Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey.

Key epidemiological parameters, including the effective reproduction number, R(t), and the instantaneous growth rate, r(t), generated from an ensemble of models, have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland (UK). However, estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the "emergency" to "endemic" phase of the pandemic. The Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) provided an opportunity to continue estimating these parameters in the absence of other data streams. We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time. The resulting fitted curve was used to estimate the "ONS-based" R(t) and r(t) across the four nations of the UK. Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters. Depending on the nation and parameter, we found that up to 77% of the variance in the government-published estimates can be explained by the ONS-based estimates, demonstrating the value of this singular data stream to track the epidemic in each of the four nations. We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates. Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations, further underlining the enormous value of such population-level studies of infection. This is not intended as an alternative to ensemble modelling, rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.

Marburg virus disease outbreaks, mathematical models, and disease parameters: a systematic review.

The 2023 Marburg virus disease outbreaks in Equatorial Guinea and Tanzania highlighted the importance of better understanding this lethal pathogen. We did a systematic review (PROSPERO CRD42023393345) of peer-reviewed articles reporting historical outbreaks, modelling studies, and epidemiological parameters focused on Marburg virus disease. We searched PubMed and Web of Science from database inception to March 31, 2023. Two reviewers evaluated all titles and abstracts with consensus-based decision making. To ensure agreement, 13 (31%) of 42 studies were double-extracted and a custom-designed quality assessment questionnaire was used for risk of bias assessment. We present detailed information on 478 reported cases and 385 deaths from Marburg virus disease. Analysis of historical outbreaks and seroprevalence estimates suggests the possibility of undetected Marburg virus disease outbreaks, asymptomatic transmission, or cross-reactivity with other pathogens, or a combination of these. Only one study presented a mathematical model of Marburg virus transmission. We estimate an unadjusted, pooled total random effect case fatality ratio of 61·9% (95% CI 38·8-80·6; I2=93%). We identify epidemiological parameters relating to transmission and natural history, for which there are few estimates. This systematic review and the accompanying database provide a comprehensive overview of Marburg virus disease epidemiology and identify key knowledge gaps, contributing crucial information for mathematical models to support future Marburg virus disease epidemic responses.

Public awareness of and opinions on the use of mathematical transmission modelling to inform public health policy in the United Kingdom.

Mathematical modelling is used to inform public health policy, particularly so during the COVID-19 pandemic. As the public are key stakeholders, understanding the public perceptions of these tools is vital. To complement our previous study on the science-policy interface, novel survey data were collected via an online panel ('representative' sample) and social media ('non-probability' sample). Many questions were asked twice, in reference to the period 'prior to' (retrospectively) and 'during' the COVID-19 pandemic. Respondents reported being increasingly aware of modelling in informing policy during the pandemic, with higher levels of awareness among social media respondents. Modelling informing policy was perceived as more reliable during the pandemic than in reference to the pre-pandemic period in both samples. Trust in government public health advice remained high within both samples but was lower during the pandemic in comparison with the (retrospective) pre-pandemic period. The decay in trust was greater among social media respondents. Many respondents explicitly made the distinction that their trust was reserved for 'scientists' and not 'politicians'. Almost all respondents believed governments have responsibility for communicating modelling to the public. These results provide a reminder of the skewed conclusions that could be drawn from non-representative samples.

Alternative epidemic indicators for COVID-19 in three settings with incomplete death registration systems.

Not all COVID-19 deaths are officially reported, and particularly in low-income and humanitarian settings, the magnitude of reporting gaps remains sparsely characterized. Alternative data sources, including burial site worker reports, satellite imagery of cemeteries, and social media-conducted surveys of infection may offer solutions. By merging these data with independently conducted, representative serological studies within a mathematical modeling framework, we aim to better understand the range of underreporting using examples from three major cities: Addis Ababa (Ethiopia), Aden (Yemen), and Khartoum (Sudan) during 2020. We estimate that 69 to 100%, 0.8 to 8.0%, and 3.0 to 6.0% of COVID-19 deaths were reported in each setting, respectively. In future epidemics, and in settings where vital registration systems are limited, using multiple alternative data sources could provide critically needed, improved estimates of epidemic impact. However, ultimately, these systems are needed to ensure that, in contrast to COVID-19, the impact of future pandemics or other drivers of mortality is reported and understood worldwide.

Tracking the incidence and risk factors for SARS-CoV-2 infection using historical maternal booking serum samples.

The early transmission dynamics of SARS-CoV-2 in the UK are unknown but their investigation is critical to aid future pandemic planning. We tested over 11,000 anonymised, stored historic antenatal serum samples, given at two north-west London NHS trusts in 2019 and 2020, for total antibody to SARS-CoV-2 receptor binding domain (anti-RBD). Estimated prevalence of seroreactivity increased from 1% prior to mid-February 2020 to 17% in September 2020. Our results show higher prevalence of seroreactivity to SARS-CoV-2 in younger, non-white ethnicity, and more deprived groups. We found no significant interaction between the effects of ethnicity and deprivation. Derived from prevalence, the estimated incidence of seroreactivity reflects the trends observed in daily hospitalisations and deaths in London that followed 10 and 13 days later, respectively. We quantified community transmission of SARS-CoV-2 in London, which peaked in late March / early April 2020 with no evidence of community transmission until after January 2020. Our study was not able to determine the date of introduction of the SARS-CoV-2 virus but demonstrates the value of stored antenatal serum samples as a resource for serosurveillance during future outbreaks.

Milestone house: The story of a hospice for people with HIV/AIDS.

The HIV/AIDS epidemic of the early 1980s has been extensively documented, with all its epidemiological, scientific and medical impacts. Cultural implications for many sectors of society have been profound and long-lasting. Some areas merit reflection for their ingenuity in the face of a crisis and the effect they have had on clinical practice. This report gives an account of a venture into the unknown territory of palliative care. Described here is a group of young people and their families propelled into a nightmarish condition. Those involved were outlawed and stigmatised, suffering from a disease leading to their inevitable death. By association with this group, carers and medical staff were left to interpret unhelpful guidelines and to venture into unknown therapeutic territory, sometimes beyond the margins of conventional practice.

Adapting field-mosquito collection techniques in a perspective of near-infrared spectroscopy implementation.

BACKGROUND: Near-infrared spectroscopy (NIRS) has the potential to be a useful tool for assessing key entomological parameters of malaria-transmitting mosquitoes, including age, infectious status and species identity. However, before NIRS can be reliably used in the field at scale, methods for killing mosquitoes and conserving samples prior to NIRS scanning need to be further optimized. Historically, mosquitoes used in studies have been killed with chloroform, although this approach is not without health hazards and should not be used in human dwellings. For the application of NIRS scanning it is also unclear which mosquito preservation method to use. The aim of the study reported here was to investigate the use of pyrethrum spray, a commercially available insecticide spray in Burkina Faso, for killing mosquitoes METHODS: Laboratory-reared Anopheles gambiae and Anopheles coluzzii were killed using either a pyrethrum insecticide spray routinely used in studies involving indoor mosquito collections (Kaltox Paalga®; Saphyto, Bobo-Dioulasso, Burkina Faso) or chloroform ("gold standard"). Preservative methods were also investigated to determine their impact on NIRS accuracy in predicting the species of laboratory-reared Anopheles and wild-caught mosquito species. After analysis of fresh samples, mosquitoes were stored in 80% ethanol or in silica gel for 2 weeks and re-analyzed by NIRS. In addition, experimentally infected An. coluzzii and wild-caught An. gambiae sensu lato (s.l.) were scanned as fresh samples to determine whether they contained sporozoites, then stored in the preservatives mentioned above for 2 weeks before being re-analyzed. RESULTS: The difference in the accuracy of NIRS to differentiate between laboratory-reared An. gambiae mosquitoes and An. coluzzii mosquitoes killed with either insecticide (90%) or chloroform (92%) was not substantial. NIRS had an accuracy of 90% in determining mosquito species for mosquitoes killed with chloroform and preserved in ethanol or silica gel. The accuracy was the same when the pyrethrum spray was used to kill mosquitoes followed by preservation in silica gel, but was lower when ethanol was used as a preservative (80%). Regarding infection status, NIRS was able to differentiate between infected and uninfected mosquitoes, with a slightly lower accuracy for both laboratory and wild-caught mosquitoes preserved in silica gel or ethanol. CONCLUSIONS: The results show that NIRS can be used to classify An. gambiae s.l. species killed by pyrethrum spray with no loss of accuracy. This insecticide may have practical advantages over chloroform for the killing of mosquitoes in NIRS analysis.

Back to the Future: Quantifying Wing Wear as a Method to Measure Mosquito Age.

Vector biologists have long sought the ability to accurately quantify the age of wild mosquito populations, a metric used to measure vector control efficiency. This has proven difficult due to the difficulties of working in the field and the biological complexities of wild mosquitoes. Ideal age grading techniques must overcome both challenges while also providing epidemiologically relevant age measurements. Given these requirements, the Detinova parity technique, which estimates age from the mosquito ovary and tracheole skein morphology, has been most often used for mosquito age grading despite significant limitations, including being based solely on the physiology of ovarian development. Here, we have developed a modernized version of the original mosquito aging method that evaluated wing wear, expanding it to estimate mosquito chronological age from wing scale loss. We conducted laboratory experiments using adult Anopheles gambiae held in insectary cages or mesocosms, the latter of which also featured ivermectin bloodmeal treatments to change the population age structure. Mosquitoes were age graded by parity assessments and both human- and computational-based wing evaluations. Although the Detinova technique was not able to detect differences in age population structure between treated and control mesocosms, significant differences were apparent using the wing scale technique. Analysis of wing images using averaged left- and right-wing pixel intensity scores predicted mosquito age at high accuracy (overall test accuracy: 83.4%, average training accuracy: 89.7%). This suggests that this technique could be an accurate and practical tool for mosquito age grading though further evaluation in wild mosquito populations is required.

Disease transmission and control modelling at the science-policy interface.

The coronavirus disease 2019 (COVID-19) pandemic has disrupted the lives of billions across the world. Mathematical modelling has been a key tool deployed throughout the pandemic to explore the potential public health impact of an unmitigated epidemic. The results of such studies have informed governments' decisions to implement non-pharmaceutical interventions to control the spread of the virus. In this article, we explore the complex relationships between models, decision-making, the media and the public during the COVID-19 pandemic in the United Kingdom of Great Britain and Northern Ireland (UK). Doing so not only provides an important historical context of COVID-19 modelling and how it has shaped the UK response, but as the pandemic continues and looking towards future pandemic preparedness, understanding these relationships and how they might be improved is critical. As such, we have synthesized information gathered via three methods: a survey to publicly list attendees of the Scientific Advisory Group for Emergencies, the Scientific Pandemic Influenza Group on Modelling and other comparable advisory bodies, interviews with science communication experts and former scientific advisors, and reviewing some of the key COVID-19 modelling literature from 2020. Our research highlights the desire for increased bidirectional communication between modellers, decision-makers and the public, as well as the need to convey uncertainty inherent in transmission models in a clear manner. These aspects should be considered carefully ahead of the next emergency response.

Communicating uncertainty in epidemic models.

While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner.

Detection of Plasmodium falciparum in laboratory-reared and naturally infected wild mosquitoes using near-infrared spectroscopy.

There is an urgent need for high throughput, affordable methods of detecting pathogens inside insect vectors to facilitate surveillance. Near-infrared spectroscopy (NIRS) has shown promise to detect arbovirus and malaria in the laboratory but has not been evaluated in field conditions. Here we investigate the ability of NIRS to identify Plasmodium falciparum in Anopheles coluzzii mosquitoes. NIRS models trained on laboratory-reared mosquitoes infected with wild malaria parasites can detect the parasite in comparable mosquitoes with moderate accuracy though fails to detect oocysts or sporozoites in naturally infected field caught mosquitoes. Models trained on field mosquitoes were unable to predict the infection status of other field mosquitoes. Restricting analyses to mosquitoes of uninfectious and highly-infectious status did improve predictions suggesting sensitivity and specificity may be better in mosquitoes with higher numbers of parasites. Detection of infection appears restricted to homogenous groups of mosquitoes diminishing NIRS utility for detecting malaria within mosquitoes.

Modelling intensive care unit capacity under different epidemiological scenarios of the COVID-19 pandemic in three Western European countries.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has placed enormous strain on intensive care units (ICUs) in Europe. Ensuring access to care, irrespective of COVID-19 status, in winter 2020-2021 is essential. METHODS: An integrated model of hospital capacity planning and epidemiological projections of COVID-19 patients is used to estimate the demand for and resultant spare capacity of ICU beds, staff and ventilators under different epidemic scenarios in France, Germany and Italy across the 2020-2021 winter period. The effect of implementing lockdowns triggered by different numbers of COVID-19 patients in ICUs under varying levels of effectiveness is examined, using a 'dual-demand' (COVID-19 and non-COVID-19) patient model. RESULTS: Without sufficient mitigation, we estimate that COVID-19 ICU patient numbers will exceed those seen in the first peak, resulting in substantial capacity deficits, with beds being consistently found to be the most constrained resource. Reactive lockdowns could lead to large improvements in ICU capacity during the winter season, with pressure being most effectively alleviated when lockdown is triggered early and sustained under a higher level of suppression. The success of such interventions also depends on baseline bed numbers and average non-COVID-19 patient occupancy. CONCLUSION: Reductions in capacity deficits under different scenarios must be weighed against the feasibility and drawbacks of further lockdowns. Careful, continuous decision-making by national policymakers will be required across the winter period 2020-2021.

The J-IDEA Pandemic Planner: A Framework for Implementing Hospital Provision Interventions During the COVID-19 Pandemic.

BACKGROUND: Planning for extreme surges in demand for hospital care of patients requiring urgent life-saving treatment for coronavirus disease 2019 (COVID-19), while retaining capacity for other emergency conditions, is one of the most challenging tasks faced by health care providers and policymakers during the pandemic. Health systems must be well-prepared to cope with large and sudden changes in demand by implementing interventions to ensure adequate access to care. We developed the first planning tool for the COVID-19 pandemic to account for how hospital provision interventions (such as cancelling elective surgery, setting up field hospitals, or hiring retired staff) will affect the capacity of hospitals to provide life-saving care. METHODS: We conducted a review of interventions implemented or considered in 12 European countries in March to April 2020, an evaluation of their impact on capacity, and a review of key parameters in the care of COVID-19 patients. This information was used to develop a planner capable of estimating the impact of specific interventions on doctors, nurses, beds, and respiratory support equipment. We applied this to a scenario-based case study of 1 intervention, the set-up of field hospitals in England, under varying levels of COVID-19 patients. RESULTS: The Abdul Latif Jameel Institute for Disease and Emergency Analytics pandemic planner is a hospital planning tool that allows hospital administrators, policymakers, and other decision-makers to calculate the amount of capacity in terms of beds, staff, and crucial medical equipment obtained by implementing the interventions. Flexible assumptions on baseline capacity, the number of hospitalizations, staff-to-beds ratios, and staff absences due to COVID-19 make the planner adaptable to multiple settings. The results of the case study show that while field hospitals alleviate the burden on the number of beds available, this intervention is futile unless the deficit of critical care nurses is addressed first. DISCUSSION: The tool supports decision-makers in delivering a fast and effective response to the pandemic. The unique contribution of the planner is that it allows users to compare the impact of interventions that change some or all inputs.

Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China.

OBJECTIVES: In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China. METHODS: From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces. RESULTS: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic. CONCLUSIONS: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic.

Adapting hospital capacity to meet changing demands during the COVID-19 pandemic.

BACKGROUND: To calculate hospital surge capacity, achieved via hospital provision interventions implemented for the emergency treatment of coronavirus disease 2019 (COVID-19) and other patients through March to May 2020; to evaluate the conditions for admitting patients for elective surgery under varying admission levels of COVID-19 patients. METHODS: We analysed National Health Service (NHS) datasets and literature reviews to estimate hospital care capacity before the pandemic (pre-pandemic baseline) and to quantify the impact of interventions (cancellation of elective surgery, field hospitals, use of private hospitals, deployment of former medical staff and deployment of newly qualified medical staff) for treatment of adult COVID-19 patients, focusing on general and acute (G&A) and critical care (CC) beds, staff and ventilators. RESULTS: NHS England would not have had sufficient capacity to treat all COVID-19 and other patients in March and April 2020 without the hospital provision interventions, which alleviated significant shortfalls in CC nurses, CC and G&A beds and CC junior doctors. All elective surgery can be conducted at normal pre-pandemic levels provided the other interventions are sustained, but only if the daily number of COVID-19 patients occupying CC beds is not greater than 1550 in the whole of England. If the other interventions are not maintained, then elective surgery can only be conducted if the number of COVID-19 patients occupying CC beds is not greater than 320. However, there is greater national capacity to treat G&A patients: without interventions, it takes almost 10,000 G&A COVID-19 patients before any G&A elective patients would be unable to be accommodated. CONCLUSIONS: Unless COVID-19 hospitalisations drop to low levels, there is a continued need to enhance critical care capacity in England with field hospitals, use of private hospitals or deployment of former and newly qualified medical staff to allow some or all elective surgery to take place.