Replacement dynamics and the pathogenesis of the Alpha, Delta and Omicron variants of SARS-CoV-2.

New SARS-CoV-2 variants causing COVID-19 are a major risk to public health worldwide due to the potential for phenotypic change and increases in pathogenicity, transmissibility and/or vaccine escape. Recognising signatures of new variants in terms of replacing growth and severity are key to informing the public health response. To assess this, we aimed to investigate key time periods in the course of infection, hospitalisation and death, by variant. We linked datasets on contact tracing (Contact Tracing Advisory Service), testing (the Second-Generation Surveillance System) and hospitalisation (the Admitted Patient Care dataset) for the entire length of contact tracing in the England - from March 2020 to March 2022. We modelled, for England, time delay distributions using a Bayesian doubly interval censored modelling approach for the SARS-CoV-2 variants Alpha, Delta, Delta Plus (AY.4.2), Omicron BA.1 and Omicron BA.2. This was conducted for the incubation period, the time from infection to hospitalisation and hospitalisation to death. We further modelled the growth of novel variant replacement using a generalised additive model with a negative binomial error structure and the relationship between incubation period length and the risk of a fatality using a Bernoulli generalised linear model with a logit link. The mean incubation periods for each variant were: Alpha 4.19 (95% credible interval (CrI) 4.13-4.26) days; Delta 3.87 (95% CrI 3.82-3.93) days; Delta Plus 3.92 (95% CrI 3.87-3.98) days; Omicron BA.1 3.67 (95% CrI 3.61-3.72) days and Omicron BA.2 3.48 (95% CrI 3.43-3.53) days. The mean time from infection to hospitalisation was for Alpha 11.31 (95% CrI 11.20-11.41) days, Delta 10.36 (95% CrI 10.26-10.45) days and Omicron BA.1 11.54 (95% CrI 11.38-11.70) days. The mean time from hospitalisation to death was, for Alpha 14.31 (95% CrI 14.00-14.62) days; Delta 12.81 (95% CrI 12.62-13.00) days and Omicron BA.2 16.02 (95% CrI 15.46-16.60) days. The 95th percentile of the incubation periods were: Alpha 11.19 (95% CrI 10.92-11.48) days; Delta 9.97 (95% CrI 9.73-10.21) days; Delta Plus 9.99 (95% CrI 9.78-10.24) days; Omicron BA.1 9.45 (95% CrI 9.23-9.67) days and Omicron BA.2 8.83 (95% CrI 8.62-9.05) days. Shorter incubation periods were associated with greater fatality risk when adjusted for age, sex, variant, vaccination status, vaccination manufacturer and time since last dose with an odds ratio of 0.83 (95% confidence interval 0.82-0.83) (P value < 0.05). Variants of SARS-CoV-2 that have replaced previously dominant variants have had shorter incubation periods. Conversely co-existing variants have had very similar and non-distinct incubation period distributions. Shorter incubation periods reflect generation time advantage, with a reduction in the time to the peak infectious period, and may be a significant factor in novel variant replacing growth. Shorter times for admission to hospital and death were associated with variant severity - the most severe variant, Delta, led to significantly earlier hospitalisation, and death. These measures are likely important for future risk assessment of new variants, and their potential impact on population health.

Electronic data collection, management and analysis tools used for outbreak response in low- and middle-income countries: a systematic review and stakeholder survey.

BACKGROUND: Use of electronic data collection, management and analysis tools to support outbreak response is limited, especially in low income countries. This can hamper timely decision-making during outbreak response. Identifying available tools and assessing their functions in the context of outbreak response would support appropriate selection and use, and likely more timely data-driven decision-making during outbreaks. METHODS: We conducted a systematic review and a stakeholder survey of the Global Outbreak Alert and Response Network and other partners to identify and describe the use of, and technical characteristics of, electronic data tools used for outbreak response in low- and middle-income countries. Databases included were MEDLINE, EMBASE, Global Health, Web of Science and CINAHL with publications related to tools for outbreak response included from January 2010-May 2020. Software tool websites of identified tools were also reviewed. Inclusion and exclusion criteria were applied and counts, and proportions of data obtained from the review or stakeholder survey were calculated. RESULTS: We identified 75 electronic tools including for data collection (33/75), management (13/75) and analysis (49/75) based on data from the review and survey. Twenty-eight tools integrated all three functionalities upon collection of additional information from the tool developer websites. The majority were open source, capable of offline data collection and data visualisation. EpiInfo, KoBoCollect and Open Data Kit had the broadest use, including for health promotion, infection prevention and control, and surveillance data capture. Survey participants highlighted harmonisation of data tools as a key challenge in outbreaks and the need for preparedness through training front-line responders on data tools. In partnership with the Global Health Network, we created an online interactive decision-making tool using data derived from the survey and review. CONCLUSIONS: Many electronic tools are available for data -collection, -management and -analysis in outbreak response, but appropriate tool selection depends on knowledge of tools' functionalities and capabilities. The online decision-making tool created to assist selection of the most appropriate tool(s) for outbreak response helps by matching requirements with functionality. Applying the tool together with harmonisation of data formats, and training of front-line responders outside of epidemic periods can support more timely data-driven decision making in outbreaks.

COVID-19: Identifying countries with indicators of success in responding to the outbreak.

Background: In designing responses to the COVID-19 pandemic, it is critical to understand what has already worked well. We aimed to identify countries with emerging success stories from whom policymakers might draw important lessons.  Methods: We developed a process to first include countries with large enough populations that results were unlikely to be due to chance, that had sufficient cases for response mechanisms to be tested, and that shared the necessary publicly available data. Within these countries, we looked at indicators suggesting success in terms of detecting disease, containing the outbreak, and treating those who were unwell. To support comparability, we measured indicators per capita (per million) and across time. We then used the indicators to identify three countries with emerging success stories to include some diversity in global region, population demographics and form of government. Results: We identified 66 countries that met our inclusion criteria on 18 th May 2020. Several of these countries had indicators of success against the set indicators at different times in the outbreak. Vietnam had high levels of testing and successful containment with no deaths reported. South Korea had high levels of testing early in the outbreak, supporting containment. Germany had high levels of sustained testing and slower increases in cases and deaths than seen in other comparable settings. Conclusions: At the time of our assessment, Vietnam and South Korea were able to contain the outbreak of COVID-19 and avoid the exponential growth in cases seen elsewhere. Germany had more cases and deaths, but was nevertheless able to contain and mitigate the outbreak. Despite the many limitations to the data currently available, looking at comparative data can help identify countries from whom we can draw lessons, so that countries can inform and adapt their strategies for success in response to COVID-19.

Learning from each other in the COVID-19 pandemic [version 2;peer review: 1 approved, 2 approved with reservations]

The increase in cases of coronavirus disease 2019 (COVID-19) worldwide has been paralleled by increasing information, and misinformation. Accurate public health messaging is essential to counter this, but education may also have a role. Early in the outbreak, The London School of Hygiene & Tropical Medicine partnered with FutureLearn to develop a massive open online course (MOOC) on COVID-19. Our approach was grounded in social constructivism, supporting participation, sharing uncertainties, and encouraging discussion. The first run of the course included over 200,000 participants from 184 countries, with over 88,000 comments at the end of the three-week run. Many participants supported each other’s learning in their responses and further questions. Our experience suggests that open education can complement traditional messaging, potentially providing a sustainable approach to countering the spread of misinformation in public health.

Learning from each other in the COVID-19 pandemic.

The increase in cases of coronavirus disease 2019 (COVID-19) worldwide has been paralleled by increasing information, and misinformation. Accurate public health messaging is essential to counter this, but education may also have a role. Early in the outbreak, The London School of Hygiene & Tropical Medicine partnered with FutureLearn to develop a massive open online course (MOOC) on COVID-19. Our approach was grounded in social constructivism, supporting participation, sharing uncertainties, and encouraging discussion. The first run of the course included over 200,000 participants from 184 countries, with over 88,000 comments at the end of the three-week run. Many participants supported each other's learning in their responses and further questions. Our experience suggests that open education, and supporting the development of communities of learners, can complement traditional messaging, providing a sustainable approach to countering the spread of misinformation.

A snapshot of the practicality and barriers to COVID-19 interventions: Public health and healthcare workers' perceptions in high and low- and middle-income countries.

BACKGROUND: In response to the COVID-19 pandemic, governments have implemented a range of non-pharmaceutical interventions (NPIs) and pharmaceutical interventions (PIs) to reduce transmission and minimise morbidity and mortality, whilst maintaining social and economic activities. The perceptions of public health workers (PHWs) and healthcare workers (HCWs) are essential to inform future COVID-19 strategies as they are viewed as trusted sources and are at the forefront of COVID-19 response. The objectives of this study were to 1) describe the practicality of implementing NPIs and PIs and 2) identify potential barriers to implementation, as perceived by HCWs and PHWs. METHODS: We conducted a cross-sectional study of PHWs and HCWs perceptions of the implementation, practicality of, and barriers to implementation of NPIs and PIs using an online survey (28/9/2020-1/11/2020) available in English, French and Portuguese. We used descriptive statistics and thematic analysis to analyse quantitative and qualitative responses. RESULTS: In total, 226 respondents (67 HCWs and 159 PHWs) from 52 countries completed the survey and 222 were included in the final analysis. Participants from low and middle-income countries (LMICs) accounted for 63% of HCWs and 67% of PHWs, with the remaining from high-income (HICs). There was little difference between the perceptions of PHWs and HCWs in HICs and LMICs, with the majority regarding a number of common NPIs as difficult to implement. However, PHWs in HICs perceived restrictions on schools and educational institutions to be more difficult to implement, with a lack of childcare support identified as the main barrier. Additionally, most contact tracing methods were perceived to be more difficult to implement in HICs than LMICs, with a range of barriers reported. A lack of public support was the most commonly reported barrier to NPIs overall across both country income and professional groups. Similarly, public fear of vaccine safety and lack of vaccine supply were the main reported barriers to implementing a COVID-19 vaccine. However, PHWs and HCWs in LMICs perceived a lack of financial support and the vaccine being manufactured in another country as additional barriers. CONCLUSION: This snapshot provides insight into the difficulty of implementing interventions as perceived by PHWs and HCWs. There is no one-size-fits-all solution to implementing interventions, and barriers in different contexts do vary. Barriers to implementing a vaccine programme expressed here by HCWs and PHCWs have subsequently come to the fore internationally.