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A new spatially explicit multivariate method, spatial principal component analysis (sPCA) is proposed to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations. The sPCA yields scores both the genetic variability and the spatial structure among individuals.

Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. In particular, genetic data coupled with dates and locations of sampled isolates can be used to reconstruct the spatiotemporal dynamics of pathogens during outbreaks. Thus far, phylogenetic methods have been used to tackle this issue. Although these approaches have proved useful for informing on the spread of pathogens, they do not aim at directly reconstructing the underlying transmission tree. Instead, phylogenetic models infer most recent common ancestors between pairs of isolates, which can be inadequate for densely sampled recent outbreaks, where the sample includes ancestral and descendent isolates. In this paper, we introduce a novel method based on a graph approach to reconstruct transmission trees directly from genetic data. Using simulated data, we show that our approach can efficiently reconstruct genealogies of isolates in situations where classical phylogenetic approaches fail to do so. We then illustrate our method by analyzing data from the early stages of the swine-origin A/H1N1 influenza pandemic. Using 433 isolates sequenced at both the hemagglutinin and neuraminidase genes, we reconstruct the likely history of the worldwide spread of this new influenza strain. The presented methodology opens new perspectives for the analysis of genetic data in the context of disease outbreaks. [ABSTRACT FROM AUTHOR]

Background: Marburg virus disease is an acute haemorrhagic fever caused by Marburg virus. Marburg virus is zoonotic, maintained in nature in Egyptian fruit bats, with occasional spillover infections into humans and nonhuman primates. Although rare, sporadic cases and outbreaks occur in Africa, usually associated with exposure to bats in mines or caves, and sometimes with secondary human-to-human transmission. Outbreaks outside of Africa have also occurred due to importation of infected monkeys. Although all previous Marburg virus disease outbreaks have been brought under control without vaccination, there is nevertheless the potential for large outbreaks when implementation of public health measures is not possible or breaks down. Vaccines could thus be an important additional tool, and development of several candidate vaccines is under way., Methods: We developed a branching process model of Marburg virus transmission and investigated the potential effects of several prophylactic and reactive vaccination strategies in settings driven primarily by multiple spillover events as well as human-to-human transmission. Linelist data from the 15 outbreaks up until 2022, as well as an Approximate Bayesian Computational framework, were used to inform the model parameters., Results: Our results show a low basic reproduction number which varied across outbreaks, from 0.5 [95% CI 0.05-1.8] to 1.2 [95% CI 1.0-1.9] but a high case fatality ratio. Of six vaccination strategies explored, the two prophylactic strategies (mass and targeted vaccination of high-risk groups), as well as a combination of ring and targeted vaccination, were generally most effective, with a probability of potential outbreaks being terminated within 1 year of 0.90 (95% CI 0.90-0.91), 0.89 (95% CI 0.88-0.90), and 0.88 (95% CI 0.87-0.89) compared with 0.68 (0.67-0.69) for no vaccination, especially if the outbreak is driven by zoonotic spillovers and the vaccination campaign initiated as soon as possible after onset of the first case., Conclusions: Our study shows that various vaccination strategies can be effective in helping to control outbreaks of MVD, with the best approach varying with the particular epidemiologic circumstances of each outbreak., (© 2023. The Author(s).)

The 10th Ebola virus disease (EVD) outbreak in the Democratic Republic of the Congo (DRC) drew substantial attention from the international community, which in turn invested more than US$1 billion in EVD control over two years (2018-2020). This is the first EVD outbreak to take place in a conflict area, which led to a shift in strategy from a pure public health response (PHR) to a multisectoral humanitarian response. A wide range of disease control and mitigation activities were implemented and were outlined in the five budgeted Strategic Response Plans used throughout the 26 months. This study used the budget/expenditure and output indicators for disease control and mitigation interventions compiled by the government of DRC and development and humanitarian partners to estimate unit costs of key Ebola control interventions. Of all the investment in EVD control, 68% was spent on PHR. The remaining 32% covered security, community support interventions for the PHR. The disbursement for the public health pillar was distributed as follows: (1) coordination (18.8%), (2), clinical management of EVD cases (18.4%), (3) surveillance and vaccination (15.9%), (4) infection prevention and control/WASH (13.8%) and (5) risk communication (13.7%). The unit costs of key EVD control interventions were as follows: US$66 182 for maintaining a rapid response team per month, US$4435 for contact tracing and surveillance per identified EVD case, US$1464 for EVD treatment per case, US$59.4 per EVD laboratory test, US$120.7 per vaccinated individual against EVD and US$175.0 for mental health and psychosocial support per beneficiary. The estimated unit costs of key EVD disease control interventions provide crucial information for future infectious disease control planning and budgeting, as well as prioritisation of disease control interventions., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Whilst it is now widely recognised that routine immunisation (RI) was disrupted by the COVID-19 pandemic in 2020, and further so in 2021, the extent of continued interruptions in 2022 and/or rebounds to previous trends remains unclear. We modelled country-specific RI trends using validated estimates of national coverage from the World Health Organisation and United Nation Children's Fund for 182 countries (accounting for > 97% of children globally), to project expected diphtheria, tetanus, and pertussis-containing vaccine first-dose (DTP1), third-dose (DTP3) and measles-containing vaccine first-dose (MCV1) coverage for 2020-2022 based on pre-pandemic trends (from 2000 to 2019). We provide further evidence of peak pandemic immunisation disruption in 2021, followed by tentative recovery in 2022. We report a 3.4% (95 %CI: [2.5%; 4.4%]) decline in global DTP3 coverage in 2021 compared to 2000-2019 trends, from an expected 89.8% to reported 86.4%. This coverage gap reduced to a 2.7% (95 %CI: [1.8%; 3.6%]) decline in 2022, with reported coverage rising to 87.2%. Similar results were seen for DTP1 and MCV1. Whilst partial rebounds are encouraging, global coverage decline translates to a 17-year setback in RI to 2005 levels, and the majority of countries retain coverage at or lower than pre-pandemic levels. The Americas, Africa, and Asia were the most impacted regions; and low- and middle-income countries the most affected income groups. The number of annual Zero Dose (ZD) children - indicating those receiving no immunisations - increased from 12.1 million (M) globally in 2019 to a peak of 16.7 M in 2021, then reduced to 13.1 M in 2022. Overall, we estimate an excess of 8.8 M ZD children cumulatively in 2020-2022 compared to pre-pandemic levels. This work can be used as an objective baseline to inform future interventions to prioritise and target interventions, and facilitate catch-up of growing populations of under- and un-immunised children., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: It is noted that BE has been employed by the Clinton Health Access Initiative in the Global Vaccines team in the last three years; and is currently employed by Gavi, the Vaccine Alliance. All research contained in this manuscript was conducted during a doctorate qualification, outside and independent of employment. Neither facilities, data, nor any other forms of input from the Clinton Health Access Initiative or Gavi, were used in this study. The research and manuscript are independent of the Clinton Health Access Initiative and Gavi, and the findings have not been discussed, reviewed, or endorsed by the Clinton Health Access Initiative, the Gavi Secretariat, or any Alliance members., (© 2023 The Authors.)

Background: Outbreaks of Marburg virus disease (MVD) are rare and small in size, with only 18 recorded outbreaks since 1967, only two of which involved more than 100 cases. It has been proposed, therefore, that Phase 3 trials for MVD vaccines should be held open over multiple outbreaks until sufficient end points accrue to enable vaccine efficacy (VE) to be calculated. Here we estimate how many outbreaks might be needed for VE to be estimated., Methods: We adapt a mathematical model of MVD transmission to simulate a Phase 3 individually randomised placebo controlled vaccine trial. We assume in the base case that vaccine efficacy is 70% and that 50% of individuals in affected areas are enrolled into the trial (1:1 randomisation). We further assume that the vaccine trial starts two weeks after public health interventions are put in place and that cases occurring within 10 days of vaccination are not included in VE calculations., Results: The median size of simulated outbreaks was 2 cases. Only 0.3% of simulated outbreaks were predicted to have more than 100 MVD cases. 95% of simulated outbreaks terminated before cases accrued in the placebo and vaccine arms. Therefore the number of outbreaks required to estimate VE was large: after 100 outbreaks, the estimated VE was 69% but with considerable uncertainty (95% CIs: 0%-100%) while the estimated VE after 200 outbreaks was 67% (95% CIs: 42%-85%). Altering base-case assumptions made little difference to the findings. In a sensitivity analysis, increasing R 0 by 25% and 50% led to an estimated VE after 200 outbreaks of 69% (95% CIs: 53-85%) and 70% (95% CIs: 59-82%), respectively., Conclusions: It is unlikely that the efficacy of any candidate vaccine can be calculated before more MVD outbreaks have occurred than have been recorded to date. This is because MVD outbreaks tend to be small, public health interventions have been historically effective at reducing transmission, and vaccine trials are only likely to start after these interventions are already in place. Hence, it is expected that outbreaks will terminate before, or shortly after, cases start to accrue in the vaccine and placebo arms., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: W John Edmunds reports financial support was provided by Department of Health and Social Care. Thibaut Jombart reports financial support was provided by MRC Centre for Global Infectious Disease Analysis. W John Edmunds reports financial support was provided by Japan Agency for Medical Research and Development. George Qian reports a relationship with Pfizer that includes: funding grants and non-financial support. GQ works on a separate project that is funded by Pfizer (please see ’Other Support’ section)., (© 2023 The Author(s).)

Background: The serial interval is a key epidemiological measure that quantifies the time between an infector's and an infectee's onset of symptoms. This measure helps investigate epidemiological links between cases, and is an important parameter in transmission models used to estimate transmissibility and inform control strategies. The emergence of multiple variants of concern (VOC) during the SARS-CoV-2 pandemic has led to uncertainties about potential changes in the serial interval of COVID-19. We estimated the household serial interval of multiple VOC using data collected by the Virus Watch study. This online, prospective, community cohort study followed-up entire households in England and Wales since mid-June 2020., Methods: This analysis included 5842 symptomatic individuals with confirmed SARS-CoV-2 infection among 2579 households from Sept 1, 2020, to Aug 10, 2022. SARS-CoV-2 variant designation was based upon national surveillance data of variant prevalence by date and geographical region. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, given assumptions on the incubation period and generation time distributions using the R package outbreaker2., Findings: We characterised the serial interval of COVID-19 by VOC. The mean serial interval was shortest for omicron BA5 (2·02 days; 95% credible interval [CrI] 1·26-2·84) and longest for alpha (3·37 days; 2·52-4·04). The mean serial interval before alpha (wild-type) was 2·29 days (95% CrI 1·39-2·94), 3·11 days (2·28-3·90) for delta, 2·72 days (2·01-3·47) for omicron BA1, and 2·67 days (1·90-3·46) for omicron BA2. We estimated that 17% (95% CrI 5-26) of serial interval values are negative across all variants., Interpretation: Most methods estimating the reproduction number from incidence time series do not allow for a negative serial interval by construction. Further research is needed to extend these methods and assess biases introduced by not accounting for negative serial intervals. To our knowledge, this study is the first to use a Bayesian framework to estimate the serial interval of all major SARS-CoV-2 VOC from thousands of confirmed household cases., Funding: UK Medical Research Council and Wellcome Trust., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Whilst COVID-19 vaccination strategies continue to receive considerable emphasis worldwide, the extent to which routine immunisation (RI) has been impacted during the first year of the pandemic remains unclear. Understanding the existence, extent, and variations in RI disruptions globally may help inform policy and resource prioritisation as the pandemic continues. We modelled historical, country-specific RI trends using publicly available vaccination coverage data for diphtheria, tetanus and pertussis-containing vaccine first-dose (DTP1) and third-dose (DTP3) from 2000 to 2019. We report a 2·9% (95 % CI : [2·2%; 3·6%]) global decline in DTP3 coverage from an expected 89·2% to a reported 86·3%; and a 2·2% decline in DTP1 coverage (95 % CI : [1·6%; 2·8%]). These declines translate to levels of coverage last observed in 2005, thus suggesting a potential 15-years setback in RI improvements. Further research is required to understand which factors - e.g., health seeking behaviours or non-pharmaceutical interventions - linked to the COVID-19 crisis impacted vaccination coverage., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: It is noted that BE has been employed by the Clinton Health Access Initiative in the Global Vaccines team in the last three years; and is currently employed by Gavi. All research contained in this manuscript was conducted during a postgraduate qualification, outside and independent of employment. Neither facilities, data, nor any other forms of input from the Clinton Health Access Initiative or Gavi, were used in this study. The research and manuscript are independent of the Clinton Health Access Initiative and Gavi, and the findings have not been discussed, reviewed, or endorsed by the Clinton Health Access Initiative, the Gavi Secretariat, or any Alliance members., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The fraction of cases reported, known as 'reporting', is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018-2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5-10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities., Competing Interests: The authors have declared that no competing interests exist.

Hospital outbreaks of COVID19 result in considerable mortality and disruption to healthcare services and yet little is known about transmission within this setting. We characterise within hospital transmission by combining viral genomic and epidemiological data using Bayesian modelling amongst 2181 patients and healthcare workers from a large UK NHS Trust. Transmission events were compared between Wave 1 (1st March to 25th J'uly 2020) and Wave 2 (30th November 2020 to 24th January 2021). We show that staff-to-staff transmissions reduced from 31.6% to 12.9% of all infections. Patient-to-patient transmissions increased from 27.1% to 52.1%. 40%-50% of hospital-onset patient cases resulted in onward transmission compared to 4% of community-acquired cases. Control measures introduced during the pandemic likely reduced transmissions between healthcare workers but were insufficient to prevent increasing numbers of patient-to-patient transmissions. As hospital-acquired cases drive most onward transmission, earlier identification of nosocomial cases will be required to break hospital transmission chains., (© 2022. The Author(s).)

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018-2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d'Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)