A single holiday was the turning point of the COVID-19 policy of Israel

Klausner, Ziv; Fattal, Eyal; Hirsch, E.; Shapira, Shmuel C.

doi:10.1016/j.ijid.2020.10.016

Cited by 23 publications

(18 citation statements)

References 33 publications

(55 reference statements)

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“…Particularly, it meant that older age groups who would usually be less risk averse in terms of social distancing, etc., would be meeting younger age groups where incidence was highest. Similar experiences have been observed in USA, Israel, China and elsewhere, which demonstrated that festive holidays, which tend to draw individuals from distant places into close contact for prolonged periods, are associated with a spike in COVID-19 incidence [ 32 , 33 , 34 ]. This naturally increases the chance of introduction of new strains, e.g., B.1.1.7 [ 35 ].…”

Section: Discussionsupporting

confidence: 68%

Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland

Madden

Teljeur

et al. 2021

IJERPH

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Like most countries worldwide, the coronavirus disease (COVID-19) has adversely affected Ireland. The aim of this study was to (i) investigate the spatio-temporal trend of COVID-19 incidence; (ii) describe mobility trends as measured by aggregated mobile phone records; and (iii) investigate the association between deprivation index, population density and COVID-19 cases while accounting for spatial and temporal correlation. Standardised incidence ratios of cases were calculated and mapped at a high spatial resolution (electoral division level) over time. Trends in the percentage change in mobility compared to a pre-COVID-19 period were plotted to investigate the impact of lockdown restrictions. We implemented a hierarchical Bayesian spatio-temporal model (Besag, York and Mollié (BYM)), commonly used for disease mapping, to investigate the association between covariates and the number of cases. There have been three distinct “waves” of COVID-19 cases in Ireland to date. Lockdown restrictions led to a substantial reduction in human movement, particularly during the 1st and 3rd wave. Despite adjustment for population density (incidence ratio (IR) = 1.985 (1.915–2.058)) and the average number of persons per room (IR = 10.411 (5.264–22.533)), we found an association between deprivation index and COVID-19 incidence (IR = 1.210 (CI: 1.077–1.357) for the most deprived quintile compared to the least deprived). There is a large range of spatial heterogeneity in COVID-19 cases in Ireland. The methods presented can be used to explore locally intensive surveillance with the possibility of localised lockdown measures to curb the transmission of infection, while keeping other, low-incidence areas open. Our results suggest that prioritising densely populated deprived areas (that are at increased risk of comorbidities) during vaccination rollout may capture people that are at risk of infection and, potentially, also those at increased risk of hospitalisation.

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Section: Discussionsupporting

confidence: 68%

Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland

Madden

Teljeur

et al. 2021

IJERPH

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“…The characteristics of the selected articles are listed in Supplementary Table S3 . Among the 60 estimates of transmission heterogeneity, four (6.7%) from two studies estimated the dispersion parameter ( k ) of SARS [16] , [18] , 11 (18.3%) from eight articles estimated the k for MERS [16] , [17] , [30] , [31] , [32] , [33] , [34] , [35] , and 45 (75.0%) from 17 articles estimated the k for COVID-19 [14] , [15] , [19] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] . Forty estimates (66.7%) were based on transmission pair data (i.e., offspring case number generated by each index case), and four estimates were calculated using epidemic/cluster size data.…”

Section: Resultsmentioning

confidence: 99%

“…Twenty-five estimations (41.7%) reported only k without the other two measures [33] , [41] , [43] , [46] , [49] , [50] . We conducted a secondary analysis to generate an estimated k value for seven articles that reported the ‘20/80’ rule [31] , [32] , [34] , [35] , [40] , [43] , [44] instead of a k value.…”

Section: Resultsmentioning

confidence: 99%

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Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review

Wang

Chen

Guo

et al. 2021

Computational and Structural Biotechnology Journal

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Background Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19) have caused substantial public health burdens and global health threats. Understanding the superspreading potential of a virus is important for characterizing transmission patterns and informing strategic decision-making in disease control. This systematic review aimed to summarize the existing evidence on superspreading features and to compare the heterogeneity in transmission within and among various coronavirus epidemics of SARS, MERS and COVID-19. Methods PubMed, Medline, and Embase databases were extensively searched for original studies on the transmission heterogeneity of SARS, MERS, and COVID-19 published in English between January 1, 2003, and February 10, 2021. After screening the articles, we extracted data pertaining to the estimated dispersion parameter ( k ) which has been a commonly-used measurement for transmission heterogeneity and superspreading potential. Findings We included a total of 60 estimates of transmission heterogeneity from 26 studies on outbreaks in 22 regions. The majority (90%) of the k estimates were small, with values less than 1, indicating an over-dispersed transmission pattern. The point estimates of k for SARS and MERS ranged from 0.12 to 0.20 and from 0.06 to 2.94, respectively. Among 45 estimates of individual-level transmission heterogeneity for COVID-19 from 17 articles, 91% were derived from Asian regions. The point estimates of k for COVID-19 ranged between 0.1 and 5.0. Conclusions We detected a substantial over-dispersed transmission pattern in all three coronaviruses, while the k estimates varied by differences in study design and public health capacity. Our findings suggested that even with a reduced R value, the epidemic still has a high resurgence potential due to transmission heterogeneity.

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“…Our model allows to simulate the impact of these measures and make comparison with the evolution without intervention. We hope authorities will use this tool, in addition to established venues [ 8 , 61 ], to simulate different lockdown policies for choosing the best exit strategy [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases

Tsori

Granek

2021

PLoS ONE

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We suggest a novel mathematical framework for the in-homogeneous spatial spreading of an infectious disease in human population, with particular attention to COVID-19. Common epidemiological models, e.g., the well-known susceptible-exposed-infectious-recovered (SEIR) model, implicitly assume uniform (random) encounters between the infectious and susceptible sub-populations, resulting in homogeneous spatial distributions. However, in human population, especially under different levels of mobility restrictions, this assumption is likely to fail. Splitting the geographic region under study into areal nodes, and assuming infection kinetics within nodes and between nearest-neighbor nodes, we arrive into a continuous, “reaction-diffusion”, spatial model. To account for COVID-19, the model includes five different sub-populations, in which the infectious sub-population is split into pre-symptomatic and symptomatic. Our model accounts for the spreading evolution of infectious population domains from initial epicenters, leading to different regimes of sub-exponential (e.g., power-law) growth. Importantly, we also account for the variable geographic density of the population, that can strongly enhance or suppress infection spreading. For instance, we show how weakly infected regions surrounding a densely populated area can cause rapid migration of the infection towards the populated area. Predicted infection “heat-maps” show remarkable similarity to publicly available heat-maps, e.g., from South Carolina. We further demonstrate how localized lockdown/quarantine conditions can slow down the spreading of disease from epicenters. Application of our model in different countries can provide a useful predictive tool for the authorities, in particular, for planning strong lockdown measures in localized areas—such as those underway in a few countries.

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A single holiday was the turning point of the COVID-19 policy of Israel

Cited by 23 publications

References 33 publications

Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland

Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland

Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review

Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases

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