scholarly journals Vaccinating Adolescents and Children Significantly Reduces COVID-19 Morbidity and Mortality across All Ages: A Popula-Tion-Based Modeling Study Using the UK as an Example

Vaccines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1180
Author(s):  
Tinevimbo Shiri ◽  
Marc Evans ◽  
Carla A. Talarico ◽  
Angharad R. Morgan ◽  
Maaz Mussad ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Age Groups ◽  
Population Level ◽  
Vital Role ◽  
Morbidity And Mortality ◽  
Transmission Model ◽  
Base Case ◽  
Global Public Health ◽  
The Uk ◽  
The Impact

Debate persists around the risk–benefit balance of vaccinating adolescents and children against COVID-19. Central to this debate is quantifying the contribution of adolescents and children to the transmission of SARS-CoV-2, and the potential impact of vaccinating these age groups. In this study, we present a novel SEIR mathematical disease transmission model that quantifies the impact of different vaccination strategies on population-level SARS-CoV-2 infections and clinical outcomes. The model employs both age- and time-dependent social mixing patterns to capture the impact of changes in restrictions. The model was used to assess the impact of vaccinating adolescents and children on the natural history of the COVID-19 pandemic across all age groups, using the UK as an example. The base case model demonstrates significant increases in COVID-19 disease burden in the UK following a relaxation of restrictions, if vaccines are limited to those ≥18 years and vulnerable adolescents (≥12 years). Including adolescents and children in the vaccination program could reduce overall COVID-related mortality by 57%, and reduce cases of long COVID by 75%. This study demonstrates that vaccinating adolescents and children has the potential to play a vital role in reducing SARS-CoV-2 infections, and subsequent COVID-19 morbidity and mortality, across all ages. Our results have major global public health implications and provide valuable information to inform a potential pandemic exit strategy.

scholarly journals Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies

2021 ◽  
Vol 17 (1) ◽  
pp. e1008619
Author(s):  
Matt J. Keeling ◽  
Edward M. Hill ◽  
Erin E. Gorsich ◽  
Bridget Penman ◽  
Glen Guyver-Fletcher ◽  
...  
Keyword(s):  
Critical Care ◽  
Health Service ◽  
Age Groups ◽  
Transmission Model ◽  
Bed Occupancy ◽  
Time Data ◽  
Social Distancing ◽  
Number Of Patients ◽  
The Uk ◽  
The Impact

Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a “stay at home” order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. We find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Our work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.

scholarly journals Impact of vaccination and non-pharmaceutical interventions on SARS-CoV-2 dynamics in Switzerland

2021 ◽  
Author(s):  
Andrew J. Shattock ◽  
Epke A. Le Rutte ◽  
Robert P Duenner ◽  
Swapnoleena Sen ◽  
Sherrie L Kelly ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Vaccination Coverage ◽  
Vaccination Campaign ◽  
Population Level ◽  
Morbidity And Mortality ◽  
Transmission Model ◽  
Third Wave ◽  
The Impact ◽  
Pharmaceutical Interventions

As vaccination coverage against SARS-CoV-2 increases amidst the emergence and spread of more infectious and potentially more deadly viral variants, decisions on timing and extent of relaxing effective, but unsustainable, non-pharmaceutical interventions (NPIs) need to be made. An individual-based transmission model of SARS-CoV-2 dynamics, OpenCOVID, was developed to compare the impact of various vaccination and NPI strategies on the COVID-19 epidemic in Switzerland. We estimate that any relaxation of NPIs in March 2021 will lead to increasing cases, hospitalisations, and deaths resulting in a "third wave" in spring and into summer 2021. However, we find a cautious phased relaxation can substantially reduce population-level morbidity and mortality. We find that faster vaccination campaign can offset the size of such a wave, allowing more flexibility for NPI to be relaxed sooner. Our sensitivity analysis revealed that model results are particularly sensitive to the infectiousness of variant B.1.1.7.

scholarly journals Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategies

2020 ◽  
Author(s):  
Matt J. Keeling ◽  
Edward M. Hill ◽  
Erin E. Gorsich ◽  
Bridget Penman ◽  
Glen Guyver-Fletcher ◽  
...  
Keyword(s):  
Critical Care ◽  
Health Service ◽  
Age Groups ◽  
Transmission Model ◽  
Bed Occupancy ◽  
Time Data ◽  
Social Distancing ◽  
Number Of Patients ◽  
The Uk ◽  
The Impact

AbstractBackgroundEfforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a “stay at home” order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking.MethodsWe present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission.FindingsWe find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required.DiscussionOur work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.

scholarly journals The differential impact of physical distancing strategies on social contacts relevant for the spread of COVID-19: Evidence from a multi-country survey

2020 ◽  
Author(s):  
Emanuele Del Fava ◽  
Jorge Cimentada ◽  
Daniela Perrotta ◽  
André Grow ◽  
Francesco Rampazzo ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Age Groups ◽  
Social Contact ◽  
The United States ◽  
Social Contacts ◽  
Contact Patterns ◽  
The Uk ◽  
Facebook Advertising ◽  
Net Reproduction ◽  
The Impact

AbstractPhysical distancing measures are intended to mitigate the spread of COVID-19, even though their impact on social contacts and disease transmission remains unclear. Obtaining timely data on social contact patterns can help to assess the impact of such protective measures. We conducted an online opt-in survey based on targeted Facebook advertising campaigns across seven European countries (Belgium, France, Germany, Italy, Netherlands, Spain, United Kingdom (UK)) and the United States (US), achieving a sample of 53,708 questionnaires in the period March 13–April 13, 2020. Post-stratification weights were produced to correct for biases. Data on social contact numbers, as well as on protective behaviour and perceived level of threat were collected and used to the expected net reproduction number by week, Rt, with respect to pre-pandemic data. Compared to social contacts reported prior to COVID-19, in mid-April daily social contact numbers had decreased between 49% in Germany and 83% in Italy, ranging from below three contacts per day in France, Spain, and the UK up to four in Germany and the Netherlands. Such reductions were sufficient to bring Rt to one or even below in all countries, except Germany. Evidence from the US and the UK showed that the number of daily social contacts mainly decreased after governments issued the first physical distancing guidelines. Finally, although contact numbers decreased uniformly across age groups, older adults reported the lowest numbers of contacts, indicating higher levels of protection. We provided a comparable set of statistics on social contact patterns during the COVID-19 pandemic for eight high-income countries, disaggregated by week. As these estimates offer a more grounded alternative to the theoretical assumptions often used in epidemiological models, the scientific community could draw on this information for developing more realistic epidemic models of COVID-19.

Impact of Novel Coronavirus (COVID-19) on Pregnant Women: A Review

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Saima Habeeb ◽  
Manju Chugani
Keyword(s):  
Pregnant Women ◽  
Vertical Transmission ◽  
Respiratory Infections ◽  
Age Groups ◽  
Well Being ◽  
Fetal Outcome ◽  
Global Public Health ◽  
Coronavirus Infection ◽  
Novel Coronavirus ◽  
The Impact

: The novel coronavirus infection (COVID‐19) is a global public health emergency.Since its outbreak in Wuhan, China in December 2019, the infection has spread at an alarming rate across the globe and humans have been locked down to their countries, cities and homes. As of now, the virus has affected over 20million people globally and has inflicted over 7 lac deaths. Nevertheless, the recovery rate is improving with each passing day and over 14 million people have recuperated so far. The statistics indicate that nobody is immune to the disease as the virus continues to spread among all age groups; newborns to the elders, and all compartmentsincluding pregnant women. However, pregnant women may be more susceptible to this infection as they are, in general, highly vulnerable to respiratory infections. There is no evidence for vertical transmission of the COVID-19 virus among pregnant women, but an increased prevalence of preterm deliveries. Besides this, the COVID-19 may alter immune response at the maternal-fetal interface and affect the well-being of mothers as well as infants. Unfortunately, there is limited evidence available in the open literature regarding coronavirus infection during pregnancy and it now appears that certain pregnant women have infected during the present 2019-nCoV pandemic. In this short communication, we study the impact of the COVID-19 infection on vertical transmission and fetal outcome among pregnant women.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fatima Khadadah ◽  
Abdullah A. Al-Shammari ◽  
Ahmad Alhashemi ◽  
Dari Alhuwail ◽  
Bader Al-Saif ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Model ◽  
Socioeconomic Groups ◽  
Before And After ◽  
Cross Transmission ◽  
Community Transmission ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Abstract Background Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. Methods We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ($$ {\mathcal{R}}_e $$ R e ). We estimated the basic reproduction number ($$ {\mathcal{R}}_0 $$ R 0 ) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated $$ {\mathcal{R}}_e $$ R e values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. Results We estimate $$ {\mathcal{R}}_e $$ R e at 1·08 (95% CI: 1·00–1·26) for P1 and 2·36 (2·03–2·71) for P2. On March 22nd, $$ {\mathcal{R}}_e $$ R e for P1 and P2 are estimated at 1·19 (1·04–1·34) and 1·75 (1·26–2·11) respectively. After the partial curfew had taken effect, $$ {\mathcal{R}}_e $$ R e for P1 dropped modestly to 1·05 (0·82–1·26) but almost doubled for P2 to 2·89 (2·30–3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. Conclusion Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

scholarly journals Sheep scab spatial distribution: the roles of transmission pathways

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Emily Joanne Nixon ◽  
Ellen Brooks-Pollock ◽  
Richard Wall
Keyword(s):  
Disease Transmission ◽  
Movement Control ◽  
Cost Effective ◽  
Infected Sheep ◽  
Disease Spread ◽  
Psoroptes Ovis ◽  
Long Distance ◽  
Sheep Scab ◽  
The Uk ◽  
The Impact

Abstract Background Ovine psoroptic mange (sheep scab) is a highly pathogenic contagious infection caused by the mite Psoroptes ovis. Following 21 years in which scab was eradicated in the UK, it was inadvertently reintroduced in 1972 and, despite the implementation of a range of control methods, its prevalence increased steadily thereafter. Recent reports of resistance to macrocyclic lactone treatments may further exacerbate control problems. A better understanding of the factors that facilitate its transmission are required to allow improved management of this disease. Transmission of infection occurs within and between contiguous sheep farms via infected sheep-to-sheep or sheep–environment contact and through long-distance movements of infected sheep, such as through markets. Methods A stochastic metapopulation model was used to investigate the impact of different transmission routes on the spatial pattern of outbreaks. A range of model scenarios were considered following the initial infection of a cluster of highly connected contiguous farms. Results Scab spreads between clusters of neighbouring contiguous farms after introduction but when long-distance movements are excluded, infection then self-limits spatially at boundaries where farm connectivity is low. Inclusion of long-distance movements is required to generate the national patterns of disease spread observed. Conclusions Preventing the movement of scab infested sheep through sales and markets is essential for any national management programme. If effective movement control can be implemented, regional control in geographic areas where farm densities are high would allow more focussed cost-effective scab management. Graphical Abstract

scholarly journals Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

2019 ◽  
Vol 16 (155) ◽  
pp. 20190080 ◽  
Author(s):  
Sasikiran Kandula ◽  
Sen Pei ◽  
Jeffrey Shaman
Keyword(s):  
Real Time ◽  
Age Groups ◽  
Forecast Accuracy ◽  
Population Level ◽  
Transmission Model ◽  
Surveillance Systems ◽  
Hospitalization Rates ◽  
The Usa ◽  
Near Term ◽  
Google Search

Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for five age groups and the overall population during five seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.

scholarly journals SARS-CoV-2 infection in London, England: changes to community point prevalence around lockdown time, March–May 2020

2020 ◽  
pp. jech-2020-214730 ◽  
Author(s):  
Michael Edelstein ◽  
Chinelo Obi ◽  
Meera Chand ◽  
Susan Hopkins ◽  
Kevin Brown ◽  
...  
Keyword(s):  
Population Level ◽  
Small Sample ◽  
Point Prevalence ◽  
European Countries ◽  
Limited Information ◽  
The Uk ◽  
The Impact

BackgroundThe UK has been one of the European countries most affected by COVID-19 pandemic. The UK implemented a lockdown in March 2020, when testing policy at the time was focusing on hospitalised cases. Limited information is therefore available on the impact of the lockdown on point prevalence in the community. We assessed COVID-19 point prevalence in London between early April and early May 2020, which approximately reflect infection around the time of the lockdown and 3–5 weeks into lockdown.MethodsWe tested 1064 participants of a community surveillance cohort for acute COVID-19 infection using PCR in London in April and May 2020 and described positivity as well as characteristics and symptoms of the participants.ResultsPoint prevalence decreased from 2.2% (95% CI 1.4 to 3.5) in early April to 0.2% (95% CI 0.03 to 1.6) in early May. 22% of those who tested positive in April were asymptomatic. Extrapolation from reports of confirmed cases suggest that 5–7.6% of total infections were confirmed by testing during this period.ConclusionCOVID-19 point prevalence in the community sharply decreased after lockdown was implemented. This study is based on a small sample and regular seroprevalence studies are needed to better characterise population-level immunity.

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