to mimic prospective prediction. A range of goodness of prediction measures were analyzed across the different time horizons. RESULTS A particularly important result is that the peak intensity of case load is often under-estimated, while random spikes in case load can be mimicked using time dependent random effects. It is also clear that during the early wave of the pandemic simpler model forms are favored, but subsequently lagged spatial dependence models for cases are favored, even if the sophisticated models perform better overall. DISCUSSION The models fitted mimic the situation where at a given time the history of the process is known but the future must be predicted based on the current evolution which has been observed. Using an overall 'best' model for prediction based on retrospective fitting of the complete pandemic waves is an assumption. However it is also clear that this case count model is well favored over other forms. During the first wave a simpler time series model predicts case counts better for counties than a spatially dependent one. The picture is more varied for morality. CONCLUSIONS From a predictive point of view it is clear that spatio-temporal models applied to county level Covid-19 data within the US vary in how well they fit over time and also how well they predict future events. At different times, SIR case count models and also mortality models with cumulative counts perform better in terms of prediction. A fundamental result is that predictive capability of models varies over time and using the same model could lead to poor predictive performance. In addition it is clear that models addressing the spatial context for case counts (i.e. with lagged neighborhood terms) and cumulative case counts for mortality data are clearly better at modeling spatio-temporal data which is commonly available for the Covid-19 pandemic in different areas of the globe.
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Affiliation(s)
- Andrew B Lawson
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon Street, Charleston, 29425, USA.
- School of Medicine, Usher Institute, University of Edinburgh, Edinburgh, UK.
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8
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Dong Q, Kline D, Hepler SA. A Bayesian Spatio-temporal Model to Optimize Allocation of Buprenorphine in North Carolina. STATISTICS AND PUBLIC POLICY (PHILADELPHIA, PA.) 2023; 10:2218448. [PMID: 37545670 PMCID: PMC10398789 DOI: 10.1080/2330443x.2023.2218448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 05/17/2023] [Accepted: 05/22/2023] [Indexed: 08/08/2023]
Abstract
The opioid epidemic is an ongoing public health crisis. In North Carolina, overdose deaths due to illicit opioid overdose have sharply increased over the last 5-7 years. Buprenorphine is a U.S. Food and Drug Administration approved medication for treatment of opioid use disorder and is obtained by prescription. Prior to January 2023, providers had to obtain a waiver and were limited in the number of patients that they could prescribe buprenorphine. Thus, identifying counties where increasing buprenorphine would yield the greatest overall reduction in overdose death can help policymakers target certain geographical regions to inform an effective public health response. We propose a Bayesian spatiotemporal model that relates yearly, county-level changes in illicit opioid overdose death rates to changes in buprenorphine prescriptions. We use our model to forecast the statewide count and rate of illicit opioid overdose deaths in future years, and we use nonlinear constrained optimization to identify the optimal buprenorphine increase in each county under a set of constraints on available resources. Our model estimates a negative relationship between death rate and increasing buprenorphine after accounting for other covariates, and our identified optimal single-year allocation strategy is estimated to reduce opioid overdose deaths by over 5.
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Affiliation(s)
- Qianyu Dong
- Department of Statistical Sciences, Wake Forest University
| | - David Kline
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine
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9
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Rollier M, Miranda GHB, Vergeynst J, Meys J, Alleman T, Baetens JM. Mobility and the spatial spread of sars-cov-2 in Belgium. Math Biosci 2023; 360:108957. [PMID: 36804448 PMCID: PMC9934928 DOI: 10.1016/j.mbs.2022.108957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 11/10/2022] [Accepted: 12/19/2022] [Indexed: 02/18/2023]
Abstract
We analyse and mutually compare time series of covid-19 -related data and mobility data across Belgium's 43 arrondissements (NUTS 3). In this way, we reach three conclusions. First, we could detect a decrease in mobility during high-incidence stages of the pandemic. This is expressed as a sizeable change in the average amount of time spent outside one's home arrondissement, investigated over five distinct periods, and in more detail using an inter-arrondissement "connectivity index" (CI). Second, we analyse spatio-temporal covid-19-related hospitalisation time series, after smoothing them using a generalise additive mixed model (GAMM). We confirm that some arrondissements are ahead of others and morphologically dissimilar to others, in terms of epidemiological progression. The tools used to quantify this are time-lagged cross-correlation (TLCC) and dynamic time warping (DTW), respectively. Third, we demonstrate that an arrondissement's CI with one of the three identified first-outbreak arrondissements is correlated to a substantial local excess mortality some five to six weeks after the first outbreak. More generally, we couple results leading to the first and second conclusion, in order to demonstrate an overall correlation between CI values on the one hand, and TLCC and DTW values on the other. We conclude that there is a strong correlation between physical movement of people and viral spread in the early stage of the sars-cov-2 epidemic in Belgium, though its strength weakens as the virus spreads.
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Affiliation(s)
- Michiel Rollier
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
| | - Gisele H B Miranda
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium; Division of Computational Science and Technology, KTH Royal Institute of Technology, Tomtebodavägen 23A, Solna, 17165, Sweden
| | - Jenna Vergeynst
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium; BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Joris Meys
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Tijs Alleman
- BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Jan M Baetens
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
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10
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Muegge R, Dean N, Jack E, Lee D. National lockdowns in England: The same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically? Spat Spatiotemporal Epidemiol 2023; 44:100559. [PMID: 36707192 PMCID: PMC9719849 DOI: 10.1016/j.sste.2022.100559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/22/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Quantifying the impact of lockdowns on COVID-19 mortality risks is an important priority in the public health fight against the virus, but almost all of the existing research has only conducted macro country-wide assessments or limited multi-country comparisons. In contrast, the extent of within-country variation in the impacts of a nation-wide lockdown is yet to be thoroughly investigated, which is the gap in the knowledge base that this paper fills. Our study focuses on England, which was subject to 3 national lockdowns between March 2020 and March 2021. We model weekly COVID-19 mortality counts for the 312 Local Authority Districts in mainland England, and our aim is to understand the impact that lockdowns had at both a national and a regional level. Specifically, we aim to quantify how long after the implementation of a lockdown do mortality risks reduce at a national level, the extent to which these impacts vary regionally within a country, and which parts of England exhibit similar impacts. As the spatially aggregated weekly COVID-19 mortality counts are small in size we estimate the spatio-temporal trends in mortality risks with a Poisson log-linear smoothing model that borrows strength in the estimation between neighbouring data points. Inference is based in a Bayesian paradigm, using Markov chain Monte Carlo simulation. Our main findings are that mortality risks typically begin to reduce between 3 and 4 weeks after lockdown, and that there appears to be an urban-rural divide in lockdown impacts.
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Affiliation(s)
- Robin Muegge
- School of Mathematics and Statistics, University of Glasgow, United Kingdom.
| | - Nema Dean
- School of Mathematics and Statistics, University of Glasgow, United Kingdom.
| | - Eilidh Jack
- School of Mathematics and Statistics, University of Glasgow, United Kingdom.
| | - Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, United Kingdom.
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11
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Reis RF, Congdon P. Editorial: Epidemiological considerations in COVID-19 forecasting. FRONTIERS IN EPIDEMIOLOGY 2023; 2:1119559. [PMID: 38455284 PMCID: PMC10910939 DOI: 10.3389/fepid.2022.1119559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 12/20/2022] [Indexed: 03/09/2024]
Affiliation(s)
- Ruy Freitas Reis
- Instituto de Ciências Exatas, Departamento de Ciência da Computação, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
- Pós-Graduação em Modelagem Computacional, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | - Peter Congdon
- Queen Mary University of London, London, United Kingdom
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12
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Botz J, Wang D, Lambert N, Wagner N, Génin M, Thommes E, Madan S, Coudeville L, Fröhlich H. Modeling approaches for early warning and monitoring of pandemic situations as well as decision support. Front Public Health 2022; 10:994949. [PMID: 36452960 PMCID: PMC9702983 DOI: 10.3389/fpubh.2022.994949] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/21/2022] [Indexed: 11/15/2022] Open
Abstract
The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.
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Affiliation(s)
- Jonas Botz
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Danqi Wang
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | | | | | | | | | - Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Department of Computer Science, University of Bonn, Bonn, Germany
| | | | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
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13
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Johnson DP, Lulla V. Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network. Front Public Health 2022; 10:876691. [PMID: 36388264 PMCID: PMC9650227 DOI: 10.3389/fpubh.2022.876691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 10/10/2022] [Indexed: 01/21/2023] Open
Abstract
As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability-including environmental determinants of COVID-19 infection-into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for "what-if" analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications.
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Affiliation(s)
- Daniel P. Johnson
- Department of Geography, Indiana University – Purdue University at Indianapolis, Indianapolis, IN, United States,*Correspondence: Daniel P. Johnson
| | - Vijay Lulla
- Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States
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14
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Momenyan S, Torabi M. Modeling the spatio‑temporal spread of COVID‑19 cases, recoveries and deaths and effects of partial and full vaccination coverage in Canada. Sci Rep 2022; 12:17817. [PMID: 36280746 PMCID: PMC9589715 DOI: 10.1038/s41598-022-21369-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 09/27/2022] [Indexed: 01/19/2023] Open
Abstract
The purposes of our study are to map high-risk areas in Canada as well as quantifying the effects of vaccination intervention and socio-demographic factors on the transmission rates of infection, recovery, and death related to COVID-19. The data of this research included weekly number of COVID‑19 cases, recovered, and dead individuals from 2020 through 2021 in Canada at health region and provincial levels. These data were associated with cumulative rates of partial and full vaccination and socio-demographic factors. We applied the spatio-temporal Susceptible-Exposed-Infected-Removed (SEIR), and Susceptible-Exposed-Infected-Removed-Vaccinated (SEIRV) models. The results indicated the partial vaccination rate has a greater effect compared with full vaccination rate on decreasing the rate of infectious cases (risk ratio (RR) = 0.18; 95%CrI: 0.16-0.2; RR = 0.60; 95%CrI: 0.55-0.65, respectively) and increasing the rate of recovered cases (RR = 1.39; 95%CrI: 1.28-1.51; RR = 1.21; 95%CrI: 1.23-1.29, respectively). However, for mortality risk reduction, only increasing full vaccination rate was significantly associated (RR = 0.09; 95%CrI: 0.05-0.14). In addition, our results showed that regions with higher rates of elderly and aboriginal individuals, higher population density, and lower socioeconomic status (SES) contribute more to the risk of infection transmission. Rates of elderly and aboriginal individuals and SES of regions were significantly associated with recovery rate. However, elderly individuals rate of regions was only a significant predictor of mortality risk. Based on the results, protection against mild and severe COVID-19 infection after the primary vaccination series decreased.
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Affiliation(s)
- Somayeh Momenyan
- grid.21613.370000 0004 1936 9609Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0W3 Canada
| | - Mahmoud Torabi
- grid.21613.370000 0004 1936 9609Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0W3 Canada ,grid.21613.370000 0004 1936 9609Department of Statistics, Faculty of Science, University of Manitoba, Winnipeg, Canada
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15
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Asif Z, Chen Z, Stranges S, Zhao X, Sadiq R, Olea-Popelka F, Peng C, Haghighat F, Yu T. Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review. SUSTAINABLE CITIES AND SOCIETY 2022; 81:103840. [PMID: 35317188 PMCID: PMC8925199 DOI: 10.1016/j.scs.2022.103840] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/10/2022] [Accepted: 03/12/2022] [Indexed: 05/05/2023]
Abstract
COVID-19 is deemed as the most critical world health calamity of the 21st century, leading to dramatic life loss. There is a pressing need to understand the multi-stage dynamics, including transmission routes of the virus and environmental conditions due to the possibility of multiple waves of COVID-19 in the future. In this paper, a systematic examination of the literature is conducted associating the virus-laden-aerosol and transmission of these microparticles into the multimedia environment, including built environments. Particularly, this paper provides a critical review of state-of-the-art modelling tools apt for COVID-19 spread and transmission pathways. GIS-based, risk-based, and artificial intelligence-based tools are discussed for their application in the surveillance and forecasting of COVID-19. Primary environmental factors that act as simulators for the spread of the virus include meteorological variation, low air quality, pollen abundance, and spatial-temporal variation. However, the influence of these environmental factors on COVID-19 spread is still equivocal because of other non-pharmaceutical factors. The limitations of different modelling methods suggest the need for a multidisciplinary approach, including the 'One-Health' concept. Extended One-Health-based decision tools would assist policymakers in making informed decisions such as social gatherings, indoor environment improvement, and COVID-19 risk mitigation by adapting the control measurements.
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Affiliation(s)
- Zunaira Asif
- Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada
| | - Zhi Chen
- Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada
| | - Saverio Stranges
- Department of Epidemiology and Biostatistics, Western University, Ontario, Canada
- Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Xin Zhao
- Department of Animal Science, McGill University, Montreal, Canada
| | - Rehan Sadiq
- School of Engineering (Okanagan Campus), University of British Columbia, Kelowna, BC, Canada
| | | | - Changhui Peng
- Department of Biological Sciences, University of Quebec in Montreal, Canada
| | - Fariborz Haghighat
- Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada
| | - Tong Yu
- Department of Civil and Environmental Engineering, University of Alberta, Canada
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16
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Vitale V, D'Urso P, De Giovanni L. Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data. SPATIAL STATISTICS 2022; 49:100529. [PMID: 34277332 PMCID: PMC8277433 DOI: 10.1016/j.spasta.2021.100529] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/30/2021] [Accepted: 07/05/2021] [Indexed: 05/04/2023]
Abstract
The spatial epidemic dynamics of COVID-19 outbreak in Italy were modelled by means of an Object-Oriented Bayesian Network in order to explore the dependence relationships, in a static and a dynamic way, among the weekly incidence rate, the intensive care units occupancy rate and that of deaths. Following an autoregressive approach, both spatial and time components have been embedded in the model by means of spatial and time lagged variables. The model could be a valid instrument to support or validate policy makers' decisions strategies.
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Affiliation(s)
- Vincenzina Vitale
- Department of Social and Economic Sciences, Sapienza University of Rome, P.za Aldo Moro, 5, 00185 Rome, Italy
| | - Pierpaolo D'Urso
- Department of Social and Economic Sciences, Sapienza University of Rome, P.za Aldo Moro, 5, 00185 Rome, Italy
| | - Livia De Giovanni
- Department of Political Sciences, LUISS University, Viale Romania, 32, 00197 Rome, Italy
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17
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Abstract
Coronavirus Disease 2019 (COVID-19) continues to spread throughout the world, and it is necessary for us to implement effective methods to prevent and control the spread of the epidemic. In this paper, we propose a new model called Spatial–Temporal Attention Graph Convolutional Networks (STAGCN) that can analyze the long-term trend of the COVID-19 epidemic with high accuracy. The STAGCN employs a spatial graph attention network layer and a temporal gated attention convolutional network layer to capture the spatial and temporal features of infectious disease data, respectively. While the new model inherits the symmetric “space-time space” structure of Spatial–Temporal Graph Convolutional Networks (STGCN), it enhances its ability to identify infectious diseases using spatial–temporal correlation features by replacing the graph convolutional network layer with a graph attention network layer that can pay more attention to important features based on adaptively adjusted feature weights at different time points. The experimental results show that our model has the lowest error rate compared to other models. The paper also analyzes the prediction results of the model using interpretable analysis methods to provide a more reliable guide for the decision-making process during epidemic prevention and control.
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18
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Congdon P. A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates. JOURNAL OF GEOGRAPHICAL SYSTEMS 2022; 24:583-610. [PMID: 35496370 PMCID: PMC9039004 DOI: 10.1007/s10109-021-00366-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/07/2021] [Indexed: 06/14/2023]
Abstract
The COVID-19 epidemic has raised major issues with regard to modelling and forecasting outcomes such as cases, deaths and hospitalisations. In particular, the forecasting of area-specific counts of infectious disease poses problems when counts are changing rapidly and there are infection hotspots, as in epidemic situations. Such forecasts are of central importance for prioritizing interventions or making severity designations for different areas. In this paper, we consider different specifications of autoregressive dependence in incidence counts as these may considerably impact on adaptivity in epidemic situations. In particular, we introduce parameters to allow temporal adaptivity in autoregressive dependence. A case study considers COVID-19 data for 144 English local authorities during the UK epidemic second wave in late 2020 and early 2021, which demonstrate geographical clustering in new cases-linked to the then emergent alpha variant. The model allows for both spatial and time variation in autoregressive effects. We assess sensitivity in short-term predictions and fit to specification (spatial vs space-time autoregression, linear vs log-linear, and form of space decay), and show improved one-step ahead and in-sample prediction using space-time autoregression including temporal adaptivity.
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Affiliation(s)
- Peter Congdon
- School of Geography, Queen Mary University of London, Mile End Rd, London, E1 4NS UK
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19
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Nightingale ES, Abbott S, Russell TW, Lowe R, Medley GF, Brady OJ. The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices. BMC Public Health 2022; 22:716. [PMID: 35410184 PMCID: PMC8996221 DOI: 10.1186/s12889-022-13069-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/14/2022] [Indexed: 11/18/2022] Open
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. METHODS We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 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. RESULTS A 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%. CONCLUSIONS Limitations 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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Affiliation(s)
- Emily S Nightingale
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK.
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Barcelona Supercomputing Centre (BSC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Graham F Medley
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
| | - Oliver J Brady
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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20
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Analysis on the characteristics of spatio-temporal evolution and aggregation trend of early COVID-19 in mainland China. Sci Rep 2022; 12:4380. [PMID: 35288642 PMCID: PMC8919916 DOI: 10.1038/s41598-022-08403-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 02/21/2022] [Indexed: 01/08/2023] Open
Abstract
To analyze the spatio-temporal aggregation of COVID-19 in mainland China within 20 days after the closure of Wuhan city, and provide a theoretical basis for formulating scientific prevention measures in similar major public health events in the future. Draw a distribution map of the cumulative number of COVID-19 by inverse distance weighted interpolation; analyze the spatio-temporal characteristics of the daily number of COVID-19 in mainland China by spatio-temporal autocorrelation analysis; use the spatio-temporal scanning statistics to detect the spatio-temporal clustering area of the daily number of new diagnosed cases. The cumulative number of diagnosed cases obeyed the characteristics of geographical proximity and network proximity to Hubei. Hubei and its neighboring provinces were most affected, and the impact in the eastern China was more dramatic than the impact in the western; the global spatio-temporal Moran’s I index showed an overall downward trend. Since the 10th day of the closure of Wuhan, the epidemic in China had been under effective control, and more provinces had shifted into low-incidence areas. The number of new diagnosed cases had gradually decreased, showing a random distribution in time and space (P< 0.1), and no clusters were formed. Conclusion: the spread of COVID-19 had obvious spatial-temporal aggregation. China’s experience shows that isolation city strategy can greatly contain the spread of the COVID-19 epidemic.
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21
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Al-Shaery AM, Hejase B, Tridane A, Farooqi NS, Al Jassmi H. Evaluating COVID-19 control measures in mass gathering events with vaccine inequalities. Sci Rep 2022; 12:3652. [PMID: 35256669 PMCID: PMC8901904 DOI: 10.1038/s41598-022-07609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 02/16/2022] [Indexed: 11/11/2022] Open
Abstract
With the increasing global adoption of COVID-19 vaccines, limitations on mass gathering events have started to gradually loosen. However, the large vaccine inequality recorded among different countries is an important aspect that policymakers must address when implementing control measures for such events. In this paper, we propose a model for the assessment of different control measures with the consideration of vaccine inequality in the population. Two control measures are considered: selecting participants based on vaccine efficacy and restricting the event capacity. We build the model using agent-based modeling to capture the spatiotemporal crowd dynamics and utilize a genetic algorithm to assess the control strategies. This assessment is based on factors that are important for policymakers such as disease prevalence, vaccine diversity, and event capacity. A quantitative evaluation of vaccine diversity using the Simpson's Diversity Index is also provided. The Hajj ritual is used as a case study. We show that strategies that prioritized lowering the prevalence resulted in low event capacity but facilitated vaccine diversity. Moreover, strategies that prioritized diversity resulted in high infection rates. However, increasing the prioritization of participants with high vaccine efficacy significantly decreased the disease prevalence. Strategies that prioritized ritual capacity did not show clear trends.
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Affiliation(s)
- Ali M Al-Shaery
- Department of Civil Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Bilal Hejase
- Department of Electrical Engineering, Ohio State University, Columbus, OH, 43210, USA
| | - Abdessamad Tridane
- Mathematical Sciences Department, College of Science, United Arab Emirates University, Al Ain, UAE.
| | - Norah S Farooqi
- College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Hamad Al Jassmi
- Emirates Center for Mobility Research, United Arab Emirates University, Al Ain, UAE
- Department of Civil and Environmental Engineering, College of Engineering, United Arab Emirates University, Al Ain, UAE
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22
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The Geographical Distribution and Influencing Factors of COVID-19 in China. Trop Med Infect Dis 2022; 7:tropicalmed7030045. [PMID: 35324592 PMCID: PMC8949350 DOI: 10.3390/tropicalmed7030045] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 02/20/2022] [Accepted: 03/03/2022] [Indexed: 12/10/2022] Open
Abstract
The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person’s perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.
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23
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Dong T, Benedetto U, Sinha S, Fudulu D, Dimagli A, Chan J, Caputo M, Angelini G. Deep recurrent reinforced learning model to compare the efficacy of targeted local versus national measures on the spread of COVID-19 in the UK. BMJ Open 2022; 12:e048279. [PMID: 35190408 PMCID: PMC8861888 DOI: 10.1136/bmjopen-2020-048279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVES To prevent the emergence of new waves of COVID-19 caseload and associated mortalities, it is imperative to understand better the efficacy of various control measures on the national and local development of this pandemic in space-time, characterise hotspot regions of high risk, quantify the impact of under-reported measures such as international travel and project the likely effect of control measures in the coming weeks. METHODS We applied a deep recurrent reinforced learning based model to evaluate and predict the spatiotemporal effect of a combination of control measures on COVID-19 cases and mortality at the local authority (LA) and national scale in England, using data from week 5 to 46 of 2020, including an expert curated control measure matrix, official statistics/government data and a secure web dashboard to vary magnitude of control measures. RESULTS Model predictions of the number of cases and mortality of COVID-19 in the upcoming 5 weeks closely matched the actual values (cases: root mean squared error (RMSE): 700.88, mean absolute error (MAE): 453.05, mean absolute percentage error (MAPE): 0.46, correlation coefficient 0.42; mortality: RMSE 14.91, MAE 10.05, MAPE 0.39, correlation coefficient 0.68). Local lockdown with social distancing (LD_SD) (overall rank 3) was found to be ineffective in preventing outbreak rebound following lockdown easing compared with national lockdown (overall rank 2), based on prediction using simulated control measures. The ranking of the effectiveness of adjunctive measures for LD_SD were found to be consistent across hotspot and non-hotspot regions. Adjunctive measures found to be most effective were international travel and quarantine restrictions. CONCLUSIONS This study highlights the importance of using adjunctive measures in addition to LD_SD following lockdown easing and suggests the potential importance of controlling international travel and applying travel quarantines. Further work is required to assess the effect of variant strains and vaccination measures.
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Affiliation(s)
- Tim Dong
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Umberto Benedetto
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Shubhra Sinha
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Daniel Fudulu
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Arnaldo Dimagli
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jeremy Chan
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Massimo Caputo
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gianni Angelini
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
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24
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Lym Y, Kim KJ. Exploring the effects of PM 2.5 and temperature on COVID-19 transmission in Seoul, South Korea. ENVIRONMENTAL RESEARCH 2022; 203:111810. [PMID: 34343550 PMCID: PMC8324501 DOI: 10.1016/j.envres.2021.111810] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 07/19/2021] [Accepted: 07/28/2021] [Indexed: 05/11/2023]
Abstract
With a recent surge of the new severe acute respiratory syndrome-coronavirus 2 (SARS-Cov-2, COVID-19) in South Korea, this study attempts to investigate the effects of environmental conditions such as air pollutants (PM2.5) and meteorological covariate (Temperature) on COVID-19 transmission in Seoul. To account for unobserved heterogeneity in the daily confirmed cases of COVID-19 across 25 contiguous districts within Seoul, we adopt a full Bayesian hierarchical approach for the generalized linear mixed models. A formal statistical analysis suggests that there exists a positive correlation between a 7-day lagged effect of PM2.5 concentration and the number of confirmed COVID-19 cases, which implies an elevated risk of the infectious disease. Conversely, temperature has shown a negative correlation with the number of COVID-19 cases, leading to reduction in relative risks. In addition, we clarify that the random fluctuation in the relative risks of COVID-19 mainly originates from temporal aspects, whereas no significant evidence of variability in relative risks is observed in terms of spatial alignment of the 25 districts. Nevertheless, this study provides empirical evidence using model-based formal assessments regarding COVID-19 infection risks in 25 districts of Seoul from a different perspective.
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Affiliation(s)
- Youngbin Lym
- Center for Innovation Strategy and Policy, KAIST, South Korea
| | - Ki-Jung Kim
- Department of Smart Car Engineering, Doowon Technical University, South Korea.
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25
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Nikparvar B, Rahman MM, Hatami F, Thill JC. Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network. Sci Rep 2021; 11:21715. [PMID: 34741093 PMCID: PMC8571358 DOI: 10.1038/s41598-021-01119-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/20/2021] [Indexed: 12/13/2022] Open
Abstract
Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.
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Affiliation(s)
- Behnam Nikparvar
- The William States Lee College of Engineering, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Md Mokhlesur Rahman
- The William States Lee College of Engineering, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
| | - Faizeh Hatami
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Jean-Claude Thill
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
- School of Data Science, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
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26
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Épidémiologie de la COVID-19, focus sur le pôle de gériatrie des hôpitaux universitaires de Strasbourg. NPG NEUROLOGIE - PSYCHIATRIE - GÉRIATRIE 2021. [PMCID: PMC8188508 DOI: 10.1016/j.npg.2021.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Dans cet article, les auteurs proposent une synthèse concernant l’épidémiologie de la COVID-19, maladie responsable d’une pandémie mondiale depuis son émergence en décembre 2019. L’objectif est d’évoquer les personnes âgées, particulièrement impactées, étant donné que l’âge supérieur à 65 ans et les comorbidités sont des facteurs de risque de formes graves. En France, 90 % des décès ont touché la population âgée. Il s’agit également de faire un focus sur la situation du pôle de gériatrie des hôpitaux universitaires de Strasbourg où, lors de la première vague épidémique, la mortalité a été de 35 % en gériatrie aiguë et de 25 % en USLD. Après la survenue de 100 000 décès en France, l’espoir est grand de contrôler l’épidémie grâce à la stratégie vaccinale.
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Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis. Processes (Basel) 2021. [DOI: 10.3390/pr9081267] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources.
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