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2.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-338399

ABSTRACT

Background: New variants of SARS-CoV-2 are constantly discovered. Administration of COVID-19 vaccines and booster doses, combined with applications of non-pharmaceutical interventions (NPIs), is often used to prevent outbreaks of emerging variants. Such outbreak dynamics are further complicated by the population's behavior and demographic composition. Hence, realistic simulations are needed to estimate the efficiency of proposed vaccination strategies in conjunction with NPIs. Methods: We developed an individual-based model of COVID-19 dynamics that considers age-dependent parameters such as contact matrices, probabilities of symptomatic and severe disease, and households' age distribution. As a case study, we simulate outbreak dynamics under the demographic compositions of two Israeli cities with different household sizes and age distributions. We compare two vaccination strategies: vaccinate individuals in a currently prioritized age group, or dynamically prioritize neighborhoods with a high estimated reproductive number. Total infections and hospitalizations are used to compare the efficiency of the vaccination strategies under the two demographic structures, in conjunction with different NPIs. Results: We demonstrate the effectiveness of vaccination strategies targeting highly infected localities and of NPIs actively detecting asymptomatic infections. We further show that there are different optimal vaccination strategies for each demographic composition of sub-populations, and that their application is superior to a uniformly applied strategy. Conclusion: Our study emphasizes the importance of tailoring vaccination strategies to subpopulations' infection rates and to the unique characteristics of their demographics (e.g., household size and age distributions). The presented simulation framework and our findings can help better design future responses against the following emerging variants.

3.
JCI Insight ; 7(13)2022 07 08.
Article in English | MEDLINE | ID: covidwho-1861743

ABSTRACT

The role of immune responses to previously seen endemic coronavirus epitopes in severe acute respiratory coronavirus 2 (SARS-CoV-2) infection and disease progression has not yet been determined. Here, we show that a key characteristic of fatal outcomes with coronavirus disease 2019 (COVID-19) is that the immune response to the SARS-CoV-2 spike protein is enriched for antibodies directed against epitopes shared with endemic beta-coronaviruses and has a lower proportion of antibodies targeting the more protective variable regions of the spike. The magnitude of antibody responses to the SARS-CoV-2 full-length spike protein, its domains and subunits, and the SARS-CoV-2 nucleocapsid also correlated strongly with responses to the endemic beta-coronavirus spike proteins in individuals admitted to an intensive care unit (ICU) with fatal COVID-19 outcomes, but not in individuals with nonfatal outcomes. This correlation was found to be due to the antibody response directed at the S2 subunit of the SARS-CoV-2 spike protein, which has the highest degree of conservation between the beta-coronavirus spike proteins. Intriguingly, antibody responses to the less cross-reactive SARS-CoV-2 nucleocapsid were not significantly different in individuals who were admitted to an ICU with fatal and nonfatal outcomes, suggesting an antibody profile in individuals with fatal outcomes consistent with an "original antigenic sin" type response.


Subject(s)
COVID-19 , Spike Glycoprotein, Coronavirus , Antibodies, Viral , Antibody Formation , Epitopes , Humans , SARS-CoV-2
4.
Int J Infect Dis ; 117: 361-368, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1729819

ABSTRACT

BACKGROUND: During Feb-Apr. 2020, many countries implemented non-pharmaceutical interventions (NPIs), such as school closures and lockdowns, to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. Overall, these interventions seem to have reduced the spread of the pandemic. We hypothesized that the official and effective start dates of NPIs can be noticeably different, for example, due to slow adoption by the population, and that these differences can lead to errors in the estimation of the impact of NPIs. METHODS: SEIR models were fitted to case data from 12 regions to infer the effective start dates of interventions and compare these with the official dates. The impact of NPIs was estimated from the inferred model parameters. RESULTS: We infer mostly late effective start dates of interventions. For example, Italy implemented a lockdown on Mar 11, but we infer the effective start date on Mar 17 (+3.05-2.01 days 95% CI). Moreover, we find that the impact of NPIs can be underestimated if it is assumed they start on their official date. CONCLUSIONS: Differences between the official and effective start of NPIs are likely. Neglecting such differences can lead to underestimation of the impact of NPIs, which could cause decision-makers to escalate interventions and guidelines.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control , Humans , Pandemics/prevention & control , SARS-CoV-2 , Schools
5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-309082

ABSTRACT

Testing individuals for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen causing the coronavirus disease 2019 (COVID-19), is crucial for curtailing transmission chains. Moreover, rapidly testing many potentially infected individuals is often a limiting factor in controlling COVID-19 outbreaks. Hence, pooling strategies, wherein individuals are grouped and tested simultaneously, are employed. We present a novel pooling strategy that implements D-Optimal Pooling Experimental design (DOPE). DOPE defines optimal pooled tests as those maximizing the mutual information between data and infection states. We estimate said mutual information via Monte-Carlo sampling and employ a discrete optimization heuristic for maximizing it. DOPE outperforms common pooling strategies both in terms of lower error rates and fewer tests utilized. DOPE holds several additional advantages: it provides posterior distributions of the probability of infection, rather than only binary classification outcomes;it naturally incorporates prior information of infection probabilities and test error rates;and finally, it can be easily extended to include other, newly discovered information regarding COVID-19. Hence, we believe that implementation of Bayesian D-optimal experimental design holds a great promise for the efforts of combating COVID-19 and other future pandemics.

7.
R Soc Open Sci ; 8(11): 210704, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1503812

ABSTRACT

Pooling is a method of simultaneously testing multiple samples for the presence of pathogens. Pooling of SARS-CoV-2 tests is increasing in popularity, due to its high testing throughput. A popular pooling scheme is Dorfman pooling: test N individuals simultaneously, if the test is positive, each individual is then tested separately; otherwise, all are declared negative. Most analyses of the error rates of pooling schemes assume that including more than a single infected sample in a pooled test does not increase the probability of a positive outcome. We challenge this assumption with experimental data and suggest a novel and parsimonious probabilistic model for the outcomes of pooled tests. As an application, we analyse the false-negative rate (i.e. the probability of a negative result for an infected individual) of Dorfman pooling. We show that the false-negative rates under Dorfman pooling increase when the prevalence of infection decreases. However, low infection prevalence is exactly the condition when Dorfman pooling achieves highest throughput efficiency. We therefore urge the cautious use of pooling and development of pooling schemes that consider correctly accounting for tests' error rates.

8.
J Am Med Inform Assoc ; 28(12): 2562-2570, 2021 11 25.
Article in English | MEDLINE | ID: covidwho-1338716

ABSTRACT

OBJECTIVE: Testing individuals for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen causing the coronavirus disease 2019 (COVID-19), is crucial for curtailing transmission chains. Moreover, rapidly testing many potentially infected individuals is often a limiting factor in controlling COVID-19 outbreaks. Hence, pooling strategies, wherein individuals are grouped and tested simultaneously, are employed. Here, we present a novel pooling strategy that builds on the Bayesian D-optimal experimental design criterion. MATERIALS AND METHODS: Our strategy, called DOPE (D-Optimal Pooling Experimental design), is built on a novel Bayesian formulation of pooling. DOPE defines optimal pooled tests as those maximizing the mutual information between data and infection states. We estimate said mutual information via Monte-Carlo sampling and employ a discrete optimization heuristic to maximize it. RESULTS: We compare DOPE to other, commonly used pooling strategies, as well as to individual testing. DOPE dominates the other strategies as it yields lower error rates while utilizing fewer tests. We show that DOPE maintains this dominance for a variety of infection prevalence values. DISCUSSION: DOPE has several additional advantages over common pooling strategies: it provides posterior distributions of the probability of infection rather than only binary classification outcomes; it naturally incorporates prior information of infection probabilities and test error rates; and finally, it can be easily extended to include other, newly discovered information regarding COVID-19. CONCLUSION: DOPE can substantially improve accuracy and throughput over current pooling strategies. Hence, DOPE can facilitate rapid testing and aid the efforts of combating COVID-19 and other future pandemics.


Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , Humans , Pandemics , Research Design
9.
J R Soc Interface ; 18(178): 20201014, 2021 05.
Article in English | MEDLINE | ID: covidwho-1234202

ABSTRACT

During infectious disease epidemics, an important question is whether cases travelling to new locations will trigger local outbreaks. The risk of this occurring depends on the transmissibility of the pathogen, the susceptibility of the host population and, crucially, the effectiveness of surveillance in detecting cases and preventing onward spread. For many pathogens, transmission from pre-symptomatic and/or asymptomatic (together referred to as non-symptomatic) infectious hosts can occur, making effective surveillance challenging. Here, by using SARS-CoV-2 as a case study, we show how the risk of local outbreaks can be assessed when non-symptomatic transmission can occur. We construct a branching process model that includes non-symptomatic transmission and explore the effects of interventions targeting non-symptomatic or symptomatic hosts when surveillance resources are limited. We consider whether the greatest reductions in local outbreak risks are achieved by increasing surveillance and control targeting non-symptomatic or symptomatic cases, or a combination of both. We find that seeking to increase surveillance of symptomatic hosts alone is typically not the optimal strategy for reducing outbreak risks. Adopting a strategy that combines an enhancement of surveillance of symptomatic cases with efforts to find and isolate non-symptomatic infected hosts leads to the largest reduction in the probability that imported cases will initiate a local outbreak.


Subject(s)
COVID-19 , SARS-CoV-2 , Disease Outbreaks/prevention & control , Humans
10.
BMC Med ; 19(1): 19, 2021 01 12.
Article in English | MEDLINE | ID: covidwho-1024366

ABSTRACT

BACKGROUND: Cross-reactivity to SARS-CoV-2 from exposure to endemic human coronaviruses (eHCoV) is gaining increasing attention as a possible driver of both protection against infection and COVID-19 severity. Here we explore the potential role of cross-reactivity induced by eHCoVs on age-specific COVID-19 severity in a mathematical model of eHCoV and SARS-CoV-2 transmission. METHODS: We use an individual-based model, calibrated to prior knowledge of eHCoV dynamics, to fully track individual histories of exposure to eHCoVs. We also model the emergent dynamics of SARS-CoV-2 and the risk of hospitalisation upon infection. RESULTS: We hypothesise that primary exposure with any eHCoV confers temporary cross-protection against severe SARS-CoV-2 infection, while life-long re-exposure to the same eHCoV diminishes cross-protection, and increases the potential for disease severity. We show numerically that our proposed mechanism can explain age patterns of COVID-19 hospitalisation in EU/EEA countries and the UK. We further show that some of the observed variation in health care capacity and testing efforts is compatible with country-specific differences in hospitalisation rates under this model. CONCLUSIONS: This study provides a "proof of possibility" for certain biological and epidemiological mechanisms that could potentially drive COVID-19-related variation across age groups. Our findings call for further research on the role of cross-reactivity to eHCoVs and highlight data interpretation challenges arising from health care capacity and SARS-CoV-2 testing.


Subject(s)
COVID-19 , Coronavirus Infections , Cross Protection/immunology , Cross Reactions/immunology , SARS-CoV-2/immunology , Age Factors , COVID-19/epidemiology , COVID-19/immunology , COVID-19/physiopathology , Coronavirus/classification , Coronavirus/immunology , Coronavirus Infections/epidemiology , Coronavirus Infections/immunology , Coronavirus Infections/therapy , Endemic Diseases , Hospitalization/statistics & numerical data , Humans , Immunity, Heterologous/immunology , Patient-Specific Modeling , Severity of Illness Index
11.
J Travel Med ; 27(5)2020 Aug 20.
Article in English | MEDLINE | ID: covidwho-529772

ABSTRACT

BACKGROUND: Substantial limitations have been imposed on passenger air travel to reduce transmission of severe acute respiratory syndrome coronavirus 2 between regions and countries. However, as case numbers decrease, air travel will gradually resume. We considered a future scenario in which case numbers are low and air travel returns to normal. Under that scenario, there will be a risk of outbreaks in locations worldwide due to imported cases. We estimated the risk of different locations acting as sources of future coronavirus disease 2019 outbreaks elsewhere. METHODS: We use modelled global air travel data and population density estimates from locations worldwide to analyse the risk that 1364 airports are sources of future coronavirus disease 2019 outbreaks. We use a probabilistic, branching-process-based approach that considers the volume of air travelers between airports and the reproduction number at each location, accounting for local population density. RESULTS: Under the scenario we model, we identify airports in East Asia as having the highest risk of acting as sources of future outbreaks. Moreover, we investigate the locations most likely to cause outbreaks due to air travel in regions that are large and potentially vulnerable to outbreaks: India, Brazil and Africa. We find that outbreaks in India and Brazil are most likely to be seeded by individuals travelling from within those regions. We find that this is also true for less vulnerable regions, such as the United States, Europe and China. However, outbreaks in Africa due to imported cases are instead most likely to be initiated by passengers travelling from outside the continent. CONCLUSIONS: Variation in flight volumes and destination population densities creates a non-uniform distribution of the risk that different airports pose of acting as the source of an outbreak. Accurate quantification of the spatial distribution of outbreak risk can therefore facilitate optimal allocation of resources for effective targeting of public health interventions.


Subject(s)
Air Travel , Coronavirus Infections/transmission , Pneumonia, Viral/transmission , Risk Assessment , Africa/epidemiology , Airports , Betacoronavirus , COVID-19 , China/epidemiology , Communicable Diseases, Imported , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Europe/epidemiology , Global Health , Humans , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Population Surveillance , SARS-CoV-2 , South America/epidemiology , Travel Medicine , United States/epidemiology
12.
J Clin Med ; 9(5)2020 May 01.
Article in English | MEDLINE | ID: covidwho-154862

ABSTRACT

Interventions targeting symptomatic hosts and their contacts were successful in bringing the 2003 SARS pandemic under control. In contrast, the COVID-19 pandemic has been harder to contain, partly because of its wide spectrum of symptoms in infectious hosts. Current evidence suggests that individuals can transmit the novel coronavirus while displaying few symptoms. Here, we show that the proportion of infections arising from hosts with few symptoms at the start of an outbreak can, in combination with the basic reproduction number, indicate whether or not interventions targeting symptomatic hosts are likely to be effective. However, as an outbreak continues, the proportion of infections arising from hosts with few symptoms changes in response to control measures. A high proportion of infections from hosts with few symptoms after the initial stages of an outbreak is only problematic if the rate of new infections remains high. Otherwise, it can simply indicate that symptomatic transmissions are being prevented successfully. This should be considered when interpreting estimates of the extent of transmission from hosts with few COVID-19 symptoms.

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