Methods We used a stochastic individual-based simulation model, adapted from a screening model originally developed for 2019-nCoV, to estimate the proportion of infected travellers who would be detected at exit screening, become severely ill during the flight, be detected at entry screening, or remain undetected throughout. Natural-history parameters (incubation period and onset-to-severe-disease interval) were derived from the 2007 Uganda outbreak and a Bayesian re-analysis of the 2012 Isiro (DRC) outbreak line list, since patient-level data from the current 2026 outbreak are not yet available. We modelled a representative 12-hour DRC/Uganda-to-international connecting itinerary with 86% screening sensitivity at both exit and entry, and conducted sensitivity analyses varying flight duration, the asymptomatic/afebrile fraction, the epidemic’s doubling time, and screening sensitivity.

Results Under the baseline scenario, we estimate that 73% (95% CrI: 68–77%) of BVD-infected travellers would evade combined exit and entry screening entirely, primarily because most board their flight before symptom onset. Among those who successfully boarded, 92% (95% CrI: 90–94%) would arrive undetected. Even under a theoretical best-case scenario of 100% screening sensitivity at both stages, 72% of infected travellers would still go undetected on arrival. The undetected fraction increased further during active epidemic growth, rising to as much as 85% under the fastest estimated doubling time, and was most sensitive to the incubation period, ranging from 36–82% undetected across incubation periods of 1–14 days.

Conclusion Syndromic airport screening alone is unlikely to meaningfully reduce the risk of international BVD spread via air travel, because most infected travellers depart while still pre-symptomatic. Resources are likely better directed towards outbreak control at source, clinician preparedness and referral pathways in receiving countries, and structured post-departure self-monitoring guidance for travellers from affected regions. Full methods, results, sensitivity analyses and an interactive Shiny app for exploring screening parameters are available in the accompanying report, the preprint on medRxiv, and the GitHub repository.

Background An outbreak of Bundibugyo ebolavirus in Ituri, Democratic Republic of the Congo was reported in May 2026. Neighbouring countries in Africa are at the greatest risk of cross-border spread, but the severe nature of Ebola virus disease has raised concerns globally around the risk of international transmission. Decision-makers outside Africa may be considering policies of varying stringency ranging from watchful waiting to border closures, and therefore need to understand the risks of Ebola importation in the context of previous epidemics.

Methods We conducted manual and AI-assisted searches to find all known Ebola cases that have occurred outside of the African continent and manually reviewed all identified case reports, public health bulletins and news articles to understand the risk of Ebola importation due to travel from Africa to other continents. We collected epidemiological data, including key dates, and analysed the historical risk of Ebola importation as well as the time-varying risk of Ebola importation during the 2014-2016 West African Ebola epidemic.

Results From the first recognised Ebola outbreak in 1976 to May 2026, there have been 28 confirmed cases of Ebola virus disease outside of Africa caused by epidemic-linked transmission of Zaire ebolavirus, Sudan ebolavirus, or Bundibugyo ebolavirus. Among these 28 cases, 21 (75%) were in individuals medically evacuated from Africa due to confirmed Ebola virus infection, three (11%) were in front-line health care workers returning from an Ebola outbreak whose symptoms were detected after border screening, one (4%) was in a traveller with no responsive role, and three (11%) were secondary cases in health care workers who treated another Ebola patient outside Africa. Based on Ebola epidemics since 2000, we estimated a crude overall risk of 0.81 Ebola cases outside Africa per 1,000 reported Ebola cases in Africa. Focusing on non-medically evacuated cases only, the crude overall risk is 0.17 Ebola cases outside Africa per 1,000 reported Ebola cases in Africa.

Interpretation The risk of undetected Ebola transmission outside Africa is low. Nearly all (27 of 28) confirmed Ebola cases reported outside Africa were linked to known occupational exposures, with reasons for travel specific to outbreak response. Our results suggest that the risk of case exportations could be substantially mitigated by infection prevention measures at the outbreak source and among outbreak response workers, in concert with enhanced travel screening and monitoring for returning response workers.

The published paper is available at Eurosurveillance.

The preprint report is available here.

The ongoing epidemic of Ebola Bundibugyo Virus Disease (BVD) in the eastern Democratic Republic of Congo (DRC) and Uganda appears to be propagating. Just as during the previous large DRC epidemic (2018-2020), there have been concerning reports of a breakdown in community trust and violence surrounding the burial of hospitalised cases. These reports underscore the importance of ensuring culturally appropriate, accessible, compassionate inhumation of people who die from BVD, the bodies of whom remain infectious after their death.

Safe and dignified burial (SDB) is a recognised pillar of Ebola response and is supported by evidence as a superior alternative to either (i) no support for safe burial, which can result in unchecked propagation; or, perhaps worse still, (ii) militarised, coercive approaches to patient isolation and burial management: these impede grief, worsen mental health, have been shown to result in mistrust and can disincentivise care-seeking [10–13]. At least in DRC, SDB has previously been offered presumptively, i.e. for any deaths in the community regardless of whether they had received an Ebola test or diagnosis.

We use a simple transmission model to explore different scenarios of how SDB could affect the epidemic’s trajectory, and thus inform decisions about which targets the SDB service should set out to achieve. We take as our analysis unit an average-sized health zone in eastern DRC in which SDB is implemented once the outbreak reaches a certain size. We reach the following conclusions:

1. Achieving high SDB coverage and effectiveness is very important, underscoring the need for community engagement and trust-building, as well as strong collaboration between the SDB service and other response pillars (in particular surveillance/contact tracing).

2. Under certain conditions of low transmissibility, SDB could make the difference between outbreak propagation and extinction. The combination of highly performant SDB and, say, isolation and case management would probably make a formidable contribution to transmission. Moreover, SDB as a trust- building measure is likely to encourage care-seeking and case reporting, and could thus act synergistically with other interventions.

3. The longer it takes for SDB to get underway, the harder it will take to bring outbreaks under control. With this in mind, it may be efficient to preposition and activate SDB even in areas where no transmission is yet observed: while we have not explored the spatial dynamics of the epidemic here, such a strategy may prevent geographic spillover.

The full report is available here in English and in French.

BVDOutbreakSize

Summary

• 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.

Read the pre-print here. Accompanying code can be found here.

Read the full report:

23rd Dec 2021: Updated version 2 of report.

16th Dec 2021: Preprint on medRxiv.

11th Dec 2021: Original report.

Background

Mobility data have demonstrated major changes in human movement patterns in response to COVID-19 and associated interventions in many countries. This involves sub-national redistribution, short-term relocations, and international migration. Aggregated mobile phone location data combined with small-area census population data allow changes in the population distribution of the UK to be quantified with high spatial and temporal granularity.

Methods

In this paper, we combine detailed data from Facebook, measuring the location of approximately 6 million daily active Facebook users in 5km² tiles in the UK with census-derived population estimates to measure population mobility and redistribution. We provide time-varying population estimates and assess spatial population changes with respect to population density and four key reference dates in 2020 (first UK lockdown, end of term, beginning of term, Christmas).

Results

We show how population estimates derived from Facebook data vary compared to mid-2020 small area population estimates by UK national statistics agencies. We also estimate that between March 2020 and March 2021, the total population of the UK declined and we identify important spatial variations in this population change, showing that low-density areas have experienced lower population decreases than urban areas. We estimate that, for the top 10% highest population tiles, the population has decreased by 6.6%. Finally, we provide evidence that geographic redistributions of population within the UK coincide with dates of non-pharmaceutical interventions including lockdowns and movement restrictions, as well as seasonal patterns of migration around holiday dates.

Conclusions

The methods used in this study reveal significant changes in population distribution at high spatial and temporal resolutions that have not previously been quantified by available demographic surveys in the UK. We found early indicators of potential longer-term changes in the population distribution of the UK although it is not clear if these changes will persist after the COVID-19 pandemic.

Read an earlier pre-print here. Population estimates for 2019 Local Authority Districts are available here.

Background

In 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, necessitating the use of alternative data sources of mortality. We obtained satellite imagery data of the main cemeteries of Mogadishu (Somalia), which showed a sustained rise in burials above the pre-pandemic baseline in the period February-July 2020. We fitted a dynamic transmission model to this indirect measure of excess mortality to estimate the date of introduction and transmissibility of SARS-CoV-2, as well as the effect of non-pharmaceutical interventions in this low-income, crisis-affected setting.

Methods

We 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.

Results

Under 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 and their long plateau. The effect of control measures on transmissibility appeared small or moderate (below 30%).

Conclusions

Subject 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.

a. Quality of fits for different infection fatality ratios and seed sizes
Goodness of fit as measured by DIC (deviance information criterion) at different values of seed size and population-wide IFR. The labels above the colored lines show median estimates for R0 and the date of introduction, and the NPI-induced reduction in transmissibility during the first NPI period below.
b. Estimates of date of introduction, R0 and scaling factor for NPI stringency index
Median values and credible intervals for the fitting parameters (introduction date, NPI_scale, R0) and quality of fits at different assumed values of the infection fatality ratio (x-axis) and seed size (colors). In the top panel, labels below the lines show median estimates of the date of introduction. Shaded areas around the median (black) are 50% (darker) and 95% credible intervals.

Simulated deaths compared to burial data
Dynamics generated by sampling the posterior distributions of fitting parameters, at a seed size of 200 and four IFR values from 0.15% to 1.13%. The best fit (lowest DIC value) is at IFR=0.36%. The dashed black line and circles show the daily number of excess burials. Only the period from 23 February to 24 August was used for fitting.

Read the pre-print here. Accompanying code can be found here.

Background: Many countries require incoming air travellers to quarantine on arrival and/or undergo testing to limit importation of SARS-CoV-2.

Methods: We developed mathematical models of SARS-CoV-2 viral load trajectories over the course of infection to assess the effectiveness of quarantine and testing strategies. We consider the utility of pre and post-flight Polymerase Chain Reaction (PCR) and lateral flow testing (LFT) to reduce transmission risk from infected arrivals and to reduce the duration of, or replace, quarantine. We also estimate the effect of each strategy relative to domestic incidence, and limits of achievable risk reduction, for 99 countries where flight data and case numbers are estimated.

Results: We find that LFTs immediately pre-flight are more effective than PCR tests 3 days before departure in decreasing the number of departing infectious travellers. Pre-flight LFTs and post-flight quarantines, with tests to release, may prevent the majority of transmission from infectious arrivals while reducing the required duration of quarantine; a pre-flight LFT followed by 5 days in quarantine with a test to release would reduce the expected number of secondary cases generated by an infected traveller compared to symptomatic self-isolation alone, R_s, by 85% (95% UI: 74%, 96%) for PCR and 85% (95% UI: 70%, 96%) for LFT, even assuming imperfect adherence to quarantine (28% of individuals) and self-isolation following a positive test (86%). Under the same adherence assumptions, 5 days of daily LFT testing would reduce R_s by 91% (95% UI: 75%, 98%).

Conclusions: Strategies aimed at reducing the risk of imported cases should be considered with respect to: domestic incidence, transmission, and susceptibility; measures in place to support quarantining travellers; and incidence of new variants of concern in travellers’ origin countries. Daily testing with LFTs for 5 days is comparable to 5 days of quarantine with a test on exit or 14 days with no test.

Read the pre-print here. Accompanying code can be found here.

Click here to read our full preprint.

Background

During the COVID-19 pandemic, the UK government imposed public health policies in England to reduce social contacts in hopes of curbing virus transmission. We measured contact patterns weekly from March 2020 to March 2021 to estimate the impact of these policies, covering three national lockdowns interspersed by periods of lower restrictions.

Methods:

Data were collected using online surveys of representative samples of the UK population by age and gender. We calculated the mean daily contacts reported using a (clustered) bootstrap and fitted a censored negative binomial model to estimate age-stratified contact matrices and estimate proportional changes to the basic reproduction number under controlled conditions using the change in contacts as a scaling factor.

Results

The survey recorded 101,350 observations from 19,914 participants who reported 466,710 contacts over 53 weeks. Contact patterns changed over time and by participants’ age, personal risk factors, and perception of risk. The mean of reported contacts among adults have reduced compared to previous surveys with adults aged 18 to 59 reporting a mean of 2.39 (95% CI 2.20 - 2.60) contacts to 4.93 (95% CI 4.65 - 5.19) contacts, and the mean contacts for school-age children was 3.07 (95% CI 2.89 - 3.27) to 15.11 (95% CI 13.87 - 16.41). The use of face coverings outside the home has remained high since the government mandated use in some settings in July 2020.

Conclusions

The CoMix survey provides a unique longitudinal data set for a full year since the first lockdown for use in statistical analyses and mathematical modelling of COVID-19 and other diseases. Recorded contacts reduced dramatically compared to pre-pandemic levels, with changes correlated to government interventions throughout the pandemic. Despite easing of restrictions in the summer of 2020, mean reported contacts only returned to about half of that observed pre-pandemic.