Infectious disease in an era of global change

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Abstract

The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013–2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technological, demographic and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population numbers continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.

Abstract

Global change, including climate change, urbanization and global travel and trade, has affected the emergence and spread of infectious diseases. In the Review, Baker, Metcalf and colleagues examine how global change affects infectious diseases, highlighting examples ranging from COVID-19 to Zika virus disease.

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Most cited references 158

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Antibiotic resistance-the need for global solutions.

Ramanan Laxminarayan, Adriano Duse, Chand Wattal … (2013)

The causes of antibiotic resistance are complex and include human behaviour at many levels of society; the consequences affect everybody in the world. Similarities with climate change are evident. Many efforts have been made to describe the many different facets of antibiotic resistance and the interventions needed to meet the challenge. However, coordinated action is largely absent, especially at the political level, both nationally and internationally. Antibiotics paved the way for unprecedented medical and societal developments, and are today indispensible in all health systems. Achievements in modern medicine, such as major surgery, organ transplantation, treatment of preterm babies, and cancer chemotherapy, which we today take for granted, would not be possible without access to effective treatment for bacterial infections. Within just a few years, we might be faced with dire setbacks, medically, socially, and economically, unless real and unprecedented global coordinated actions are immediately taken. Here, we describe the global situation of antibiotic resistance, its major causes and consequences, and identify key areas in which action is urgently needed. Copyright © 2013 Elsevier Ltd. All rights reserved.

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Machine learning: Trends, perspectives, and prospects.

M. I. Jordan, T. M. Mitchell (2015)

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

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Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

Joel Hellewell, Sam Abbott, Amy Gimma … (2020)

Summary Background Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19. Methods We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R 0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings Simulated outbreaks starting with five initial cases, an R 0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R 0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R 0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R 0 of 2·5 more than 70% of contacts had to be traced, and for an R 0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R 0 was 1·5. For R 0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts. Funding Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.

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Author and article information

Contributors

Rachel E. Baker:

ORCID: http://orcid.org/0000-0002-2661-8103

racheleb@princeton.edu

C. Jessica E. Metcalf: cmetcalf@princeton.edu

Journal

Journal ID (nlm-ta): Nat Rev Microbiol

Journal ID (iso-abbrev): Nat Rev Microbiol

Title: Nature Reviews. Microbiology

Publisher: Nature Publishing Group UK (London )

ISSN (Print): 1740-1526

ISSN (Electronic): 1740-1534

Publication date (Electronic): 13 October 2021

Pages: 1-13

Affiliations

[1 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, Department of Ecology and Evolutionary Biology, , Princeton University, ; Princeton, NJ USA

[2 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, Princeton High Meadows Environmental Institute, , Princeton University, ; Princeton, NJ USA

[3 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Demography, , University of California, Berkeley, ; Berkeley, CA USA

[4 ]GRID grid.294303.f, Rocky Mountain Biological Laboratory, ; Crested Butte, CO USA

[5 ]Mahaliana Labs SARL, Antananarivo, Madagascar

[6 ]Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar

[7 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, EPPIcenter Program, Division of HIV, ID, and Global Medicine, Department of Medicine, , University of California, San Francisco, ; San Francisco, CA USA

[8 ]GRID grid.5491.9, ISNI 0000 0004 1936 9297, WorldPop, School of Geography and Environmental Science, University of Southampton, ; Southampton, UK

[9 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Bioengineering, , McGill University, ; Montreal, Quebec Canada

[10 ]GRID grid.428397.3, ISNI 0000 0004 0385 0924, Programme in Emerging Infectious Diseases, Duke-NUS Medical School, ; Singapore, Singapore

[11 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke Global Health Institute, , Duke University, ; Durham, NC USA

[12 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, , Johns Hopkins University, ; Baltimore, MD USA

[13 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, Princeton School of Public and International Affairs, , Princeton University, ; Princeton, NJ USA

Author information

Rachel E. Baker http://orcid.org/0000-0002-2661-8103

Ian F. Miller http://orcid.org/0000-0002-2673-9618

Lin-Fa Wang http://orcid.org/0000-0003-2752-0535

Article

Publisher ID: 639

DOI: 10.1038/s41579-021-00639-z

PMC ID: 8513385

PubMed ID: 34646006

SO-VID: 330d8b0f-dbc7-484e-9e40-0b4349f4659c

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This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

Infectious disease in an era of global change

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Novel Coronavirus Disease COVID-19

Most cited references 158

Antibiotic resistance-the need for global solutions.

Machine learning: Trends, perspectives, and prospects.

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

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