Controlling infectious disease outbreaks: Lessons from mathematical modelling.

Accepted versio

Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers

We find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where R falls from supercritical to subcritical values) may be ascertained 5–10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data

Enhancing Bayesian risk prediction for epidemics using contact tracing

Contact tracing data collected from disease outbreaks has received relatively little attention in the epidemic modelling literature because it is thought to be unreliable: infection sources might be wrongly attributed, or data might be missing due to resource contraints in the questionnaire exercise. Nevertheless, these data might provide a rich source of information on disease transmission rate. This paper presents novel methodology for combining contact tracing data with rate-based contact network data to improve posterior precision, and therefore predictive accuracy. We present an advancement in Bayesian inference for epidemics that assimilates these data, and is robust to partial contact tracing. Using a simulation study based on the British poultry industry, we show how the presence of contact tracing data improves posterior predictive accuracy, and can directly inform a more effective control strategy.Comment: 40 pages, 9 figures. Submitted to Biostatistic

Reconciling Techno-simplicity and Eco-complexity for future food security

Ecological intensification has been proposed as a paradigm for ensuring global food security while preserving biodiversity and ecosystem integrity. Ecologicalintensification was originally coined to promote precise site-specific farming practices aimed at reducing yield gaps, while avoiding negative environmental impacts (techno-simplicity). Recently, it has been extended to stress the importance of landscape complexity to preserve biodiversity and ecosystem services (eco-complexity). While these perspectives on ecological intensification may seem distinct, they are not incompatible and should be interwoven to create more comprehensive and practical solutions. Here, we argue that designing cropping systems to be more diverse, across space and time would be an effective route to accomplish environmentally-friendly intensification of crop production. Such a novel approach will require better integration of knowledge at the landscape level for increasing agro-biodiversity(focused on interventions outside fields) with strategies diversifying croppingsystems to manage weeds and pests (focused on interventions inside fields).Fil: Poggio, Santiago Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Producción Vegetal; ArgentinaFil: Macfadyen, Sarina. CSIRO; AustraliaFil: Bohan, David A.. Institut National de la Recherche Agronomique; Franci

Challenges for modelling interventions for future pandemics

Funding: This work was supported by the Isaac Newton Institute (EPSRC grant no. EP/R014604/1). MEK was supported by grants from The Netherlands Organisation for Health Research and Development (ZonMw), grant number 10430022010001, and grant number 91216062, and by the H2020 Project 101003480 (CORESMA). RNT was supported by the UKRI, grant number EP/V053507/1. GR was supported by Fundação para a Ciência e a Tecnologia (FCT) project reference 131_596787873. and by the VERDI project 101045989 funded by the European Union. LP and CO are funded by the Wellcome Trust and the Royal Society (grant 202562/Z/16/Z). LP is also supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1) and by The Alan Turing Institute for Data Science and Artificial Intelligence. HBS is funded by the Wellcome Trust and Royal Society (202562/Z/16/Z), and the Alexander von Humboldt Foundation. DV had support from the National Council for Scientific and Technological Development of Brazil (CNPq - Refs. 441057/2020-9, 424141/2018-3, 309569/2019-2). FS is supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). EF is supported by UKRI (Medical Research Council)/Department of Health and Social Care (National Insitute of Health Research) MR/V028618/1. JPG's work was supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care.Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.Publisher PDFPeer reviewe

Pandemic Influenza: Ethics, Law, and the Public\u27s Health

Highly pathogenic Influenza (HPAI) has captured the close attention of policy makers who regard pandemic influenza as a national security threat. Although the prevalence is currently very low, recent evidence that the 1918 pandemic was caused by an avian influenza virus lends credence to the theory that current outbreaks could have pandemic potential. If the threat becomes a reality, massive loss of life and economic disruption would ensue. Therapeutic countermeasures (e.g., vaccines and antiviral medications) and public health interventions (e.g., infection control, social separation, and quarantine) form the two principal strategies for prevention and response, both of which present formidable legal and ethical challenges that have yet to receive sufficient attention. In part II, we examine the major medical countermeasures being being considered as an intervention for an influenza pandemic. In this section, we will evaluate the known effectiveness of these interventions and analyze the ethical claims relating to distributive justice in the allocation of scarce resources. In part III, we will discuss public health interventions, exploring the hard tradeoffs between population health on the one hand and personal (e.g., autonomy, privacy, and liberty) and economic (e.g., trade, tourism, and business) interests on the other. This section will focus on the ethical and human rights issues inherent in population-based interventions. Pandemics can be deeply socially divisive, and the political response to these issues not only impacts public health preparedness, but also reflects profoundly on the kind of society we aspire to be

Weak States and Global Threats: Assessing Evidence of Spillovers

A key motivation behind recent donor attention and financial resources devoted to developing countries is the presumed connection between weak and failing states, on the one hand, and a variety of transnational threats, on the other. Indeed, it has become conventional wisdom that poorly performing states generate multiple cross-border “spillovers,” including terrorism, weapons proliferation, organized crime, regional instability, global pandemics, and energy insecurity. What is striking is how little empirical evidence underpins such sweeping assertions. A closer look suggests that the connection between state weakness and global threats is less clear and more variable than typically assumed. Both the type and extent of “spillovers” depend in part on whether the weakness in question is a function of state capacity, will, or a combination of the two. Moreover, a preliminary review suggests that some trans-border threats are more likely to emerge not from the weakest states but from stronger states that possess narrower but critical gaps in capacity and will. Crafting an effective U.S. and international strategy towards weak states and the cross-border spillovers they sometimes generate will depend on a deeper understanding of the underlying mechanisms linking these two sets of phenomena. The challenge for analysts and policymakers will be to get greater clarity about which states are responsible for which threats and design development and other external interventions accordingly. This working paper represents an initial foray in this direction, suggesting avenues for future research and policy development.weak state, failing state, regional instability, global threats