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Evidence-based medicine (EBM’s) traditional methods, especially randomised controlled trials (RCTs) and meta-analyses, along with risk-of-bias tools and checklists, have contributed significantly to the science of COVID-19. But these methods and tools were designed primarily to answer simple, focused questions in a stable context where yesterday’s research can be mapped more or less unproblematically onto today’s clinical and policy questions. They have significant limitations when extended to complex questions about a novel pathogen causing chaos across multiple sectors in a fast-changing global context. Non-pharmaceutical interventions which combine material artefacts, human behaviour, organisational directives, occupational health and safety, and the built environment are a case in point: EBM’s experimental, intervention-focused, checklist-driven, effect-size-oriented and deductive approach has sometimes confused rather than informed debate. While RCTs are important, exclusion of other study designs and evidence sources has been particularly problematic in a context where rapid decision making is needed in order to save lives and protect health. It is time to bring in a wider range of evidence and a more pluralist approach to defining what counts as ‘high-quality’ evidence. We introduce some conceptual tools and quality frameworks from various fields involving what is known as mechanistic research, including complexity science, engineering and the social sciences. We propose that the tools and frameworks of mechanistic evidence, sometimes known as ‘EBM+’ when combined with traditional EBM, might be used to develop and evaluate the interdisciplinary evidence base needed to take us out of this protracted pandemic. Further articles in this series will apply pluralistic methods to specific research questions.

ABSTRACTThe reemergence of smallpox as a bioterrorism attack is now an increasing and legitimate concern. Advances in synthetic biology have now made it possible for the virus to be synthesized in a laboratory, with methods publicly available. Smallpox introduction into a susceptible population, with increased immunosuppression and an aging population, raises questions of how vaccination should be used in an epidemic situation when supply may be limited. We constructed three modified susceptible-latent-infectious-recovered (SEIR) models to simulate targeted, ring and mass vaccination in response to a smallpox outbreak in Sydney, Australia. We used age-specific distributions of susceptibility, infectivity, contact rates, and tested outputs under different assumptions. The number of doses needed of second- and third-generation vaccines are estimated, along with the total number of deaths at the end of the epidemic. We found a faster response is the key and ring vaccination of traced contacts is the most effective strategy and requires a smaller number of doses. However if public health authorities are unable to trace a high proportion of contacts, mass vaccination with at least 125,000 doses delivered per day is required. This study informs a better preparedness and response planning for vaccination in a case of a smallpox outbreak in a setting such as Sydney.

BackgroundThe presymptomatic transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been documented in limited clusters, and it is predicted through modelling. However, there is a lack of evidence from observations with a large sample size.MethodsWe used data from meticulous contact tracing of people exposed to cases of SARS-CoV-2 to estimate the proportion of cases that result from the presymptomatic transmission of the virus in Beijing during January 2020 and February 2020.ResultsThe results showed that presymptomatic transmission occurred in at least 15% of 100 secondary COVID-19 cases. The earliest presymptomatic contact event occurred 5 days prior to the index case’s onset of symptoms, and this occurred in two clusters.ConclusionsThe finding suggested that the contact tracing period should be earlier and highlighted the importance of preventing transmission opportunities well before the onset of symptoms.

Smallpox has been eradicated but advances in synthetic biology have increased the risk of its re-emergence. Residual immunity in individuals who were previously vaccinated may mitigate the impact of an outbreak, but there is a high degree of uncertainty about the duration and degree of residual immunity. Both cell-mediated and humoral immunity are thought to be important but the exact mechanisms of protection are unclear. Guidelines usually suggest vaccine-induced immunity wanes to zero after 3-10 years post vaccination, whereas other estimates show long term immunity over decades.

Objective To determine if there was herd protection conferred to unprotected healthcare workers (HCWs) by N95 respirators worn by colleagues.Methods Data were analysed from a prospective cluster randomized clinical trial conducted in Beijing, China between 1 December 2008 and 15 January 2009. A minimum compliance level (MCL) of N95 respirators for prevention of clinical respiratory illness (CRI) was set based on various compliance cut-offs. The CRI rates were compared between compliant (≥MCL) and non-compliant (

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to—not a replacement of—traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Cloth masks can be an alternative to medical masks during pandemics. Recent studies have examined the performance of fabrics under various conditions; however, the performance against violent respiratory events such as human sneezes is yet to be explored. Accordingly, we present a comprehensive experimental study using sneezes by a healthy adult and a tailored image-based flow measurement diagnostic system evaluating all dimensions of protection of commonly available fabrics and their layered combinations: the respiratory droplet blocking efficiency, water resistance, and breathing resistance. Our results reveal that a well-designed cloth mask can outperform a three-layered surgical mask for such violent respiratory events. Specifically, increasing the number of layers significantly increases the droplet blocking efficiency, on average by ∼20 times per additional fabric layer. A minimum of three layers is necessary to resemble the droplet blocking performance of surgical masks, and a combination of cotton/linen (hydrophilic inner layer)–blends (middle layer)–polyester/nylon (hydrophobic outer layer) exhibited the best performance among overall indicators tested. In an optimum three-layered design, the average thread count should be greater than 200, and the porosity should be less than 2%. Furthermore, machine washing at 60 °C did not significantly impact the performance of cloth masks. These findings inform the design of high-performing homemade cloth masks.