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1.
Preprint in English | medRxiv | ID: ppmedrxiv-22268891

ABSTRACT

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSOver the past two decades, vaccination programmes for vaccine-preventable diseases (VPDs) have expanded across low- and middle-income countries (LMICs). However, the rise of COVID-19 resulted in global disruption to routine immunisation (RI) activities. Such disruptions could have a detrimental effect on public health, leading to more deaths from VPDs, particularly without mitigation efforts. Hence, as RIs resume, it is important to estimate the effectiveness of different approaches for recovery. MethodsWe apply an impact extrapolation method developed by the Vaccine Impact Modelling Consortium to estimate the impact of COVID-19-related disruptions with different recovery scenarios for ten VPDs across 112 LMICs. We focus on deaths averted due to RIs occurring in the years 2020-2030 and investigate two recovery scenarios relative to a no-COVID-19 scenario. In the recovery scenarios, we assume a 10% COVID-19-related drop in RI coverage in the year 2020. We then linearly interpolate coverage to the year 2030 to investigate two routes to recovery, whereby the immunization agenda (IA2030) targets are reached by 2030 or fall short by 10%. ResultsWe estimate that falling short of the IA2030 targets by 10% leads to 11.26% fewer fully vaccinated persons (FVPs) and 11.34% more deaths over the years 2020-2030 relative to the no-COVID-19 scenario, whereas, reaching the IA2030 targets reduces these proportions to 5% fewer FVPs and 5.22% more deaths. The impact of the disruption varies across the VPDs with diseases where coverage expands drastically in future years facing a smaller detrimental effect. ConclusionOverall, our results show that drops in RI coverage could result in more deaths due to VPDs. As the impact of COVID-19-related disruptions is dependent on the vaccination coverage that is achieved over the coming years, the continued efforts of building up coverage and addressing gaps in immunity are vital in the road to recovery. SUMMARYO_ST_ABSWhat is already known?C_ST_ABSO_LIThe impact of vaccination programmes without COVID-19-related disruption has been assessed by the Vaccine Impact Modelling Consortium. C_LIO_LIThe COVID-19 pandemic has disrupted vaccination programmes resulting in a decline in coverage in the year 2020, the ramifications of this is unclear. C_LI What are the new findings?O_LIWe estimate the impact of disruptions to routine immunisation coverage and different routes to recovery. We compare to a scenario without COVID-19-related disruptions (assuming no drops in immunisation coverage). C_LIO_LIWe estimate that reaching the Immunization Agenda (IA2030) targets leads to 5% fewer FVPs and 5.22% more deaths over the years 2020 to 2030 relative to the scenario with no COVID-19-related disruptions, whereas falling short of the IA2030 targets by 10% leads to 11.26% fewer fully vaccinated persons (FVPs) and 11.34% more deaths. C_LIO_LIThe impact of the disruption varies across the vaccine-preventable diseases with those forecasted to have vast expansions in coverage post-2020 able to recover more. C_LI What do the new findings imply?O_LIA drop in vaccination coverage results in fewer vaccinated individuals and thus more deaths due to vaccine-preventable diseases. To mitigate this, building up coverage of routine immunisations and addressing immunity gaps with activities such as catch-up campaigns are vital in the road to recovery. C_LI

2.
Preprint in English | medRxiv | ID: ppmedrxiv-21267858

ABSTRACT

The Omicron B.1.1.529 SARS-CoV-2 variant was first detected in late November 2021 and has since spread to multiple countries worldwide. We model the potential consequences of the Omicron variant on SARS-CoV-2 transmission and health outcomes in England between December 2021 and April 2022, using a deterministic compartmental model fitted to epidemiological data from March 2020 onwards. Because of uncertainty around the characteristics of Omicron, we explore scenarios varying the extent of Omicrons immune escape and the effectiveness of COVID-19 booster vaccinations against Omicron, assuming the level of Omicrons transmissibility relative to Delta to match the growth in observed S gene target failure data in England. We consider strategies for the re-introduction of control measures in response to projected surges in transmission, as well as scenarios varying the uptake and speed of COVID-19 booster vaccinations and the rate of Omicrons introduction into the population. These results suggest that Omicron has the potential to cause substantial surges in cases, hospital admissions and deaths in populations with high levels of immunity, including England. The reintroduction of additional non-pharmaceutical interventions may be required to prevent hospital admissions exceeding the levels seen in England during the previous peak in winter 2020-2021.

3.
Preprint in English | medRxiv | ID: ppmedrxiv-21266598

ABSTRACT

1Forecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways.

4.
Preprint in English | medRxiv | ID: ppmedrxiv-21266930

ABSTRACT

BackgroundIn settings where the COVID-19 vaccine supply is constrained, extending the intervals between the first and second doses of the COVID-19 vaccine could let more people receive their first doses earlier. Our aim is to estimate the health impact of COVID-19 vaccination alongside benefit-risk assessment of different dosing intervals for low- and middle-income countries of Europe. MethodsWe fitted a dynamic transmission model to country-level daily reported COVID-19 mortality in 13 low- and middle-income countries in the World Health Organization European Region (Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Georgia, Republic of Moldova, Russian Federation, Serbia, North Macedonia, Turkey, and Ukraine). A vaccine product with characteristics similar to the Oxford/AstraZeneca COVID-19 (AZD1222) vaccine was used in the base case scenario and was complemented by sensitivity analyses around efficacies related to other COVID-19 vaccines. Both fixed dosing intervals at 4, 8, 12, 16, and 20 weeks and dose-specific intervals that prioritise specific doses for certain age groups were tested. Optimal intervals minimise COVID-19 mortality between March 2021 and December 2022. We incorporated the emergence of variants of concern into the model, and also conducted a benefit-risk assessment to quantify the trade-off between health benefits versus adverse events following immunisation. FindingsIn 12 of the 13 countries, optimal strategies are those that prioritise the first doses among older adults (60+ years) or adults (20-59 years). These strategies lead to dosing intervals longer than six months. In comparison, a four-week fixed dosing interval may incur 10.2% [range: 4.0% - 22.5%; n = 13 (countries)] more deaths. There is generally a negative association between dosing interval and COVID-19 mortality within the range we investigated. Assuming a shorter first dose waning duration of 120 days, as opposed to 360 days in the base case, led to shorter optimal dosing intervals of 8-12 weeks. Benefit-risk ratios were the highest for fixed dosing intervals of 8-12 weeks. InterpretationWe infer that longer dosing intervals of over six months, which are substantially longer than the current label recommendation for most vaccine products, could reduce COVID-19 mortality in low- and middle-income countries of WHO/Europe. Certain vaccine features, such as fast waning of first doses, significantly shorten the optimal dosing intervals. FundingWorld Health Organization

5.
Preprint in English | medRxiv | ID: ppmedrxiv-21266584

ABSTRACT

England has experienced a heavy burden of COVID-19, with high infection levels observed throughout the summer months of 2021. Alongside the emergence of evidence suggesting that COVID-19 vaccine protection wanes over time, booster vaccinations began for individuals aged 50 and above in September 2021. Using a model fitted to 18 months of epidemiological data, we project potential dynamics of SARS-CoV-2 transmission in England to September 2022. We consider key uncertainties including behavioural change, waning vaccine protection, strategies for vaccination, and the reintroduction of public health and social measures. We project the current wave of transmission will peak in Autumn 2021, with low levels of transmission in early 2022. The extent to which SARS-CoV-2 transmission resurges in 2022 depends largely on assumptions around waning vaccine protection and booster vaccinations. Widespread booster vaccinations or the reimposition of mild public health and social measures such as work-from-home policies could largely mitigate the wave of COVID-19 transmission projected to occur in England in Spring/Summer 2022.

6.
Preprint in English | medRxiv | ID: ppmedrxiv-21264701

ABSTRACT

BackgroundThis study measured the long-term health-related quality of life of non-hospitalised COVID-19 cases with PCR-confirmed SARS-CoV-2(+) infection using the recommended instrument in England (the EQ-5D). MethodsProspective cohort study of SARS-CoV-2(+) cases aged 12-85 years and followed up for six months from 01 December 2020, with cross-sectional comparison to SARS-CoV-2(-) controls. Main outcomes were loss of quality-adjusted life days (QALDs); physical symptoms; and COVID-19-related private expenditures. We analysed results using multivariable regressions with post-hoc weighting by age and sex, and conditional logistic regressions for the association of each symptom and EQ-5D limitation on cases and controls. ResultsOf 548 cases (mean age 41.1 years; 61.5% female), 16.8% reported physical symptoms at month 6 (most frequently extreme tiredness, headache, loss of taste and/or smell, and shortness of breath). Cases reported more limitations with doing usual activities than controls. Almost half of cases spent a mean of {pound}18.1 on non-prescription drugs (median: {pound}10.0), and 52.7% missed work or school for a mean of 12 days (median: 10). On average, all cases lost 15.9 (95%-CI: 12.1, 19.7) QALDs, while those reporting symptoms at month 6 lost 34.1 (29.0, 39.2) QALDs. Losses also increased with older age. Cumulatively, the health loss from morbidity contributes at least 21% of the total COVID-19-related disease burden in England. ConclusionsOne in 6 cases report ongoing symptoms at 6 months, and 10% report prolonged loss of function compared to pre-COVID-19 baselines. A marked health burden was observed among older COVID-19 cases and those with persistent physical symptoms. summaryLosses of health-related quality of life in non-hospitalised COVID-19 cases increase by age and for cases with symptoms after 6 months. At a population level, at least 21% of the total COVID-19-related disease burden in England is attributable to morbidity.

7.
Preprint in English | medRxiv | ID: ppmedrxiv-21265245

ABSTRACT

BackgroundWe aimed to evaluate the impact of various allocation strategies of COVID-19 vaccines and antiviral such that the pandemic exit strategy could be tailored to risks and preferences of jurisdictions in the East Asia and Pacific region (EAP) to improve its efficiency and effectiveness. MethodsVaccine efficacies were estimated from the titre distributions of 50% plaque reduction neutralization test (PRNT50), assuming that PRNT50 titres of primary vaccination decreased by 2-10 folds due to antibody waning and emergence of VOCs, and an additional dose of vaccine would increase PRNT50 titres by 3- or 9-fold. We then used an existing SARS-CoV-2 transmission model to assess the outcomes of vaccine allocation strategies with and without the use of antivirals for symptomatic patients in Japan, Hong Kong and Vietnam. FindingsIncreasing primary vaccination coverage was the most important contributing factor in reducing the total and peak number of COVID-19 hospitalizations, especially when population vaccine coverage or vaccine uptake among older adults was low. Providing antivirals to 50% of symptomatic infections only further reduced total and peak hospitalizations by 10-13%. The effectiveness of an additional dose of vaccine was highly dependent on the immune escape potential of VOCs and antibody waning, but less dependent on the boosting efficacy of the additional dose. InterpretationIncreasing primary vaccination coverage should be prioritised in the design of allocation strategies of COVID-19 vaccines and antivirals in the EAP region. Heterologous vaccination with any available vaccine as the additional dose could be considered when planning pandemic exit strategies tailored to the circumstances of EAP jurisdictions. FundingHealth and Medical Research Fund, General Research Fund, AIR@InnoHK

8.
Preprint in English | medRxiv | ID: ppmedrxiv-21260272

ABSTRACT

BackgroundCountries in the World Health Organization (WHO) European Region differ in terms of the COVID-19 vaccine roll-out speed. We evaluated the health and economic impact of different age-based vaccine prioritisation strategies across this demographically and socio-economically diverse region. MethodsWe fitted country-specific age-stratified compartmental transmission models to reported COVID-19 mortality in the WHO European Region to inform the immunity level before vaccine roll-out. Building upon broad recommendations from the WHO Strategic Advisory Group of Experts on Immunisation (SAGE), we examined four strategies that prioritise: all adults (V+), younger (20-59 year-olds) followed by older adults (60+) (V20), older followed by younger adults (V60), and the oldest adults (75+) (V75) followed by incremental expansion to successively younger five-year age groups. We explored four roll-out scenarios based on projections or recent observations (R1-4) - the slowest scenario (R1) covers 30% of the total population by December 2022 and the fastest (R4) 80% by December 2021. Five decision-making metrics were summarised over 2021-22: mortality, morbidity, and losses in comorbidity-adjusted life expectancy (cLE), comorbidity- and quality-adjusted life years (cQALY), and the value of human capital (HC). Six sets of infection-blocking and disease-reducing vaccine efficacies were considered. FindingsThe optimal age-based vaccine prioritisation strategies were sensitive to country characteristics, decision-making metrics and roll-out speeds. Overall, V60 consistently performed better than or comparably to V75. There were greater benefits in prioritising older adults when roll-out is slow and when VE is low. Under faster roll-out, V+ was the most desirable option. InterpretationA prioritisation strategy involving more age-based stages (V75) does not necessarily lead to better health and economic outcomes than targeting broad age groups (V60). Countries expecting a slow vaccine roll-out may particularly benefit from prioritising older adults. FundingWorld Health Organization, Bill and Melinda Gates Foundation, the Medical Research Council (United Kingdom), the National Institute of Health Research (United Kingdom), the European Commission, the Foreign, Commonwealth and Development Office (United Kingdom), Wellcome Trust Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and medRxiv for articles published in English from inception to 9 Jun 2021, with the search terms: ("COVID-19" OR "SARS-CoV-2") AND ("priorit*) AND ("model*") AND ("vaccin*") and identified 66 studies on vaccine prioritization strategies. Of the 25 studies that compared two or more age-based prioritisation strategies, 12 found that targeting younger adults minimised infections while targeting older adults minimised mortality; an additional handful of studies found similar outcomes between different age-based prioritisation strategies where large outbreaks had already occurred. However, only two studies have explored age-based vaccine prioritisation using models calibrated to observed outbreaks in more than one country, and no study has explored the effectiveness of vaccine prioritisation strategies across settings with different population structures, contact patterns, and outbreak history. Added-value of this studyWe evaluated various age-based vaccine prioritisation strategies for 38 countries in the WHO European Region using various health and economic outcomes for decision-making, by parameterising models using observed outbreak history, known epidemiologic and vaccine characteristics, and a range of realistic vaccine roll-out scenarios. We showed that while targeting older adults was generally advantageous, broadly targeting everyone above 60 years might perform better than or comparably to a more detailed strategy that targeted the oldest age group above 75 years followed by those in the next younger five-year age band. Rapid vaccine roll-out has only been observed in a small number of countries. If vaccine coverage can reach 80% by the end of 2021, prioritising older adults may not be optimal in terms of health and economic impact. Lower vaccine efficacy was associated with greater relative benefits only under relatively slow roll-out scenarios considered. Implication of all the available evidenceCOVID-19 vaccine prioritization strategies that require more precise targeting of individuals of a specific and narrow age range may not necessarily lead to better outcomes compared to strategies that prioritise populations across broader age ranges. In the WHO European Region, prioritising all adults equally or younger adults first will only optimise health and economic impact when roll-out is rapid, which may raise between-country equity issues given the global demand for COVID-19 vaccines.

9.
Preprint in English | medRxiv | ID: ppmedrxiv-21259104

ABSTRACT

BackgroundHow best to prioritise COVID-19 vaccination within and between countries has been a public health and an ethical challenge for decision-makers globally. We systematically reviewed epidemiological and economic modelling evidence on population priority groups to minimise COVID-19 mortality, transmission and morbidity outcomes. MethodsWe searched the National Institute of Health iSearch COVID-19 Portfolio (a database of peer-reviewed and pre-print articles), Econlit, the Centre for Economic Policy Research and the National Bureau of Economic Research for mathematical modelling studies evaluating the impact of prioritising COVID-19 vaccination to population target groups. We narratively synthesised the main study conclusions on prioritisation and the conditions under which the conclusions changed. FindingsThe search identified 1820 studies. 36 studies met the inclusion criteria and were narratively synthesised. 83% of studies described outcomes in high-income countries. We found that for countries seeking to minimise deaths, prioritising vaccination of senior adults was the optimal strategy and for countries seeking to minimise cases the young were prioritised. There were several exceptions to the main conclusion, notably reductions in deaths could be increased, if groups at high risk of both transmission and death could be further identified. Findings were also sensitive to the level of vaccine coverage. InterpretationThe evidence supports WHO SAGE recommendations on COVID-19 vaccine prioritisation. There is however an evidence gap on optimal prioritisation for low- and middle-income countries, studies that included an economic evaluation, and studies that explore prioritisation strategies if the aim is to reduce overall health burden including morbidity.

10.
Preprint in English | medRxiv | ID: ppmedrxiv-21258924

ABSTRACT

IntroductionIn countries with weak surveillance systems confirmed COVID-19 deaths are likely to underestimate the death toll of the pandemic. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data on burial patterns in Mogadishu, Somalia during 2020 to estimate the date of introduction, transmissibility and other epidemiologic characteristics of SARS-CoV-2 in this low-income, crisis-affected setting. MethodsWe performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number (R0) and the effect of non-pharmaceutical interventions in Mogadishu up to September 2020. ResultsUnder the assumption that excess deaths in Mogadishu February-September 2020 were directly attributable to SARS-CoV-2 infection we arrived at median estimates of October-November 2019 for the date of introduction and low R0 estimates (1.3-1.5) stemming from the early and slow rise of excess deaths. The effect of control measures on transmissibility appeared small. ConclusionSubject to study assumptions, a very early SARS-CoV-2 introduction event may have occurred in Somalia. Estimated transmissibility in the first epidemic wave was lower than observed in European settings.

11.
Preprint in English | medRxiv | ID: ppmedrxiv-21257215

ABSTRACT

BackgroundThe COVID-19 pandemic has disrupted delivery of immunisation services globally. Many countries have postponed vaccination campaigns out of concern about infection risks to staff delivering vaccination, the children being vaccinated and their families. The World Health Organization recommends considering both the benefit of preventive campaigns and the risk of SARS-CoV-2 transmission when making decisions about campaigns during COVID-19 outbreaks, but there has been little quantification of the risks. MethodsWe modelled excess SARS-CoV-2 infection risk to vaccinators, vaccinees and their caregivers resulting from vaccination campaigns delivered during a COVID-19 epidemic. Our model used population age-structure and contact patterns from three exemplar countries (Burkina Faso, Ethiopia, and Brazil). It combined an existing compartmental transmission model of an underlying COVID-19 epidemic with a Reed-Frost model of SARS-CoV-2 infection risk to vaccinators and vaccinees. We explored how excess risk depends on key parameters governing SARS-CoV-2 transmissibility, and aspects of campaign delivery such as campaign duration, number of vaccinations, and effectiveness of personal protective equipment (PPE) and symptomatic screening. ResultsInfection risks differ considerably depending on the circumstances in which vaccination campaigns are conducted. A campaign conducted at the peak of a SARS-CoV-2 epidemic with high prevalence and without special infection mitigation measures could increase absolute infection risk by 32% to 45% for vaccinators, and 0.3% to 0.5% for vaccinees and caregivers. However, these risks could be reduced to 3.6% to 5.3% and 0.1% to 0.2% respectively by use of PPE that reduces transmission by 90% (as might be achieved with N95 respirators or high-quality surgical masks) and symptomatic screening. ConclusionsSARS-CoV-2 infection risks to vaccinators, vaccinees and caregivers during vaccination campaigns can be greatly reduced by adequate PPE, symptomatic screening, and appropriate campaign timing. Our results support the use of adequate risk mitigation measures for vaccination campaigns held during SARS-CoV-2 epidemics, rather than cancelling them entirely.

12.
Preprint in English | medRxiv | ID: ppmedrxiv-21255642

ABSTRACT

BackgroundThis study developed deep learning models to monitor global intention and confidence of Covid-19 vaccination in real time. MethodsWe collected 6.73 million English tweets regarding Covid-19 vaccination globally from January 2020 to February 2021. Fine-tuned Transformer-based deep learning models were used to classify tweets in real time as they relate to Covid-19 vaccination intention and confidence. Temporal and spatial trends were performed to map the global prevalence of Covid-19 vaccination intention and confidence, and public engagement on social media was analyzed. FindingsGlobally, the proportion of tweets indicating intent to accept Covid-19 vaccination declined from 64.49% on March to 39.54% on September 2020, and then began to recover, reaching 52.56% in early 2021. This recovery in vaccine acceptance was largely driven by the US and European region, whereas other regions experienced the declining trends in 2020. Intent to accept and confidence of Covid-19 vaccination were relatively high in South-East Asia, Eastern Mediterranean, and Western Pacific regions, but low in American, European, and African regions. 12.71% tweets expressed misinformation or rumors in South Korea, 14.04% expressed distrust in government in the US, and 16.16% expressed Covid-19 vaccine being unsafe in Greece, ranking first globally. Negative tweets, especially misinformation or rumors, were more engaged by twitters with fewer followers than positive tweets. InterpretationThis global real-time surveillance study highlights the importance of deep learning based social media monitoring to detect emerging trends of Covid-19 vaccination intention and confidence to inform timely interventions. FundingNational Natural Science Foundation of China. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWith COVID-19 vaccine rollout, each country should investigate its vaccination intention in local contexts to ensure massive vaccination. We searched PubMed for all articles/preprints until April 9, 2021 with the keywords "("Covid-19 vaccines"[Mesh] OR Covid-19 vaccin*[TI]) AND (confidence[TI] OR hesitancy[TI] OR acceptance[TI] OR intention[TI])". We identified more than 100 studies, most of which are country-level cross-sectional surveys, and the largest global survey of Covid-19 vaccine acceptance only covered 32 countries to date. However, how Covid-19 vaccination intention changes over time remain unknown, and many countries are not covered in previous surveys yet. A few studies assessed public sentiments towards Covid-19 vaccination using social media data, but only targeting limited geographical areas. There is a lack of real-time surveillance, and no study to date has globally monitored Covid-19 vaccination intention in real time. Added value of this studyTo our knowledge, this is the largest global monitoring study of Covid-19 vaccination intention and confidence with social media data in over 100 countries from the beginning of the pandemic to February 2021. This study developed deep learning models by fine-tuning a Bidirectional Encoder Representation from Transformer (BERT)-based model with 8000 manually-classified tweets, which can be used to monitor Covid-19 vaccination beliefs using social media data in real time. It achieves temporal and spatial analyses of the evolving beliefs to Covid-19 vaccines across the world, and also an insight for many countries not yet covered in previous surveys. This study highlights that the intention to accept Covid-19 vaccination have experienced a declining trend since the beginning of the pandemic in all world regions, with some regions recovering recently, though not to their original levels. This recovery was largely driven by the US and European region (EUR), whereas other regions experienced the declining trends in 2020. Intention to accept and confidence of Covid-19 vaccination were relatively high in South-East Asia region (SEAR), Eastern Mediterranean region (EMR), and Western Pacific region (WPR), but low in American region (AMR), EUR, and African region (AFR). Many AFR countries worried more about vaccine effectiveness, while EUR, AMR, and WPR concerned more about vaccine safety (the most concerns with 16.16% in Greece). Online misinformation or rumors were widespread in AMR, EUR, and South Korea (12.71%, ranks first globally), and distrust in government was more prevalent in AMR (14.04% in the US, ranks first globally). Our findings can be used as a reference point for survey data on a single country in the future, and inform timely and specific interventions for each country to address Covid-19 vaccine hesitancy. Implications of all the available evidenceThis global real-time surveillance study highlights the importance of deep learning based social media monitoring as a quick and effective method for detecting emerging trends of Covid-19 vaccination intention and confidence to inform timely interventions, especially in settings with limited sources and urgent timelines. Future research should build multilingual deep learning models and monitor Covid-19 vaccination intention and confidence in real time with data from multiple social media platforms.

13.
Preprint in English | medRxiv | ID: ppmedrxiv-21255949

ABSTRACT

BackgroundEven with good progress on vaccination, SARS-CoV-2 infections in the UK may continue to impose a high burden of disease and therefore pose substantial challenges for health policy decision makers. Stringent government-mandated physical distancing measures (lockdown) have been demonstrated to be epidemiologically effective, but can have both positive and negative economic consequences. The duration and frequency of any intervention policy could, in theory, could be optimised to maximise economic benefits while achieving substantial reductions in disease. MethodsHere we use a pre-existing SARS-CoV-2 transmission model to assess the health and economic implications of different strengths of control through time in order to identify optimal approaches to non-pharmaceutical intervention stringency in the UK, considering the role of vaccination in reducing the need for future physical distancing measures. The model is calibrated to the COVID-19 epidemic in England and we carry out retrospective analysis of the optimal timing of precautionary breaks in 2020 and the optimal relaxation policy from the January 2021 lockdown, considering the willingness to pay for health improvement. ResultsWe find that the precise timing and intensity of interventions is highly dependent upon the objective of control. As intervention measures are relaxed, we predict a resurgence in cases, but the optimal intervention policy can be established dependent upon the willingness to pay (WTP) per QALY loss avoided. Our results show that establishing an optimal level of control can result in a reduction in net monetary loss of billions of pounds, dependent upon the precise WTP value. ConclusionsIt is vital, as the UK emerges from lockdown, but continues to face an on-going pandemic, to accurately establish the overall health and economic costs when making policy decisions. We demonstrate how some of these can be quantified, employing mechanistic infectious disease transmission models to establish optimal levels of control for the ongoing COVID-19 pandemic.

14.
Preprint in English | medRxiv | ID: ppmedrxiv-21252338

ABSTRACT

BackgroundMultiple COVID-19 vaccines appear to be safe and efficacious, but only high-income countries have the resources to procure sufficient vaccine doses for most of their eligible populations. The World Health Organization has published guidelines for vaccine prioritisation, but most vaccine impact projections have focused on high-income countries, and few incorporate economic considerations. To address this evidence gap, we projected the health and economic impact of different vaccination scenarios in Sindh province, Pakistan (population: 48 million). Methods and FindingsWe fitted a compartmental transmission model to COVID-19 cases and deaths in Sindh from 30 April to 15 September 2020. We then projected cases, deaths, and hospitalization outcomes over 10 years under different vaccine scenarios. Finally, we combined these projections with a detailed economic model to estimate incremental costs (from healthcare and partial societal perspectives), disability adjusted life years (DALYs), and incremental cost-effectiveness ratio (ICER) for each scenario. We project that one-year of vaccine distribution, at delivery rates consistent with COVAX projections, using an infection-blocking vaccine at $3/dose with 70% efficacy and 2.5 year duration of protection is likely to avert around 0.9 (95% Credible Interval: 0.9, 1.0) million cases, 10.1 (95% CrI: 10.1, 10.3) thousand deaths and 70.1 (95% CrI: 69.9, 70.6) thousand DALYs, with an ICER of $27.9 per DALY averted from the health system perspective. Varying these assumptions, we generally find that prioritizing the older (65+) population prevents more deaths, but broad distribution from the outset is economically comparable in many scenarios, and either scheme can be cost-effective for low per-dose costs. However, high vaccine prices ($10/dose) may not be cost-effective. The principal drivers of the health outcomes are the fitted values for the overall transmission scaling parameter and disease natural history parameters from other studies, particularly age specific probabilities of infection and symptomatic disease, as well as social contact rates. Other parameters are investigated in sensitivity analyses. These projections are limited by the mechanisms present in the model. Because the model is a single-population compartmental model, detailed impacts of non-pharmaceutical interventions (NPIs) such as household isolation cannot be practically represented or evaluated in combination with vaccine programmes. Similarly, the model cannot consider prioritizing groups like healthcare or other essential workers. Additionally, because the future impact and implementation cost of NPIs is uncertain, how these would interact with vaccination remains an open question. ConclusionsCOVID-19 vaccination can have a considerable health impact, and is likely to be cost-effective if more optimistic vaccine scenarios apply. Preventing severe disease is an important contributor to this impact, but the advantage of focusing initially on older, high-risk populations may be smaller in generally younger populations where many people have already been infected, typical of many low- and -middle income countries, as long as vaccination gives good protection against infection as well as disease. Author SummaryO_ST_ABSWhy Was This Study Done?C_ST_ABS- The evidence base for health and economic impact of COVID-19 vaccination in low- and middle-income settings is limited. - Searching PubMed, medRxiv, and econLit using the search term ("coronavirus" OR "covid" OR "ncov") AND ("vaccination" OR "immunisation") AND ("model" OR "cost" OR "economic") for full text articles published in any language between 1 January 2020 and 20 January 2021, returned 29 (PubMed), 1,167 (medRxiv) and 0 (econLit) studies: 20 overall were relevant, with only 4 exclusively focused on low- or middle-income countries (India, China, Mexico), while 3 multi-country analyses also included low- or middle-income settings, - However only three of these studies are considered economic outcomes, all of them comparing the costs of vaccination to the costs of non-pharmaceutical interventions and concluding that both are necessary to reduce infections and maximise economic benefit. - The majority of studies are set in high-income settings and conclude that targeting COVID-19 vaccination to older age groups is the preferred strategy to minimise mortality, particularly when vaccine supplies are constrained, while other age- or occupational risk groups should be priorities when vaccine availability increases or when other policy objectives are pursued. What Did the Researchers Do and Find?- We combined epidemiological and economic analysis of COVID-19 vaccination based on real-world disease and programmatic information in the Sindh province of Pakistan. - We found vaccination in this setting is likely to be highly cost-effective, and even cost saving, as long as the vaccine is reasonably priced and efficacy is high. - Unlike studies in high-income settings, we also found that vaccination programmes targeting all adults may have almost as much benefit as those initially targeted at older populations, likely reflecting the higher previous infection rates and different demography in these settings. What Do These Findings Mean?- Lower- and middle-income countries (LMICs) and international bodies providing guidance for LMICs need to consider evidence specific to these settings when making recommendations about COVID-19 vaccination. - Further data and model-based analyses in such settings are urgently needed in order to ensure that vaccination decisions are appropriate to these contexts.

15.
Preprint in English | medRxiv | ID: ppmedrxiv-21250489

ABSTRACT

BackgroundChildhood immunisation services have been disrupted by COVID-19. WHO recommends considering outbreak risk using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the pandemic. MethodsWe used 2-3 models per infection to estimate the health impact of 50% reduced routine vaccination coverage and delaying campaign vaccination for measles, meningococcal A and yellow fever vaccination in 3-6 high burden countries per infection. ResultsReduced routine coverage in 2020 without catch-up vaccination may increase measles and yellow fever disease burden in the modelled countries. Delaying planned campaigns may lead to measles outbreaks and increases in yellow fever burden in some countries. For meningococcal A vaccination, short term disruptions in 2020 are unlikely to have a significant impact. ConclusionThe impact of COVID-19-related disruption to vaccination programs varies between infections and countries. FundingBill & Melinda Gates Foundation and Gavi, the Vaccine Alliance Impact statementRoutine and campaign vaccination disruption in 2020 may lead to measles outbreaks and yellow fever burden increases in some countries, but is unlikely to greatly increase meningococcal A burden. SummaryO_ST_ABSBackgroundC_ST_ABSChildhood immunisation services have been disrupted by the COVID-19 pandemic. WHO recommends considering outbreak risk using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the pandemic. MethodsWe used 2-3 models per infection to estimate the health impact of 50% reduced routine vaccination coverage in 2020 and delay of campaign vaccination from 2020 to 2021 for measles vaccination in Bangladesh, Chad, Ethiopia, Kenya, Nigeria, and South Sudan, for meningococcal A vaccination in Burkina Faso, Chad, Niger, and Nigeria, and for yellow fever vaccination in the Democratic Republic of Congo, Ghana, and Nigeria. Our counterfactual comparative scenario was sustaining immunisation services at coverage projections made prior to COVID-19 (i.e. without any disruption). FindingsReduced routine vaccination coverage in 2020 without catch-up vaccination may lead to an increase in measles and yellow fever disease burden in the modelled countries. Delaying planned campaigns in Ethiopia and Nigeria by a year may significantly increase the risk of measles outbreaks (both countries did complete their supplementary immunisation activities (SIAs) planned for 2020). For yellow fever vaccination, delay in campaigns leads to a potential disease burden rise of >1 death per 100,000 people per year until the campaigns are implemented. For meningococcal A vaccination, short term disruptions in 2020 are unlikely to have a significant impact due to the persistence of direct and indirect benefits from past introductory campaigns of the 1 to 29-year-old population, bolstered by inclusion of the vaccine into the routine immunisation schedule accompanied by further catch-up campaigns. InterpretationThe impact of COVID-19-related disruption to vaccination programs varies between infections and countries. Planning and implementation of campaigns should consider country and infection-specific epidemiological factors and local immunity gaps worsened by the COVID-19 pandemic when prioritising vaccines and strategies for catch-up vaccination. FundingBill & Melinda Gates Foundation and Gavi, the Vaccine Alliance

16.
Preprint in English | medRxiv | ID: ppmedrxiv-20248822

ABSTRACT

A novel SARS-CoV-2 variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in November 2020 and is rapidly spreading towards fixation. Using a variety of statistical and dynamic modelling approaches, we estimate that this variant has a 43-90% (range of 95% credible intervals 38-130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine roll-out, COVID-19 hospitalisations and deaths across England in 2021 will exceed those in 2020. Concerningly, VOC 202012/01 has spread globally and exhibits a similar transmission increase (59-74%) in Denmark, Switzerland, and the United States.

17.
Preprint in English | medRxiv | ID: ppmedrxiv-20220962

ABSTRACT

BackgroundShort-term forecasts of infectious disease can aid situational awareness and planning for outbreak response. Here, we report on multi-model forecasts of Covid-19 in the UK that were generated at regular intervals starting at the end of March 2020, in order to monitor expected healthcare utilisation and population impacts in real time. MethodsWe evaluated the performance of individual model forecasts generated between 24 March and 14 July 2020, using a variety of metrics including the weighted interval score as well as metrics that assess the calibration, sharpness, bias and absolute error of forecasts separately. We further combined the predictions from individual models into ensemble forecasts using a simple mean as well as a quantile regression average that aimed to maximise performance. We compared model performance to a null model of no change. ResultsIn most cases, individual models performed better than the null model, and ensembles models were well calibrated and performed comparatively to the best individual models. The quantile regression average did not noticeably outperform the mean ensemble. ConclusionsEnsembles of multi-model forecasts can inform the policy response to the Covid-19 pandemic by assessing future resource needs and expected population impact of morbidity and mortality.

18.
Preprint in English | medRxiv | ID: ppmedrxiv-20214585

ABSTRACT

The time-varying reproduction number (Rt: the average number secondary infections caused by each infected person) may be used to assess changes in transmission potential during an epidemic. While new infections are not usually observed directly, they can be estimated from data. However, data may be delayed and potentially biased. We investigated the sensitivity of Rt estimates to different data sources representing Covid-19 in England, and we explored how this sensitivity could track epidemic dynamics in population sub-groups. We sourced public data on test-positive cases, hospital admissions, and deaths with confirmed Covid-19 in seven regions of England over March through August 2020. We estimated Rt using a model that mapped unobserved infections to each data source. We then compared differences in Rt with the demographic and social context of surveillance data over time. Our estimates of transmission potential varied for each data source, with the relative inconsistency of estimates varying across regions and over time. Rt estimates based on hospital admissions and deaths were more spatio-temporally synchronous than when compared to estimates from all test-positives. We found these differences may be linked to biased representations of subpopulations in each data source. These included spatially clustered testing, and where outbreaks in hospitals, care homes, and young age groups reflected the link between age and severity of disease. We highlight that policy makers could better target interventions by considering the source populations of Rt estimates. Further work should clarify the best way to combine and interpret Rt estimates from different data sources based on the desired use.

19.
Preprint in English | medRxiv | ID: ppmedrxiv-20200857

ABSTRACT

BackgroundIn response to the coronavirus disease 2019 (COVID-19), the UK adopted mandatory physical distancing measures in March 2020. Vaccines against the newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may become available as early as late 2020. We explored the health and economic value of introducing SARS-CoV-2 immunisation alongside physical distancing scenarios in the UK. MethodsWe used an age-structured dynamic-transmission and economic model to explore different scenarios of immunisation programmes over ten years. Assuming vaccines are effective in 5-64 year olds, we compared vaccinating 90% of individuals in this age group to no vaccination. We assumed either vaccine effectiveness of 25% and 1-year protection and 90% re-vaccinated annually, or 75% vaccine effectiveness and 10-year protection and 10% re-vaccinated annually. Natural immunity was assumed to last 45 weeks in the base case. We also explored the additional impact of physical distancing. We considered benefits from disease prevented in terms of quality-adjusted life-years (QALYs), and costs to the healthcare payer versus the national economy. We discounted at 3.5% annually and monetised health impact at {pound}20,000 per QALY to obtain the net monetary value, which we explored in sensitivity analyses. FindingsWithout vaccination and physical distancing, we estimated 147.9 million COVID-19 cases (95% uncertainty interval: 48.5 million, 198.7 million) and 2.8 million (770,000, 4.2 million) deaths in the UK over ten years. Vaccination with 75% vaccine effectiveness and 10-year protection may stop community transmission entirely for several years, whereas SARS-CoV-2 becomes endemic without highly effective vaccines. Introducing vaccination compared to no vaccination leads to economic gains (positive net monetary value) of {pound}0.37 billion to +{pound}1.33 billion across all physical distancing and vaccine effectiveness scenarios from the healthcare perspective, but net monetary values of physical distancing scenarios may be negative from societal perspective if the daily national economy losses are persistent and large. InterpretationOur model findings highlight the substantial health and economic value of introducing SARS-CoV-2 vaccination. Given uncertainty around both characteristics of the eventually licensed vaccines and long-term COVID-19 epidemiology, our study provides early insights about possible future scenarios in a post-vaccination era from an economic and epidemiological perspective. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and medRxiv for economic evaluations of SARS-CoV-2 vaccines with the search string (coronavirus OR COVID OR SARS-CoV-2) AND (vaccin* OR immunisation) AND ((economic evaluation) OR (cost effectiveness analysis)) AND 2020[dp] on September 21, 2020, with no language restrictions. We found one pre-print that valued health outcomes in monetary terms and explored the additional impact of vaccines in a cost-benefit analysis of physical distancing for the USA; no study focused on vaccines in a full economic evaluation. Added value of this studyWith a growing number of vaccine candidates under development and having entered clinical trials, our study is to our knowledge the first to explore the health and economic value of introducing a national SARS-CoV-2 immunisation programme. A programme with high vaccine effectiveness and long-lasting protection may stop the community transmission entirely for a couple of years, but even a vaccine with 25% vaccine effectiveness is worthwhile to use; even at short-lived natural and vaccine-induced protections. After an initial lockdown, voluntary physical distancing as a sole strategy risks a large second epidemic peak, unless accompanied by highly effective immunisation. Compared to no vaccination, introducing vaccination leads to positive net monetary value across physical distancing scenarios from the healthcare perspective, subject to the long-run vaccine price and cost-effectiveness of other treatments (e.g. new drugs). The net monetary value of immunisation decreases if vaccine introduction is delayed, natural immunity is long or vaccine-induced protection is short. Intermittent physical distancing leads to negative net benefits from the perspective of the wider economy if the daily national income losses are persistent and large. Implications of all the available evidenceOur model findings highlight the health and economic value of introducing SARS-CoV-2 vaccination to control the COVID-19 epidemic. Despite the many uncertainties, continued physical distancing may be needed to reduce community transmission until vaccines with sufficiently high vaccine effectiveness and long-lasting protection are available. Our study provides first broad health-economic insights rather than precise quantitative projections given the many uncertainties and unknown characteristics of the vaccine candidates and aspects of the long-term COVID-19 epidemiology, and the value of vaccines will ultimately depend on other socioeconomic and health-related policies and population behaviours.

20.
Preprint in English | medRxiv | ID: ppmedrxiv-20186502

ABSTRACT

As several countries gradually release social distancing measures, rapid detection of new localised COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (Automatic Selection of Models and Outlier Detection for Epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterise the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggest ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. We illustrate our method using publicly available data of NHS Pathways reporting potential COVID-19 cases in England at a fine spatial scale, for which we provide a template automated analysis pipeline. ASMODEE is implemented in the free R package trendbreaker.

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