Assessing the impacts of timing on the health benefits, cost-effectiveness and relative affordability of COVID-19 vaccination programmes in 27 African Countries (preprint)

Background: The COVID-19 vaccine supply shortage in 2021 constrained rollout efforts in Africa while populations experienced waves of epidemics. As supply picks up, a key question becomes if vaccination remains an impactful and cost-effective strategy given changes in the timing of implementation. Methods: We assessed the impact of timing using an epidemiological and economic model. We fitted our mathematical epidemiological model to reported COVID-19 deaths in 27 African countries to estimate the existing immunity (resulting from infection) before substantial vaccine rollout. We then projected health outcomes for different programme start dates (2021-01-01 to 2021-12-01, n = 12) and roll-out rates (slow, medium, fast; 275, 826, and 2066 doses/ million population-day, respectively) for viral vector and mRNA vaccines. Rollout rates used were derived from observed uptake trajectories. We collected data on vaccine delivery costs by country income group. Lastly, we calculated incremental cost-effectiveness ratios and relative affordability. Findings: Vaccination programmes with early start dates incur the most health benefits and are most cost-effective. While incurring the most health benefits, fast vaccine roll-outs are not always the most cost-effective. At a willingness-to-pay threshold of 0.5xGDP per capita, vaccine programmes starting in August 2021 using mRNA and viral vector vaccines were cost-effective in 6-10 and 17-18 of 27 countries, respectively. Interpretation: African countries with large proportions of their populations unvaccinated by late 2021 may find vaccination programmes less cost-effective than they could have been earlier in 2021. Lower vaccine purchasing costs and/or the emergence of new variants may improve cost-effectiveness. Funding: Bill and Melinda Gates Foundation, World Health Organization, National Institute of Health Research (UK), Health Data Research (UK)

Test to release from isolation after testing positive for SARS-CoV-2 (preprint)

The rapid spread and high transmissibility of the Omicron variant of SARS-CoV-2 is likely to lead to a significant number of key workers testing positive simultaneously. Under a policy of self-isolation after testing positive, this may lead to extreme staffing shortfalls at the same time as e.g. hospital admissions are peaking. Using a model of individual infectiousness and testing with lateral flow tests (LFT), we evaluate test-to-release policies against conventional fixed-duration isolation policies in terms of excess days of infectiousness, days saved, and tests used. We find that the number of infectious days in the community can be reduced to almost zero by requiring at least 2 consecutive days of negative tests, regardless of the number of days' wait until testing again after initially testing positive. On average, a policy of fewer days' wait until initiating testing (e.g 3 or 5 days) results in more days saved vs. a 10-day isolation period, but also requires a greater number of tests. Due to a lack of specific data on viral load progression, infectivity, and likelihood of testing positive by LFT over the course of an Omicron infection, we assume the same parameters as for pre-Omicron variants and explore the impact of a possible shorter proliferation phase.

Dosing interval strategies for two-dose COVID-19 vaccination in 13 low- and middle-income countries of Europe: health impact modelling and benefit-risk analysis (preprint)

Background: In 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. Methods: We 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. Findings: In 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. Interpretation: We 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.

Transmission dynamics of SARS-CoV-2 in a strictly-Orthodox Jewish community in the UK (preprint)

Some social settings such as households and workplaces, have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020, reaching 64.3% seroprevalence within 10 months, and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data, and a network model of people and places represented as a stochastic graph rewriting system, we estimated the relative contribution of transmission in households, schools and religious institutions to the epidemic, and the relative risk of infection in each of these settings. All congregate settings were important for transmission, with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher general-community transmission rate for women (3.3-fold), and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support (preprint)

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

The N-terminal domain of spike glycoprotein mediates SARS-CoV-2 infection by associating with L-SIGN and DC-SIGN (preprint)

The widespread occurrence of SARS-CoV-2 has had a profound effect on society and a vaccine is currently being developed. Angiotensin-converting enzyme 2 (ACE2) is the primary host cell receptor that interacts with the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Although pneumonia is the main symptom in severe cases of SARS-CoV-2 infection, the expression levels of ACE2 in the lung is low, suggesting the presence of another receptor for the spike protein. In order to identify the additional receptors for the spike protein, we screened a receptor for the SARS-CoV-2 spike protein from the lung cDNA library. We cloned L-SIGN as a specific receptor for the N-terminal domain (NTD) of the SARS-CoV-2 spike protein. The RBD of the spike protein did not bind to L-SIGN. In addition, not only L-SIGN but also DC-SIGN, a closely related C-type lectin receptor to L-SIGN, bound to the NTD of the SARS-CoV-2 spike protein. Importantly, cells expressing L-SIGN and DC-SIGN were both infected by SARS-CoV-2. Furthermore, L-SIGN and DC-SIGN induced membrane fusion by associating with the SARS-CoV-2 spike protein. Serum antibodies from infected patients and a patient-derived monoclonal antibody against NTD inhibited SARS-CoV-2 infection of L-SIGN or DC-SIGN expressing cells. Our results highlight the important role of NTD in SARS-CoV-2 dissemination through L-SIGN and DC-SIGN and the significance of having anti-NTD neutralizing antibodies in antibody-based therapeutics.

Comparison of SARS-CoV-2 infection in two non-human primate species: rhesus and cynomolgus macaques (preprint)

SARS-CoV-2 is a coronavirus that sparked the current COVID-19 pandemic. To stop the shattering effect of COVID-19, effective and safe vaccines, and antiviral therapies are urgently needed. To facilitate the preclinical evaluation of intervention approaches, relevant animal models need to be developed and validated. Rhesus macaques (Macaca mulatta) and cynomolgus macaques (Macaca fascicularis) are widely used in biomedical research and serve as models for SARS-CoV-2 infection. However, differences in study design make it difficult to compare and understand potential species-related differences. Here, we directly compared the course of SARS-CoV-2 infection in the two genetically closely-related macaque species. After inoculation with a low passage SARS-CoV-2 isolate, clinical, virological, and immunological characteristics were monitored. Both species showed slightly elevated body temperatures in the first days after exposure while a decrease in physical activity was only observed in the rhesus macaques and not in cynomolgus macaques. The virus was quantified in tracheal, nasal, and anal swabs, and in blood samples by qRT-PCR, and showed high similarity between the two species. Immunoglobulins were detected by various enzyme-linked immunosorbent assays (ELISAs) and showed seroconversion in all animals by day 10 post-infection. The cytokine responses were highly comparable between species and computed tomography (CT) imaging revealed pulmonary lesions in all animals. Consequently, we concluded that both rhesus and cynomolgus macaques represent valid models for evaluation of COVID-19 vaccine and antiviral candidates in a preclinical setting.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research (preprint)

Motivation: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Results: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. Availability: https://neo4covid19.ncats.io . Keywords: SARS-CoV-2, COVID-19, network pharmacology, graph database, Neo4j, data integration, drug repositioning

Human movement can inform the spatial scale of interventions against COVID-19 transmission (preprint)

The UK enacted an intensive, nationwide lockdown on March 23 2020 to mitigate transmission of COVID-19. As restrictions began to ease, resurgence in transmission has been targeted by geographically-limited interventions of various stringencies. Determining the optimal spatial scale for local interventions is critical to ensure interventions reach the most at risk areas without unnecessarily restricting areas at low risk of resurgence. Here we use detailed human mobility data from Facebook to determine the spatially-explicit network community structure of the UK before and during the lockdown period, and how that has changed in response to the easing of restrictions and to locally-targeted interventions. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown. During this period, there was no evidence of re-routing in the network. Communities in which locally-targeted interventions have happened following resurgence did not show reorganization but did show small decreases in measurable mobility effects in the Facebook dataset. We propose that geographic communities detected in Facebook or other mobility data be part of decision making for determining the spatial extent or boundaries of interventions in the UK. These data are available in near real-time, and allow quantification of changes in the distribution of the population across the UK, as well as people's travel patterns to give data-driven metrics for geographically-targeted interventions.

Changing travel patterns in China during the early stages of the COVID-19 pandemic (preprint)

Understanding changes in human mobility in the early stages of the COVID-19 pandemic is crucial for assessing the impacts of travel restrictions designed to reduce disease spread. Here, relying on data from mainland China, we investigated the spatio-temporal characteristics of human mobility between 1st January and 1st March 2020 and discussed their public health implications. An outbound travel surge from Wuhan before travel restrictions were implemented was also observed across China due to the Lunar New Year, indicating that holiday travel may have played a larger role in mobility changes compared to impending travel restrictions. Holiday travel also shifted healthcare pressure related to COVID-19 towards locations with lower access to care. Network analyses showed no sign of major changes in the transportation network after Lunar New Year. Changes observed were temporary and have not yet led to structural reorganisation of the transportation network at the time of this study. One sentence summaryUnderstanding travel before, during, and after the introduction of travel restrictions in China in response to the COVID-19 Pandemic.

The contribution of asymptomatic SARS-CoV-2 infections to transmission - a model-based analysis of the Diamond Princess outbreak (preprint)

BackgroundSome key gaps in the understanding of SARS-CoV-2 infection remain. One of them is the contribution to transmission from individuals experiencing asymptomatic infections. We aimed to characterise the proportion and infectiousness of asymptomatic infections using data from the outbreak on the Diamond Princess cruise ship. MethodsWe used a transmission model of COVID-19 with asymptomatic and presymptomatic states calibrated to outbreak data from the Diamond Princess, to quantify the contribution of asymptomatic infections to transmission. Data available included the date of symptom onset for symptomatic disease for passengers and crew, the number of symptom agnostic tests done each day, and date of positive test for asymptomatic and presymptomatic individuals. FindingsOn the Diamond Princess 74% (70-78%) of infections proceeded asymptomatically, i.e. a 1:3.8 case-to-infection ratio. Despite the intense testing 53%, (51-56%) of infections remained undetected, most of them asymptomatic. Asymptomatic individuals were the source for 69% (20-85%) of all infections. While the data did not allow identification of the infectiousness of asymptomatic infections, assuming no or low infectiousness resulted in posterior estimates for the net reproduction number of an individual progressing through presymptomatic and symptomatic stages in excess of 15. InterpretationAsymptomatic SARS-CoV-2 infections may contribute substantially to transmission. This is essential to consider for countries when assessing the potential effectiveness of ongoing control measures to contain COVID-19. FundingERC Starting Grant (#757699), Wellcome trust (208812/Z/17/Z), HDR UK (MR/S003975/1)